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IEQ, sleep quality, and IoT: meta-analysis on improving IEQ and sleep quality using IoT
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IEQ, sleep quality, and IoT: meta-analysis on improving IEQ and sleep quality using IoT
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Content
IEQ, SLEEP QUALITY, AND IoT
Meta-Analysis on Improving IEQ and Sleep Quality Using IoT
by
Jun Wang
A Thesis Presented to the
FACULTY OF THE USC SCHOOL OF ARCHITECTURE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF BUILDING SCIENCE
May 2022
Copyright 2022 Jun Wang
ii
Acknowledgement
I would really appreciate Professor Choi, Professor Schiler and Professor Noble for their time and
effort on instructions and engagements.
I would like to appreciate my parents to give me a chance to study in University of Southern
California to learn more knowledge.
I would like to appreciate all professors in Master of Building Science program for their efforts
and time on lectures and instructions.
Committee Chair: Joon-Ho Choi, Ph.D.
USC, School of Architecture, Los Angeles, CA
joonhoch@usc.edu
Committee Member: Marc Schiler, FASES
USC, School of Architecture, Los Angeles, CA
marcs@usc.edu
Committee Member: Douglas Noble, Ph.D., FAIA
USC, School of Architecture, Los Angeles, CA
dnoble@usc.edu
iii
Table of Contents
Acknowledgement .......................................................................................................................... ii
List of Tables ............................................................................................................................... viii
List of Figures ................................................................................................................................ ix
Abstract ........................................................................................................................................ xiv
1 Introduction ............................................................................................................................. 1
1.1 Indoor Environment Quality ........................................................................................................ 1
1.1.1 Impacts of Indoor Environment Quality on Human ............................................................................... 2
1.1.2 Importance of Thermal Comfort ............................................................................................................ 2
1.1.3 Importance of Indoor Air quality ........................................................................................................... 4
1.1.4 Importance of Lighting Intensity ............................................................................................................ 5
1.1.5 Importance of Acoustics ......................................................................................................................... 6
1.2 Sleep Quality ................................................................................................................................ 7
1.2.1 Sleep and Sleep Quality ......................................................................................................................... 7
1.2.2 Importance of Sleep Quality .................................................................................................................. 8
1.3 The Internet of Things ................................................................................................................. 9
1.3.1 Content of The Internet of Things .......................................................................................................... 9
1.3.2 Wearable Sensors ................................................................................................................................. 10
1.3.3 Smart Homes ........................................................................................................................................ 10
1.3.4 Importance of IoT, Wearable Sensors And Smart Home ..................................................................... 11
1.4 Goals and Objects ...................................................................................................................... 11
1.5 Summary .................................................................................................................................... 12
2 Background Research ........................................................................................................... 13
iv
2.1 IEQ ............................................................................................................................................. 13
2.1.1 Impact of IEQ in Human Wellbeing .................................................................................................... 13
2.1.2 Component Influenced Thermal Comfort ............................................................................................ 14
2.1.3 Component Influenced Indoor Air Quality .......................................................................................... 16
2.1.4 Component Influenced Lighting Quality ............................................................................................. 17
2.1.5 Component Influenced Acoustic Environment .................................................................................... 18
2.1.6 Limitation of Current Research Studies ............................................................................................... 18
2.2 Sleep quality .............................................................................................................................. 19
2.2.1 What is Sleep Quality ........................................................................................................................... 19
2.2.2 What Environmental Component Will Influence Sleep Quality .......................................................... 19
2.2.3 Limitation of Current Research Studies ............................................................................................... 22
2.3 IoT .............................................................................................................................................. 22
2.3.1 IoT ........................................................................................................................................................ 22
2.3.2 Wearable Sensors Which Could Be Used in Sleep Time ..................................................................... 23
2.4 Summary .................................................................................................................................... 24
3 Method .................................................................................................................................. 26
3.1 Overview of Methodology ......................................................................................................... 26
3.2 Literature Review ...................................................................................................................... 27
3.2.1 Search Progress .................................................................................................................................... 27
3.2.2 Identify and Record .............................................................................................................................. 28
3.2.3 Analysis and Summary ......................................................................................................................... 30
3.3 Interact the Relationship with IoT and Wearable Sensors ......................................................... 32
3.4 Development of Suggestions for Better Sleep Quality and Future Study Guideline ................. 32
3.4.1 Development of Suggestion for Sleep Quality Research Guideline .................................................... 32
3.4.2 Development of Suggestion for Future Study Direction of IEQ and Sleep Quality ............................ 33
v
3.5 Summary .................................................................................................................................... 33
4 Data ....................................................................................................................................... 34
4.1 Impact Factor of Current Publication ........................................................................................ 34
4.2 Basic Information and Research Condition for Research Studies Related to IEQ and sleep quality
35
4.2.1 Common Issue and Goals ..................................................................................................................... 35
4.2.2 IEQ Component, Research Type, and Research Season ...................................................................... 35
4.2.3 Nation and Participants ........................................................................................................................ 38
4.2.4 Experiment Data Analysis Methodology ............................................................................................. 41
4.3 Research Investigated Parameters for Research Study Related to IEQ and sleep quality ......... 43
4.3.1 Outdoor Parameters .............................................................................................................................. 43
4.3.2 Indoor Parameters and Measurements ................................................................................................. 44
4.3.3 Indoor Parameters Experiment Condition ............................................................................................ 48
4.3.4 Biological Signal .................................................................................................................................. 55
4.3.5 Sleep Quality ........................................................................................................................................ 58
4.4 Research Results for Research Studies Related to IEQ and sleep quality ................................. 60
4.4.1 Results of Air Temperature .................................................................................................................. 60
4.4.2 Results of Relative Humidity ............................................................................................................... 63
4.4.3 Results of CO2 Density ......................................................................................................................... 63
4.4.4 Results of Air Velocity ......................................................................................................................... 64
4.4.5 Results of Lighting Intensity ................................................................................................................ 64
4.4.6 Results of Noise Level ......................................................................................................................... 64
4.5 Current published Research Studies Related to IoT and Wearable Sensors .............................. 65
4.5.1 IoT System ........................................................................................................................................... 65
4.5.2 Wearable Sensor and Smart Home Device .......................................................................................... 66
vi
4.6 Summary .................................................................................................................................... 68
5 Discussion and Results ......................................................................................................... 69
5.1 Analysis of Basic Information and Research Condition for Research Studies Related to IEQ and
sleep Quality ........................................................................................................................................... 69
5.1.1 Analysis of IEQ Components, Research Type and Research Season .................................................. 69
5.1.1 Analysis of National Locations and Participants ................................................................................. 70
5.1.2 Analysis of Experiment Data Analysis Methods ................................................................................. 71
5.1.3 Analysis of Experiment Survey Type .................................................................................................. 71
5.1.4 Analysis of Experiment Procedure ....................................................................................................... 74
5.2 Analysis of Research Investigated Parameters and Results for Research Studies Related to IEQ
and Sleep Quality .................................................................................................................................... 76
5.2.1 Analysis of Outdoor Parameters .......................................................................................................... 76
5.2.2 Analysis of Indoor Parameters ............................................................................................................. 77
5.2.3 Analysis of Biological Signal ............................................................................................................... 81
5.2.4 Analysis of Sleep quality ...................................................................................................................... 83
5.3 Analysis of Research Results Related to IEQ and Sleep Quality .............................................. 84
5.3.1 Analysis of Results of Different Indoor Parameters ............................................................................ 84
5.3.2 Integrated Relationship Between IEQ and Sleep Quality .................................................................... 87
5.4 Analysis of Research Studies Related to IoT and Wearable Sensor .......................................... 89
5.5 Summary and Recommendations for Future Sleep Quality Study ............................................ 92
5.5.1 Recommendations for Future Sleep Quality Study Setting ................................................................. 92
5.5.2 Recommendation for Sleep Quality Study Direction ........................................................................... 92
6 Conclusion and Future Work ................................................................................................ 94
6.1 Conclusion ................................................................................................................................. 94
vii
6.2 Limitations ................................................................................................................................. 96
6.3 Future Work ............................................................................................................................... 97
6.3.1 Near Term Future Work ....................................................................................................................... 97
6.3.2 Long Term Future Work ...................................................................................................................... 97
6.4 Summary .................................................................................................................................... 98
References ..................................................................................................................................... 99
viii
List of Tables
Table 4-1 Air Velocity Experiment Conditions(Pan, Lian, and Lan 2012; N. Zhang, Cao, and
Zhu 2018; Cao et al. 2021; 2020; Sekhar and Goh 2011; C. Song, Liu, et al. 2020; L. Lan et
al. 2018b; Li Lan et al. 2014; Irshad et al. 2018; Y. Liu et al. 2014; Li Lan et al. 2019; Tsang,
Mui, and Wong 2021; C. Song, Zhao, et al. 2020; L. Lan, Lian, and Lin 2016; Xiong et al.
2020) ............................................................................................................................................. 54
Table 5-1 Interaction of Biological Signal and Sleep Quality parameters ................................... 83
Table 5-2 Interaction of IEQ Parameters and Sleep Quality Parameters ...................................... 88
Table 5-3 IoT System Type .......................................................................................................... 91
ix
List of Figures
Figure 1-1 Glare because of contrast ratio between window and computer screen ........................ 6
Figure 3-1 Overview of Methodology .......................................................................................... 26
Figure 3-2 Record Components of Relationship Between IEQ and Sleep Quality ...................... 30
Figure 3-3 Record Components of IoT and Wearable Sensors .................................................... 30
Figure 4-1 Sleep Research Percentage by IEQ Components ........................................................ 36
Figure 4-2 Research Type ............................................................................................................. 37
Figure 4-3 Experiment Setting ...................................................................................................... 37
Figure 4-4 Sleep Study by Season ................................................................................................ 38
Figure 4-5 Sleep Study by Nation ................................................................................................. 39
Figure 4-6 Sleep Study by Participants Number ........................................................................... 40
Figure 4-7 Sleep Study by Gender ................................................................................................ 40
Figure 4-8 Sleep Experiment Study by Age Group ...................................................................... 41
Figure 4-9 Data Analysis Software ............................................................................................... 42
Figure 4-10 Data Analysis Algorithm ........................................................................................... 42
Figure 4-11 Outdoor Parameter Measured ................................................................................... 44
Figure 4-12 Indoor Parameters Measured ..................................................................................... 45
Figure 4-13 Indoor Air Temperature/Relative Humidity Measurements ..................................... 45
Figure 4-14 Indoor CO2 Density Measurements .......................................................................... 46
x
Figure 4-15 Indoor Air Velocity Measurements ........................................................................... 47
Figure 4-16 Indoor Lighting Intensity Measurements .................................................................. 47
Figure 4-17 Indoor Noise Level Measurements ........................................................................... 48
Figure 4-18 Summer Experiment Temperature Conditions (Xiong et al. 2020; M. Kim, Chun,
and Han 2010; Tsuzuki et al. 2015; L. Lan, Lian, and Lin 2016; Imagawa and Rijal 2015a; Li
Lan et al. 2014; 2019; L. Lan et al. 2018a; Sekhar and Goh 2011; Cao et al. 2020; Budiawan
et al. 2021; Xu, Lian, Shen, Lan, et al. 2021) ............................................................................... 49
Figure 4-19 Transition Season Experiment Temperature Conditions (M. Kim, Chun, and Han
2010; Tsuzuki et al. 2015; X. Zhang et al. 2021; Irshad et al. 2018; Strøm-Tejsen et al. 2016;
Cordoza et al. 2019; Li Lan et al. 2021) ....................................................................................... 49
Figure 4-20 Winter Experiment Temperature Conditions (M. Kim, Chun, and Han 2010;
Tsuzuki et al. 2015; C. Song, Zhao, et al. 2020; Tsang, Mui, and Wong 2021; Y. Liu et al.
2014; C. Song, Liu, et al. 2020; Budiawan et al. 2021; Cao et al. 2021; Pan, Lian, and Lan
2011; 2012) ................................................................................................................................... 50
Figure 4-21 Summer Experiment Relative Humidity Conditions (Xiong et al. 2020; M. Kim,
Chun, and Han 2010; Tsuzuki et al. 2015; Imagawa and Rijal 2015a; Li Lan et al. 2019; 2014;
Sekhar and Goh 2011; Cao et al. 2020; Budiawan et al. 2021; Xu, Lian, Shen, Lan, et al. 2021;
L. Lan et al. 2018a) ....................................................................................................................... 51
Figure 4-22 Transition Season Experiment Relative Humidity Conditions (Li Lan et al. 2021;
Chenxi Liao and Jelle Laverge 2019; Strøm-Tejsen et al. 2016; Irshad et al. 2018; X. Zhang
et al. 2021; Tsuzuki et al. 2015; M. Kim, Chun, and Han 2010) .................................................. 51
xi
Figure 4-23 Winter Experiment Related Humidity Conditions (Pan, Lian, and Lan 2011; Cao
et al. 2021; Budiawan et al. 2021; C. Song, Liu, et al. 2020; Y. Liu et al. 2014; Tsang, Mui,
and Wong 2021; C. Song, Zhao, et al. 2020; Tsuzuki et al. 2015; M. Kim, Chun, and Han
2010) ............................................................................................................................................. 52
Figure 4-24 CO2 Density Experiment Conditions (Li Lan et al. 2021; Xu, Lian, Shen, Lan, et
al. 2021; N. Zhang, Cao, and Zhu 2018; Cao et al. 2021; 2020; Chenxi Liao and Jelle Laverge
2019; Strøm-Tejsen et al. 2016; Sekhar and Goh 2011; L. Lan et al. 2018b; Irshad et al. 2018;
Li Lan et al. 2019; Xu, Lian, Shen, Cao, et al. 2021; X. Zhang et al. 2021; M. Kim, Chun, and
Han 2010; Xiong et al. 2020) ........................................................................................................ 53
Figure 4-25 Lighting Intensity Experiment Conditions (Cho et al. 2015; Cao et al. 2021;
Hubalek, Brink, and Schierz 2010; Dong and Zhang 2021; Shishegar et al. 2021; van
Lieshout-van Dal, Snaphaan, and Bongers 2019; Wen et al. 2021; Tsuzuki et al. 2015; M.
Kim, Chun, and Han 2010) ........................................................................................................... 54
Figure 4-26 Noise Level Experiment Conditions (Li Lan et al. 2021; Xu, Lian, Shen, Lan, et
al. 2021; Basner, Müller, and Elmenhorst 2011; Cao et al. 2021; 2020; Röösli et al. 2019;
Chenxi Liao and Jelle Laverge 2019; Radun, Hongisto, and Suokas 2019; Smith et al. 2019;
M. Kim, Chun, and Han 2010) ..................................................................................................... 55
Figure 4-27 Biological Signal Measured ...................................................................................... 56
Figure 4-28 EEG Measurement .................................................................................................... 57
Figure 4-29 Body Temperature Measurement .............................................................................. 57
Figure 4-30 EMG Measurement ................................................................................................... 58
xii
Figure 4-31 Heart Rate Measurement ........................................................................................... 58
Figure 4-32 Sleep Quality Parameters .......................................................................................... 59
Figure 4-33 Sleep Quality Measurements ..................................................................................... 59
Figure 4-34 Summer Experiment Temperature Results (Xu, Lian, Shen, Lan, et al. 2021;
Budiawan et al. 2021; Cao et al. 2020; Sekhar and Goh 2011; L. Lan et al. 2018b; Li Lan et
al. 2014; 2019; Imagawa and Rijal 2015b; M. Kim, Chun, and Han 2010) ................................. 62
Figure 4-35 Winter Experiment Temperature Results (Pan, Lian, and Lan 2012; 2011; Cao et
al. 2021; Budiawan et al. 2021; Y. Liu et al. 2014; Tsang, Mui, and Wong 2021; M. Kim,
Chun, and Han 2010) .................................................................................................................... 62
Figure 4-36 Relative Humidity Experiment Result (Chenxi Liao and Jelle Laverge 2019; Cao
et al. 2021) .................................................................................................................................... 63
Figure 5-1 IEQ Survey .................................................................................................................. 72
Figure 5-2 Sleep Quality Survey ................................................................................................... 73
Figure 5-3 Other Survey Type ...................................................................................................... 73
Figure 5-4 Sleep Procedure of Lab-Setting Experiment ............................................................... 75
Figure 5-5 Sleep Procedure of Field Study Experiment ............................................................... 75
Figure 5-6 Summer Experiment Temperature Condition VS ASHRAE standard ....................... 78
Figure 5-7 Transition Season Experiment Temperature Condition VS ASHRAE standard ........ 78
Figure 5-8 Winter Experiment Temperature condition VS ASHRAE Standard .......................... 79
Figure 5-9 Summer Experiment Relative Humidity condition VS ASHRAE Standard .............. 79
xiii
Figure 5-10 Transition Season Experiment Relative Humidity condition VS ASHRAE
Standard ........................................................................................................................................ 80
Figure 5-11 Winter Experiment Relative Humidity condition VS ASHRAE Standard ............... 80
xiv
Abstract
Indoor environment quality (IEQ) is a significant component in people's daily life because people
spend most of their time to stay indoors. The IEQ has been thoroughly studied in previous research.
However, some people are suffering from inadequate sleep quality because of the uncomfortable
indoor environment. The comfortable indoor environment could be uncomfortable for people to
sleep. The research on the relationship between IEQ and sleep quality is insufficient. Some
research uses different methods to explore the relationship between sleep quality and IEQ;
however, it did not have a complete and systematic principle and did not interact with the real-life
application. Therefore, it is necessary to review research to integrate the relationship between sleep
quality and IEQ and interact with the Internet of things, smart homes, or wearable devices. The
wearable sensors will report the people's heart rate, eyes movement, and indoor environment
condition to the HAVC system, ventilation system, lighting system, and so on. Then the system
will change the indoor environment condition to provide a better sleeping environment, which
contributes to better people's sleep quality. To sum up, it is essential to provide a meta-analysis of
the bulk of research about IEQ, sleeping quality, and IoT to suggest a novel tendency of the
development to improve sleeping quality through using IoT and wearable sensors to maintain
indoor environment quality automatically.
