Feasibility of mechanomyography-based fatigue classification for passive lower-limb exoskeleton evaluation: A pilot study

Clinical Snapshot

Key Findings

• P Value: p < 0.001 for correlation with perceived exertion

• Effect Size: Mean accuracy 0.913 ± 0.057

• Primary Outcome: Fatigue detection accuracy using 1/5 voting rule

• Nnt Or Sensitivity: AUC 0.792 ± 0.073, detection error 1.4 ± 0.8 seconds

• Confidence Interval: Not reported in abstract

Clinical Application

Technically feasible but requires specialized equipment and expertise Relevant for Australian workplace safety initiatives, potential applications in mining and construction industries where exoskeletons are emerging Workers using lower-limb exoskeletons, rehabilitation patients, occupational health settings

Abstract

Mechanomyography (MMG) enables non-invasive monitoring of muscle mechanical activity, while its utility in time-resolved fatigue detection during dynamic human-exoskeleton interaction remains underexplored. This pilot study explored the feasibility of combining MMG with machine learning to characterize neuromuscular fatigue and evaluate passive lower-limb exoskeleton assistance during repetitive 10 kg squat-lifting tasks. MMG signals from five lower-limb muscles were extracted for time-domain, frequency-domain and nonlinear features, and fatigue identification was implemented via a spectral-based criterion and multi-muscle voting optimization. A radial basis function-enhanced random forest (RBF-RF) model integrated with data augmentation was validated through leave-one-subject-out cross-validation. The results demonstrated that the 1/5 voting rule achieved optimal performance, with mean accuracy of 0.913 ± 0.057, AUC of 0.792 ± 0.073, and a low fatigue detection error of 1.4 ± 0.8 s. Data augmentation steadily improved model robustness, and predicted fatigue levels were significantly correlated with subjective perceived exertion (ρ = 0.756, p < 0.001). This pilot study demonstrates the feasibility of MMG-based fatigue monitoring for wearable assistive systems. The proposed framework supports objective, high-temporal-resolution fatigue monitoring, and may serve as a viable tool for assessing wearable assistive systems. Further large-cohort studies are required to validate its generalizability for practical applications.

References

1. 1.Wang, S., Guan, X., Li, H., Zhang, R., Bai, Y., & Zhou, Q. (2026). Feasibility of mechanomyography-based fatigue classification for passive lower-limb exoskeleton evaluation: A pilot study. PLoS One. https://doi.org/10.1109/jsen.2024.3523512