本研究旨在開發一個基於手部骨架動作辨識技術於組裝生產作業現場的組裝動作分析架構。在Gilbreth提出的動作研究中，工作場域中的所有手部動作（Action）都由17種動素（Therblig）組成。本研究利用動素分析的概念，針對兩個主題進行研究：1) 分析使用相同的影像資料時，使用不同的標籤與關節數量是否會影響人體動作辨識模型的預測準確度；2) 提出一個以人因工程的動素辨識進而推測組裝動作的方法。本研究以骨架資訊擷取套件OpenPose所輸出的骨架資訊，利用時空圖卷積網路（Spatial Temporal - Graph Convolutional Network, ST-GCN）對組裝作業員的連續裝配動作或動素進行辨識。首先，先針對不同的標籤與關節數量進行人體動作辨識模型的預測準確度分析，然後再藉由動素辨識結果的組合，使用動態規劃方法（Dynamic Time Warping, DTW）來預測可能的組裝動作，以達到預測動作的目的。本實驗以主機板組裝中常見的動作作為模型訓練及測試。第一部分實驗結果發現，動作標籤搭配26個關節點可使辨識模型得到最佳的準確度，而動素標籤則依照動素定義僅需要慣用手23點的資訊即可得到最佳準確度。第二部分的實驗研究結果顯示以動素預測結果預測動作準確率為73.75%，而使用動素結合相應物件（Therblig-Item）的預測結果預測動作準確度可以提升至81.25%，顯示動素結合相對應物件可更有效預地測出動作。本研究結果可發現利用動素辨識可降低更換動作造成重新訓練模型的訓練成本。

This research aims to develop a hand gesture recognition framework for an assembly production site. In human motion study, Gilbreth considers that all hand actions in the workplace are composed of 17 therbligs. This research utilizes the concept of therblig analysis to investigate two topics: 1) analyze the usage of different labels and number of joints which might affect the prediction accuracy of human action recognition models when using the same image data. 2) propose a method for inferring assembly actions by using the result of therblig recognition. In this study, the skeleton information output from OpenPose was used to recognize the human action of the assembly operation using Spatial Temporal - Graph Convolutional Networks (ST-GCN). First, the prediction accuracy of the human action recognition model under different labels and the number of joints was analyzed. Then the possible assembly actions are classification by Dynamic Time Warping through the combination of the therblig recognition results. In the first part of the experiment, it was found that the action labeling with 26 joints resulted in the best accuracy of the recognition model, while the therblig labeling with only 23 joints of the handedness was required to obtain the best accuracy according to the therblig definition. The second part of the experiment showed that the accuracy of action classification with therblig prediction result was 73.75%. In addition, the accuracy of action classification with therblig combined with the corresponding object (Therblig-Item) could be increased to 81.25%. The result of the experiments can conclude that the proposed method can reduce the cost of retraining the model for action recognition by using the therblig recognition.

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