1
1 Introduction
People spend a significant amount of time in the indoor environment. A comfortable indoor
environment quality can contribute to a good mood, high work efficiency, and healthy body. It is
essential to maintain a comfortable indoor environment for people. One-third of people's daily life
is sleeping. Inadequate sleep time and quality will contribute to low productivity. Long-term
inadequate sleep quality will contribute to the more serious situation such as deterioration of
memory. Researchers have established a correlation between a comfortable indoor environment
and sleep quality. Adjusting indoor conditions during sleep to more closely match the comfort
range of an individual might help improve sleep. However, it is difficult for people to manipulate
the indoor environment during sleeping time. Using of strategy of advanced technology such as
IoT and wearable sensors might allow indoors conditions to be more closely matched to the
occupant during sleep periods.
1.1 Indoor Environment Quality
People spend 85-90% of their daily time in the indoor environment (Ganesh et al. 2021). Many
people work indoors, study indoors and sleep indoor (Ganesh et al. 2021). Indoor environment
quality (IEQ) includes several different aspects, such as thermal condition, acoustics, visual and
indoor air quality (IAQ) (Ganesh et al. 2021). IEQ has a significant influence on human wellbeing.
Many experts have studied IEQ in these different aspects in recent decades (Ganesh et al. 2021).
The most significant component that influences the indoor environment quality is thermal comfort
and indoor air quality (Ganesh et al. 2021). Thermal comfort includes indoor air temperature,
relative humidity, and air speed. Indoor air quality includes different density of air particulars such
2
as CO2, Fine particulate matter (PM2.5), inhalable particle matter (PM10) and volatile organic
compounds (VOCs).
1.1.1 Impacts of Indoor Environment Quality on Human
IEQ has a significant impact on people's physical and psychological aspects. Uncomfortable IEQ
will contribute to the occupants' dissatisfaction (Ganesh et al. 2021). It will contribute to lower
productivity, negative performance, and sick building syndrome (SBS) (Ganesh et al. 2021). SBS
includes coughing, loss of memory, headache, dizzying, depression and so on (Seltzer 1994).
What's more, it will contribute to some chronic health problems called building-related illness
(BRI), which includes asthma, infection, and other common SBS (Ganesh et al. 2021) (al Horr et
al. 2016). Some SBS will not recover after people leave the indoor environment (Ganesh et al.
2021). On the other hand, a comfortable indoor environment can reduce the possibility of illness
and increase people's work performance.
1.1.2 Importance of Thermal Comfort
Indoor environment quality includes thermal comfort, air quality, acoustics, and lighting (Ganesh
et al. 2021). Thermal comfort is a significant component of indoor environmental quality. Thermal
comfort includes at least four different aspects: air temperature, relative humidity, air velocity, and
mean radiant temperature. People's skin will directly feel the indoor temperature, relative humidity,
and air velocity (Ganesh et al. 2021). Then the people's bodies and brains will react to these
parameters. When people stay in a comfortable environment, they can work efficiently and have a
good mood (Ncube and Riffat 2012). If people stay in a hot and humid environment, they might
feel unsatisfied and sleepy. It will contribute to people's distraction and low productivity. If the
3
people stay in a cold environment, they may feel too cold to work effectively. It will contribute to
low productivity too. Furthermore, the human thermoregulatory system interacts with the sleep
regulatory system (Li Lan et al. 2019). The thermal situation will directly influence people’s sleep
quality. During the pre-sleep time, it is essential to provide a warm environment for people because
a warm environment allows people to fall asleep easier than a normal or cold environment (C.
Song, Zhao, et al. 2020). On the other hand, a warmer environment will increase sleep fragments
(C. Song, Zhao, et al. 2020). It will contribute to bad sleep quality. Therefore, it is necessary to
provide a comfortable and suitable thermal environment for people in both daytime and nighttime.
Nowadays, thermal comfort systems can control indoor temperature automatically. People can
manipulate the indoor environment. People can set a specific temperature range, and the HVAC
system will react to the set point. When the indoor environment temperature is higher than the
setpoint in summer, the HVAC system will start work automatically. It will send cold air, which
will contribute to the decrease of indoor temperature. However, it is not convenient because people
need to operate the system and set the temperature. One research also shows that 60% of
Americans are not satisfied with the thermal environment (Choi and Yeom 2019). Especially,
people cannot manipulate the set point during the sleep period. Sometimes, the indoor temperature
will not be suitable for people to have a good sleep quality. When people wake up during the night,
they might choose to force themselves to sleep instead of changing the setting point. Furthermore,
the comfortable temperature for good sleep quality is different between pre-sleep and during-sleep
(C. Song, Zhao, et al. 2020). The temperature which allows people to fall asleep quickly might not
be the suitable temperature for people during sleep time.
4
1.1.3 Importance of Indoor Air quality
The indoor air quality will influence people's productivity (al Horr et al. 2016). Good indoor air
quality will contribute to better working performance (al Horr et al. 2016). A bad indoor air quality
will contribute to some health-related issues such as asthma symptoms, allergies, and some Sick
Building Syndrome (al Horr et al. 2016). The indoor air quality will be affected by air pollution.
The main air pollution in the indoor environment is Formaldehyde and Volatile Organic
Compounds (VOCs). VOCs are from natural pollution such as the ocean, volcano, and forest and
man-made pollution such as furniture, carpets, and painting. Indoor air quality can be enhanced by
ventilation. What's more, natural ventilation can contribute to the reduction of energy-saving too.
Some research studies indicated that air conditioning systems would have a higher relationship
with SBS than natural ventilation. Another important indoor air quality is the density of Carbon
Dioxide. Some research studies indicated that high CO2 density would contribute to lower
productivity and some SBS too. Fine particulate matter (PM2.5) and inhalable particle matter
(PM10) are also significant indoor air pollution. Inhalation of PM2.5 or PM10 over a long period
will contribute to the damage of the lung and heart (L. Zhao et al. 2021). It will also lead to
subsequent health problems such as respiratory and cardiovascular diseases (L. Zhao et al. 2021).
Although air conditioning could help people to have a better thermal comfortable environment,
the air quality in the room which operates the air conditioning will have worse air quality due to
the enclosed space created by the air condition system. Indoor air quality could be maintained
either by increasing the ventilation rate or decreasing the density of air pollution (al Horr et al.
2016). Natural ventilation and mechanical ventilation are two main methods (al Horr et al. 2016).
People who stay in a room with natural ventilation/mechanical ventilation will have a better air
quality than people who stay in a room with operated air conditioning.
5
1.1.4 Importance of Lighting Intensity
Indoor lighting intensity includes two different lighting sources: daylighting and artificial lighting
(al Horr et al. 2016). Daylighting is from the outside and is influenced by the aperture of the
building, such as windows and doors (al Horr et al. 2016). Daylight is important because it will
influence peoples' internal clock. Daylighting controls the emission of melatonin and cortisol (al
Horr et al. 2016). These two hormones control the circadian rhythm phase shift (al Horr et al.
2016). Occupants use a lot of artificial lighting in the indoor environment. Then energy
consumption of artificial lighting occupied 33% of the average consumption of UK building
energy (al Horr et al. 2016). Artificial lighting also influences the emission of melatonin which
controls the human body's internal clock (al Horr et al. 2016). Inadequate artificial light will
contribute to workers' dissatisfaction (al Horr et al. 2016). People who work in a dark environment
will be hard to see the working content. It will contribute to lower productivity and work efficiency.
What's more, it will also lead to fatigue. On the other hand, the excess of daylight and artificial
light will contribute to glare and visual discomfort. For example, the glare will cause eye fatigue
when people work in front of the computer located beside the window (Figure1-1). The glare exists
because of significant contract ratio between window and computer screen. The computer screen
is dark for people to complete their work. Long-period work with glare contributes to eye fatigue
and health problem.
6
Figure 1-1 Glare because of contrast ratio between window and computer screen
1.1.5 Importance of Acoustics
The acoustic environment is also important because people will have a low work efficiency when
they experience excessive noise. Most office work has a demand for noise control to provide a
better acoustic environment for people to gain better work efficiency (al Horr et al. 2016). A bad
and noisy acoustic environment will contribute to people's dissatisfaction with the ambient
environment. A continuous loud sound will lend to the increase of stress and level of blood
pressure. It will contribute to distraction and lower productivity. Not only the outdoor component
such as traffic noise but also the indoor component such as windpipe, fax machine, and so on will
produce noisy sound (al Horr et al. 2016). Acoustic sensation has the equivalent impact on people
7
as the thermal sensation (al Horr et al. 2016). The effects on productivity from 1°C temperature
change were the same as from 2.6 dB noise level change (al Horr et al. 2016).
1.2 Sleep Quality
1.2.1 Sleep and Sleep Quality
Sleeping takes up one-third of life (Li Lan and Lian 2016). Every night, people lay on the bed and
close their eyes to fall asleep. A simple expression of sleep is that sleep is a behavior that will
disengage from the environment and will not respond to the ambient environment (Carskadon and
Dement n.d.).
Sleep includes two different periods. These two different periods alternate across a sleeping time
with an approximately 90 minutes interval (Li Lan and Lian 2016). The first period is non-rapid
eye movement (NREM) (Carskadon and Dement n.d.).NREM includes four stages [1]. The first
stage of it happens after people close their eyes. It is the start of sleep (Leung and Ge 2013). The
second stage of NREM will shut down the muscles (Leung and Ge 2013). The third and fourth
stages of NREM are deep sleep (Leung and Ge 2013). These two stages are also called slow-wave
sleep because of the slow brain wave during stages (Leung and Ge 2013). The NREM represents
consistent deep sleep (Carskadon and Dement n.d.). It is hard to wake people up during the NREM
period. By contrast, rapid eye movement (REM), which is the second period of sleep time,
represents light sleep. During the rapid eye movement period, people's muscles are atonic, and
people's eyes will move rapidly (Li Lan and Lian 2016). Sleep begins with the NREM period.
After the first NREM period is REM. Then a deep NREM stage will occur. These two periods
8
contribute to the whole progress of people's sleep. 75-80% of sleep is NREM, and 20-25% is REM
(Elliot et al. 1980).
Sleeping quality includes different aspects of sleep. Sleep quality includes sleep latency, sleep
duration, the restfulness of sleep, sleep fragment, and so on (Buysse et al. 1989). Sleep latency is
the time of pre-sleeping. It represents the extent to which people have difficulty falling asleep.
Sleep duration is the total time of sleep. Sleep restfulness represents people's feelings. Sleep
fragment means people's wake times during sleep. It represents the frequency and duratipon of
when people get disrupted during sleep. Furthermore, sleep quality also includes a subjective
feeling based on people's opinions (Buysse et al. 1989).
1.2.2 Importance of Sleep Quality
Sleep quality is an essential activity in people's life. Sleep not only helps people to be relaxed but
also help people to let their body recovery (Li Lan and Lian 2016). The reason that sleep is
important also includes the following parts. First, poor sleep quality will contribute to a tired body
(McCoy and Strecker 2011). It will contribute to the distraction of daytime working or study
(McCoy and Strecker 2011). It also contributes to lower efficiency of work or study. Second, poor
sleep quality will contribute to damage of wellbeing (Buysse et al. 1989). Poor sleep will contribute
to young people's weight increase and damage of young people's brains (He, Lian, and Chen 2019).
Elderly people who have poor sleep will have a high risk of gaining cardiovascular and
cerebrovascular diseases (Y. Chen et al. 2018). Third, poor sleep will contribute to damage of
cognitive performance and ability (McCoy and Strecker 2011). The damage of cognitive
performance includes a decrease in attention, executive function, memory, emotion, and sensation.
9
Good sleep quality will allow people to have a better memory and cognitive ability (McCoy and
Strecker 2011). What is more, poor sleep will contribute to a high risk of mortality (Buysse et al.
1989) (Y. Chen et al. 2018). Therefore, good sleep quality is essential to people because it will
contribute to a healthy body and well cognitive ability.
It is common to find people who are complaining about their sleeping quality (Buysse et al. 1989).
Research indicates that 15-35% of people frequently complain about the distribution of sleep
quality (Buysse et al. 1989). Their complaint includes the difficulty of falling asleep or maintaining
sleep (Buysse et al. 1989). Sleep quality does not have a standard to measure. It is a subjective
feeling of people. It is hard to detect people's subjective feelings.
1.3 The Internet of Things
1.3.1 Content of The Internet of Things
One tendency of world development is that people can connect to anything anytime and anywhere
through the Internet (Kaur 2018). The Internet of things (IoT) allows the tendency to become
possible (Kaur 2018). IoT uses different objects, including a sensor, Near Field Communication
(NFC), Radio Frequency Identification (RFID), mobile phone, computer, cloud-related, actuator
and so on, to connect and interact with each other (Kaur 2018). These tools can sense, listen, and
communicate with each other [2]. The combination of these tools even can perform a task (Kaur
2018). IoT allows people to have a better life quality (Kaur 2018). IoT is widely used in different
aspects such as transportation, smart homes, medical system, industrial production, etc. (Medhat
et al. 2019).
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1.3.2 Wearable Sensors
Wearable sensors are sensors that people can wear in their body parts such as the wrist, neck, and
so on. Some wearable sensors can detect people's heart rate, skin temperature, and other biological
signals (S. Liu et al. 2019). The development of the sensors is complete and mature and has
commercial products (S. Liu et al. 2019). It allows people to record their health data and biological
signal in a portable and convenient method. What's more, some companies developed wearable
sensors such as Fitbit, which could record people's sleep stage, duration of the sleep period, sleep
onset latency, and so on. It allows people to monitor their sleep quality.
1.3.3 Smart Homes
Our daily life quality can be improved by IoT through smart homes devices and applications. Smart
homes allow people to have a comfortable, economical, and secure operation. For instance, smart
refrigerators, TVs, washers, cooling, and heating devices have already connected to the Internet
(W. M. Kang, Moon, and Park 2017). The residents who own these smart devices can manipulate
the device and control the different parameters of their home based on their preference through
their smartphone or computer (W. M. Kang, Moon, and Park 2017). For example, the door will
automatically open when people arrive home. The sensor will detect people's biological signals.
Once it detects the master's biological signal, it will send a message to the door switch to open the
door. Another example is lighting. People can turn on the light or change the lighting intensity in
the different rooms through their smartphones. People can control and monitor their entire home
with smartphones. What is more, some virtual AI assistance, such as Alexa developed by Amazon,
has been installed in some smart homes (Yang, Lee, and Lee 2018). People can get the information
they want, such as climate and daily schedule, by calling 'Alexa' (Yang, Lee, and Lee 2018). Apple
11
also developed an AI speaker called Apple HomeKit. The Chinese company Xiaomi targets smart
homes as a long-term development (Yang, Lee, and Lee 2018). Xiaomi company now has its own
application called Mijia. In this application, people can connect and use every smart device brought
from Xiaomi company. These kinds of automatic operations allow people to conveniently control
their homes based on their preference through the smartphone. IoT and AI contribute to the
development of smart home services.
1.3.4 Importance of IoT, Wearable Sensors And Smart Home
IoT allows people to have a better life quality and a more convenient life. People's daily life and
work will be improved by IoT. It helps people to save time for manipulation. It also contributes to
an increase in the world economy. The worldwide smart home market will increase to USD 119.26
Billion by 2022 (Yang, Lee, and Lee 2018). More companies are targeting IoT as their long-term
development goal. IoT is the future development tendency.
1.4 Goals and Objects
It is essential to provide a meta-analysis of current research studies on IEQ, sleep quality, and
current advanced IoT technology. A significant amount of journal articles, conference papers, and
books about IEQ, sleep quality, and IoT will be reviewed. It is essential to compare the different
research studies to find the relationship between sleep quality and indoor environment quality and
find the tendency of the future study. Then it is essential to review the IoT and wearable sensors
to find the tendency of IoT and wearable sensors development. Finally, the relationship between
sleep quality and indoor environment quality should be interacted with IoT and wearable sensors
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to suggest a novel method or novel technology that can adjust the indoor environment condition
for sleeping people. In IoT or smart homes environment, the sensor could detect people's feelings
and biological signals of the indoor environment and automatically adjust the indoor environment
based on people's biological signals. What is more, the parameter which was not fully studied in
previous research studies should be suggested being one important parameter in the future study.
It is also essential to provide a visionary plan for the research study or development of novel
technology which will contribute to better sleep quality.
1.5 Summary
Indoor environment quality is a significant component of people's life. It will impact people's
productivity, wellness, and sleep quality. Sleep is an essential activity that occupies one-third time
of people's life. Good sleep quality will contribute to a healthy body and high work or study
efficiency. However, 15-35% of people complain about their sleep quality. It is essential to
improve people's sleep quality. Indoor environment quality is highly related to sleep quality. A
comfortable indoor environment will contribute to good sleep quality. The daytime indoor
environment quality has been thoroughly studied in the previous research studies. On the contract,
there is limited research studies related to the relationship between IEQ and sleep quality. The
limited research studies also are diverse and biased because most of them focus on a specific aspect.
Nevertheless, it is hard for people to control the indoor environmental condition during sleep time.
Therefore, it is essential to find a novel and possible method which uses IoT and wearable sensors
to automatically adjust indoor environment conditions during sleep time.
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2 Background Research
IEQ has a significant influence on human wellbeing. Thermal comfort is the most important
portion of IEQ because people directly detect the indoor temperature through the skin. There are
several different biological signals that can represent people's thermal satisfaction. The research
studies on daily time have been studied for a long time. However, the relationship between sleep
quality and IEQ has only begun to be seriously studied in recent years. The research studies are
inadequate and partial. What's more, the maintenance of sleeping indoor environment is difficult.
It is essential to interact with the IoT and wearable sensors, which have also been studied in recent
years.
2.1 IEQ
2.1.1 Impact of IEQ in Human Wellbeing
IEQ will influence people's work efficiency and productivity. It includes acoustic, lighting, thermal
condition, and indoor air quality (Ganesh et al. 2021). A comfortable acoustic environment can
protect the occupants away from the distribution of noise (Ganesh et al. 2021). Fourteen dBA
background noise and eighteen five dBA white noise will not influence people's satisfaction
(Ganesh et al. 2021). What's more, the noise last time also influences people's feelings (Ganesh et
al. 2021). A duration of less than 30 minutes will not influence people's satisfaction (Ganesh et al.
2021). However, a duration of beyond 120 minutes will heavily influence people's satisfaction
(Ganesh et al. 2021). A long time exposed to a noisy environment can contribute to headaches
(Ganesh et al. 2021). Lighting quality will also influence people's productivity and work efficiency.
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Working in a low light condition will contribute to severe damage after work and insufficient sleep
quality. Thermal comfort is the most important parameter in the IEQ.
2.1.2 Component Influenced Thermal Comfort
Some experts have already studied indoor thermal comfort for a long time. Some codes such as
ASHRAE, ISO, and EN also have been established for a long time.
