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Analysis of Muscle Fatigue Progression Using Geometric Features of Surface Electromyography Signals and Explainable XGBoost Classifier

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Abstract

Purpose

Analysing the progression of muscle fatigue is paramount as it can be helpful in monitoring the myoelectric manifestations of fatigue conditions and predicting any abnormalities at an early stage. In this work, a detection model for tracking the muscle fatigue progression is developed using geometric features of surface electromyography (sEMG) signals and explainable eXtreme Gradient Boosting (XGBoost) classifier.

Methods

The signals are recorded under dynamic contractions from fifty-eight healthy adult volunteers. These signals are preprocessed and divided into three equal zones: nonfatigue, transition-to-fatigue and fatigue. These segments are subjected to Hilbert transform, and the resultant coefficients are represented in a complex plane to form a shape. The geometric features namely, perimeter, area, second moment, inertia and instantaneous spectral centroid (ISC) are extracted from the shape and a classification model is developed using XGBoost and Shapley Additive exPlanations (SHAP) approach.

Results

Four features, viz. perimeter, area, second moment and inertia, follow an increasing trend towards fatigue, whereas ISC decreases towards fatigue. The features facilitate the discrimination of nonfatigue and transition-to-fatigue zones. However, there is an overlap between transition-to-fatigue and fatigue zones. Interestingly, the classification model achieves a balanced accuracy and F-score of 96.83% and 95.25% for differentiating transition-to-fatigue and fatigue zones. SHAP values reveal that the impact of ISC is more for the classification of three zones.

Conclusion

The geometric features are able to characterise the sEMG signals during the progression of fatigue. The proposed approach could be helpful to track the muscle fatigue progression in applications such as sports biomechanics, rehabilitation and myoelectric prosthesis.

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Data Availability

Data generated during the current study are available from the corresponding author on reasonable request.

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Funding

The authors state that they did not receive any grants, funding, or other assistance to prepare this manuscript.

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Authors and Affiliations

Authors

Contributions

NP—Conceptualisation, Methodology, Validation, Investigation, Writing—original draft, Writing—review and editing, Visualisation. KDB—Conceptualization, Methodology, Software, Investigation, Validation, Writing—original draft. SRM—Investigation, Software, Visualization. PAK—Investigation, Supervision.

Corresponding author

Correspondence to N. Punitha.

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Competing interest

The authors do not have any applicable financial/non-financial interest to declare.

Ethical Approval

The study adheres to the Declaration of Helsinki and was approved by the Institutional Review Board.

Consent to Participate

Informed consent was obtained from all individual participants included in the study.

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The authors affirm that the participants provided consent for the publication of the data.

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Punitha, N., Divya Bharathi, K., Manuskandan, S.R. et al. Analysis of Muscle Fatigue Progression Using Geometric Features of Surface Electromyography Signals and Explainable XGBoost Classifier. J. Med. Biol. Eng. 44, 191–197 (2024). https://doi.org/10.1007/s40846-024-00858-8

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  • DOI: https://doi.org/10.1007/s40846-024-00858-8

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