Wearable Wireless Low-power Sensor Devices with Energy Harvesting for In-home Personal Healthcare
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- Abstract
- Wearable health-monitoring devices are becoming very popularly, especially in enabling the noninvasive diagnosis of physiology function of the human body nowadays. The ABI Research has projected that by 2016, wearable wireless medical device sales will reach more than 100 million devices annually, and the market for wearable technologies in healthcare is projected to exceed $2.9 billion in 2016. However, most wearable sensor nodes that are used for continuously monitoring vital signals consume more power and also have high cost and bulky size. Therefore, this research works focus on design and implementation of long-life and batteryless wearable healthcare devices by using low-power components, low-power and non-power wireless transceivers, and energy harvesting sources. This study implemented a ZigBee-based low-power sensor node that consumes low power of only ~14 mW for monitoring ECG and PPG signals. The proposed node can easily function to operate in a body sensor network. The lifetime of the node can be up to 82 hours with only one AAA rechargeable battery (800 mAh); the battery can be charged by RF energy harvester to prolong battery life. Another wearable system is the BLE-based body sensor device. The device can easily communicate with mobile devices for monitoring biomedical signals. To extend lifetime, a high-efficiency harvester was designed to collect available solar energy in indoor and outdoor environments. In addition, this study also developed a robust algorithm for real-time peak detection of ECG and PPG waveforms under challenge conditions to monitoring heart rate, heart rate variability, and continuous blood pressure. To test the proposed algorithm, a PC mouse-based PPG sensor was implemented to collect PPG waveforms during normal operation duration of user. Finally, a batteryless sensor device that can measure the ECG signal was designed and implemented by using the RFID technology and RF energy harvester. Therefore this research results can satisfy the requirement of long-life wearable low-power devices for monitoring in-home healthcare conditions.
- Issued Date
- 2016
- Awarded Date
- 2016. 2
- Type
- Dissertation
- Publisher
- Department of Electronic Engineering, Pukyong National University
- Affiliation
- Pukyong National University
- Department
- 대학원 전자공학과
- Advisor
- WAN-YOUNGCHUNG
- Table Of Contents
- List of Figures vi
List of Tables x
List of Abbreviations xi
Acknowledgements xiv
Abstract xv
1 Introduction 1
1.1 Motivations 2
1.2 Challenges 4
1.2.1 Power Consumption 5
1.2.2 Sensors and Signal Conditions 5
1.2.3 Wireless Communication Standards 7
1.2.4 Energy Harvesting Sources 7
1.3 Contributions 8
1.4 Outline of the Dissertation 9
2 Background and Related Works 12
2.1 Biomedical Signals 12
2.1.1 Electrocardiography (ECG) 12
2.1.2 Photoplethysmography (PPG) 17
2.2 Energy Harvesting Sources 20
2.2.1 RF Wave Energy Source 22
2.2.2 Solar energy source 24
2.3 Wireless Communication Standards 27
2.3.1 ZigBee (IEEE-802.15.4) 29
2.3.2 Bluetooth Standard 30
2.3.3 RFID Standard 31
2.4 Wearable Technology 33
2.5 Chapter Summary 34
3 Energy Harvesting Sources for Long-life Devices 35
3.1 RF Energy Source 37
3.2 Solar Energy Source 41
3.3 Chapter Summary 46
4 ZigBee-based Low-Power Body Sensor Node 47
4.1 System Overview 48
4.2 Hardware Design 49
4.2.1 Control Module 49
4.2.2 Power Supply Module 51
4.2.3 ECG Circuit 52
4.2.4 PPG Circuit 54
4.3 Software Design 55
4.3.1 NesC/TinyOS Programming Language 55
4.3.2 Application Programming Design 56
4.4 System Setup 59
4.5 Experimental Results 62
4.6 Chapter Summary 65
5 BLE-based Wearable Body Sensor Device 66
5.1 System Overview 67
5.2 System Design 68
5.2.1 ECG & PPG Module 69
5.2.2 Optimal Solar Energy Harvester 70
5.2.3 Bluetooth Low Energy (BLE) 76
5.3 Experimental Results 77
5.4 Chapter Summary 83
6 Robust Real-time Peak Detection Algorithm with PC Mouse-based PPG Sensor Device 84
6.1 System Design 85
6.1.1 PPG Sensor 85
6.1.2 Reference Data Set 89
6.2 Peak Detection Algorithm 93
6.2.1 Adaptive Peak Detection 95
6.2.2 Random Error Detection 98
6.3 Experimental Results 102
6.4 Chapter Summary 108
7 Batteryless ECG Sensor Device 109
7.1 UHF RFID System 109
7.1.1 Types of RFID Tags 110
7.1.2 UHF RFID Air Communication Protocol 112
7.1.3 Read Range Calculation 114
7.2 System Design 116
7.2.1 RFID Tag 118
7.2.2 MCU, ECG, and Harvester Modules 121
7.2.3 Proposed System and Experimental Parameter 122
7.3 Real-time ECG R-peak Detection Algorithm 124
7.3.1 Characteristics of ECG Waveforms under Challenge Conditions 125
7.3.2 Algorithm 126
7.5 Chapter Summary 130
8 Conclusions 131
List of References 133
APPENDIX A 146
APPENDIX B 148
APPENDIX C 150
List of Pulications 152
Awards 156
- Degree
- Doctor
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