在運動和復健中透過生理訊號去監測生命徵象能讓使用者更了解到他們的生理狀態，使用穿戴式裝置搭配智慧型手機的使用更能幫助使用者在進行運動時警覺本身的健康狀態，在中風病人的上肢復健方面也能提供良好的幫助。基於上述理由，本論文開發一套穿戴式系統用於體育鍛鍊和上肢復健的應用上。
本論文開發一套穿戴式系統其結合即時姿態辨識、心率監測及量化動作監控功能於一穿戴式裝置內，該系統包含一個穿戴式裝置、智慧型手機應用程式以及雲端伺服器。MSP430為一個超低功耗微控制器，其用於穿戴式裝置中負責進行心率計算、計算行走及跑步步數、計算伏地挺身及仰臥起坐次數以及姿態辨識。智慧型手機則藉由無線傳輸接收上述由穿戴式裝置中量測到之資訊並透過UI將資訊提供給使用者，同時智慧型手機會將資料上傳到伺服器。此外在ECG的部分，本論文提出了一套基於ECG的波峰偵測演算法，在使用MIT-BIHST Change資料庫下，能達到99.7%的準確率。此外本論文在跑步機上驗證演算法時，在速度1.8 km/hr到9km/h的速度下，心率量測方面能達到準確度98.89%。俯臥撑，仰臥起坐，行走和行走計數器的準確度分別為99％，100％，99.21％和98.57％。在各項姿態辨識中此系統準確率為100%。
上肢復健系統，IMU的準確性是使用Qualisys作為黃金標準進行評估，經過實驗，三個不同方向的角度測量誤差的均方根誤差（Root-Mean-Square Error, RMSE）達到1.2度。結果顯示本系統對於上肢復健系統可用於臨床應用。

Monitoring vital signs along with physical and physiological data during exercise is able to establish the comprehensive health status awareness. The integration of wearable devices and smartphone technology for physical exercise shifts the daily behavior of the user to be more aware about their health and fitness. The implementation of the technology integration moves forward to the field of post-stroke rehabilitation for supporting patient recovery. Based on this consideration, we proposed the wearable integrated system for physical exercise and upper limb rehabilitation application.
For physical activities detection, we developed an integrated wearable system that combines real-time posture detection, heart rate (HR) monitoring, and quantitative activity monitoring into a robust wearable device. The system includes a wearable device, a smartphone application, and a server. MSP430, an ultralow-power microprocessor, is used in the wearable device to calculate HR, count walking and running steps, tabulate push-ups and sit-ups, and detect posture. Data transmissions are conducted from the wearable device to a smartphone to display the information to the user, record all received data, and upload data to a server. ECG peak detection algorithm achieved accuracy of 99.7% using the MIT-BIH ST Change Database. Accuracy of 98.89% was achieved for HR and 99%, 100%, 99.21%, and 98.57% accuracy for push-up, sit-up, walking, and running counter respectively. The results are 100% accurate in various postures.
For the upper-limb rehabilitation, the validity of the IMU sensor has been evaluated using Qualisys motion capture camera as gold standard, which the angle measurement error (RMSE) in three different orientations is achieved to be 1.2⁰. The preliminary results show that this system has feasibility to be utilized in the clinical application.

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