The most significant factor of thermal comfort is the air temperature because it is the main variable
factor, and it directly influences the occupants' satisfaction with the thermal environment (Ganesh
et al. 2021). The dry-bulb air temperature should be between 18 to 23 for a comfortable thermal
condition (Ganesh et al. 2021). What's more, the air temperature can be directly detected by the
people's skin (Ganesh et al. 2021). This feeling will contribute to people's judgment of thermal
comfort (Ganesh et al. 2021). Occupants' performance will decrease by 2% when the indoor
temperature increases by one Celsius degree in the range of 25 °C–30 °C away from the desirable
temperature (al Horr et al. 2016). The second important component of thermal satisfaction is air
speed (Ganesh et al. 2021). Air speed will influence people's convection heat loss and contribute
to a change of thermal satisfaction. The acceptable air speed is relatively low, and most are under
0.3m/s (Ganesh et al. 2021). In some humid regions, people used to use small fans to control the
indoor temperature (Ganesh et al. 2021). The airflow from the small fan could increase the
comfortable temperature range to 28°C (Ganesh et al. 2021). The third component of thermal
satisfaction is related humidity (RH) (Ganesh et al. 2021). The low RH will contribute to a dry
ambient environment and stimulation of the nose and eye (Ganesh et al. 2021). The RH should be
between 25% to 70% for normal people and be between 30% to 50% for sensitive people (Ganesh
et al. 2021).
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Several journal articles demonstrate their study of the relationship between thermal comfort and
biological signals such as people's skin temperature, heart rate signal, electroencephalogram (EEG)
signal, et al.
The article "Analysis of human electroencephalogram features in different indoor environments,"
written by Hongyu Guan and published in December 2020, demonstrated that the
electroencephalogram signal is strongly connected with indoor comfort (Guan et al. 2020). The
article held an experiment to test their hypothesis (Guan et al. 2020). The experiment included
eight participants with a healthy body (Guan et al. 2020). The experiment was conducted in a small
room with an air-conditioning system at Qingdao University of Technology (Guan et al. 2020).
Two indoor conditions were comfortable, and uncomfortable situations were set to test the
hypothesis (Guan et al. 2020). Participants wear a cap with some electrodes which can record their
EEG signal (Guan et al. 2020). The results showed that the total EEG energy would increase when
people stay in an uncomfortable environment (Guan et al. 2020). Therefore, the EEG signal has
an intimate relationship with indoor environment comfort, including thermal comfort (Guan et al.
2020). EEG signal might be used as the guideline to control the indoor temperature in the future
study.
The research "Development of the data-driven thermal satisfaction prediction model as a function
of human physiological responses in a built environment," written by Joon-Ho Choi and Dongwoo
Yeom and published in March 2019, insisted that the heart rate and the body skin temperature are
significantly related to thermal satisfaction (Choi and Yeom 2019). They conducted an experiment
at USC (Choi and Yeom 2019). The experiment was conducted in a 2.9m×2.9m experiment
chamber (Choi and Yeom 2019). The experiment includes an HVAC system, a desk, and a chair
(Choi and Yeom 2019). Eighteen participants completed the experiment and took the sixteen valid
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data as a result (Choi and Yeom 2019). The progress of the experiment is as follows. The
participants enter the chamber with 20℃ (Choi and Yeom 2019). Then the researchers collected
the skin temperature data, heart rate data, and questionnaire (Choi and Yeom 2019). The room
temperature raised one degree every ten minutes until the room temperature rose up to 30℃ [3].
The participants stay in the room for 100 hours (Choi and Yeom 2019). The data was collected
through a Labview-based Data Acquisition (DAQ) system every 10 seconds (Choi and Yeom
2019). For the skin temperature, the researchers collected several local skin areas temperatures
(Choi and Yeom 2019). The local skin areas are shown in figure.5. The researchers used Excel,
Minitab, and WEKA software to analyze the data (Choi and Yeom 2019). Excel and Minitab are
essential data analysis software (Choi and Yeom 2019). WEKA is a data-mining software that is
free used (Choi and Yeom 2019). WEKA is used to test the precise of the data connection (Choi
and Yeom 2019). What is more, the researchers take gender and BIM into the analysis (Choi and
Yeom 2019). They combine different skin temperature signals and components to predict the
thermal comfort level. Then they find that the most precise combo is combat forehead temperature,
wrist temperature, and gender (Choi and Yeom 2019).
2.1.3 Component Influenced Indoor Air Quality
Indoor air quality study includes three physical parameters: indoor air pollution level, ventilation
rate, and outdoor air monitor (al Horr et al. 2016). Indoor air pollution includes several different
components such as CO2, Formaldehyde, Volatile Organic Compounds (VOCs), PM2.5, PM10,
and so on (Canha et al. 2017). A high ventilation rate with good outdoor air quality will contribute
to the improvement of indoor air quality. In contrast, if the outdoor air quality is bad, it is essential
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to reduce the indoor ventilation rate to maintain the indoor air quality. Yu Zhao et al. demonstrated
that indoor PM density has an intimate relationship with outdoor PM density (Y. Zhao et al. 2017).
People's satisfaction with indoor air quality increased when the pollution source was removed from
the office (Wargocki and Wyon 2017). The research used carpet as the air pollution source in an
office (Wargocki and Wyon 2017). 70% of people report dissatisfaction with indoor air quality
when the carpet is in the office (Wargocki and Wyon 2017). In contrast, 30% of people report
dissatisfaction with indoor air quality when the carpet is removed (Wargocki and Wyon 2017).
People's satisfaction with indoor air quality also could be increased by increasing the ventilation
rate (Wargocki and Wyon 2017). Another method to improve people's indoor air satisfaction is to
increase the outdoor air supply (Wargocki and Wyon 2017).
2.1.4 Component Influenced Lighting Quality
Lighting regulates people’s internal clock, performance, and physiology (al Horr et al. 2016). The
light environment significantly affects people’s visual comfort (Hu et al. 2021). Lighting quality
includes illuminance, luminance, and correlated color temperature. The comfortable illuminance
range is 100 lux to 3000 lux (Hu et al. 2021). China “Architectural Lighting Design Standards”
recommend that average illuminance for an ordinary office should not be less than 300 lux (Lu et
al. 2020). The British Institute of Lighting Engineering recommends that offices’ illuminance
should be 500 lux (Lu et al. 2020). People’s satisfaction with light has a positive relationship with
illuminance under the range of 0-600lux (Hu et al. 2021). Lower CCT could provide a restfulness,
security, and positive environment for people (Lu et al. 2020). Shinomura et al. indicated that low
color temperature contributes to more fatigue (Lu et al. 2020). A higher color temperature allows
people to stay alertness and concentrate (Lu et al. 2020).
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2.1.5 Component Influenced Acoustic Environment
The acoustic quality study mainly focused on three parts: sound source type, sound pressure level
(SPL), and reverberation time (RT) (Meng, An, and Yang 2021). Sound source type could be
attributed to two main parts: exterior and interior (Meng, An, and Yang 2021). The exterior source
includes transportation sound, natural sound (such as noise created by wind), outdoor activities
sound, and so on. Interior source includes mechanical source, human voice, electrical signal sound,
and so on. Sound pressure level could be measured in decibels (dB) (Soares 2008). According to
the research produced by K W Mui, the comfortable noise level range in the air conditioning
situation is between 45-70 dB (Mui and Wong 2016). Another research produced by Huang L et
al. also indicated that the acceptable noise level was below 49.6 dB (Huang et al. 2012).
Reverberation time could also influence work performance (Meng, An, and Yang 2021). The
increase of reverberation time will lower the accuracy of memory of people (Meng, An, and Yang
2021). Qi Meng et al. demonstrate that noise could influence the response time of visual cognitive
performance tasks (Meng, An, and Yang 2021).
2.1.6 Limitation of Current Research Studies
As discussed above, IEQ is significantly important to people's health and productivity when people
spend 90% of their time in the indoor environment. The research studies related to daytime indoor
environment quality is thorough. A lot of research studies have been done in different aspects of
the daytime indoor environment. Some research studies even demonstrated that different aspects
would have internal influences on each other. However, compared with health and productivity,
sleep quality which will also be influenced by indoor environment quality, has not been sufficiently
researched, and the current research studies also have some limitations. As a result, current
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literature reported by numerous research units or organizations is not robust enough to generalize
the findings and research outcomes for a real-world application.
2.2 Sleep quality
2.2.1 What is Sleep Quality
Sleep quality is a partly subjective opinion based on people's experience in sleep time. It can be
predicted by the time of REM and NREM during sleep time. It also can be predicted by using the
PSQI questionnaire and sleep quality questionnaires after people wake up. Sleep quality also can
be predicted by some wearable sensors such as Fitbit.
Sleeping thermal satisfaction prediction has several models. First is the PMV-PPD model (Q. Zhao,
Lian, and Lai 2021). Another model established from predicting daily thermal satisfaction: the
two-node model is a four-node model (Q. Zhao, Lian, and Lai 2021). The two-node model includes
body core temperature and skin temperature (Q. Zhao, Lian, and Lai 2021). The four-node model
was developed by Pan Dongmei et al. (Dongmei et al. 2012). It includes the core body temperature,
naked skin temperature, temperature of skin covered by a quilt, and temperature of skin connected
with mattress (Dongmei et al. 2012).
2.2.2 What Environmental Component Will Influence Sleep Quality
Sleep quality will be influenced by thermal condition, indoor air quality, acoustic and lighting
intensity.
First and foremost, the thermal condition is the most important component which will directly
influence people's sleep quality. The research was done by Xinbo Xu et al. which separates the
questionnaire through the mail, the Internet, and journal magazines in Shanghai, China (Xu, Lan,
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et al. 2021). They collected the questionnaires and analyze the questionnaire (Xu, Lan, et al. 2021).
The results show that people's sleep quality will be influenced by the indoor temperature (Xu, Lan,
et al. 2021). Li Lan et al. did research based on an experiment with 18 healthy Chinese students.
It was held with three different air temperatures. Each of the participants attended these three
different situations separately. They collected EEG, air temperature, and people's satisfaction of
thermal comfort and sleep quality. The results indicated that 26 °C is the neutral temperature (Li
Lan et al. 2014). The temperature which deviates from neutral temperature will contribute to more
sleep onset latency and less slow-wave sleep (Li Lan et al. 2014). It also illustrated that air
temperature would have a significant influence on people's sleep quality (Li Lan et al. 2014).
What's more, the neutral temperature for people in sleeping time is higher than in weak time (Li
Lan et al. 2014). Nan Zhang et al. also illustrated that 26°C is the neutral temperature which will
contribute to less sleep onset latency (N. Zhang, Cao, and Zhu 2019). They did an experiment with
eight healthy women who experienced all three different air temperature situations. The
researchers recorded the air temperature, global temperature, relative humidity, EEG, EOG, EMG,
skin temperature, and satisfaction questionnaire results. What's more, Kraiwuth Kallawicha et al.
did a questionnaire collection and PSQI collection (Kallawicha et al. 2021). The result also shows
that a warm environment can contribute to less sleep onset latency. On the other hand, as
mentioned in "Effects of phased sleeping thermal environment regulation on human thermal
comfort and sleep quality" written by Cong Song et al., during the pre-sleep time, the thermal
condition should be warm enough, which contribute to less sleep latency (C. Song, Zhao, et al.
2020). However, the thermal condition should be lower than pre-sleep time because the warm
thermal condition will contribute to sleep fragment and a high ratio of rapid-eye-movement (C.
Song, Zhao, et al. 2020). They did an experiment with 12 participants. The researchers collected
21
the data and used data mining methods to analyze the data. The relationship between thermal
condition and sleep quality has been studied thoroughly than other parameters. When researching
for sleep quality and indoor environment quality, there is a bulk of study related to thermal comfort.
The second component which affects sleep quality is indoor air quality. The density of CO2 will
impact people's sleep quality. Xiaojing Zhang et al. indicated that the increase of CO2 density
would decrease people's sleep quality (X. Zhang et al. 2021). They also demonstrated that a high
ventilation rate would decrease the CO2 density and increase the sleep quality of people (X. Zhang
et al. 2021).
Thirdly, the acoustic environment will influence people's sleep quality. The indoor noise sound
was emitted by air conditioning, windpipe, and fans. The outdoor noise sounds include traffic
sound, wind sound, and so on. Noise level is negatively related to sleep quality and sleep latency
(Röösli et al. 2019). What’s more, noise exposure one hour before sleep also contributes to the
decrease of sleep quality (Röösli et al. 2019).
Fourth, the lighting intensity is a component that is frequently overlooked. Many people will close
the light when they are sleeping. However, exposure to the lighting during the daytime will
influence people's inner clock. Lighting will influence the secretion of some hormones such as
melatonin and cortisol. Penjun Wen et al. indicated that high correlated color temperature (CCT)
exposure before sleep would contribute to the decrease of adolescents' sleep quality and increase
of adolescents' daytime fatigue (Wen et al. 2021). They compared the sleep quality and daytime
sleepiness between high CCT and lower CCT in adolescents (Wen et al. 2021). The result shows
that lower CCT will contribute to the lower melatonin level in the next day (Wen et al. 2021).
22
2.2.3 Limitation of Current Research Studies
As mentioned above, most of the current research studies are related to IEQ in the daytime. The
results of the relationship between IEQ and human comfort are not applicable to the sleep condition.
During sleep period, people need specific environment such as an environment which is warmer
than daytime. Even through there are some research studies investigated the relationship between
sleep quality and IEQ, not only the number of the research studies is limited but also the results
are limited. The limited research studies on the relationship between IEQ and sleep quality are
diverse. Different research studies focused on different aspects. Most of the research studies only
focused on thermal comfort. Some research studies only focused on indoor air quality. The
different components influence sleep quality in the meantime. The results of these study are biased.
Some results indicated the thermal condition. Some research studies indicated the air quality
condition. Little research studies investigated the impacts of combination of different parameters.
It contributes to a situation that there is few guideline for the IEQ’s operation during sleep time.
Therefore, the integration of the relationship between IEQ and sleep quality should be conducted
to develop a guideline for IEQ during sleep period.
2.3 IoT
2.3.1 IoT
IoT is widely used in the world. IoT allows communication between different equipment
(Bavaresco et al. 2019). In the architecture domain, IoT can be used to manipulate the indoor
environmental condition. For example, A huge amount of HVAC systems now relates to the
23
smartphone. People can use their phones to control the air conditioning even when they are outside.
The usage of IoT has expanded in recent years.
Grahama Couldby et al. conducted an experiment about IoT used in the indoor environment
(Coulby et al. 2021). The experiment goal is to compare the low-cost sensor and expensive sensor
(Coulby et al. 2021). It used sensors to monitor the indoor environment quality include CO2 density,
PM2.5 density, temperature, humidity, lighting intensity, and noise level (Coulby et al. 2021). The
low-cost sensors include AMSCCS 811 (CO2), PMSA003 (PM2.5), Bosch BME 280 (temperature,
humidity), ROHM BH1750 (lighting intensity), InvenSense INMP441 (noise level) (Coulby et al.
2021). The expensive sensors include HOBO MX1101 (CO2, temperature, humidity), IQAir Air
Visual Pro (PM2.5), Onset HOBO MX1104 (lighting Intensity, temperature, humidity), Omega
HHSL-101 (noise level) (Coulby et al. 2021). The data from low-cost sensors were collected and
processed by HELTEC ESP32 Wi-Fi controller (Coulby et al. 2021). The controller also sent the
data to the IoT/Cloud platform ThingSpeak (Coulby et al. 2021). The data from expensive sensors
were recorded locally. The data was downloaded from the specific application at the end of the
experiment. The two pairs of data were compared, and the results showed that some low-cost
sensor is feasible in daily life. The limitation of the low-cost sensor was the low accuracy.
2.3.2 Wearable Sensors Which Could Be Used in Sleep Time
A bulk of wearable sensors can help monitor people's biological signals such as heart rate, skin
temperature, and so on. According to the "Effects of phased sleeping thermal environment
regulation on human thermal comfort and sleep quality," the skin temperature can be detected by
a wireless thermometer sensor (C. Song, Zhao, et al. 2020). Somté 32 PSG is a portable sensor that
24
could record people's Electroencephalography (EEG), Electrooculogram (EOG), and
Electromyogram (EMG). EEG, EOG, and EMG could be used to calculate people's sleep quality.
Some commercial sensors such as Fitbit and ActiGraph WGT3X-BT are well established to
monitor people's sleep quality. Fitbit is a comfortable and convenient wearable sensor that could
monitor people's sleep onset latency, sleep total duration, wake numbers after sleep, sleep stage
(N1, N2, N3, REM), and sleep quality (X. Zhang et al. 2021). ActiGraph WGT3X-BT is another
portable sensor that could monitor people's sleep quality. However, the company will not provide
the raw data to the people. It will not allow the researchers to handle raw data and get precise
results. What's more, the monitor precise also needs to be further studied.
2.4 Summary
Different aspects of indoor environment quality have been thoroughly studied in awake time.
There are limited research studies about indoor environment quality during sleep time. The limited
research studies also have some limitations. First, the research studies focused on the different
aspects of the indoor environment respectively. Some only focused on the relationship between
thermal comfort and sleep quality. Some only focused on the relationship between indoor air
quality and sleep quality. The relationship between IEQ and sleep quality still needed to be
integrated. Second, the research studies focused on different ages respectively. Third, most of the
research studies is based on the experiment conditions, which means researchers hold the
experiment in a specific chamber. The actual bedroom condition will be different from the chamber
condition. The sensor they used in the chamber will not be suitable for bedroom use. IoT and
wearable sensors should be interacted with the relationship between IEQ and sleep quality. IoT
25
and wearable sensors which are portable, and convenient allow people to control their indoor
environment efficiently and conveniently. Therefore, it is essential to convert a meta-analysis of
improving IEQ and sleep quality through using IoT.
26
3 Method
3.1 Overview of Methodology
To achieve the goal of conducting a meta-analysis of the current research studies trend of the sleep
quality discipline and providing future research studies directions to meet the intellectual
requirement of the research studies and professional communities, it is necessary to overview the
current research studies related to IEQ sleep quality and IoT and provide suggestions: 1. a novel
guideline which provide suggestions for future studies experiment setting, 2. a visionary plan of
future research studies. To obtain the goal, the methodology includes literature review, analysis,
and development of suggestion (Figure3-1).
