本論文旨在以加速度感測模組實現一結合動作辨識之能量消耗估測演算法，利用配戴於慣用手手腕及慣用腳腳踝的加速度感測模組，收集並儲存日常活動所產生之加速度訊號。首先建構一動作辨識演算法用以辨識站姿動作、坐姿動作、走路、跑步、騎腳踏車、上樓梯及下樓梯七種動作，再依照各活動類別分別建立各動作之能量消耗估測模型，最後再針對非穩態能量消耗進行估測，以完成一結合動作辨識之能量消耗估測演算法，提供使用者動作種類及能量消耗資訊。
加速度訊號經由加速度感測模組量測後，經由靜態、動態與震動分析將可區分靜態訊號、動態訊號與非人為產生的震動訊號。靜態訊號由姿態辨識演算法辨識站姿動作及坐姿動作；動態訊號由活動辨識演算法分為走路、跑步、騎腳踏車、上樓梯及下樓梯五種動作。接著使用加速度訊號的所產生的各項參數及由氣體分析儀所量測之實際能量消耗值來建構每一活動類別之能量消耗估測模型，最後利用一非穩態能量消耗估測方程式進行非穩態能量消耗的估測。
本論文結合動作辨識與能量消耗估測模型完成一適用於廣泛活動強度之能量消耗估測演算法。由實驗結果得知，動作辨識演算法對於11種日常活動之平均辨識率可達到96.8%；而結合動作辨識之能量消耗估測的平均估計標準誤差為0.726 METs，相較於未結合動作辨識之估計標準誤差0.969 METs，估測準確度之改善率為23.01%。另外，加入了非穩態能量消耗估測後對於連續時間之能量消耗估測的平均估計標準誤差為1.13METs，而未加入非穩態能量消耗估測的平均估計標準誤差為0.72METs，估測準確度之改善率為36.52%。

This thesis presents an algorithm for energy expenditure (EE) estimation of physical activities with activity classification for a wide range of daily activities using wearable physical activity sensor modules. The modules are worn on subject's dominant wrist and ankle to collect the accelerations of daily physical activity. The activity classification algorithm uses accelerations to classify seven activity categories including sitting, standing, walking, running, bicycling, walking upstairs, and walking downstairs. The oxygen uptake measured by the Cosmed K4b2 for each activity is used to construct EE mapping regression model. Finally, the proposed nonsteady state EE estimation method is constructed to compensate the results of the EE estimation algorithm.
Accelerations collected by the modules from activities are separated into three types: static, dynamic, and vibration. The static type can be divided into sitting and standing by using a posture recognition algorithm. Then, a k-nearest neighbor (k-NN) algorithm is used to classify the dynamic type including walking, running, bicycling, walking upstairs, and walking downstairs. After activity classification, oxygen uptake measured by the Cosmed K4b2 and features generated by accelerations are used to develop a linear regression model for each activity to estimate EE. A nonsteady state EE estimation equation is then used to estimate nonsteady state EE to improve the accuracy of long-term EE estimation.
In our experiments, the effectiveness of the proposed activity classification algorithm, EE mapping regression model, and nonsteady EE estimation algorithm has been successfully validated. The classification rate of 11 daily activities using the proposed activity classification algorithm is 96.8%. The averaged standard error of estimation (SEE) of EE estimation with activity classification is 0.726 METs, and the SEE of EE estimation without activity classification is 0.969 METs, which is higher than the SEE with activity classification. The performance of improvement of EE estimation with activity classification is 23.01%. The averaged SEE of EE estimation for a long-term acceleration data with nonsteady EE estimation is 0.72 METs, and the averaged SEE of EE estimation without nonsteady EE estimation is 1.31 METs. The performance of improvement of nonsteady EE estimation is 36.52%.

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