2019年新型冠狀病毒的爆發，因其強大的傳播力，人們被迫待 在家中減少外出。運動中心與健身房等場所也容易因為汗水或是 近距離的接觸進而增加染疫的風險。然而大部分的使用者在沒有 教練的協助下，常常導致動作不夠標準或是缺乏系統性的訓練， 造成運動傷害以及成效有限。因此本研究提出 Home Exercise Analysis System Based on Pressure Sensor and Deep Learning，簡稱 為 HomeXDL。HomeXDL 運用壓力及壓力重心變化結合深度學習 來達到運動紀錄以及動作正確度偵測。 資料前處理後，HomeXDL 從 CoP 軌跡以及壓力變化計算出的 特徵值，運用決策樹進行動 作種類的分辨，在深蹲、硬舉及弓箭步的動作分類中，準確度 (Accuracy)達91%以上。動作正確度判斷上，HomeXDL 運用 CNN 對上述三個動作的正確度偵測。在系統有標記的動作問題上， HomeXDL 也皆能快速且精準的偵測出來，三個動作整體準確度 也達90%以上。最後 HomeXDL 的使用者介面能及時的回饋給使 用者其運動品質與動作錯誤原因，對使用者在運動過程中有很大 的幫助。

The pandemic of the coronavirus in 2019 has strong transmission power. Therefore, people are forced to stay at home and reduce going out. Sports centers, gyms and other places are also prone to increase the risk of infection due to sweating or close contact. Coupled with the recent popularity of sports, in order to maintain a good figure, people lose weight through exercise. However, most users tend to have bad movements or lack of systematic training without the assistance of a coach, resulting in sports injuries and limited effects. Therefore, this study proposes a Home Exercise Analysis System Based on Pressure Sensor and Deep Learning, called HomeXDL for short. HomeXDL uses pressure and pressure center of gravity changes combined with deep learning to achieve motion recording and motion accuracy detection. After data preprocessing, HomeXDL uses a decision tree to distinguish action types based on the CoP trajectory and feature values calculated from pressure changes. In the action classification of squats, deadlifts, and lunges, the accuracy rate exceeds 91%. At the same time, HomeXDL uses CNN to detect the correctness of the above three actions with over 90% overall accuracy rate. Finally, the user interface of HomeXDL can give real-time-feedback to the user on the quality of the movement and the cause of the error, which is of great help to the user during exercise.

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