Figure 3-1 Overview of Methodology
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3.2 Literature Review
To obtain the goal of providing an overview of sleep quality and IEQ research studies and
technology currently available, the first step was conducting a literature review study that includes
three stages: search, identify and record, and analysis. A bulk of research studies about IEQ, sleep
quality, IoT, and wearable sensors was read, progressed, recorded, and compared.
3.2.1 Search Progress
The first stage of the literature review was searching for current peer-reviewed journal articles,
books, and conferences. The search narrowed to the recent 20 years because another goal of
providing the overview is to give further suggestions for the development of the advanced IoT,
which could help people to improve their sleep quality and for the future study of better sleep
quality. There were two different methods to do the search. First, it was convenient to use the USC
library and google scholar to search for specific terms. Second, it was necessary to search specific
terms in specific journals and conferences such as Building and environment, Indoor and
Environment, ASHRAE conference paper et al. The research studies were divided into two
different aspects: the relationship between IEQ and sleep quality, and IoT and wearable sensors.
For different aspects of the search, different terms will be used to do the search. When conducting
the relationship between IEQ and sleep quality aspect search, the term "sleeping quality," "IEQ,"
"indoor environment quality," "thermal comfort," "acoustic," "lighting," "indoor air quality" was
separately used to find the research studies related to the indoor environment, human wellbeing,
and sleep quality. For the IoT technology and wearable sensor research studies, the term "Internet
of thing," "smart home," and "wearable sensor" was used to find study. During the search stage, it
28
was essential to search for more than 70 research studies because when doing the searching stage,
only the summary was read. After identifying and recording stage, the total number of the research
studies was reduced to fifteen-nine. The goals of the research studies were improving IEQ,
contributing to a healthier life, improving sleep quality, the usage of IoT in the building
environment aspect.
It was essential to use the bookmark to record and collect the link. What's more, the link and aspect
of the research studies were recorded in a word file in order to conduct the following identify and
compare progress.
3.2.2 Identify and Record
All the research papers, books, conference proceedings were collected in a word file. In identifying
progress, this kind of research studies was read thoroughly and deeply. According to the deep
reading, more information was added to the word file. For the research studies concentrated on the
indoor environment quality and sleep quality, reading from the beginning to the end, the goals,
objectives, methodology, results, limitations were recorded in the word document. Some articles
might not be included based on some reason. Finally, it was essential to generate an excel file
based on word files. The excel file includes two sheets that record two research aspects,
respectively.
For the IEQ and sleep quality aspect, the sheet listed the author, title, date, goals, objectives,
parameters, biological signal, sleep quality, questionnaires, data analysis methods, results, and
limitations. The record of author, title, date, current issues, goals, and objectives allowed
convenient comparison (Figure 3-2). Parameter recording included the parameters recorded and
analyzed from the research studies. It separated into two parts: the outdoor parameter and the
29
indoor parameter. The outdoor parameter included the parameters which the researchers
investigated, such as outdoor temperature, relative humidity, and so on, and the measurements of
the parameters. The indoor parameter included the parameters which the researchers investigated,
such as indoor air temperature, radiant temperature, relative humidity, air velocity, lighting
intensity, CO2 density, and so on. The biological signal type such as Electroencephalography
(EEG), Electrooculogram (EOG), Electromyogram (EMG), heart rate/ Electrocardiogram (ECG),
skin temperature, and so on, and measurement of each biological signal were recorded (Figure 3-
6). The detail of sleep quality such as sleep onset latency (SL), sleep period time/sleep duration
(SPT), wake after sleep onset (WASO), and so on were recorded with their measurement. It was
also necessary to record if the researchers used the questionnaires to investigate the relationship
between sleep quality and IEQ. Finally, the data analysis methods, results, and limitations were
recorded (Figure 3-2). The sheet included every detail of all the research studies. It helps the
progress of analysis.
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Figure 3-2 Record Components of Relationship Between IEQ and Sleep Quality
The research studies for IoT and the wearable sensor were recorded in another sheet. The basic
information, function, results, and limitations were recorded (Figure3-3). It allowed the analysis
part to become more translucent, clear, and easy.
Figure 3-3 Record Components of IoT and Wearable Sensors
3.2.3 Analysis and Summary
Based on the IEQ and SQ sheet, it was essential to analyze and compare the different experiment
conditions, methods, results, and limitations. The recommendation of the experiment condition
should not be biased. For the methods, it was essential to find the method which is simple and
31
accurate to give suggestions for future study. The parameters were counted to find out which is
fully studied, and which is not fully studied. Based on the results' pros and cons, the relationship
between sleep quality and IEQ were integrated. Analysis used Excel.
The first analysis was related to the research’s condition. The number of lab-setting and on-site
experiments, the participants, weather conditions, nation, and age were analyzed in chapter 4. It
contributed to the elimination of bias in further study.
The second analysis is related to the research methodology. In the excel file, if the parameter was
studied in the research studies, it was marked by 1 in the sheet. Therefore, it was convenient to
sum the number of times that the parameter has been studied. The times and parameters were
recorded in a new sheet. Then, they were used to generate a bar chart. Based on the bar chart, the
parameter which has been studied thoroughly and the parameter which has not been studied
thoroughly is obvious. Then it was necessary to analyze the reason for adequate and inadequate.
The reason analysis is demonstrated in chapter 4.
The filter function in Excel contributes to the next step of the analysis of the effects of a specific
parameter. The filter function could filter research studies that studied the specific parameter
recorded in Excel. Then the filtered research studies list could be used to analyze the measurement
of the parameter. The commonly used measurements and instruments would be recorded. In the
next interacting stage, the commonly used instrument would be compared with the IoT
technologies and wearable sensors to determine the better and more suitable one for further study.
The filtered research studies lists were used to analyze the relationship between the parameter and
sleep quality. The overlapping range of each parameter was recorded. The overlapping range
would be one significant part of the recommendations. The measurement and relationship analysis
are demonstrated in chapter 4 and chapter 5.
32
Based on the IoT and wearable sensor chart, it was essential to find out the most convenient
equipment with affordable price and some model type sensor which have the possibility to become
a commercial product. The equipment should interact with the relationship between sleep quality
and IEQ.
3.3 Interact the Relationship with IoT and Wearable Sensors
It was essential to find out which IoT equipment has the specific function of changing the indoor
environmental condition. The IoT equipment and wearable sensors should have the function of
measuring the parameters were analyzed in the previous step. It also should be commercial
products which people could buy them in the store or online with affordable prices. The IoT
equipment also should be portable for people to take with them when they are travelling and
evection.
3.4 Development of Suggestions for Better Sleep Quality and
Future Study Guideline
3.4.1 Development of Suggestion for Sleep Quality Research Guideline
The suggestion for guidelines was based on the analysis of the current punishments. The limited
researched IEQ component, outdoor parameters, indoor parameters, and so on will be suggested.
The suggestion for future studies’ methodology depends on the analysis of the experiment type,
experiment information participants, experiment procedure, experiment survey and so on. The
recommended measurement is also based on the analysis of the measurement of different
parameters.
33
3.4.2 Development of Suggestion for Future Study Direction of IEQ and
Sleep Quality
Based on the analysis of methods, some parameter such as CO2, PM2.5 and humidity are not
studied thoroughly. It is essential to suggest future studies to focus on these parameters. Especially
the PM2.5 is related to COVID-19. This suggestion is also conducted from the methods which are
feasible but are not rapidly used. For example, the four-node method was developed in recent years
and is not frequently used in the current study.
3.5 Summary
To achieve this thesis goal, it was essential to overview the current public research studies related
to IEQ, sleep quality, and IoT. The public research studies should be recorded, summarized, and
analyzed. Then, two recommendations were provided. The first recommendation is a novel
guideline that give out some suggestion for the future studies. The second recommendation is a
trend of the sleep quality discipline, which provides future research studies directions to meet the
intellectual requirement of the research studies and professional communities.
34
4 Data
Current published research studies investigated about the IEQ, sleep quality and IoTs. After search
on the USC library, Google scholar, conference, and specific journal such as Building and
Environment, forty-four research studies related to IEQ and sleep quality and fifteen research
studies related to IoT, and wearable sensors were recorded. For research studies related to IEQ and
sleep quality, the basic information of the research studies, parameters investigated in the research
studies, and research results were recorded in this chapter. The basis analysis and chart were
represented. For research studies related to IoT and wearable sensors, their functions and
drawbacks were recorded.
4.1 Impact Factor of Current Publication
The research studies are from different journals with different index. The journal of Building and
Environment has an impact factor of 6.456. The journal of Sleep has a 5.849 impact factor. The
journal of Indoor Environment and Health has an impact factor of 5.770. The journal of Building
Engineering has an impact factor of 5.318. The journal of Atmospheric Pollution Research has an
impact factor of 4.352. The journal of Environmental Research and Public Health has an impact
factor of 3.39. The journal of Sustainability has an impact factor of 3.251. The journal of
Architectural Science Review has an impact factor of 3.1. The journal of Indoor and Built
Environment has an impact factor of 3.015 The journal of Thermal biology has an impact factor
of 2.902. The journal of Lighting Research and Technology has an impact factor of 2.767. The
journal of Buildings has an impact factor of 2.648. The journal of Science and Technology for the
Built Environment has an impact factor of 1.990.
35
4.2 Basic Information and Research Condition for Research
Studies Related to IEQ and sleep quality
4.2.1 Common Issue and Goals
Based on the recorded information in the excel files, the common issues are as follows. First, sleep
quality is important in people's life because people take one-third of their time to sleep. Second,
the current requirements of IEQ for daytime are not suitable for sleep time. Third, limited research
studies related to sleep quality and IEQ. The common goal is to investigate the relationship
between IEQ and sleep quality.
4.2.2 IEQ Component, Research Type, and Research Season
IEQ includes four components: thermal comfort, indoor air quality, lighting, acoustic. The
different studies focused on different aspects. (Figure 4-1) The most investigated component is
thermal comfort. 27 research papers (44% among 44 papers) investigate thermal comfort during
the sleep period. 14 research papers (23% among 44 papers) investigate indoor air quality during
the sleep period. 11 research papers (18% among 44 papers) investigate lighting conditions'
impacts on sleep quality. Not only the lighting condition during the sleep period but also the
lighting condition during daytime were investigated. Nine research studies (15% among 44 papers)
investigated acoustic environments during the sleep period.
36
Figure 4-1 Sleep Research Percentage by IEQ Components
Research types were also analyzed. Research type includes empirical study, literature review, and
questionnaire study (Figure 4-2). 40 papers (91% of 44 papers) are empirical research studies. Two
papers (5% of 44 papers) are questionnaire research studies. Two papers (5% of 44 papers) are
literature review research studies. Empirical research studies include lab-setting study and field
study (Figure 4-3). 21 research studies (52.5% of 44 papers) conducted lab-setting study
experiments. 19 research studies (47.5% of 44 papers) conducted field study experiments.
27, 44%
14, 23%
11, 18%
9, 15%
Percentage of IEQ components
Thermal comfort IAQ Lighting Acoustic
37
Figure 4-2 Research Type
Figure 4-3 Experiment Setting
Most of the research studies was conducted in summer and winter. 19 research cases studied the
summer condition. 17 research cases studied the winter condition. 12 research cases studied the
spring condition. Seven research cases studied the autumn condition.
91%, 40
5%, 2 5%, 2
0
5
10
15
20
25
30
35
40
45
Empirical Study Questionnaire Literature Review
Research Type
52.50%, 21
47.50%, 19
17
18
19
20
21
22
lab-setting field
Experiment Setting
38
Figure 4-4 Sleep Study by Season
4.2.3 Nation and Participants
The research studies in the papers analyzed in this study were conducted in various different
countries (Figure 4-5). The majority of the research studies was conducted in China. 23 of the
forty-four research studies were conducted in China. Three research studies were conducted in
Japan and Korea, respectively. Two research studies were conducted in Switzerland and Malaysia,
respectively. The rest of the research studies was conducted in the Netherlands, Australia, Belgium,
Denmark, Finland, Germany, Portugal, Singapore, United States, Swedish, and Thailand,
respectively.
19
17
12
7
0
2
4
6
8
10
12
14
16
18
20
Summer Winter Spring Autumn
Sleep Study by Season
39
Figure 4-5 Sleep Study by Nation
The number of participants in each study, and their genders, and ages were recorded. Each of the
different research studies recruited different participants. 15 research studies (39% of 38 empirical
research studies) recruited 11-20 participants. Eight research studies (21% of 38 empirical research
studies) recruited 1-10 participants. Eight research studies (21% of 38 empirical research studies)
recruited more than one hundred participants. Two research studies (5% of 38 empirical research
studies) recruited 41-50 participants. One research study (3% of 39 empirical research studies)
recruited 31-40 participants. One research study (3% of 39 empirical research studies) recruited
51-100 participants. y.
23
3 3
2 2
1 1 1 1 1 1 1 1 1 1 1
0
5
10
15
20
25
China
Japan
Korea
Switzerland
Malaysian
Netherlands
Australia
Belgium
Denmark
Finland
Germany
Portugal
Singapore
United State
Swedish
Thailand
Sleep Study by Nation
40
Figure 4-6 Sleep Study by Participants Number
Different research studies recruited male and female, male-only or female-only (Figure 4-7).
76%of 38 empirical research studies (29 research studies) recruited male and female. 18% of
empirical research studies (Seven research studies) recruited males only. 5% of empirical research
studies (two research studies) recruited females only.
Figure 4-7 Sleep Study by Gender
39%, 15
21%, 8 21%, 8
8%, 3
5%, 2
3%, 1 3%, 1
0
2
4
6
8
10
12
14
16
11-20 0-10 21-30 >100 41-50 31-40 51-100
Sleep Study by Participants Number
76%, 29
18%, 7
5%, 2
0
5
10
15
20
25
30
35
Male and Female Male Only Female Only
Sleep Experiment Study by Gender
41
The age group of participants was divided into four groups: children, adolescent, adults and elderly.
32 of 39 experiment papers recruited adults. Nine of 39 experiments recruited elderly people. One
research study recruited adolescents and One research study recruited children, respectively.
Figure 4-8 Sleep Experiment Study by Age Group
4.2.4 Experiment Data Analysis Methodology
Among the 39 research studies, researchers used different data analysis software and algorithms
to analyze the parameter and the relationship between IEQ and sleep quality. The software includes
SPSS, Minitab, Excel, XLSTAT, and Chart 7 (Figure 4-9). The most used software is SPSS. 12
research studies used SPSS. Two research studies used Excel. The rest research studies used
XLSTAT, Minitab, and Chart 7, respectively. Each research study used various algorithms to
obtain their research goal. The most used algorithms are t-test, correlation, regression, Shapiro-
Wilk's W test, Wilcoxon signed-ranks test, and ANOVA (Figure 4-10).
32
9
2
1
0
5
10
15
20
25
30
35
adults elderly adolescent children
Sleep Experiment Study by Age Group
42
Figure 4-9 Data Analysis Software
Figure 4-10 Data Analysis Algorithm
12
2
1 1 1
0
2
4
6
8
10
12
14
SPSS Excel XLSTAT Minitab Data Analysis
software Chart 7
Data Analysis Software
23
14 14
9 9
8
5 5
4
2 2
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
0
5
10
15
20
25
T Test
Correlation
Regression
Shapiro-Wilk’s W test
Wilcoxon Signed-Rank’s test
ANOVA
Mann–Whitney test
Kolmogorov–Smirnov test
Friedman analysis of variance…
Friedman's one-way analysis
Pearson's chi-squared test
Huynh-Feldt statistics
Friedman test for k-related…
Student–Newman–Keuls tests
Kano model
Tukey’s post hoc test
Anderson–Darling test
three-level random intercept…
Tsai–Partington numbers test,
F-test
Apriori algorithem
Multiple correspondence…
Griffiths' method
bonferroni posthoc analysis1
non-parametric test.
Fisher's exact test
Kruskal-Wallis Test
Dunn's pairwise tests
Wilcoxon non-parametric test
Akaike's Information Criterion…
PLSD
Mauchly's test
Algorithm
43
4.3 Research Investigated Parameters for Research Study Related
to IEQ and sleep quality
Different research studies investigated with heterogeneous parameters. It could be sorted into four
groups: outdoor parameters, indoor parameters, biological signals, and sleep quality. Different
parameter groups include specific parameters. The measurements are diverse. It is essential to
analyze the most investigated parameters and the least investigated parameters to provide
accommodations for future study and guidelines.
4.3.1 Outdoor Parameters
Outdoor parameters measured in the selected research studies include outdoor temperature,
outdoor relative humidity, outdoor noise, solar irradiation, and wind speed (Figure 4-11). Ten
research studies measured outdoor temperature. Five research studies measured outdoor relative
humidity. Two research studies measured outdoor noise. One research studies measured solar
irradiation. One research study measured outdoor wind speed.
44
Figure 4-11 Outdoor Parameter Measured
4.3.2 Indoor Parameters and Measurements
The second parameter group is indoor parameters. Indoor parameters measured in published
research studies include indoor temperature, radiant temperature, relative humidity, air velocity,
CO2 density, PM2.5 density, PM10 density, TVOCs density, lighting density, noise level, level of
indoor bioaerosols, and inner envelope temperature (Figure 4-12). The six most measured
parameters are indoor air temperature, indoor relative humidity, CO2 density, air velocity, lighting
density, and noise level.
The most investigated parameters are indoor temperature and relative humidity. 26 research studies
investigated indoor temperature and relative humidity, respectively. The measuring instruments of
indoor temperature and relative humidity include TR-76Ui, TR-72Ui, Hobo, DAV005, LT8A,
Netatmo, RTR-53A, SAMBA, SHT31, TR-74Ui, and WBGT-2010SD (Figure 4-13). The most
frequently used measuring instrument of indoor temperature is TR-76Ui. Seven research studies
used TR-76Ui to measure not only air temperature and relative humidity but also CO2 density. 16
10
5
2
1 1
0
2
4
6
8
10
12
Outdoor
temperature
Outdoor relative
humidity
Outdoor noise Solar irradiation Wind speed
Outdoor Parameter Measured
45
research studies measured CO2 density. The measuring instruments of indoor CO2 density include
TR-76Ui, HOBO, Netatmo, SAMBA IEQ device, SCD30, Telaire 7001, and TESTO 480 probes.
The most used measuring instrument is TR-76Ui.
