機器學習用於預測人體最大肌力表現

Abstract

前言:力量訓練中最大肌力的準確預測對於運動員的訓練計劃和表現提升至關重要。傳統方法如力竭測試和運動自覺強度量表在準確性和實用性上存在諸多限制，因此需要探索更精確且低風險的方法。本研究將探討機器學習技術在預測人體最大肌力中的應用，並比較其與傳統速度依循訓練 (Velocity-Based Training, VBT) 方法的效果。目的:本研究旨在透過機器學習與深度學習方法預測人體最大肌力，並評估不同模型和資料前處理技術對預測準確性的影響。方法:本研究使用線性位移器收集了十名受試者的單次硬舉數據及一名受試者十週的蹲舉數據。透過主成分分析進行資料降維，並使用多種機器學習模型進行預測以及比較。結果:機器學習方法在個人化預測準確度上優於VBT方法，更多的參數類型可以獲得準確度的提升，加入高斯雜訊於訓練資料後，模型的泛化能力和預測精度都獲得提升。群體預測方面，使用支持向量機模型進行預測，其均方誤差遠低於傳統VBT方法。在負荷的使用上個體預測於中等負荷獲得最佳的預測準確度，群體預測於較重負荷下得出較低的預測誤差。結論:機器學習在力量預測的表現優於VBT方法。使用資料前處理技術可以有效提升模型的性能。未來建議在更大的數據上進行研究與驗證，並考慮加入更多不同類型的參數，以進一步提高預測的準確性。

Introduction: Accurate prediction of maximum strength in strength training is crucial for developing effective training plans and enhancing athlete performance. Traditional methods, such as exhaustive testing and the Rate of Perceived Exertion scale, have limitations in terms of accuracy and practicality, necessitating the exploration of more precise and low-risk methods. This study will explore the application of machine learning techniques in predicting human maximum strength and compare their effectiveness with traditional Velocity-Based Training (VBT) methods. Purpose: This study aims to predict human maximum strength using machine learning and deep learning methods, and to evaluate the impact of different models and data preprocessing techniques on prediction accuracy. Methods: This study collected single deadlift data from ten subjects and ten-week squat data from one subject using a linear transducer. Principal Component Analysis was used for data dimensionality reduction, and various machine learning models were employed for prediction and comparison. Results: Machine learning methods outperformed VBT methods in personalized prediction accuracy. A greater variety of parameter types led to improved accuracy, and adding Gaussian noise to the training data enhanced both the model's generalization ability and prediction precision. In group predictions, the Support Vector Machine model achieved a mean squared error significantly lower than that of traditional VBT methods. For load application, individual predicting at medium loads resulted in the best prediction accuracy, while group predictions yielded lower errors under heavier loads. Conclusion: Machine learning methods outperform VBT methods in strength prediction. Data preprocessing techniques effectively enhance model performance. Future research should focus on larger datasets and consider incorporating a wider range of parameters to further improve prediction accuracy.