用可穿戴传感器预测力量训练主观疲劳度，助力个性化训练。
Estimation of Resistance Training RPE using Inertial Sensors and Electromyography
- 结合惯性与肌电传感器数据，用随机森林模型估算训练时的主观疲劳感。
- 模型准确率达41.4%完全匹配，±1误差范围内达85.9%。
- 离心阶段时间是最重要的预测因子，适合健身科技与运动康复领域。
精准估计主观用力程度（RPE）可提升力量训练的个性化反馈与伤病预防效果。本研究探讨了机器学习模型在单臂哑铃弯举训练中估算RPE的应用，利用可穿戴惯性传感器和肌电（EMG）传感器采集数据。构建了包含69组训练、超过1000次重复动作的定制数据集，并提取统计特征用于模型训练。评估的模型中，随机森林分类器表现最佳，精确匹配率41.4%，±1误差范围准确率达85.9%。尽管加入肌电数据略微提升了精度，但其效果可能受限于数据质量及电极位置敏感性。特征分析显示，离心阶段持续时间是预测力竭程度最强的指标。结果表明基于可穿戴传感器的RPE估算具有可行性，并揭示了提升模型泛化能力的关键挑战。
原文摘要 · Abstract (English)
Accurate estimation of rating of perceived exertion (RPE) can enhance resistance training through personalized feedback and injury prevention. This study investigates the application of machine learning models to estimate RPE during single-arm dumbbell bicep curls, using data from wearable inertial and electromyography (EMG) sensors. A custom dataset of 69 sets and over 1000 repetitions was collected, with statistical features extracted for model training. Among the models evaluated, a random forest classifier achieved the highest performance, with 41.4% exact accuracy and 85.9% $\pm1$ RPE accuracy. While the inclusion of EMG data slightly improved model accuracy over inertial sensors alone, its utility may have been limited by factors such as data quality and placement sensitivity. Feature analysis highlighted eccentric repetition time as the strongest RPE predictor. The results demonstrate the feasibility of wearable-sensor-based RPE estimation and identify key challenges for improving model generalizability.
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