Figure 4-12 Indoor Parameters Measured
Figure 4-13 Indoor Air Temperature/Relative Humidity Measurements
26 26
16
15
10 10
5
3
2
1 1 1
0
5
10
15
20
25
30
Indoor temperature
Relative humidity
CO2 density
Air velocity
Lighting density
Noise level
Radiant temperature
PM 2.5
TVOCs
Inner envelope temp
levels of indoor bioaerosols
PM10
Indoor Parameters Measured
7
5
2
1 1 1 1 1 1 1 1
0
1
2
3
4
5
6
7
8
TR-76Ui
TR-72Ui
Hobo Data Logger
DAV005
LT8A
Netatmo
RTR-53A
SAMBA IEQ device
SHT31
TR-74Ui
WBGT-2010SD
Indoor Air Temperature/Relative Humitidy Measurement
46
Figure 4-14 Indoor CO2 Density Measurements
The fourth parameter which is most measured is air velocity. 15 papers measured the air velocity.
The measurements of air velocity include UAS1100, Testo ZRQF-F3, Model DA-600, SAMBA
IEQ device, TESTO 480, Testo425, TSI-8475, and WFWZY-1 (Figure 4-15). The most used
equipment is UAS1100. The fifth parameter which is most measured is lighting intensity. The
measurements of lighting intensity include HPC-1 (HOPOO), ANA-F11, Actiwatch-L, LuxBlick,
Model C-7000, MS-200LED-lux meter, and Wireless Illuminance Sensor (Figure 4-16). The sixth
parameter which is most measured is noise level. The measurements of noise level include Cirrus
CR 1720, DT-173, Microtech Gefell MM 210, Netatmo, and NTi Audio AG (Figure 4-17).
8
1 1 1 1 1 1
0
1
2
3
4
5
6
7
8
9
TR-76Ui HOBO Netatmo SAMBA IEQ
device
SCD30 Telaire 7001 TESTO 480
probes
Indoor CO
2
Density Measurement
47
Figure 4-15 Indoor Air Velocity Measurements
Figure 4-16 Indoor Lighting Intensity Measurements
3
2
1 1 1 1 1 1
0
1
2
3
4
UAS1100 Testo
ZRQF-F3
Model
DA-600
SAMBA
IEQ device
TESTO
480
Testo425 TSI-8475 WFWZY-1
Indoor Air Velocity Measurement
2
1 1 1 1 1 1
0
1
2
3
HPC-1 (HOPOO)
ANA-F11
Actiwatch-L
LuxBlick
Model C-7000
MS-200LED-luxmeter
Wireless Illuminance Sensor
Indoor Lighting Intensity Measurement
48
Figure 4-17 Indoor Noise Level Measurements
4.3.3 Indoor Parameters Experiment Condition
Heterogeneous parameters were recorded in the different research studies. The experiment
conditions were specific. First, the temperature range of different research studies was recorded
based on the season. The most frequently investigated temperature range for summer is 23°C to
30°C (Figure 4-18). The most frequently investigated temperature range for transition season
(spring and autumn) are 18°C to 27°C (Figure 4-19). The most frequently investigated temperature
range for winter is 17°C to 23°C (Figure 4-20).
1 1 1 1 1
0
0.2
0.4
0.6
0.8
1
1.2
Cirrus CR 1720 DT-173 Microtech Gefell
MM 210
Netatmo NTi Audio AG
Indoor Noise level Measurement
49
Figure 4-18 Summer Experiment Temperature Conditions (Xiong et al. 2020; M. Kim, Chun, and Han 2010; Tsuzuki et al.
2015; L. Lan, Lian, and Lin 2016; Imagawa and Rijal 2015a; Li Lan et al. 2014; 2019; L. Lan et al. 2018a; Sekhar and Goh 2011;
Cao et al. 2020; Budiawan et al. 2021; Xu, Lian, Shen, Lan, et al. 2021)
Figure 4-19 Transition Season Experiment Temperature Conditions (M. Kim, Chun, and Han 2010; Tsuzuki et al. 2015; X.
Zhang et al. 2021; Irshad et al. 2018; Strøm-Tejsen et al. 2016; Cordoza et al. 2019; Li Lan et al. 2021)
24-30.5
25-31
23.2-25.5
22.5-25.5
31,32
23
27
26.3-27.9
25-28
27.8
28.6
26.5
28-31
26
30
30
20 22 24 26 28 30 32
Xinbo Xu et al. (2021)
Budianwan, Wiwik et al. (2021)
Ting Cao et al. (2020)
S.C.Sekhar et al. (2011)
Li. Lan et al. (2018)
Li Lan et al. (2014)
Li. Lan et al. (2019)
Hikaru Imagawa et al. (2014)
Li Lan et al. (2016)
Kazuyo Tsuzuki et al (2015)
Minhee Kim et al. (2010)
Jing Xiong et al. (2020)
Summer Experiment Temperature Condition
Temperature Range(°C)
21.8
21.8
13.7-27.7
24-27
17.8-29.3
18.4
22.4
22.5
10 12 14 16 18 20 22 24 26 28 30
Li Lan et al. (2021)
Chenxi Liao et al. (2019)
P. Strøm-Tejsen et al. (2015)
Kashif Irshad et al. (2018)
XiaoJing Zhang et al. (2021)
Kazuyo Tsuzuki et al. (2015)
Minhee Kim et al. (2010)
Transition Season Experiment T emperature Condition
Temperatrue Range (°C)
50
Figure 4-20 Winter Experiment Temperature Conditions (M. Kim, Chun, and Han 2010; Tsuzuki et al. 2015; C. Song,
Zhao, et al. 2020; Tsang, Mui, and Wong 2021; Y. Liu et al. 2014; C. Song, Liu, et al. 2020; Budiawan et al. 2021; Cao et al.
2021; Pan, Lian, and Lan 2011; 2012)
Second, the relative humidity conditions were recorded based on the season. For summer, the
experiment condition has a relative humidity range from 40% to 70% (Figure 4-21). For transition
seasons (spring and autumn), the experiment condition has a relative humidity range from 30% to
70% (Figure 4-22). For winter, the experiment condition has a relative humidity range from 25%
to 70% (Figure 4-23).
17
17
17
17-30
20.7
8.7-18.3
22.3-23.6
16
10.3
22.7
20
20
20
21
23
23
23
5 10 15 20 25 30
Li Pan et al. (2012)
Li Pan et al. (2011)
Ting Cao et al. (2021)
Budianwan, Wiwik et al. (2021)
Cong Song et al. (2020)
Yanfeng Liu et al. (2014)
T.W Tsang et al. (2021)
Cong Song et al. (2020)
Kazuyo Tsuzuki et al. (2015)
Minhee Kim et al. (2010)
Winter Experiment Temperature Condition
Temperature Range (°C)
51
Figure 4-21 Summer Experiment Relative Humidity Conditions (Xiong et al. 2020; M. Kim, Chun, and Han 2010; Tsuzuki
et al. 2015; Imagawa and Rijal 2015a; Li Lan et al. 2019; 2014; Sekhar and Goh 2011; Cao et al. 2020; Budiawan et al. 2021; Xu,
Lian, Shen, Lan, et al. 2021; L. Lan et al. 2018a)
Figure 4-22 Transition Season Experiment Relative Humidity Conditions (Li Lan et al. 2021; Chenxi Liao and Jelle
Laverge 2019; Strøm-Tejsen et al. 2016; Irshad et al. 2018; X. Zhang et al. 2021; Tsuzuki et al. 2015; M. Kim, Chun, and Han
2010)
50-83
45-85
40-60
34-72
40-60
40-70
40-60
50-60
50-80
60
50-70
30 40 50 60 70 80 90
Xinbo Xu et al. (2021)
Budianwan, Wiwik et al. (2021)
Ting Cao et al. (2020)
S.C.Sekhar et al. (2011)
Li Lan et al. (2017)
Li Lan et al. (2014)
Li Lan et al. (2019)
Hikaru Imagawa et al. (2014)
Kazuyo Tsuzuki et al (2015)
Minhee Kim et al. (2010)
Jing Xiong et al. (2020)
Summer experiment Relative Humidity Condition
Relatvie Humidity Range
50-70
30-50
40-50
47-82
25.5-74
50-70
39.1
0 10 20 30 40 50 60 70 80 90
Li Lan et al. (2021)
Chenxi Liao et al. (2019)
P. Strøm-Tejsen et al. (2015)
Kashif Irshad et al. (2018)
XiaoJing Zhang et al. (2021)
Kazuyo Tsuzuki et al. (2015)
Minhee Kim et al. (2010)
Transition Season Experiment Relative Humidity Condition
Relatvei Humidity Range(%)
52
Figure 4-23 Winter Experiment Related Humidity Conditions (Pan, Lian, and Lan 2011; Cao et al. 2021; Budiawan et al.
2021; C. Song, Liu, et al. 2020; Y. Liu et al. 2014; Tsang, Mui, and Wong 2021; C. Song, Zhao, et al. 2020; Tsuzuki et al. 2015;
M. Kim, Chun, and Han 2010)
The third recorded parameter is CO2 density. The most frequently investigated range is from
400ppm to 2500 ppm (Figure 4-24). The fourth recorded parameter is air velocity (Table 4-1).
Most of the research studies controlled air velocity less than 0.2 m/s, which meets the requirement
of ASHRAE 55 (2004) (“Thermal Environmental Conditions for Human Occupancy” 2004). The
fifth most investigated parameter is lighting intensity (Figure 4-25). Different research cases
studied different lighting intensity parameters during different time periods. The sixth most
investigated parameter is noise level. The most frequently investigated range is from 30-50 dB
(Figure 4-26).
50
40
25-65
40-69
20-80
60-80
31-55
40-60
20-30
55 70
20 30 40 50 60 70 80 90
Li Pan et al. (2011)
Ting Cao et al. (2021)
Budianwan, Wiwik et al. 2021)
Cong Song et al. (2020)
Yanfeng Liu et al. (2014)
T.W Tsang et al. (2021)
Cong Song et al. (2020)
Kazuyo Tsuzuki et al. (2015)
Minhee Kim et al. (2010)
Winter Experiment Related Humidity Condition
Relatvie Humidity Range (%)
53
Figure 4-24 CO2 Density Experiment Conditions (Li Lan et al. 2021; Xu, Lian, Shen, Lan, et al. 2021; N. Zhang, Cao, and
Zhu 2018; Cao et al. 2021; 2020; Chenxi Liao and Jelle Laverge 2019; Strøm-Tejsen et al. 2016; Sekhar and Goh 2011; L. Lan et
al. 2018b; Irshad et al. 2018; Li Lan et al. 2019; Xu, Lian, Shen, Cao, et al. 2021; X. Zhang et al. 2021; M. Kim, Chun, and Han
2010; Xiong et al. 2020)
800-1700
400-2300
700-1800
<1000
<800
0-2600
660-2585
420-1089
600-1100
620-750
1000-1700
800
468-4327
428-1276
300-1400
1900 3000
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Li Lan et al. (2021)
Xinbo Xu et al. (2021)
Nan Zhang et al. (2018)
Ting Cao et al. (2021)
Ting Cao et al. (2020)
Chenxi Liao et al. (2019)
P. Strøm-Tejsen et al. (2015)
S.C.Sekhar et al. (2011)
Li Lan et al. (2017)
Kashif Irshad et al. (2018)
Li Lan et al. (2019)
Xinbo Xu et al. (2019)
XiaoJing Zhang et al. (2021)
Minhee Kim et al. (2010)
Jing Xiong et al. (2020)
CO
2
Density Experiment Condition
CO2 Density Range (PPM)
54
Table 4-1 Air Velocity Experiment Conditions (Pan, Lian, and Lan 2012; N. Zhang, Cao, and Zhu 2018; Cao et al. 2021;
2020; Sekhar and Goh 2011; C. Song, Liu, et al. 2020; L. Lan et al. 2018b; Li Lan et al. 2014; Irshad et al. 2018; Y. Liu et al.
2014; Li Lan et al. 2019; Tsang, Mui, and Wong 2021; C. Song, Zhao, et al. 2020; L. Lan, Lian, and Lin 2016; Xiong et al. 2020)
Figure 4-25 Lighting Intensity Experiment Conditions (Cho et al. 2015; Cao et al. 2021; Hubalek, Brink, and Schierz 2010; Dong
and Zhang 2021; Shishegar et al. 2021; van Lieshout-van Dal, Snaphaan, and Bongers 2019; Wen et al. 2021; Tsuzuki et al.
2015; M. Kim, Chun, and Han 2010)
Author Experiment Condition
Li Pan et al. (2012) Controlled (0.112+-0.022 mile/h)
Nan Zhang et al. (2018) not listed
Ting Cao et al. (2021) controlled ≤0.2 m/s
Ting Cao et al. (2020) controlled ≤0.2 m/s
S.C.Sekhar et al. (2011) <0.1m/s
Cong Song et al. (2020) 0.10 ± 0.04, 0.07 ± 0.04, 0.12 ± 0.04, 0.12 ±
0.04, 0.11 ± 0.02, 0.10 ± 0.04, 0.09 ± 0.03,
0.08 ± 0.03, 0.10 ± 0.04
Li Lan et al. (2017) not listed
Li Lan et al. (2014) 0.08±0.03, 0.06 ± 0.02
Kashif Irshad et al. (2018) not listed
Yanfeng Liu et al. (2014) 0.02 ± 0.02, 0.10 ± 0.02, 0.13 ± 0.03, 0.15 ±
0.04
Li Lan et al. (2019) 0.6 task fan, 0.7 ceiling fan
T.W Tsang et al. (2021) 0.0004 ± 0.00026, 0.00043 ± 0.00022
Cong Song et al. (2020) not listed
Li Lan et al. (2016) not listed
Jing Xiong et al. (2020) not listed
55
Figure 4-26 Noise Level Experiment Conditions (Li Lan et al. 2021; Xu, Lian, Shen, Lan, et al. 2021; Basner, Müller, and
Elmenhorst 2011; Cao et al. 2021; 2020; Röösli et al. 2019; Chenxi Liao and Jelle Laverge 2019; Radun, Hongisto, and Suokas
2019; Smith et al. 2019; M. Kim, Chun, and Han 2010)
4.3.4 Biological Signal
The third parameter group is the biological signals. Biological signals are essential parameters that
could indicate people's sleep quality and satisfaction with IEQ (Figure 4-27). The most frequently
investigated biological signal is Electroencephalography (EEG). 14 research studies measured
EEG. The second most frequently investigated biological signal is Body temperature. 13 research
studies measured body temperature. The third frequently investigated biological signal is
Electromyogram (EMG). Ten research studies measured EMG. Electrooculogram (EOG) is the
fourth frequently investigated biological signal. Nine research studies measured EOG. Heart rate
is the fifth frequently investigated biological signal. Eight research studies measured heart rate.
The activities were measured four times. Blood flow in the figure was measured three times.
Proteinuria/urine has been measured twice. Melatonin and salivary biomarkers were measured
once, respectively.
35
35-63
45,50,55,60,65
≤40
≤40
30.2
30-50
25-46
35,40,45
30-40
50
0 10 20 30 40 50 60 70
Li Lan et al. (2021)
Xinbo Xu et al. (2021)
Mathias Basner et al. (2011)
Ting Cao et al. (2021)
Ting Cao et al. (2020)
Martin Röösli et al. (2019)
Chenxi Liao et al. (2019)
Jenni Radun et al. (2019)
Michael G.Smith et al. (2019)
Minhee Kim et al. (2010)
Noise Level Experiment Condition
Noise Level Range (dB)
56
Figure 4-27 Biological Signal Measured
Measurements of the different biological signals are diverse. For the EEG, the most used
measurement is polysomnography (PSG) (Figure 4-28). The most used measurement for body
temperature is Pt1000 (Figure 4-29). The most frequently used measurement for EMG and EOG
is PSG (Figure 4-30). The most frequently used measurement for heart rate (ECG) is PSG and
Fitbit (Figure 4-31).
14
13
10
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1 1
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4
6
8
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Electroencephalography(EEG)
Body temperature
Electromyogram (EMG)
Electrooculogram (EOG)
HR/ECG
activities
Blood flow in figure
Proteinuria/urine
Melatonin
Salivary biomarkers
Biological Signal Measured
57
Figure 4-28 EEG Measurement
Figure 4-29 Body Temperature Measurement
10
3
1
0
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4
6
8
10
12
PSG Electrodes NOX A1
EEG Measurement
5
4
1 1 1 1
0
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Pt1000 PyroButtons DS1922L iButton LA8B MSR145B50
Body Temperature Measurement
58
Figure 4-30 EMG Measurement
Figure 4-31 Heart Rate Measurement
4.3.5 Sleep Quality
Diverse sleep quality parameters are investigated in the research studies (Figure 4-32). The most
frequently investigated rank of these parameters is sleep period time, sleep efficiency, sleep latency,
9
1
0
1
2
3
4
5
6
7
8
9
10
PSG Nox A1
EMG Measurement
2 2
1 1 1 1
0
0.5
1
1.5
2
2.5
Fitbit PSG Nox A1 DB-12;EL-194 Electrode Smart watch
(Polar Electro
Oy)
Heart Rate Measurement
59
duration of N1, N2, N3 and REM, wake after sleep onset, SWS, wake numbers, movement index,
average wake time, and fragmentation index. The measurements of sleep quality include
polysomnography (PSG), actigraphy, EEG, actometer device, Cannula, and Caremonitor (Figure
4-33). The most frequently used measurements are PSG and actigraphy (Figure 4-33).
Figure 4-32 Sleep Quality Parameters
Figure 4-33 Sleep Quality Measurements
18 18
17
14
12
6
5
3
1 1
0
2
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Sleep period time (spt)
Sleep efficiency (SE)
Sleep latency (SL)
Total duration of N1,N2,N3…
Wake after sleep onset…
SWS
Wake numbers
Movement index
Average week time
Fragementation index
Sleep Quality Parameter
12
10
2
1 1 1
0
2
4
6
8
10
12
14
PSG actigraphy EEG actimeter
device
Cannula
connected to
ResMed’s
ApneaLink
Caremonitor
Sleep Quality Measurment
60
4.4 Research Results for Research Studies Related to IEQ and
sleep quality
4.4.1 Results of Air Temperature
26 research studies investigated indoor temperature. The first consistent result is that indoor
temperature, which is the most important component that influences sleep quality, has intimated
relationship with sleep quality (Pan, Lian, and Lan 2012; Xu, Lian, Shen, Lan, et al. 2021; Cao et
al. 2021; Xu, Lan, et al. 2021; Li Lan et al. 2014; Y. Liu et al. 2014; C. Song, Zhao, et al. 2020; X.
