本論文實現一套熱量消耗分析系統，其利用感測器記錄使用者身體活動之加速度訊號以及心電訊號，並透過訊號演算法分析訊號以獲得熱量消耗之結果。此系統包含一部分析伺服器及一套穿戴式感測器。穿戴式感測器包含三個具無線傳輸功能的加速度感測器及心電收集裝置，感測器負責收集加速度訊號和心電訊號，其中主感測器上有藍芽無線傳輸模組，可將訊息透過藍芽與電腦溝通；分析伺服器負責資料儲存、演算法分析以及結果呈現。
在代謝當量估測演算法中，本論文利用配戴於使用者手腕、腰部和腳部之加速度感測器進行加速度訊號蒐集，加入使用者的心跳值，並且以K4b2氣體交換分析儀所量測之熱量消耗做為標準，建構一活動所產生熱量消耗之迴歸方程式。在進行代謝當量估測演算法前，加速度訊號須先經過重力濾除與視窗化的前處理，再將處理過之訊號取其count值，加入心跳以及使用者個人參數(身高、體重、性別、和年齡)建構活動的代謝當量映射迴歸方程式，以估測熱量消耗。在本論文中利用心跳值的加入，改善單純只用加速度值所建構之迴歸方程式，其原因為改善無法量測肌肉等長運動之加速度訊號，以及上下樓梯與地板之反作用力之影響。

This thesis presents an energy consumption estimation (ECE) system using the feature information from a set of wearable biosignal sensors recording user’s motion accelerations and electrocardiography (ECG). The ECE system consists of a set of wearable biosignal sensors (one host recorder and two client sensors), ECG holter and an analysis server. Each client sensor comprises an accelerometer (G-sensor), a RF module, and a microprocessor. The host recorder integrates a client sensor with a microcontroller unit (MCU), a memory unit and a Bluethooth module. The set of the biosignal sensors are responsible to collect motion accelerations from user’s hand, waist and ankle, respectively, and transmit to the memory unit of the host sensor. To analyze the collected accelerations, users can transmit the accelerations to the analysis server through Bluetooth transmission. Within the analysis server there are three modules: data collection module, analysis module and database module. The analysis module computes energy expenditure by mapping accelerations to MET values. Since the energy consumption by some activities cannot be measured from only accelerations, ECG is applied to improve the estimation accuracy of the system.
The procedures of the MET mapping algorithm are as follows. First, a low-pass filter is applied to remove the noise, and then a high-pass filter to remove the influence of gravity. A sliding window of size 60 seconds is adopted to calculate the count value that interprets continuous acceleration signals as discrete values. In this study, a K4b2 oxymeter is used to measure METs for different activities and the measured METs are regarded as the ground truth. Then the count values, heart rates, and personal information (height, weight, gender and age) are used to construct a regression model for estimating the MET values for different physical activities. The experimental results indicate that motion accelerations with heart-rate information, energy consumption can be more accurately estimated, especially for activities associated with reaction force such as climbing stairs or doing therapeutic exercises.

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