Modeling local muscle fatigue phases from EMG: A comparative study of classification and regression approaches

Identifying local muscle fatigue phases through surface electromyography (sEMG) is essential for developing real-time, non-invasive monitoring systems. This study compares two modeling strategies—classification and regression—for detecting three fatigue phases (non-fatigue, transition, fatigue) using sEMG signals alone. Data were collected during sustained handgrip tasks from healthy subjects, and eleven EMG features were extracted. Labels were assigned based on observed force decline thresholds: upper than 90%, between 70–90% and lower than 70% of Maximum voluntary contraction (MVC), serving as ground truth during training. Three classification models:  SVM (Support Vector Machine), LDA (Linear Discriminant Analysis), QDA (Quadratic Discriminant Analysis) and a Multiple Linear Regression (MLR) model were trained and tested. With classifiers, QDA yielded the highest accuracy of 82% and the most consistent phase mapping. However, MLR achieved higher performance in reconstructing continuous force output with r = 0.96, enabling smoother and more physiologically realistic phase segmentation. Visual comparisons showed that classification outputs tended to be fragmented, particularly in the transition phase while regression maintained temporal coherence. These results research show that regression provides a more robust and interpretable framework for modeling fatigue progression from EMG signals, although classification models may still be useful in applications requiring discrete outputs.