Zhang et al. 2021; Xiong et al. 2020). A too cold or too warm environment will contribute to worse
sleep quality. Pan et al. indicated that 17°C in winter would contribute to more waking times, more
sleep onset latency, more REM duration, and less SWS duration compared with 20°C and 23°C
(Pan, Lian, and Lan 2012). Xu et al. indicated that warmer temperatures would contribute to the
increase of SWS (Xu, Lian, Shen, Lan, et al. 2021). However, the experiments were conducted for
summer with a temperature range from 24°C-30°C. Song et al. also indicated that a warmer
environment during sleep time would contribute to fragmentation of sleep. It indicated that a
warmer environment within the range from 16°C to 21°C with bed heating would contribute to a
decrease in sleep quality. The second consistent result is that comfortable temperature during the
sleep period is different from comfortable temperature during the daytime (Cao et al. 2020; N.
Zhang, Cao, and Zhu 2018; C. Song, Liu, et al. 2020; Kallawicha et al. 2021; Li Lan et al. 2014).
15 research studies demonstrate the comfortable temperature for good sleep quality. For the
summer experiment, the most comfortable temperature zone is from 22°C to 27°C. Some research
studies only demonstrated a specific number of comfortable temperatures. Xu et al. indicated that
24.8°C is the most comfortable temperature (Xu, Lian, Shen, Lan, et al. 2021). Lan et al. indicated
61
that 26°C is the most comfortable temperature. These two are both in the range of 22°C-27°C (Li
Lan et al. 2014). Lan et al. also indicated that 31°C to 32°C with local body cooling and 29°C with
a ceiling fan is comfortable for people to sleep (L. Lan et al. 2018b; Li Lan et al. 2019). However,
the temperature range should be excluded because it involved the assistance system. Based on the
results of winter comfortable temperature range, it could be concluded that the most comfortable
temperature in winter is 23°C.
Gender differences are also investigated in the research studies. Women prefer warm environment
than men (Pan, Lian, and Lan 2011; Cao et al. 2020; Tsang, Mui, and Wong 2021; Li Lan et al.
2019). Different ages also requested different temperatures. Elderly people have poor sleep quality
than other age groups (Li Lan et al. 2021; N. Zhang, Cao, and Zhu 2018). Elderly people tend to
prefer warmer indoor temperatures compared to other ages [4]. Elderly people's sleep quality is
more easily disrupted by thermal comfort(Li Lan et al. 2019).
62
Figure 4-34 Summer Experiment Temperature Results (Xu, Lian, Shen, Lan, et al. 2021; Budiawan et al. 2021; Cao et al.
2020; Sekhar and Goh 2011; L. Lan et al. 2018b; Li Lan et al. 2014; 2019; Imagawa and Rijal 2015b; M. Kim, Chun, and Han
2010)
Figure 4-35 Winter Experiment Temperature Results (Pan, Lian, and Lan 2012; 2011; Cao et al. 2021; Budiawan et al.
2021; Y. Liu et al. 2014; Tsang, Mui, and Wong 2021; M. Kim, Chun, and Han 2010)
24.8
28.1
22.1-27.1
22.5-25.5
31,32
26
29
26.4-27.1
24-26
22 23 24 25 26 27 28 29 30 31 32
Xinbo Xu et al. (2021)
Budianwan, Wiwik et al. (2021)
Ting Cao et al. (2020)
S.C.Sekhar et al. (2011)
Li. Lan et al. (2018)
Li Lan et al. (2014)
Li. Lan et al. (2019)
Hikaru Imagawa et al. (2014)
Minhee Kim et al. (2010)
Summer Experiment Temperature Results
Temperature Range(°C)
23
20
20
23.5
15.8
23.05
24-26
23
18.3
15 17 19 21 23 25 27
Li Pan et al. (2012)
Li Pan et al. (2011)
Ting Cao et al. (2021)
Budianwan, Wiwik et al. (2021)
Yanfeng Liu et al. (2014)
T.W Tsang et al. (2021)
Minhee Kim et al. (2010)
Winter Experiment Temperature Results
Temperature Range (°C)
63
4.4.2 Results of Relative Humidity
Only two articles give out the comfortable RH value See Figure 4-36. Liao et al. indicated that 64%
is the comfortable relative humidity with 24.8°C (Chenxi Liao and Jelle Laverge 2019). Cao et al.
indicated that 55-70% is the comfortable relative humidity range (Cao et al. 2021). Tsuzuki et al
indicated that high RH would contribute to distorting of sleep quality (Tsuzuki et al. 2015). Other
research studies only recorded the relative humidity. They did not investigate the relationship
between RH and sleep quality.
Figure 4-36 Relative Humidity Experiment Result (Chenxi Liao and Jelle Laverge 2019; Cao et al. 2021)
4.4.3 Results of CO2 Density
The third investigated parameter is CO2 density. Only one research study indicated that 800ppm
is the most comfortable CO2 density compared with the other two (1900ppm and 3000ppm). Most
of the research studies indicated that high CO2 density is negatively related to sleep quality (Xu,
Lian, Shen, Cao, et al. 2021). Consistent result is that CO2 density is negatively related to sleep
quality (Li Lan et al. 2021; Xu, Lian, Shen, Lan, et al. 2021; Ramos et al. 2022; Sekhar and Goh
2011; Xu, Lian, Shen, Cao, et al. 2021; X. Zhang et al. 2021; Tsuzuki et al. 2015; Xiong et al.
2020).
64
4.4.4 Results of Air Velocity
The fourth investigated parameter is air velocity. None of the research study demonstrated the
most comfortable air velocity. What's more, none of the researchers investigate the relationship
between air velocity and sleep quality. Lan et al. indicated that ceiling fan is the most comfortable
method compared to task fan and air conditioning to remove the heating load for elderly people
during sleep periods (Li Lan et al. 2019).
4.4.5 Results of Lighting Intensity
The fifth investigated parameter is lighting intensity. Most of the studies focus on the daytime
lighting intensity impacts because most people sleep without light. Cho et al. indicated that even
dim artificial light with 5 lux illuminances would contribute to an increase of waking, N1 period,
and REM and decreasing of N2 and total sleep time (Cho et al. 2015). It will contribute to bad
sleep quality. Tsuzuki et al. indicated that exposure 4-hours before sleep is important (Tsuzuki et
al. 2015). Bedtime will be delayed because light exposure increased 4-hours before sleep (Tsuzuki
et al. 2015). Ting et al. indicated that the lighting exposure before sleep should be 150 lux, after
sleep should be 30 lux to provide a better sleep environment for people (Cao et al. 2021). Sleep
quality will be increase when the daily lighting exposure increase (Shishegar et al. 2021; Dong
and Zhang 2021; Hubalek, Brink, and Schierz 2010). Shishegar et al. also indicated that the CCT
would influence sleep quality (Shishegar et al. 2021).
4.4.6 Results of Noise Level
Most of the studies related to noise level focus on the relationship between sleep quality and noise.
Noise level is negatively related to sleep quality (Li Lan et al. 2021; Xu, Lian, Shen, Lan, et al.
65
2021; Basner, Müller, and Elmenhorst 2011; Röösli et al. 2019; Radun, Hongisto, and Suokas
2019; Smith et al. 2019). Basner et al. indicate that noise level beyond 55dB the waking increased
(Basner, Müller, and Elmenhorst 2011). Röösli et al. indicated that when the noise level increased
by ten dBA, the sleep efficiency reduced by 1.11%, and sleep latency increased by 5.67 minutes
(Röösli et al. 2019). Radun et al. indicate that when the sound level is between 25-46dB, the sleep
disturbance caused by wind tube noise will increase by the sound level increase (Radun, Hongisto,
and Suokas 2019). Smith et al. indicate that noise level over 40dB will contribute to the heart rate
increase and disruption of sleep (Smith et al. 2019). The results of the threshold are totally different.
And there are few results showing the comfortable noise level range
4.5 Current published Research Studies Related to IoT and
Wearable Sensors
4.5.1 IoT System
Six research studies demonstrated the different IoT systems that could maintain different indoor
parameters. The first system used the combination of a wrist-worn temperature sensor, thermal
camera, and ambient temperature sensor to predict human thermal satisfaction (Aryal and Becerik-
Gerber 2019). The system is 84% accurate. The used sensor in the system is cheap (Price: FLIR
$250, DHT22 $5, Smartwatch with temperature sensor $100). However, the system was only
tested in the daytime. It should be further studied to verify if it could predict the thermal comfort
for sleep time. The system only focuses on thermal comfort.
The second IoT system combines HD32.3 WBGT hardware, which is a data logger for WBGT,
PMV, and PPD measurements. It used machine learning algorithm ANNs to predict disabled
66
people's thermal comfort (Brik et al. 2021). However, it also only focuses on thermal comfort in
the daytime.
The third IoT system is called LATEST (von Frankenberg et al. 2020). IEQ sensors cooperate with
biological sensors to control the thermal condition (Brik et al. 2021). People also could use the app
installed on their phones to manipulate the thermal condition (Brik et al. 2021). The LATEST
system worked efficiently to provide a comfortable thermal environment for people. However, it
only focuses on thermal comfort in the daytime.
The fourth IoT system combined a wrist-worn band and thermal camera to predict thermal comfort
(Yoshikawa et al. 2019). It could predict thermal comfort in an effective way. It only focuses on
daytime thermal comfort.
The fifth IoT system is an indoor air monitor system (X. Chen et al. 2014). Monitor Dylos DC1700
collected the PM 2.5 density data and sent it to the cloud. Based on a long period of data collection,
ANN was used to suggest when the HVAC system should be operated. The system focuses on
daytime indoor air quality, especially PM2.5.
The sixth IoT system is a sleep quality monitor system. It combines the KTH live-in-lab system
which can monitor the ambient environment and control the building system and OURA Ring
which could monitor the sleep quality, pulse rate, body temperature. It could monitor the sleep
quality and indoor environment quality.
4.5.2 Wearable Sensor and Smart Home Device
Eight different wearable sensors were demonstrated. Each sensor has a different function and
apparency, respectively. The first wearable sensor is installed in the smartphone called Smart
phone-illuminance (Wahl, Kantermann, and Amft 2014). It could detect the illuminance of the
67
ambient environment (Wahl, Kantermann, and Amft 2014). The second wearable sensor is a Smart
eye mask. It equipped the eye mask with an infrared sensor to measure sleep quality (Matsui,
Terada, and Tsukamoto 2017). The prototype is portable. People could bring it with them
anywhere. Also, the feeling of wearing it is the same as a regular eye mask. It will not contribute
to uncomfortable feelings and distortion of sleep. However, it is just a prototype. The productivity
and commercial value need further investigation. The third wearable sensor is Fitbit which could
measure sleep quality during sleep time (Purta et al. 2016). It is already a commercial and portable
product that people could buy in in different methods such as online shopping. However, when
people wear Fitbit, their sleep quality will be influenced by the wearing feeling. The accuracy of
Fitbit still needs further investigation. The fourth wearable sensor is EEG cap-Brainwear (Vargas
et al. n.d.). It installed EEG electro in a cap (Vargas et al. n.d.). It could measure the sleep quality
during sleep time (Vargas et al. n.d.). Same as Fitbit, the wearing feel might influence people's
sleep quality. It is also a prototype. The commercial value and accuracy need to be investigated in
the future.
The fifth wearable sensor is the OURA ring which could measure sleep quality (Malakhatka et al.
2021). It is a portable and commercial product same as Fitbit. The drawback is also similar. The
wearing feeling and accuracy is a problem that should be further investigated. The sixth wearable
sensor is an Ear-EEG device that could measure EEG (Mandekar et al. 2021). It is portable and
easy to use in daily life. However, it is a prototype. The seventh wearable sensor is Ran's Night
which could be attached to the cloth to measure body temperature (Katsumata et al. 2019). The
accuracy is uncertain. The eighth wearable sensor is HALEY which is a clock that could measure
indoor noise levels (Lucherelli et al. 2014). It could detect ambient noise and send it to the users'
68
phone in different places of the body (Lucherelli et al. 2014). However, it is just a prototype that
needs further development and investigation.
Two smart home devices which could monitor indoor air quality were developed. The first smart
home device is an Indoor air quality monitor: Dylos DC1700 (X. Chen et al. 2014). It could
measure indoor air quality, especially PM2.5. The second smart home device is Aircase (H. Zhao,
Roddiger, and Beigl 2021). It installed a different sensor in an early charge case (H. Zhao,
Roddiger, and Beigl 2021). The sensors include an air quality sensor (BOSCH® BME680) to sense
the indoor temperature, relative humidity, air pressure, and volatile organic compounds (VOC), a
CO2 sensor (WINSEN® MH- Z19B) and a light sensor (SHARP® GA1A1S202WP) (H. Zhao,
Roddiger, and Beigl 2021). It is a portable but not commercial product. The third device is inAir
(S. Kim and Paulos 2009). It is a commercially available air quality sensor with an iPod Touch
and an Arduino micro-controller. It could detect the indoor air quality and show the results in the
iPod Touch (S. Kim and Paulos 2009).
4.6 Summary
Limited research studies have been conducted to investigate the impacts of IEQ on sleep quality.
Most research studies focused on investigating thermal comfort and indoor air quality, respectively.
Most of the research studies recruited adults in summer and winter. The relationship between IEQ
components and sleep quality has been studied, respectively. However, the relationship should be
integrated. Future studies should focus on the combination of different parameters. The
comfortable parameter zone is uncertain. Some IoT systems and wearable sensors were developed
and investigated. It is essential to interact with the IoT and wearable sensor with IEQ to provide a
better sleep quality environment.
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5 Discussion and Results
5.1 Analysis of Basic Information and Research Condition for
Research Studies Related to IEQ and sleep Quality
5.1.1 Analysis of IEQ Components, Research Type and Research Season
According to Figure 4‑1, the frequently investigated IEQ components sequence is thermal comfort,
IAQ, lighting intensity, and acoustic. Thermal comfort is the most studied component. Thermal
comfort is directly felt by humans. In objective consideration, air temperature is the most critical
component (N. Zhang, Cao, and Zhu 2018). Cao et al. also demonstrated that thermal comfort is
the most important factor affecting sleep quality (Cao et al. 2021). IAQ will influence people’s
respiration and can disrupt sleep progress.
Lighting intensity and acoustics are the least studied components. However, daylighting will
influence people’s circadian rhythms. It will influence people’s sleep quality because light will
suppress the emission of melatonin. It should be suggested to be investigated in future research
studies. Light intensity during daytime, lighting intensity before waking up, and before sleep
impacts on sleep quality should be future investigated. Acoustic will contribute to awake during
the sleep period. White noise before sleep and technology which could absorb noise sound used
during sleep time should be further investigated to if it contributes to better sleep quality.
According to Figure 4‑2, 39 research studies conducted the experiment, and two research studies
conducted the literature review. More literature review research studies should be conducted in
future research studies. Literature review research studies could review the current publishment
and provide more suggestion for future studies. Field study should be the main experiment setting
70
in the future. Sleep quality will be influenced by the lab-setting condition. Participants need time
to accommodate the experiment condition. Some participants might have a sleep disorder because
of the changes in the sleep environment. The lab-setting also has a problem in changing the
investigated parameters and controlling other parameters, which is hard to control in real life. The
lab-setting results may not be fully accurate due to the distraction of lab-setting equipment.
However, for the field study, it is hard to control conditions such as the pajamas and bed fabrics
such as quilts, pillows, etc., which are essential parameters. Pajama and bed fabrics will also
influence sleep quality. It is necessary to control these parameters during the field study.
According to Figure 4‑4, empirical research studies focused on summer and winter. However,
transition season should also be investigated. The temperature, relative humidity, air velocity, and
so on are different from each season. Every season should be taken seriously and thoughtful
consideration and investigation.
5.1.1 Analysis of National Locations and Participants
Most of the research studies have been conducted in various countries, such as China, Japan, Korea,
and multiple European countries, including Denmark (Figure 4-5). Future studies should focus on
another region. What’s more, it is also essential to investigate IEQ impacts on the foreigners’ sleep
quality. As mentioned in Wiwik et al.’s research, comfort temperature for Indonesian in Japan is
higher than for Japanese in Japan (Budiawan et al. 2021). Some research studies should be
conducted to explore the IEQ impacts on foreigners’, such as Chinese, Japanese, Korean,
Australian, and so on, sleep quality in the United States.
According to Figure 4-6, 80% of current research studies recruited less than 30 participants. Future
research studies should recruit more participants to increase the accuracy of results and eliminate
71
bias. The age of the participants should cover all age groups, especially for vulnerable age groups
such as children, adolescents, and the elderly, respectively. Poor indoor environment quality will
contribute to worse sleep quality among children, adolescent, and elderly people than adults.
Women should be investigated more in the future because women need a warmer environment
than men (Pan, Lian, and Lan 2011; Cao et al. 2020; Tsang, Mui, and Wong 2021; Li Lan et al.
2019).
5.1.2 Analysis of Experiment Data Analysis Methods
According to Figure 4-9, the most used data analysis software is SPSS. It could be used in future
work. The machine learning algorithm should be based on the hypothesis and goals of the research
studies. According to Figure 4-10, the recommended algorithms including t-test, correlation,
regression, and ANOVA.
5.1.3 Analysis of Experiment Survey Type
The majority of research studies collect the IEQ comfort, IEQ sensation, and sleep quality
questionnaires. The most frequently used IEQ comfort survey is a 5-point scale survey based on
the ASHRAE standard (Figure 5-1). It investigated the satisfaction of IEQ components. The most
used IEQ sensation survey is a 7-point scale IEQ sensation vote based on the ASHRAE standard
(Figure 5-1). It investigated the sensitivity of IEQ components. It is necessary to conduct the IEQ
satisfaction 5-points survey and IEQ sensation 7 points survey based on the ASHRAE standard to
evaluate the subjective assessment.
72
Figure 5-1 IEQ Survey
The most frequently used sleep quality surveys are specific surveys related to “calmness of sleep,
ease of falling to sleep, ease of awaking, freshness after awaking, satisfaction about sleep,” survey
based on Groningen Sleep Quality Scale (GSQS), and survey based on PSQI which could assess
sleep quality (Figure 5-2). GSQS is a brief and simple questionnaire with 15 true or false questions
which could access the sleep quality of the previous night. Compared with the specific survey
related to five aspects of sleep quality, the GSQS survey is more complex and more detailed.
