用AI视觉实时分析深蹲动作，自动纠错并提升训练效果。
Investigation of intelligent barbell squat coaching system based on computer vision and machine learning
- 通过计算机视觉提取关节角度等4个关键特征，结合机器学习诊断动作问题。
- 六类错误识别F1最高达100%，单次诊断耗时不足0.5秒。
- 实测证明系统能显著提升用户深蹲技术，适合健身初学者和教练使用。
研究发现力量训练可降低各年龄段慢性病与身体退化风险，因此独立训练时具备动作诊断系统至关重要。本研究开发了一套基于人工智能与计算机视觉的杠铃深蹲实时辅导系统，可即时诊断动作问题并提供反馈，同时支持回放模式供用户回顾。首先确定了深蹲的四个核心特征：躯干关节角度、足背屈程度、膝髋运动比值及杠铃稳定性。收集77名参与者共8,151次深蹲数据，分为正确动作与六类错误。采用三种机器学习架构训练诊断模型，并应用SHAP方法进行特征选择，提升预测准确率并降低计算时间。结果显示，六类问题的F1分数分别为86.86%、69.01%、77.42%、90.74%、95.83%和100%；单次诊断耗时低于0.5秒。通过对比两组受试者（有/无系统）训练效果，发现使用系统的参与者在系统评估和专业举重教练评分中均显著改善技术。结论表明，该研究整合人工智能、计算机视觉与多变量处理技术，构建了一个实时、易用的杠铃深蹲反馈与训练系统。
原文摘要 · Abstract (English)
Purpose: Research has revealed that strength training can reduce the incidence of chronic diseases and physical deterioration at any age. Therefore, having a movement diagnostic system is crucial for training alone. Hence, this study developed an artificial intelligence and computer vision-based barbell squat coaching system with a real-time mode that immediately diagnoses the issue and provides feedback after each squat. In addition, a replay mode allows users to examine their previous squats and check their comments. Initially, four primary characteristics of the barbell squat were identified: body joint angles, dorsiflexion, the ratio of knee-to-hip movement, and barbell stability. Methods: We collect 8,151 squats from 77 participants, categorizing them as good squats and six issues. Then, we trained the diagnosis models with three machine-learning architectures. Furthermore, this research applied the SHapley Additive exPlanations (SHAP) method to enhance the accuracy of issue prediction and reduce the computation time by feature selection. Results: The F1 score of the six issues reached 86.86%, 69.01%, 77.42%, 90.74%, 95.83%, and 100%. Each squat diagnosis took less than 0.5 seconds. Finally, this study examined the efficacy of the proposed system with two groups of participants trained with and without the system. Subsequently, participants trained with the system exhibited substantial improvements in their squat technique, as assessed both by the system itself and by a professional weightlifting coach. Conclusion: This is a comprehensive study that integrates artificial intelligence, computer vision and multivariable processing technologies, aimed at building a real-time, user-friendly barbell squat feedback and training system.
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