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姓名 黃星瑜(Hsing-Yu Huang) 查詢紙本館藏 畢業系所 資訊管理學系在職專班 論文名稱 使用卷積神經網絡於棒球投手上肢部位傷害預測
(Predicting Upper Extremity Injuries in Major League Baseball Pitchers with Convolutional Neural Networks)相關論文 檔案 [Endnote RIS 格式]
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摘要(中) 運動傷害對於運動員的生涯影響極大，尤其職業運動員一旦傷病，輕者則缺席賽季，嚴重則可能斷送整個個人職業生涯; 而對於職業球團，若選手容易受傷無法出賽將需要更複雜的球員調度，因此也盡力的避免選手傷害的發生。但根據美國職棒大聯盟的統計，球員因傷無法出賽的比例卻逐年升高，其中投手相較於其他守備位置的球員，面臨著更高的上肢部位的運動傷害風險。因此，有效的預測傷害的發生將有助於傷害的避免以及即早的治療，以減少對球員造成之永久傷害以及對球團的損失。而運用統計學、機器學習以及深度學習於傷害預測的相關研究皆提出各不同理論的預測模型，希望在實務上能夠降低損失發生的機會。
本研究以大聯盟選手於2020年的傷兵名單為樣本，選取12名上肢部位受傷投手並擷取其傷害發生前以及未受傷的影像資料，並透過深度學習架構OpenPose將影像資料萃取出人體骨幹，並實驗使用人體骨架動作辨識是否在卷積神經網路模型上得到不錯的傷害預測結果。而實驗結果顯示，在VGG19的模型下預測表現為最佳。相較於超深層卷積神經網絡ResNet-50，VGG19的預測準確率為最高，顯示較深的卷積神經網絡沒有帶來較高的準確率。摘要(英) Sports injuries have a great impact on the career of athletes, especially for professional athletes. Once the injury caused, the one with the minor injuries may be out of the season, and one with the serious injuries may ruin his/her whole career. For professional teams, if the players are injury-prone, they are constantly having to shuffle their pitching rotations. Therefore, learn how to prevent these injuries to players is an important task, both for players and in coaches. However, according to the statistics, the proportion of players unable to play due to injuries has been increasing year by year. Baseball pitchers have been shown to be at a higher risk for sustaining injuries, especially upper extremity injuries than position players. In related literatures, some statistics, machine learning, and deep learning methods have been used for injury prediction.
This study selects 12 pitchers who suffered from upper extremity injuries from the MLB where were in the injured list of 2020. Particularly, their pre-injury and uninjured video data were captured as the samples and the human skeleton was extracted as the input data by using OpenPose. In addition, three different convolutional neural network (CNN) models were chosen to find out the best one by training human skeleton data. The experimental results show that the best performance is obtained by the VGG19 model. Compared with the ultra-deep convolutional neural network ResNet-50, VGG19 has the highest prediction accuracy, showing that the deeper convolutional neural network does not bring higher accuracy.關鍵字(中) ★ 棒球選手投球傷害
★ 深度學習關鍵字(英) 論文目次 目錄
摘要……………………………………………………………………………………………………………………...…………I
ABSTRACT II
誌謝...................................................................................................................................................................III
目錄………………………………………………………………………………………………………………………………IV
圖目錄…………………………………………………………………………………………………………………………….V
表目錄………………………………………………………………………………………………………………………….…V
第一章 緒論 1
1-1 研究背景 1
1-2 研究動機 2
1-3 研究目的 3
1-4 研究架構 4
第二章 文獻探討 5
2-1 運動傷害相關研究 5
2-2 卷積神經網絡CONVOLUTIONAL NEURAL NETWORKS 9
2-2-1 VGGNet網路架構 11
2-2-2 ResNet 殘差網路架構 12
2-2-3 3D Convolutional Neural Networks 15
2-3 OPENPOSE 17
第三章 研究方法 18
3-1 資料來源 18
3-2 資料前處理 20
3-2-1 擷取主要動作片段 20
3-2-2 剔除干擾骨架 21
3-3 研究設計及架構 22
3-4 模型評估 24
第四章 實驗 25
4-1 模型參數設定 25
4-2 預測模型結果 26
4-2-1預測模型結果之比較 26
4-2-2預測模型混淆矩陣之比較 27
第五章 研究結論及建議 30
5-1 研究結論 30
5-2 研究限制 31
5-3 未來研究方向 31
參考文獻 32
圖目錄
圖 1. 1994年至2014年每年美國職棒大聯盟投手進行UCLR的總數 5
圖 2. 手臂肱骨位置圖 6
圖 3. 該研究實驗流程 7
圖 4. 卷積神經網絡基本結構 9
圖 5. 卷積層的特徵萃取方式 10
圖 6. 池化層的運作 11
圖 7. 感受野(Receptive field):每個神經元對應到上一層大小的區域 11
圖 8. 殘差學習結構 13
圖 9. CNN與3D CNN運作方式 15
圖 10. 3D CNN架構 16
圖 11. Openpose對於人體骨架 25個關鍵點的偵測模型 17
圖 12. 投球動作階段 20
圖 13. 資料前處理並過濾 21
圖 14. 本研究之實驗設計 23
圖 15. 隨機搜索和網格搜索，隨機搜尋較能快速的收斂 25
圖 16. VGG19混淆矩陣 27
圖 17. ResNet50混淆矩陣 28
圖 18. 3D-CNN混淆矩陣 28
表目錄
表 1. MLB投手受傷部位統計 1
表 2. 運動傷害相關論文 8
表 3. VGGNet的結構 12
表 4. 不同深度的ResNet 13
表 5. 樣本蒐集名冊 18
表 6. 影片取樣總數 19
表 7. 各模型樣本總數 19
表 8. 經前處理後各模型樣本總數 22
表 9. 二元分類的混淆矩陣。 24
表 10. 本研究模型參數一覽表 26
表 11. 各模型表現比較 26
表 12. 各模型訓練時間比較 27參考文獻 [1] Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of machine learning research, 13(2).
[2] Cao, Z., Hidalgo, G., Simon, T., Wei, S. E., & Sheikh, Y. (2019). OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields. IEEE transactions on pattern fanalysis and machine intelligence, 43(1), 172-186.
[3] D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, "Learning spatiotemporal features with 3d convolutional networks," in Proceedings of the IEEE international conference on computer vision, 2015, pp. 4489-4497.
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https://www.train2playsports.com/baseball-pitching-signs-symptoms-pitcher-fatigue-reduce-arm-injury/ (2017)
[11] Johnson, W. R., Mian, A., Lloyd, D. G., & Alderson, J. A. (2019). On-field player workload exposure and knee injury risk monitoring via deep learning. Journal of biomechanics, 93, 185-193.
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https://towardsdatascience.com/finding-donors-classification-project-with-pyspark-485fb3c94e5e#_=_指導教授 蔡志豐(Chih-Fong Tsai) 審核日期 2021-8-2 推文 plurk
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