Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology · 2026
A Machine learning framework for calf muscle fatigue assessment using IMU sensors and EMG-Based labeling.
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Manoharan Saravanan, Jadhav Ashirwad, Dominic Dobson, Srinivasan Ramji, Natarajan Balasubramaniam, Srinivasan Babji
Аннотация
PURPOSE: Calf muscle fatigue detection is essential for sports performance monitoring, injury prevention, rehabilitation, and tasks involving prolonged standing or repetitive lower-limb movements. While surface Electromyography (sEMG) is commonly used for fatigue assessment, its sensitivity to noise, electrode placement, and motion artifacts limits its practicality for continuous monitoring.
SCOPE: This study proposes an IMU-based approach for calf muscle fatigue detection, using sEMG only during the training phase for generating accurate fatigue labels. EMG-derived indicators such as Root Mean Square and Median Frequency were compared with multiple IMU features to identify those most strongly aligned with EMG behavior. Participants performed fatigue-inducing calf exercises, and percentage deviations from baseline EMG values were used to assign fatigue levels. Using these labels, IMU-based classification models were developed, including Random Forest for binary classification and a feedforward neural network for multi-class analysis.
CONCLUSION: The Random Forest classifier achieved 90.53% accuracy for the right calf and 90.19% for the left in binary classification, while the neural network reached 81.42% and 84.67% in multi-class classification. Subject-wise three-class classification further improved accuracy to 92.9% and 91.96% using Random Forest. These results demonstrate that IMU-only models can reliably detect calf muscle fatigue and are suitable for applications in sports, clinical practice, and daily activities, providing continuous, non-invasive monitoring to optimize performance and prevent overuse injuries.
SCOPE: This study proposes an IMU-based approach for calf muscle fatigue detection, using sEMG only during the training phase for generating accurate fatigue labels. EMG-derived indicators such as Root Mean Square and Median Frequency were compared with multiple IMU features to identify those most strongly aligned with EMG behavior. Participants performed fatigue-inducing calf exercises, and percentage deviations from baseline EMG values were used to assign fatigue levels. Using these labels, IMU-based classification models were developed, including Random Forest for binary classification and a feedforward neural network for multi-class analysis.
CONCLUSION: The Random Forest classifier achieved 90.53% accuracy for the right calf and 90.19% for the left in binary classification, while the neural network reached 81.42% and 84.67% in multi-class classification. Subject-wise three-class classification further improved accuracy to 92.9% and 91.96% using Random Forest. These results demonstrate that IMU-only models can reliably detect calf muscle fatigue and are suitable for applications in sports, clinical practice, and daily activities, providing continuous, non-invasive monitoring to optimize performance and prevent overuse injuries.