隨著人口老化程度的增加，人們對於自身健康的重視也日益提高，而隨著科技的日新月異，其應用在相關醫療方面的比例也越來越多，本論文中提及的穿戴式裝置亦是受惠於所謂的”行動醫療”的發展。在本論中提出了一個由微控制器、藍芽低功耗 (Bluetooth low energy) 以及兩種不同類型的感測器所組成的穿戴式裝置，其內部的感測器可以量測心電圖 (Electrocardiography)、阻抗式肺量計 (Impedance pneumography) 以及九軸訊號。

在韌體的設計中，我們提供了三種操作模式：全模式 (All mode)、心電圖模式 (ECG mode) 以及R-R間隔模式 (R-R interval mode)，並且將所設計的低功耗設計應用在這三種模式上。首先，在全模式中會傳送心電圖、阻抗式肺量計以及三軸加速度計的資料，在心電圖模式中只傳送心電圖的資料，而R-R間隔模式則只傳送R-R間隔的資料。我們藉由收取一段時間的資料再透過藍芽傳出來的方式減少功率消耗，並且搭配R-R間隔偵測演算法來減少資料量，其中，值得一提的是R-R間隔演算法精確度可達99%，因此藍芽低功耗不僅能夠更快完成資料傳輸，同時增加睡眠時間以減少功率消耗。在未使用低功耗設計的情況下，藍芽低功耗的平均電流為9.82毫安培，整體平均電流為20.17毫安培。而在使用低功耗設計後，藍芽低功耗的平均電流降到0.09毫安培，整體平均電流為4.47毫安培。此外，全模式的整體平均功耗為29毫瓦，心電圖模式的平均功耗為24.68毫瓦，而R-R間隔模式的平均功耗為14.75毫瓦。這也就代表著最高可以節省77.83%的功率消耗，若是透過使用一顆300毫安時大小的電池做為電源供應之下，裝置可以連續使用66.98小時。

除了低功耗設計之外，考慮到人們對自身的健康愈來越重視，我們對這個裝置設計了相關的應用，像是阻塞性睡眠呼吸中止症 (Obstructive sleep apnea) 經常造成人們在睡眠時呼吸暫停，進而影響睡眠品質造成白天精神不濟。而我們知道阻塞性睡眠呼吸中止症的發生與睡眠的姿勢有一定的關連性，所以我們除了使用三軸的加速度計來量測呼吸的起伏外，還提出了一個能偵測睡眠姿勢的演算法，希望透過這個應用可以提供給醫生額外的資訊，幫助其能夠更準確診療這樣的文明疾病。

Mobile health promotes the development of the wearable devices. As a result, in this thesis we have proposed a wearable device which is composed of a micro-controller unit (MCU), a Bluetooth Low Energy (BLE) and two type sensors. These sensors in our wearable device can detect single-lead electrocardiography (ECG), impedance pneumography (IPG) and the 9-axis signals.

In the firmware design, we have provided three kinds of operating modes in the device: all mode, ECG mode and R-R interval mode, and we have used the low power design in each operating mode. Generally, all mode transmits ECG, IPG and accelerometer data; ECG
mode transmits ECG data only and R-R interval mode transmits R-R interval data only. To reduce the power consumption, we have switched the BLE between the active mode and sleep mode. We also have implemented the algorithm of R-R interval detection in the device to reduce the data amount and the accuracy has achieved 99%. Therefore, the BLE can transmit the data faster and stay in sleep mode longer. Without the low power design, the average current of BLE is 9.82 mA and the total average current is 20.17 mA. On the other hand, with the low power design, the power consumption of R-R interval mode is the minimum version, the average current of BLE is only 0.09 mA and the total average current is 4.47 mA. Besides, in all mode, the total average power is 29 mW. In ECG mode, the total average power is 24.68 mW. In R-R interval mode, the total average power is 14.75 mW. As the result,
it reduces from 66.56 mW to 14.75 mW. That means it saves the maximum 77.83% power consumption, hence, the operating time of device extend to 66.98 hours continuously with a 300 mAh battery.

Apart from the low power design, considering to people are paying more and more attention to their health, we have done an applications with our device. For example, nowadays the obstructive sleep apnea (OSA) causes the human breath pausing during sleep time. As we know the moment OSA breaks out is related to sleep posture, hence, we have used the 3-axis accelerometer to measure breath and have proposed an algorithm of sleep posture detection.

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 .. . . . . . . . . . . . . . . . . . . . . 9
2.2 Wireless Transmission Technology . . . . . . . . . . . . . 13
2.2.1 Bluetooth Low Energy (BLE) . . . . . . . . . . . . . . . 14
2.2.2 BLE Protocol Stack . . . . . . . . . . . . . . . . . . . 15
2.2.3 BLE Profile . . . . . . . . . . . . . . . . . . . . . . . 16
2.3 Related Studies Comparison . . . . . . . . . . . . . . . . 18
3 Proposed Physiological Sensing System 21
3.1 System Overview . . . . . . . . . . . . . . . . . . . . . . 21
3.2 Front-end Components . . . . . . . . . . . . . . . . . . . 23
3.2.1 ADS1292R . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2.2 MPU9250 . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3 Control-end . . . . . . . . . . . . . . . . . . . . . . . . 30
3.3.1 MSP430F5438A . . . . . . . . . . . . . . . . . . . . . . 32
3.3.2 CC2541 . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.4 Firmware Design . . . . . . . . . . . . . . . . . . . . . . 37
3.4.1 Low Power Design .. . . . . . . . . . . . . . . . . . . . 37
3.4.2 R-R Interval Detection . . . . . . . . . . . . . . . . . 42
3.4.3 System Optimization . . . . . . . . . . . . . . . . . . . 44
3.4.4 System Integration . . . . . . . . . . . . . . . . . . . 47
3.5 Sleep Posture Detection . . . . . . . . . . . . . . . . . . 50
4 Implementation Results 55
4.1 Sensor Node . . . . . . . . . . . . . . . . . . . . . . . . 55
4.1.1 Appearance . . . . . .. . . . . . . . . . . . . . . . . . 55
4.1.2 Specification . . . . .. . . . . . . . . . . . . . . . . . 56
4.1.3 Power Consumption . . . . . . . . . . . . . . . . . . . . 56
4.2 Feature Detection . . . . . . . . . . . . . . . . . . . . . 68
4.2.1 Detection of R-R Interval . . . . . . . . . . . . . . . . 68
4.2.2 Sleep Posture Detection . . . . . . . . . . . . . . . . . 70
5 Conclusions and Future Works 75
5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . 75
5.2 Future Works . . . . . . . .. . . . . . . . . . . . . . . . 76

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