Compared with the PSQI questionnaire, the GSQS survey is short and brief. GSQS could be
suggested in future research projects.
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7
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0
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6
8
10
12
IEQ Sensation 5-Point Scale IEQ Satisfaction 5-Point Scale IEQ Satisfaction 3-Point Scale
IEQ Survey
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Figure 5-2 Sleep Quality Survey
Figure 5-3 Other Survey Type
However, it is also necessary to collect data about clothing, bed fabrics, mood, daily activities
during the pre-sleep time. The limited research studies did conduct surveys about bed fabrics,
mood, and daily activities (Figure 5-3). The bed fabrics condition obviously influences people’s
thermal comfort and sleep quality. A bad mood such as depression can influence the people’s sleep
quality (Thomsen et al. 2003). The daily activities also influence sleep quality. Hight physical
13
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3 3
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5 Aspect (5-Point
Scale)
Custom Survey(5-
Point Scale)
GSQS PSQI SMH
Sleep Quality Survey
5
3 3
0
1
2
3
4
5
6
Mood Bed Fabric Daily Activities
Other Survey Type
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activities during daytime can contribute to better sleep quality (Feng et al. 2014). It is essential to
conduct survey about bed fabrics, mood, and daily activities to eliminate the influence and bias of
the results.
5.1.4 Analysis of Experiment Procedure
Most of the lab-setting research studies began their experiment at around 9:00 pm to prepare and
do the pre-sleep surveys. The sleep began among 11:00 pm and finished around 7:00 am. The total
sleep hour is between 7-8 hours which is suggested as the optimal duration of good sleep quality
(Xu, Lian, Shen, Lan, et al. 2021; Pan, Lian, and Lan 2011) (Figure 5-4 and Figure 5-5). For future
studies, the duration should keep the same as previous studies. However, the began moment and
end moment should base on the participants’ habits. The irregular bedtime will contribute to the
disorder and decrease of sleep quality (J. H. Kang and Chen 2009). It will contribute to the
inaccuracy of experiment results.
As mentioned before, the mood and daily activities will influence sleep quality. It is essential to
ask each participant to keep the same mood and daily activities to ensure the accuracy of
experiment results. Some data with the different mood and daily activities survey results should
be excluded.
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Figure 5-4 Sleep Procedure of Lab-Setting Experiment
Figure 5-5 Sleep Procedure of Field Study Experiment
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8 hour 7.5 hour 7 hour
Sleep Procedure of Lab-Setting Experiment
3
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8 hour 7.5 hour
Sleep Procedure of Field Study Experiment
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5.2 Analysis of Research Investigated Parameters and Results for
Research Studies Related to IEQ and Sleep Quality
5.2.1 Analysis of Outdoor Parameters
As illustrated in Figure 4-11, only ten research studies investigated the outdoor parameter.
However, the outdoor parameter is important. It could influence the indoor parameter through the
opening window, infiltration from façade, and thick façade. It is essential to investigate the impact
of outdoor parameters on indoor environment quality and impacts on sleep quality. The outdoor
temperature, relative humidity, air velocity will influence the indoor temperature, relative humidity,
and airflow through the opening window and infiltration of the façade.
Indoor air quality is strongly related to outdoor air quality. A high ventilation rate with good
outdoor air quality could dilute the indoor air pollution density. Zhao et al. demonstrated that
indoor PM density has an intimate relationship with outdoor PM density (Y. Zhao et al. 2017). It
is necessary to investigate the outdoor air quality’s impact on the indoor environment and sleep
quality, especially with the natural ventilation.
For lighting intensity, the outdoor streetlamps, headlights of automobile, LED-screens, and so on
will also influence the indoor lighting density during sleep period. The outdoor light can penetrate
the curtain into the indoor. As Cho et al. demonstrated that 5 lux of dim artificial light influenced
the sleep quality (Cho et al. 2015). The lighting intensity of outdoor lights and impacts should be
further taken into investigation. The light of sunrise will wake people up too. However, only Dong
et al. investigate the awaking daylight impacts on sleep quality and next-day performance (Dong
and Zhang 2021). It is essential to investigate the outdoor lighting density of artificial light and
daylight.
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5.2.2 Analysis of Indoor Parameters
According to Figure 4-12, the most investigated parameter is the air temperature and relative
humidity, which is identified as the most important component during the sleep period. People
could directly feel the air temperature and relative humidity through their skin. The uncomfortable
indoor air temperature and relative humidity will contribute to the disruption of sleep and decrease
of sleep quality. However, some research studies conducted the experiment condition exceed the
ASHRAE 55 standard. The clothing level for the cooling season could be estimated as 1.6, and the
metabolic rate is 0.7 for sleep time. The CBE Thermal Comfort Tool indicated that the comfort
zone for this situation is 22.5°C-30°C (“CBE Thermal Comfort Tool for ASHRAE-55” n.d.). Four
research studies’ experiment conditions exceeded the range. For transition season, the clothing
level could be estimated as 2.5, and the metabolic rate is 0.7 for sleep. The CBE Thermal Comfort
Tool indicated that the comfort zone for this situation is 19°C-27.5°C (“CBE Thermal Comfort
Tool for ASHRAE-55” n.d.). For the winter season, the clothing level could be estimated as 3.2,
and the metabolic rate is 0.7 for sleep. The comfortable zone based on the winter condition is
16.5°C-26.5°C (“CBE Thermal Comfort Tool for ASHRAE-55” n.d.). Three research studies’
experiment conditions exceeded the heating season requirement of 16.5-26.5°C. 14 research
studies investigated relative humidity range exceeding the ASHRAE standard (Figure 5-9, Figure
5-10, and Figure 5-11).
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Figure 5-6 Summer Experiment Temperature Condition VS ASHRAE standard
Figure 5-7 Transition Season Experiment Temperature Condition VS ASHRAE standard
24-30.5
25-31
23.2-25.5
22.5-25.5
31,32
23
27
26.3-27.9
25-28
27.8
28.6
26.5
28-31
26
30
30
20 22 24 26 28 30 32
Xinbo Xu et al. (2021)
Budianwan, Wiwik et al. (2021)
Ting Cao et al. (2020)
S.C.Sekhar et al. (2011)
Li. Lan et al. (2018)
Li Lan et al. (2014)
Li. Lan et al. (2019)
Hikaru Imagawa et al. (2014)
Li Lan et al. (2016)
Kazuyo Tsuzuki et al (2015)
Minhee Kim et al. (2010)
Jing Xiong et al. (2020)
Summer Experiment Temperature Condition
Temperature Range(°C)
ASHRAE Standarad 22.5°C-30°C
21.8
21.8
13.7-27.7
24-27
17.8-29.3
18.4
22.4
22.5
10 12 14 16 18 20 22 24 26 28 30
Li Lan et al. (2021)
Chenxi Liao et al. (2019)
P. Strøm-Tejsen et al. (2015)
Kashif Irshad et al. (2018)
XiaoJing Zhang et al. (2021)
Kazuyo Tsuzuki et al. (2015)
Minhee Kim et al. (2010)
Transition Season Experiment T emperature Condition
Temperatrue Range (°C)
ASHRAE Standard 19°C-27.5°C
79
Figure 5-8 Winter Experiment Temperature condition VS ASHRAE Standard
Figure 5-9 Summer Experiment Relative Humidity condition VS ASHRAE Standard
17
17
17
17-30
20.7
8.7-18.3
22.3-23.6
16
10.3
22.7
20
20
20
21
23
23
23
5 10 15 20 25 30
Li Pan et al. (2012)
Li Pan et al. (2011)
Ting Cao et al. (2021)
Budianwan, Wiwik et al. (2021)
Cong Song et al. (2020)
Yanfeng Liu et al. (2014)
T.W Tsang et al. (2021)
Cong Song et al. (2020)
Kazuyo Tsuzuki et al. (2015)
Minhee Kim et al. (2010)
Winter Experiment Temperature Condition
Temperature Range (°C)
50-83
45-85
40-60
34-72
40-60
40-70
40-60
50-60
50-80
60
50-70
30 40 50 60 70 80 90
Xinbo Xu et al. (2021)
Budianwan, Wiwik et al. (2021)
Ting Cao et al. (2020)
S.C.Sekhar et al. (2011)
Li Lan et al. (2017)
Li Lan et al. (2014)
Li Lan et al. (2019)
Hikaru Imagawa et al. (2014)
Kazuyo Tsuzuki et al (2015)
Minhee Kim et al. (2010)
Jing Xiong et al. (2020)
Summer experiment Relative Humidity Condition
Relatvie Humidity Range
ASHRAE Standard <65%
ASHRAE Standard 16.5°C-26.5°C
80
Figure 5-10 Transition Season Experiment Relative Humidity condition VS ASHRAE Standard
Figure 5-11 Winter Experiment Relative Humidity condition VS ASHRAE Standard
As illustrated in Figure 4-12, the least investigated parameters are PM2.5, TVOCs, PM10, and
levels of indoor bioaerosols. It is essential to conduct research studies related to the latest
investigated parameters PM2.5 and PM10 levels are positively related to the waking duration and
times and negatively related to sleep efficiency (Chenxi Liao and Jelle Laverge 2019). TVOCs are
50-70
30-50
40-50
47-82
25.5-74
50-70
39.1
0 10 20 30 40 50 60 70 80 90
Li Lan et al. (2021)
Chenxi Liao et al. (2019)
P. Strøm-Tejsen et al. (2015)
Kashif Irshad et al. (2018)
XiaoJing Zhang et al. (2021)
Kazuyo Tsuzuki et al. (2015)
Minhee Kim et al. (2010)
Transition Season Experiment Relative Humidity Condition
Relatvei Humidity Range(%)
ASHRAE Standard <65%
50
40
25-65
40-69
20-80
60-80
31-55
40-60
20-30
55 70
20 30 40 50 60 70 80 90
Li Pan et al. (2011)
Ting Cao et al. (2021)
Budianwan, Wiwik et al. 2021)
Cong Song et al. (2020)
Yanfeng Liu et al. (2014)
T.W Tsang et al. (2021)
Cong Song et al. (2020)
Kazuyo Tsuzuki et al. (2015)
Minhee Kim et al. (2010)
Winter Experiment Related Humidity Condition
Relatvie Humidity Range (%)
ASHRAE Standard <65%
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irritants and odorants. It will contribute to the injury of human respiration and disruption of sleep
(al Horr et al. 2016). TVOCs are produced by the smoking and cooking in the indoor environment,
the material of furniture, paint and so on (al Horr et al. 2016). Smoking and cooking also produce
PM2.5 and PM10. PM 2.5, PM 10 and TVOCs should be investigated in the field study rather than
lab-setting because the lab-setting experiments may not include the smoking and cooking behavior
that happens in daily life.
According to Figure 4-13 and Figure 4-14, the most frequently used measuring instrument for
indoor temperature, relative humidity and CO2 density is TR-76Ui. It is a sensor that could monitor
different parameters. What is more, the SAMBA IEQ sensor also could be used in future studies.
It could sense all components of IEQ. It is equipped with a thermal comfort sensor, indoor air
quality sensor, lighting sensor, and acoustics sensor (Parkinson, Parkinson, and de Dear 2019). It
also could cooperate with the cloud to monitor the indoor environment.
Limited research studies investigated the bed condition, which is highly related to human thermal
satisfaction. The impacts of a partial heating/cooling system allow people to have the better
thermal environment (L. Lan et al. 2018b; W. Song et al. 2020). The personal ventilation system
allows people to have the better thermal condition and air quality (Zhou, Lian, and Lan 2013).
5.2.3 Analysis of Biological Signal
The most investigated parameter is EEG which represents different sleep stages (Figure 4-27). The
brain wave from 8 Hz to 11 Hz decreased to 3 Hz to 7 Hz represent stage 1 of NREM (Pan, Lian,
and Lan 2012). The brain wave of stage 2 of NREM is sleep spindles waves (12Hz to 14 Hz)(Pan,
Lian, and Lan 2012). The brain wave of NREM’s stage 3 is characterized by a minimum of 20%
of the wave from 0.5Hz to 2Hz (Pan, Lian, and Lan 2012). The brain wave of the REM stage is a
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rapid and low-voltage brain wave (Pan, Lian, and Lan 2012). The EEG is the representation of
sleep quality. It is used to identify the different stages of sleep. One research study investigated
the relationship between EEG and sleep quality (Pan, Lian, and Lan 2012) (Table 5-1). It
demonstrated that EEG is a reliable biological signal to help researchers distinguish sleep stages
(Pan, Lian, and Lan 2012). The heart rate works as the same function when using actigraphy to
measure sleep quality. At the beginning of the sleep, the heart rate is as normal. After deep sleep,
the heart rate will decrease and tend to become calm (Irshad et al. 2018).
Limited research studies use body temperature to measure the sleep quality. However, some
research studies indicated that body temperature change tendency is almost the same (Table 5-1).
Some research studies indicated that the body temperature begins to increase to the first peak at
the beginning of sleep. The second peak appeared three hours before waking, and then it decreased
until one hour before awaking (Pan, Lian, and Lan 2012; Y. Liu et al. 2014; C. Song, Zhao, et al.
2020; L. Lan, Lian, and Lin 2016). The research studies are limited and incomprehensive. In the
future, impacts of IEQ on body temperature during sleep time should be investigated based on
different stages. It could verify if it is possible to use body temperature as an indicator of sleep
stage. What is more, the different combinations of body temperature locations should be further
investigated.
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Biological
Signal
Sleep Parameter
EEG HR Body Temperature
Sleep onset Latency Pan et al (2012) Pan et al (2012)
Lan et al. (2017)
Irshad et al. (2018)
Liu et al. (2014)
Lan et al. (2019)
Song et al. (2020)
Lan et al. (2016)
Duration of N1*,
N2**, N3***, REM
Irshad et al. (2018) Lan et al. (2017)
Irshad et al. (2018)
Liu et al. (2014)
Lan et al. (2019)
Song et al. (2020)
Lan et al. (2016)
Table 5-1 Interaction of Biological Signal and Sleep Quality parameters (*: NEM stage 1 **: NEM Stage 2 ***: NEM Stage3)
5.2.4 Analysis of Sleep quality
According to Figure 4-32, most of the research studies investigated sleep quality in different
aspects. Pan et al. and Xu et al. indicated that SWS and SOL is the most important component to
analyze sleep quality (Pan, Lian, and Lan 2012; Xu, Lian, Shen, Lan, et al. 2021). Other research
studies investigated the duration of NREM stages and REM. It is essential to investigate IEQ’s
impact on the different sleep stage duration.
As illustrated in Figure 4-33, PSG is widely used in experiments. However, it is complicated and
unportable. It also needs experts to calculate the data and transfer the electrical signals to sleep
quality. It could be used in lab-setting. As Liao et al. indicated that it takes time to install the PSG.
It will contribute to the increase of parameters such as PM2.5. On the other hand, actigraphy is
more convenient and portable than PSG. It is also some commercial products such as Fitbit, and
Philips’s watch which is convenient to buy for people. It allows researchers to conduct the field
study experiment conveniently.
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5.3 Analysis of Research Results Related to IEQ and Sleep
Quality
5.3.1 Analysis of Results of Different Indoor Parameters
a) Analysis of Results of Air Temperature
Air temperature is the most important component, which has a direct and positive relationship with
sleep quality. The air temperature requirement is different from the daytime requirement. It is
essential to investigate the comfortable air temperature for the sleep period. However, only 15
research studies demonstrate the comfortable temperature for good sleep quality. The most
comfortable range for summer conditions is from 22°C to 27°C. The most comfortable range for
winter is 23°C. Therefore, it is necessary to verify the comfortable zone’s feasibility. What is more,
the comfort zones for all seasons still need to be investigated in future studies.
Most of the research studies investigate only the sleep period. However, the pre-sleep condition
will also influence sleep quality. The temperature in the pre-sleep time will influence the sleep
onset latency. Song et al. indicated that pre-sleep period should have a warmer environment than
the sleep period (C. Song, Zhao, et al. 2020). Lan et al. indicate the C2-rise-fall condition will
contribute to longer sleep onset latency (L. Lan, Lian, and Lin 2016). The C2-rise-fall condition
means that the indoor temperature changed from 25°C-28°C -26°C (L. Lan, Lian, and Lin 2016).
They also demonstrated that the temperature should not be too low during the sleep start stage (L.
Lan, Lian, and Lin 2016; X. Zhang et al. 2021). However, Zhang et al. indicated that comfortable
temperature for the pre-sleep period is lower than the during-sleep period (X. Zhang et al. 2021).
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This research study has opposite results compared with the previous two. The comfortable pre-
sleep period temperature should be further investigated.
b) Analysis of Results of Relative Humidity
As illustrated in Chapter 4, most of the research studies only recorded the relative humidity range
during the sleep period. The comfortable zone for relative humidity during sleep time should be
explored in future research studies. Only two research studies demonstrated the comfortable
relative humidity range. Only one research study indicated that relative humidity is negatively
related to sleep quality. However, relative humidity is also a critical component that will influence
the breath of people. It will further influence people’s sleep quality. The relationship between
relative humidity should be further investigated.
c) Analysis of CO2 Density
As illustrated in chapter 4, CO2 density has intimated and negative relationship with sleep quality
(Li Lan et al. 2021; Xu, Lian, Shen, Lan, et al. 2021; Ramos et al. 2022; Sekhar and Goh 2011;
Xu, Lian, Shen, Cao, et al. 2021; X. Zhang et al. 2021; Tsuzuki et al. 2015; Xiong et al. 2020).
However, the comfort zone of CO2 density is indeterminate. Future studies should investigate the
comfortable CO2 density for sleep environments.
d) Analysis of Air Velocity
As illustrated in Table 4-1, most of the research studies only controlled the air velocity less than
0.2 m/s, which meet the requirement of the ASHRAE standard (“Thermal Environmental
Conditions for Human Occupancy” 2004). The relationship between air velocity and sleep quality
is still ambivalent. The comfortable zone for air velocity is also ambivalent. It is a vital direction
for future studies.
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e) Analysis of Lighting Intensity
Cho et al. demonstrated that dim artificial light during sleep time will influence the people’s sleep
quality (Cho et al. 2015). The indoor lighting intensity should be controlled to 0 lux. Moreover,
the outdoor lighting intensity should be investigated because it will penetrate the curtain. It will
also influence sleep quality.
