在現今物聯網及大數據時代裡，傳統網路與穿戴式傳感器的整合是物聯網技術快速發展的其中一項因素。而在傳統工業製程管理中，工業工程師通常以觀察法為製程的績效進行評估及改善，然而透過此方法不僅會耗費許多人力及時間成本，同時每個人主觀解讀不同造成評估上的差異，也可能因視覺誤差而降低評估的精確度，因此本研究設計了三組實驗分別為基礎動作實驗、組裝及裝箱作業和組裝木箱作業，以模擬現場作業人員的動作，並利用穿戴式傳感器(XSENS)收集人員在作業時的動作資料，接著輸入至監督式機器學習演算法建立分類模型，並同時探討合適的資料預處理方法，如觀察時窗切割的大小及正規化方法對於分類模型績效的影響。研究結果顯示時窗的切割大小及正規化方法對模型的績效並未有顯著的影響，而利用隨機森林及支持向量機可以準確地分類人員動作及相應的作業，顯示了機器學習分類方法應用於辨識人員作業動作的可行性。

In the era of Internet of Things and big data, the integration of traditional networks and wearable sensors is one of the factors in the rapid development of Internet of Things technology. Traditional industrial engineers usually use observational method to evaluate and improve a manufacturing process. However, it may take up a lot of human resources and time, each person’s subjective interpretation may cause differences in evaluation, and reduce the accuracy of evaluation due to visual errors. This study designed three types of experiments: basic movement, assembling and packing cartons, assembling wooden boxes, by adopting XSENS to simulate the work of actual operators. This study input the data into the supervised machine learning algorithm to establish the classification model, and discuss the appropriate data preprocessing methods, such as the segmentation size of the time window and the normalization method, and their effect on the performance of the classification model. The research results show that the segmentation size of the time window and the normalization method do not have a significant impact on the performance of the model, and Random Forest and Support Vector Machine can accurately classify the activities and the tasks designed by this study, which shows the feasibility of applying machine learning classification methods to identify operator 's working movements.

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