Work

Muscle fatigue monitoring and analysis system: Myosense

Year: 2025

Type: software-paper

Abstract: This study presents a software tool for analyzing forearm muscle fatigue in high-performance tennis players using electromyographic (EMG) signals. The system integrates a Myo armband for data acquisition, entropy-based feature extraction, and a Support Vector Machine (SVM) classifier to distinguish between fatigue and non-fatigue states. Experimental validation achieved up to 80.02% Average accuracy. The software automates signal processing, enhances reproducibility, and enables real-time monitoring. Its applications extend to wearable sports technology, training optimization, and rehabilitation. Future work includes integration with embedded systems and expanding its use in other biomechanical and medical research areas.

Source: SoftwareX

DOI: 10.1016/j.softx.2025.102360

Authors: J.D. Ramírez, J.D. Ramírez, J.L. Ramírez, J.L. Ramírez, A. Rubiano

Institution: Military University Nueva Granada

Language: English

Topic: Muscle activation and electromyography studies

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,200