Towards Real-World Identification of Fatigued Muscle Groups via Musculoskeletal Simulation

2026-05-23

AI Summary

This study addresses the limitations of traditional contact-based clinical assessments for muscle fatigue, which are ill-suited for remote, non-invasive, large-scale screening. To overcome this, the authors propose a contactless fatigue identification method that accurately differentiates fatigue states across multiple upper-limb muscle groups by comparing subjects’ free-arm movements with high-fidelity physics-based musculoskeletal simulation models under varying levels of fatigue. This work represents the first integration of high-fidelity musculoskeletal simulation with real-world motion data for fatigue diagnosis, effectively bridging the gap between simulation and reality. The approach validates feasible configurations of advanced simulators for this task and establishes a novel paradigm for remote, automated assessment of muscular function.

Abstract

Contactless diagnosis of musculoskeletal disorders can potentially improve population health as well as robot behaviours in collaborative settings. However, current diagnosis methods require an in-person physical examination in which a trained physician senses, through contact, the force applied by various muscles. Simulation tools exist, but their use for diagnosis with real data is under-explored. In this paper, we propose an algorithm for identifying which upper-limb muscle group is fatigued. Our algorithm compares the realworld free-space motion of the subject with that of a simulated musculoskeletal model, and is therefore contactless: preventing the need for invasive sensing or in-person assessment. Our algorithm simulates various fatigue conditions using a physics-based musculoskeletal model and extracts diagnostic motion features from both real and simulated data, which are compared for diagnosis. Experimental results on real data demonstrate that the proposed method can reliably distinguish between multiple muscle-groups of fatigue. Additionally, through comprehensive performance comparisons, we show how recent advanced musculoskeletal simulators can be properly configured to address the sim-to-real gap in the context of the fatigue diagnosis task. Our approach can potentially spur further research in remote and automated diagnosis, significantly lowering the barrier to large-scale and early detection.

Problem

• muscle fatigue identification
• contactless diagnosis
• musculoskeletal disorders
• real-world motion analysis

Innovation

• contactless diagnosis
• musculoskeletal simulation
• muscle fatigue identification
• sim-to-real gap
• physics-based modeling