Estimating Human Muscular Fatigue in Dynamic Collaborative Robotic Tasks with Learning-Based Models

2026-02-17

AI Summary

This study addresses the critical need for real-time, continuous assessment of muscle fatigue in dynamic human-robot collaboration to ensure safety and performance. It pioneers the formulation of fatigue estimation as a regression problem, predicting the fraction of cycles to failure (FCF)—the proportion of remaining repetitions before fatigue onset—using surface electromyography (sEMG) signals from the arm. The approach integrates time-frequency domain features with spectrograms and employs data-driven models, including Random Forest, XGBoost, and Convolutional Neural Networks (CNNs). Experimental results demonstrate that the CNN achieves the lowest prediction error (RMSE: 20.8%) and exhibits strong generalization across unseen movement patterns, such as vertical and circular motions, without requiring retraining, thereby confirming its robustness and practical applicability.

Technology Category

• Intelligent Robots: State Estimation
• Natural Language Processing: Safety and Robustness
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• muscle fatigue estimation
• physical human-robot interaction
• collaborative robotics
• sEMG
• fatigue monitoring

Innovation

• muscle fatigue estimation
• human-robot collaboration
• surface electromyography (sEMG)
• regression modeling
• cross-task generalization