The daytime lighting exposure will contribute to different sleep qualities. The lighting condition
parameters include illuminance, color temperature, luminance. Only one research project studied
the CCT influence on sleep quality. Few research studies investigated the luminance impacts on
sleep quality. It is necessary to conduct research studies related to these two parameters in the
future to identify the lighting condition impacts on sleep quality.
f) Analysis of Noise Level
The consistent result is that noise level is negatively related to sleep quality (Li Lan et al. 2021;
Xu, Lian, Shen, Lan, et al. 2021; Basner, Müller, and Elmenhorst 2011; Röösli et al. 2019; Radun,
Hongisto, and Suokas 2019; Smith et al. 2019). The threshold of noise level is totally different in
current published research studies. The comfortable range of noise should be investigated in future
studies.
Lan et al. investigated the impact of noise from ventilation. It is another direction of future research
studies. Noise levels should be connected with indoor equipment such as air conditioning,
ventilation, and so on to investigate if the noise from these kinds of indoor equipment will
compromise the positive effect from them.
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5.3.2 Integrated Relationship Between IEQ and Sleep Quality
Indoor parameters influence the sleep quality in different sleep periods and different sleep
parameters. However, current research studies are limited. The influence of air temperature on
sleep period time, sleep efficiency, duration of N1, N2, REM should be further investigated (Table
5-2). The relative humidity impacts on all sleep quality parameters should be further investigated.
The CO2 density’s influence on sleep period time, sleep onset latency, duration of N1, N2, and
REM should be further investigated. Air speed and lighting impacts on all sleep parameters should
be further investigated.
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IEQ Parameter
Sleep Parameter
Air
Temperature
Relative
Humidity
CO2 Density Air Speed Lighting Noise Level
Sleep period time
Ramos et al
(2022)
Wiwik et al.
(2021)
Lan et al.
(2021)
Ramos et al
(2022)
Lan et al.
(2019)
Cho et al.
(2015)
Shishegar et
al. (2021)
Dal et al.
(2019)
Tsuzuki et
al. (2015)
Lan et al.
(2021)
Sleep efficiency
Cao et al.
(2021)
Xiong et al.
(2020)
Lan et al.
(2021)
Strøm-Tejsen
et al. (2015)
Zhang et al.
(2021)
Cho et al.
(2015)
Kazuyo
Tsuzuki et
al. (2015)
Lan et al.
(2021)
Xu et al.
(2021)
Röösli et al.
(2019)
Sleep onset Latency
Pan et al (2012)
Pan et al (2011)
Ting Cao et al.
(v21)
Wiwik et al.
(2021)
Cao et al.
(2020)
Lan et al.
(2014)
Lan et al.
(2016)
Strøm-Tejsen
et al. (2015)
Xu et al.
(2019)
Shishegar et
al. (2021)
Röösli et al.
(2019)
Duration of N1*
Pan et al (2012)
Xiong et al.
(2020)
Lan et al.
(2021)
Xu et al.
(Sep-19)
Cho et al.
(2015)
Lan et al.
(2021)
Smith et al.
(2019)
Duration of N2**
Pan et al (2012)
Xiong et al.
(2020)
Lan et al.
(2021)
Xu et al.
(2019)
Cho et al.
(2015)
Lan et al.
(2021)
Smith et al.
(2019)
Slow Wave (N3) ***
Pan et al (2012)
Xu et al. (2021)
Pan et al (2011)
Cao et al.
(2021)
Cao et al.
(2020)
Li Lan et al.
(2014)
Cao et al.
(2021)
Xu et al.
(2021)
Lan et al.
(2021)
Xu et al.
(2019)
Xu et al.
(2019)
Xiong et al.
(2020)
Cao et al.
(2021)
Lan et al.
(2021)
Basner et al.
(2011)
Smith et al.
(2019)
Duration of REM
Pan et al (2012)
Lan et al.
(2016)
Xiong et al.
(2020)
Lan et al.
(2021)
Lan et al.
(2019)
Cho et al.
(2015)
Lan et al.
(2021)
Basner et al.
(2011)
Smith et al.
(2019)
Table 5-2 Interaction of IEQ Parameters and Sleep Quality Parameters (*: NEM stage1, **: NEM Stage2, ***: NEM Stage3)
Most of the research studied specific parameters such as air temperature, CO2 density, etc.,
respectively. Limited research projects investigated the combination of different parameters. Xu
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et al. conducted an experiment related to air temperature, relative humidity, and CO2 density (Xu,
Lian, Shen, Lan, et al. 2021). Zhang et al. investigated the air temperature, CO2 density, noise level,
and lighting density impacts on sleep quality, respectively (N. Zhang, Cao, and Zhu 2018). Xiong
et al. investigated the impacts of air temperature, and CO2 density on sleep quality, respectively
(Xiong et al. 2020). However, their studies investigated the relationship between different IEQ
parameters with sleep quality, respectively. Only limited research studies investigated the
relationship between the combination of different IEQ components and sleep quality. Cao et al.
interacted with temperature, relative humidity, and illuminance and conducted the impacts of the
combination on sleep quality (Cao et al. 2021). Li et al. combined the CO2 density and noise level
to investigate their impacts on sleep quality (Li Lan et al. 2021). Tsuzuki et al. combined the
lighting and indoor air temperature (Tsuzuki et al. 2015). It is necessary to combine the different
parameters and investigate the impacts of combination. For instance, as Li et al. indicated, the task
fan could contribute to the removal of heat stress and improvement of sleep quality (Li Lan et al.
2019). The air velocity, indoor air temperature, and relative humidity should be combined together
to investigate their influence on sleep quality.
5.4 Analysis of Research Studies Related to IoT and Wearable
Sensor
Most of the IoT systems monitored and controlled one specific IEQ parameter. They focus on
monitoring and manipulating the thermal comfort, air quality, respectively (Table 5-3). The
lighting conditions, and acoustic conditions also need to be monitored and automatically
manipulated with the IoT system. Lighting conditions and acoustic conditions will influence
people during daytime and sleep time. IoT system, which could monitor and control the indoor
90
lighting condition and acoustic condition, should be further developed to contribute to better
indoor environment quality.
Most IoT systems did not monitor the EEG which is an important biological signal represent sleep
quality. There are several wearable sensors that could easily measure the EEG, such as EEG CAP-
Brainwear and Ear-EEG device. The IoT system which could monitor the sleep quality and adjust
the indoor environment quality based on the monitored sleep quality should be developed in the
future study. HRV is another important biological signal which represents the stress level. It could
be affected by the nervous system (Xhyheri et al. 2012). HRV could be applied to IoT systems to
monitor people’s satisfaction.
On the other hand, most of the research studies on IoT system were investigated in the daytime.
Only one IoT system monitored the sleep quality (Malakhatka et al. 2021). The feasibility of using
these IoT systems during the sleep period still needs to be verified. What’s more, IoT systems
seem not much frequently adopted by various sleep quality studies. What’s more, the future study
should utilize the IoT system to conduct experiments.
Furthermore, it is necessary to develop an integrated system that could monitor and manipulate
different indoor environment parameters and sense the biological signals. The indoor environment
parameter should include air temperature, relative humidity, air velocity, air quality, lighting
condition, and acoustic condition. The IoT system should sense the different biological signals
such as HR, HRV, body temperature, and so on to represent people’s satisfaction of IEQ. Then the
data will send to cloud to use different machine learning algorithms to send command to HVAC
system, lighting control system and so on to change the indoor parameter.
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IEQ Component
Biological Signal
Indoor Air
Quality
Thermal Comfort Lighting
Condition
Acoustic
EEG
HRV
HR Elena et al. (2021) Yoshikawa et al. (2019)
Elena et al. (2021)
Yoshikawa et al. (2019)
Frankenberg et al. (2020)
Skin Temperature Elena et al. (2021) Elena et al. (2021)
Frankenberg et al. (2020)
Aryal et al. (2019)
Table 5-3 IoT System Type
The function of most wearable sensors is to monitor one specific parameter. Some wearable
sensors could monitor the indoor environment quality, such as lighting intensity, indoor noise
levels. Some wearable sensors could monitor biological signals such as EEG, HR, and body
temperature. Some wearable sensors could monitor sleep quality. Parts of them are prototypes. It
is essential to validate the commercial value and accuracy of the prototype. There are some
exceptions. Fitbit watches monitor multiple conditions such as sleep quality, heart rate, level of
oxygen in the blood and so on.
On the other hand, these wearable sensors only sense the data such as specific indoor environment
parameters and specific biological signals. They are not connected to IoT systems. It could be
developed to connect to the cloud and IoT system to contribute to better sleep quality.
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5.5 Summary and Recommendations for Future Sleep Quality
Study
5.5.1 Recommendations for Future Sleep Quality Study Setting
Based on analysis of current and previous publications, the following recommendations are
developed for the future study on IEQ and sleep quality. The recommended season is spring and
autumn. It is recommended to recruit more than 50 people to participate in the field study with
subjective and objective measurements. The participants should cover all ages and all gender. The
experiment could use different indoor sensors such as TR-76Ui and SAMBA IEQ sensor, and
Fitbit, which could monitor the sleep quality. The experiment procedure should base on
participants’ normal sleep habits. Before the participants sleep, the online survey about bed
condition, mood, daily activities, IEQ comfortable, and IEQ sensation should be filled by the
participants. Then the participants will have a 7.5-hour-8-hour sleep. After they wake up, the
participants should fill out a GSQS survey investigating sleep quality.
5.5.2 Recommendation for Sleep Quality Study Direction
First, different IEQ components’ comfort zone should be investigated based on field study,
especially for the air velocity, noise level, CO2 density, PM 2.5, and PM 10. The relationship
between indoor parameters and sleep quality should also be further validated. The second
recommendation is that it is necessary to investigate the impacts of the combination of different
IEQ components with field study or lab-setting experiments. The third recommendation is to verify
the feasibility of the currently developed IoT system. The IoT system will automatically
manipulate the indoor environment quality based on biological signals such as EEG and HR to
93
maintain a better sleep environment for people. The fourth recommendation is to utilize the IoT
systems to monitor and manipulate the experiment condition. The fifth recommendation is to
develop an integrated IoT system that could monitor and control all the IEQ components with
different wearable sensors to provide a better indoor environment for better sleep quality. The sixth
recommendation is to investigate the relationship between outdoor parameters and sleep quality.
It is necessary to investigate the impacts of outdoor parameters on sleep quality and develop novel
methods to offset the impacts.
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6 Conclusion and Future Work
6.1 Conclusion
Indoor environment quality influences people's daily life. People spend a significant amount of
time in the indoor environment. A high indoor environment quality will contribute to high
productivity, good mood, and healthy body. Indoor environment quality includes four different
aspects: thermal comfort, indoor air quality, lighting condition, and acoustic condition. Each aspect
was thoroughly studied during the daytime. However, IEQ will also influence people's sleep
quality. However, limited research studied the relationship between IEQ and sleep quality. On the
other hand, it is impossible for people to manipulate the indoor environment quality during sleep
time. Therefore, it is necessary to integrate the relationship between IEQ and sleep quality and
interact advanced IoT technology and wearable sensors with the relationship.
To obtain the goals, a meta-analysis was conducted. The first step is searching for current
publications through the USC library, Google Scholar, journal papers, and conferences. The
second step is recording the detail as much as possible in the excel file. The third step is analyzing
the data in the excel file through the different types of charts and tables. The fourth step is
developing the recommendation based on the analysis.
Forty-four research studies related to IEQ and sleep quality and 15 research studies related to IoT
and wearable sensors were recorded and examined. The journal impact factors for research studies
related to IEQ and sleep quality is from 1.99 to 6.456. most of the studies have the common goal
to investigate the relationship between IEQ and sleep quality. The most investigated component
rank is thermal comfort, IAQ, lighting condition, acoustic. Most research studies are empirical
research studies (21 lab-setting research studies and 19 field studies). The majority of the research
95
studies were conducted in China, Japan, Korea, Switzerland, Malaysia, etc. Most of the research
studies were conducted in summer and winter. The majority of the research studies recruited adult
males and adult females. Ten research studies measured outdoor parameters include outdoor
temperature, relative humidity, noise, etc. The most investigated indoor parameters are indoor air
temperature, indoor relative humidity, CO2 density, air velocity, lighting density, noise level,
PM2.5, TVOCS, level of indoor bioaerosols, PM10, respectively. The most investigated biological
signals are EEG, body temperature, EMG, EOG, heart rate, activities, blood flow in the figure,
proteinuria, melatonin, salivary biomarkers. The most investigated sleep quality parameters are
sleep onset time, sleep efficiency, sleep latency, N1, N2, N3 and REM, wake after sleep onset, and
SWS. The consistent result of current research studies is as follows. Indoor air temperature has an
intimate relationship with sleep quality. The comfortable zone for good sleep quality could be
concluded to 23°C-26°C. The relative humidity is negatively related to sleep quality. The
concentration of CO2 is negatively related to sleep quality. Enough lighting exposure during the
daytime could contribute to better sleep quality. Noise level is negatively related to sleep quality.
For current research studies related to IoT and wearable sensors, their function, advantages, and
disadvantages were recorded.
Lighting conditions and acoustic conditions during the sleep period should be further studied.
More field studies should be conducted to eliminate the inaccuracy. The transition season should
be investigated more. Research studies should be conducted in different regions and countries.
What is more, the foreigner should be investigated because their requirements are different from
local people. The experiment should follow the participants' sleep habits. The subjective survey
should include IEQ satisfaction, IEQ sensation, GSQS, mood, bed fabric, and daily activities. The
outdoor parameter and indoor parameters should be monitored and recorded. The relationship
96
between IEQ and sleep quality, and the comfort zone for different parameters should be further
investigated. The impacts of the combination of different parameters should be further investigated.
It is essential to verify the feasibility of using IoT systems during sleep time. The research studies
should utilize the IoT system more to investigate the relationship between IEQ and sleep quality.
The wearable sensor should be further developed to connect to the IoT system.
6.2 Limitations
First, the total number of research studies is limited. The meta-analysis includes 59 research studies
(44 related to IEQ and sleep quality, 15 related to IoT and wearable sensors). More studies should
be considered to investigate the relationship between IEQ and sleep quality and interact the
relationship with IoT. Furthermore, more research studies will be conducted in the future. It is
essential to consider future research studies to provide a more sophisticated analysis to investigate
the relationship between IEQ and sleep quality.
Second, the research studies related to IoT systems and wearable sensors are limited. In the future,
IoT systems and wearable sensors should be further developed. With the development of advanced
technologies, more parameters will be accommodated and measured in future studies. Therefore,
additional novel IoT systems and wearable sensors which measure and monitor different data and
parameters with the novel method should be considered to conduct the more sophisticated
experiment with sophisticated results. It will also contribute to the integration of better data
analysis.
Third, the data analysis method in the analysis is limited. Most of the analysis is based on Excel.
Most statistical analysis is based on datasets that are nominal rather than numeric. It is significantly
limited to analyzing the nominal data only, especially in terms of analysis on sleep quality
97
parameters, study methodologies, indoor environment parameters, etc. Therefore, more
sophisticated data analysis methods should be considered to investigate in-depth findings in the
collected.
6.3 Future Work
6.3.1 Near Term Future Work
The publications’ data could be further analyzed with data mining software such as SPSS and
WEKA for short-term future work. The public research papers which public in the future should
be included in the future to integrate the relationship between IEQ and sleep quality. More IoT
systems and wearable sensors should be reviewed and evaluated to interact with the IEQ and sleep
quality. The machine learning algorithm used in the current IoT system should be analyzed in the
future.
6.3.2 Long Term Future Work
It is essential to conduct an empirical research study based on the recommendations of chapter 5
to investigate the relationship between IEQ further and sleep quality and the comfort zone for
different parameters.
Different aspects influence sleep quality: Not only physical components but also psychological
components will influence sleep quality. Daily activities such as eating, working out, drinking will
also influence sleep quality. The mood, such as depression and stress, will also influence the sleep
quality. It is necessary to research these aspects’ impacts on sleep quality.
98
The development of IoT systems and wearable senor could be another future work. It is necessary
to invent a sensor that could monitor different parameters, connect to the network, and connect to
an IoT system.
6.4 Summary
A meta-analysis was conducted to integrate the relationship between IEQ and sleep quality and
interact the relationship with advanced IoT systems. The recommendation will allow future
research studies to have new directions and focus on the limited investigated aspect. It will allow
people to understand better sleep quality, which allows people to have better sleep quality in the
future.
99
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Abstract (if available)
Abstract
Indoor environment quality (IEQ) is a significant component in people's daily life because people spend most of their time to stay indoors. The IEQ has been thoroughly studied in previous research. However, some people are suffering from inadequate sleep quality because of the uncomfortable indoor environment. The comfortable indoor environment could be uncomfortable for people to sleep. The research on the relationship between IEQ and sleep quality is insufficient. Some research uses different methods to explore the relationship between sleep quality and IEQ; however, it did not have a complete and systematic principle and did not interact with the real-life application. Therefore, it is necessary to review research to integrate the relationship between sleep quality and IEQ and interact with the Internet of things, smart homes, or wearable devices. The wearable sensors will report the people's heart rate, eyes movement, and indoor environment condition to the HAVC system, ventilation system, lighting system, and so on. Then the system will change the indoor environment condition to provide a better sleeping environment, which contributes to better people's sleep quality. To sum up, it is essential to provide a meta-analysis of the bulk of research about IEQ, sleeping quality, and IoT to suggest a novel tendency of the development to improve sleeping quality through using IoT and wearable sensors to maintain indoor environment quality automatically.
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Asset Metadata
Creator
Wang, Jun
(author)
Core Title
IEQ, sleep quality, and IoT: meta-analysis on improving IEQ and sleep quality using IoT
School
School of Architecture
Degree
Master of Building Science
Degree Program
Building Science
Degree Conferral Date
2022-05
Publication Date
04/15/2022
Defense Date
03/09/2022
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
comfortable environment,indoor environment quality,IoT,OAI-PMH Harvest,sleep quality
Format
application/pdf
(imt)
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Choi, Joon-ho (
committee chair
), Noble, Douglas E. (
committee member
), Schiler, Marc (
committee member
)
Creator Email
jwang991@usc.edu,scarlettwong95@gmail.com
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-oUC110964829
Unique identifier
UC110964829
Document Type
Thesis
Format
application/pdf (imt)
Rights
Wang, Jun
Type
texts
Source
20220416-usctheses-batch-926
(batch),
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Access Conditions
The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.
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Repository Location
USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA
Repository Email
cisadmin@lib.usc.edu
Tags
comfortable environment
indoor environment quality
IoT
sleep quality