||Development of a Wearable Sensor System for Health Promotion: Physical Activity Measures and Physiological Condition Recognition
||Department of Electrical Engineering
Heart rate variability
Health promotion has become a major emphasis for the health and medical industry over the past century. This dissertation focuses on developing a wearable sensor system and its signal analysis algorithms for health promotion. A wearable sensor system with high data compression and low power requirements were developed for recording long-term acceleration and ECG signals. Based on the acceleration and ECG signals, automatic analysis algorithms for physical activity measures and physiological condition recognition were developed. For physical activity measures, this dissertation developed an energy expenditure estimation algorithm and movement analysis algorithm based on acceleration data. The proposed energy expenditure estimation models integrated an activity classification algorithm with a neural-network-based regression model constructed using features selected by sequential forward/backward selection strategies. For human movement analysis, we first developed a gait information retrieval algorithm based on information about each gait phase. Walking patterns (level walking, walking upstairs, walking downstairs) and movement distance were analyzed. For physiological condition recognition, a framework for physiological condition mining strategy was proposed. Participants with different severity levels of Parkinson’s disease were successfully classified using the proposed strategy. In addition to the physiological condition mining strategy, two HRV feature generation methods (parameter-based and trend-based feature generation) were proposed. These two feature generation methods incorporated the physiological condition mining strategy can effectively recognize driving stress under three different conditions. The effectiveness of the proposed wearable sensor system and algorithms has been successfully validated by applications for physical activity measures and physiological condition recognition.
中 文 摘 要 I
LIST OF TABLES VII
LIST OF FIGURES X
Chapter 1 Introduction 1
1.1 Motivation and Literature Survey 1
1.2 Dissertation Contributions 13
1.3 Dissertation Organization 15
Chapter 2 A Wearable Sensor Module with a Neural-Network-based Activity Classification Algorithm for Daily Energy Expenditure Estimation 17
2.1 Introduction 17
2.2 A Wearable Sensor Module 18
2.3 Neural-Network Based Activity Classification Algorithm 26
2.4 Experimental and Design 33
2.5 Summary 40
Chapter 3 Walking Pattern Classification and Movement Distance Estimation Algorithm using Gait Phase Information 41
3.1 Introduction 41
3.2 Wearable Acceleration Sensor 42
3.3 Development of a Walking Pattern Classification and Movement Estimation
using Gait Phase Information 43
3.4 Experimental Results 59
3.5 Summary 62
Chapter 4 Mining Physiological Conditions from Heart Rate Variability Analysis 64
4.1 Introduction 64
4.2 Heart Rate Variability Analysis 66
4.3 Methodologies for Mining Physiological Conditions from HRV Analysis 71
4.4 Illustrative Examples 78
4.5 Summary 90
Chapter 5 An HRV Feature-based Transformation Algorithm for Driving Stress Recognition 91
5.1 Introduction 91
5.2 An HRV Feature-Based Transformation Algorithm 93
5.3 Experimental Results 97
5.4 Summary 104
Chapter 6 Conclusions and Future Studies 105
6.1 Conclusions 105
6.2 Recommendations for Future Studies 108
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