本論文依據現有的研究結果作為根據，找出三種可能造成運動傷害的指標，並依據這三種指標分別建立三種辨識運動異常模型，先透過OpenPose[1](Z. Cao et al., 2019)提取人體關節點座標和感興趣的特徵，再經過異常值處理後輸入至本論文的辨識運動異常模型，最後判斷運動姿勢是否可能造成運動損傷？
三種運動傷害指標包含 :
(1) 跑步時膝蓋是否有外翻和內翻？
(2) 在跑步過程中，大腿蹬地往前跳時是否髖關節伸展不足？
(3) 深蹲時膝蓋是否有外翻的現象？
這三種運動表現特徵皆有可能造成膝關節傷害，數據收集會依據量測指標的不同收集不同角度的影片，指標(1)選擇相機由前方或後方拍攝跑者的影片;指標(2)選擇相機從側方拍攝跑者的影片;指標(3)選擇從前方拍攝使用者做深蹲訓練的影片。從youtube收集和自己拍攝這三種視角的影片作為本研究的資料集。本研究根據這3個運動傷害指標分別建立的運動傷害辨識模型分別達到了94.1%、93.7%和95.4%的準確度。
運動傷害模型訓練完成後和OpenPose[1](Cao et al., 2019)做整合，實作了運動傷害即時檢測系統。同時設計一個簡單易操作的系統介面，可以透過攝影機或錄製好的影片作為輸入，提供使用者隨時檢測運動姿勢是否正確，判斷是否可能會造成運動傷害？

Based on the existing research results, we find three indicators that may cause sports injuries, and we establish three models to identify abnormal motion based on these three indicators. First, we use OpenPose[1](Cao et al., 2019) as the human joint extractor in this study to obtain the joint point coordinates and the features of interest, and then input them into the proposed abnormal motion recognition model to determine whether the motion posture may cause sports injury after outlier processing.
The three sports indicators include:
(1) the knees are genu valgum or baker-kneed while running
(2) whether the hip joint is insufficiently stretched when the thighs jump forward in the late running posture
(3) the knees are genu valgum while squatting
According to the existing researches, these three kinds of motion representations may cause knee injury. The data collection method will collect videos from different angles according to different measurement indicators. Indicator (1) chooses videos recording runners running from front or back perspective; indicator (2) chooses videos recording runners running from side perspective; indicator (3) chooses videos recording user doing squat training from front side perspective. All videos were collected from Youtube or recorded by myself based on three perspectives as the data set for this research. Three models established in this study based on these three sports injury indicators achieved 94.1%, 93.7% and 95.4% accuracy respectively.
After the training of the sports injury model is completed, it is integrated with OpenPose[1](Cao et al., 2019), and we implement real-time sports injury detection system, a simple and easy-to-operate system, which can use camera stream or video as input to input into system, so that users can check whether they may cause sports injury.

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