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系統識別號 U0026-1011201116475200
論文名稱(中文) 發展可穿戴式感測器系統於健康促進:日常活動量測及生理狀況識別
論文名稱(英文) Development of a Wearable Sensor System for Health Promotion: Physical Activity Measures and Physiological Condition Recognition
校院名稱 成功大學
系所名稱(中) 電機工程學系碩博士班
系所名稱(英) Department of Electrical Engineering
學年度 100
學期 1
出版年 100
研究生(中文) 林哲偉
研究生(英文) Che-Wei Lin
學號 N2896152
學位類別 博士
語文別 英文
論文頁數 121頁
口試委員 指導教授-王振興
口試委員-詹寶珠
口試委員-楊延光
口試委員-楊宜青
口試委員-徐崇堯
召集委員-陳永昇
中文關鍵字 加速度  熱量消耗  心律變異性  健康促進 
英文關鍵字 Acceleration  Energy expenditure  Heart rate variability  Health promotion 
學科別分類
中文摘要 近年來,健康促進已成為醫學界注目且強調的重點之一,本論文發展了一套可穿戴式感測器系統及相關分析演算法於健康促進領域。首先,本論文發展了具長時間紀錄及低功耗特性之加速度及心電訊號感測器系統;根據加速度及心電訊號,本論文發展日常活動量測及生理狀況識別的自動分析演算法。在日常活動量測方面,透過身體活動之加速度訊號,本論文開發了熱量消耗估測模型及移動分析演算法;熱量消耗估測模型結合動作辨識演算法及類神經網路,透過循序前進搜尋及循序向後搜尋法,選擇出最適合建構類神經網路之特徵值。移動分析演算法則基於加速度訊號,辨識步態循環中每一個相位的發生及持續時間,基於步態相位的資訊,使用者的移動模式(走路、上樓、下樓)及移動距離可被精確的分析及估算出來。在生理狀況識別部分,本論文基於心電圖訊號,提出一個基於心律變異性的生理狀況識別演算法,應用於慢性疾病的早期偵測及壓力偵測。透過此演算法,成功的區分了患者於不同帕金森氏病程的反應。此外,本論文亦提出了基於心律變異性趨勢及基於心律變異性參數的兩種特徵產生方式,搭配基於心律變異性的生理狀況識別演算法,成功的辨識了駕駛人於不同路段的駕駛壓力。
英文摘要 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.
論文目次 中 文 摘 要 I
ABSTRACT II
ACKNOWLEGEMENT IV
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
References 111
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