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.

翻译：评估人体肌肉疲劳对于优化物理人机交互中的性能与安全至关重要。本研究提出一种数据驱动框架，利用手臂表面肌电图在动态循环式物理人机交互中估计疲劳程度。针对个体的机器学习回归模型（随机森林、XGBoost和线性回归）从三个频域特征和一个时域肌电特征预测疲劳周期分数，并与摄入滤波后肌电图谱图的卷积神经网络进行性能对比。将疲劳估计构建为回归问题（而非分类）能捕捉向疲劳状态的连续演进过程，支持早期检测、及时干预和自适应机器人控制。在十名参与者的实验中，采用导纳控制的协作机器人引导重复横向（左右）末端执行器运动直至肌肉疲劳。各参与者平均疲劳周期分数均方根误差为：CNN模型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%。为探究跨任务泛化能力，一名参与者额外执行了未训练过的垂直（上下）和圆形重复运动；仅使用横向数据训练的模型直接测试时基本保持精度，表明其对运动方向、手臂运动学和肌肉募集模式变化具有鲁棒性，而线性回归性能显著下降。总体而言，本研究表明基于特征的机器学习和基于谱图的深度学习均能估计重复性物理人机交互中的剩余工作能力，其中CNN误差最低，树模型紧随其后。所报道的新运动模式迁移能力表明，无需为每个任务重新训练即可实现实用化疲劳监测，从而提升操作员保护水平，实现疲劳感知的共享自主控制，为更安全的疲劳自适应物理人机交互控制提供支持。