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UBIQUITOUS COMPUTING FOR HUMAN ACTIVITY ANALYSIS WITH
APPLICATIONS IN PERSONALIZED HEALTHCARE
by
Mi Zhang
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
August 2013
Copyright 2013 Mi Zhang
Object Description
| Title | Ubiquitous computing for human activity analysis with applications in personalized healthcare |
| Author | Zhang, Mi |
| Author email | mizhang@usc.edu;zhangmifigo@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2013-03-25 |
| Date submitted | 2013-07-02 |
| Date approved | 2013-07-02 |
| Restricted until | 2013-07-02 |
| Date published | 2013-07-02 |
| Advisor (committee chair) | Sawchuk, Alexander A. (Sandy) |
| Advisor (committee member) |
Krishnamachari, Bhaskar Liu, Yan |
| Abstract | Ubiquitous computing envisions a world in which people can access computing resources anywhere and any time. Over the past decade, the emergence and availability of a variety of miniature devices embedded with powerful sensing, communication, and computational capabilities are turning this vision into reality. Powered by these sensing and computational devices, ubiquitous computing endeavors to provide new and better solutions to problems in many application domains with significant societal impact. These include security, healthcare, education, sustainability, energy, and social informatics. ❧ My thesis investigates how ubiquitous computing technologies bring new solutions to transform the existing healthcare system to enable personalized healthcare and improve health and well-being for both healthy and clinical populations. The first half of this thesis focuses on wearable sensor-based human activity recognition technology which acts as the fundamental technology to support a variety of personalized healthcare applications, including personal fitness monitoring, long-term preventive care, and intelligent assistance for elderly citizens. Chapter 2 presents the human activity dataset we have built based on wearable sensor. Chapter 3 to Chapter 6 presents four different algorithms to model and recognize human daily activities based on the human activity dataset introduced in Chapter 2. Specifically, Chapter 3 analyzes human activity signals based on feature selection algorithms and shows that the recognition performance can be improved by carefully selecting features for each activity separately. Chapter 4 and Chapter 5 discusses new computational models based on dictionary learning and nonlinear manifold learning respectively to solve the human activity recognition problem from a totally different perspective. Chapter 6 presents the new activity model based on the recently developed sparse representation and compressed sensing theories and demonstrates the task of looking for optimal feature to achieve the best activity recognition performance is less important within this framework. ❧ The second half of this thesis focuses on the design of a novel on-body networked sensing system called RehabSPOT for computerized rehabilitation for patients with stroke. Chapter 7 presents the system design of RehabSPOT and its value in personalized rehabilitation delivery via real-time system reconfiguration. Chapter 8 presents the computational model based on wearable sensing system to analyze patients' motor behavior to track precisely the progress patients have made during rehabilitation. |
| Keyword | human activity recognition; mobile computing; personalized healthcare; ubiquitous computing; virtual rehabilitation; wireless health |
| Language | English |
| Format (imt) | application/pdf |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Contributing entity | University of Southern California |
| Rights | Zhang, Mi |
| Physical access | 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. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@dots.usc.edu |
| Filename | etd-ZhangMi-1733.pdf |
| Archival file | uscthesesreloadpub_Volume7/etd-ZhangMi-1733.pdf |
Description
| Title | Page 1 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@dots.usc.edu |
| Full text | UBIQUITOUS COMPUTING FOR HUMAN ACTIVITY ANALYSIS WITH APPLICATIONS IN PERSONALIZED HEALTHCARE by Mi Zhang A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ELECTRICAL ENGINEERING) August 2013 Copyright 2013 Mi Zhang |
