在此篇論文中，我們提出一個低功耗的穿戴式生理感測裝置。
系統的架構包含微控制器、藍牙4.0和兩個前端感測的單晶片。我們的系統包含了三種類型的感測器，可分別量測心電訊號、呼吸的胸腔阻抗變化、和用戶的運動軌跡追蹤。以此感測器結合手機的應用程式，提供高解析度的高準確性的生理訊號，做到隨時隨地長期的生理訊號監控。
心電訊號透過電極貼片，量測胸腔體表電壓，採用16位元、250赫茲的取樣頻率。呼吸訊號的部分量測胸腔電阻變化，採用16位元、28赫茲的取樣頻率。運動軌跡感測器則由3軸的加速度計、地磁計、陀螺儀共同組成的一個慣性感測器，採用16位元、50赫茲的取樣頻率。透過手機或平板的應用程式控制，可以選擇單獨或同時接收記錄上述三種生理訊號。
透過韌體的設計，我們讓微控制器和藍芽分別在活動模式與睡眠模式間切換，以降低整個感測器的功耗。針對低功耗的設計包含了減少傳輸數據量、採取較高傳輸速度、集中傳輸使微控制器和藍芽系統的使用效率提高，減少系統的待機時間，以得到較長的時間可以停留在睡眠模式。
透過以上的這些設計，選擇心電跟呼吸感測器時，藍牙的功耗可以從32.2毫瓦減少到2.93毫瓦，所消耗的能量為原始版本的9%；選擇運動軌跡感測器時，藍牙的功耗可以從31.63毫瓦降低到4.26毫瓦，所消耗的能量為原始版本的13.5%。最大可節省29.27毫瓦。
此感測器裝備了電池容量為300豪安時的鋰電池。在3.3伏特的工作電壓下，使用心電與呼吸感測器時，功耗可從原來的39.21毫瓦降為9.93毫瓦，是原本的25.3%，可連續使用的時間從25小時提高到約100小時；使用運動軌跡感測器時，功耗從49.89毫瓦降為22.54毫瓦，為原本的45.14%，可連續使用的時間從19小時提高至44小時。

In this thesis, a wearable sensing device is proposed. The system is microcontroller-based and features Bluetooth Low Energy (BLE) technology for wireless transmission. Three types of sensors are integrated in our system. A wearable physiological device is developed to record single-lead electrocardiography (ECG) signals, respiration signals, and the motion trackings of users.
An ECG and respiration sensor is adapted to detect single-lead ECG signal and thorax impedance variation caused by respiration of the users. The sampling rate is 250 Hz for ECG and 28 Hz for respiration and the resolution is 16 bits for both. A inertial-measurement sensor with 3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer is used to monitor the motion tracking. The sampling rate is 50 Hz and the resolution is 16 bits. Choose by the smart phone or tablet, these sensors can be selected and record the physiological signals individually or collectively.
By used of the firmware design, we switch the microcontroller and the bluetooth low energy between the active mode and sleep mode to make the reduction of the power consumption. This work also do with the down sampling of the sampling rate for respiration (250 to 28). It reduce the amount of data, and therefore the BLE can transmit the data faster and go into sleep mode longer. When we adopt the sleep mode, the power consumption of the BLE reduced from 32.2 mW to 6.03 mW. When also using the down sampling and down resolution, the power consumption of the BLE reduced to 2.93 mW, which is only 48.6% of the original version with sleep mode and 9% of the original version without sleep mode. For the 9-axis sensor, the power consumption of the BLE reduced from 31.63 mW to 4.26 mW, which is only 13.5 % of the version without sleep mode.
A 300 mAh battery can make device continuously recording and transmitting data for at least 43 hours. The power consumption for the ECG and respiration sensor is about 9.93 mW under 3.3 V operation voltage, and the power consumption is 22.54 mW for 9-axis sensor. It can reduce maximum 29.27 mW on BLE after this work on the BLE.

Abstract
i
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Characteristics of the Physiological Signals and Wireless Transmission Interface 5
2.1 Introduction to the Physiological Signals . . . . . . . . . . . . . . . . . . . . 5
2.1.1 Electrocardiography (ECG) . . . . . . . . . . . . . . . . . . . . . . 5
2.1.2 Respiration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.3 Motion tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.4 Related Studies Summary . . . . . . . . . . . . . . . . . . . . . . . 14
2.2 Wireless Transmission Technology . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1 Bluetooth Low Energy (BLE) . . . . . . . . . . . . . . . . . . . . . 16
2.2.2 BLE Protocol Stack . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2.3 BLE Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3 Proposed Physiological Sensing Device 21
3.1 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 Front-End Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2.1 ADS1292R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2.2 MPU9250 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3 Micro-controller MSP430F5438A . . . . . . . . . . . . . . . . . . . . . . . 30
3.4 Bluetooth Low Energy CC2541 . . . . . . . . . . . . . . . . . . . . . . . . 32
3.5 Firmware Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.5.1 Low-power Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.5.2 MSP430 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.5.3 CC2541 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4 Implementation Results 47
4.1 Sensor Node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.1.1 Appearance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.1.2 Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.1.3 Power Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2 Physiological Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.2.1 ECG Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.2.2 9-axis Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5 Conclusion and Future Work 63
5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

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