用肌电图数据实时预测人机协作中肌肉疲劳程度，支持机器人自适应调节。
Estimating Human Muscular Fatigue in Dynamic Collaborative Robotic Tasks with Learning-Based Models
- 基于肌电特征与谱图的机器学习模型，回归预测疲劳剩余周期比例。
- 深度学习模型误差最低（平均RMSE 20.8%），树模型表现接近且具泛化能力。
- 模型可跨动作类型通用，适合无需重训的工业安全监控场景。
评估人体肌肉疲劳对优化物理人机交互（pHRI）中的性能与安全至关重要。本文提出一种数据驱动框架，利用臂部表面肌电图（sEMG）在动态、周期性的人机协作任务中估计疲劳。采用随机森林、XGBoost和线性回归等个体化机器学习回归模型，从三个频域和一个时域肌电特征预测疲劳周期比例（FCF），并与基于卷积神经网络（CNN）的谱图分析方法对比。将疲劳估计视为回归而非分类，可捕捉疲劳连续演进过程，支持早期预警与及时干预。十名参与者实验中，协作机器人在阻抗控制下引导重复左右端点运动直至肌肉疲劳。平均FCF RMSE为：CNN 20.8±4.3%，随机森林 23.3±3.8%，XGBoost 24.8±4.5%，线性回归 26.9±6.1%。为检验跨任务泛化能力，一名参与者额外执行未训练的上下与圆形重复动作；仅在横向数据上训练的模型仍保持较高精度，表明对运动方向、臂部运动学及肌肉募集变化具有鲁棒性，而线性回归性能下降。研究结果表明，基于特征的机器学习与基于谱图的深度学习均可有效估计重复性人机协作中的剩余工作能力，其中CNN误差最低，树模型紧随其后。跨动作模式的良好迁移性表明该方法具备无需每项任务重训即可实现实用疲劳监测的潜力，提升操作者保护并支持疲劳感知的共享自主控制，实现更安全的疲劳自适应人机协作。
原文摘要 · Abstract (English)
Assessing human muscle fatigue is critical for optimizing performance and safety in physical human-robot interaction(pHRI). This work presents a data-driven framework to estimate fatigue in dynamic, cyclic pHRI using arm-mounted surface electromyography(sEMG). Subject-specific machine-learning regression models(Random Forest, XGBoost, and Linear Regression predict the fraction of cycles to fatigue(FCF) from three frequency-domain and one time-domain EMG features, and are benchmarked against a convolutional neural network(CNN) that ingests spectrograms of filtered EMG. Framing fatigue estimation as regression (rather than classification) captures continuous progression toward fatigue, supporting earlier detection, timely intervention, and adaptive robot control. In experiments with ten participants, a collaborative robot under admittance control guided repetitive lateral (left-right) end-effector motions until muscular fatigue. Average FCF RMSE across participants was 20.8+/-4.3% for the CNN, 23.3+/-3.8% for Random Forest, 24.8+/-4.5% for XGBoost, and 26.9+/-6.1% for Linear Regression. To probe cross-task generalization, one participant additionally performed unseen vertical (up-down) and circular repetitions; models trained only on lateral data were tested directly and largely retained accuracy, indicating robustness to changes in movement direction, arm kinematics, and muscle recruitment, while Linear Regression deteriorated. Overall, the study shows that both feature-based ML and spectrogram-based DL can estimate remaining work capacity during repetitive pHRI, with the CNN delivering the lowest error and the tree-based models close behind. The reported transfer to new motion patterns suggests potential for practical fatigue monitoring without retraining for every task, improving operator protection and enabling fatigue-aware shared autonomy, for safer fatigue-adaptive pHRI control.
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