Remote Monitoring of Muscle Activity by Integrating Stacking Ensemble Classifier for Surface Electromyography Signals*

This paper focuses on the signal classifier component of a tele-rehabilitation framework that uses wearable surface electromyography (sEMG) devices to identify hand grasps and deliver real-time, adaptive therapy. The prediction accuracy and robustness of a stacking ensemble classifier significantly — with the combination of Support Vector Machine (SVM), Random Forest (RF), and a Logistic Regression meta—learner-improves performance, enabling continuous and personalized monitoring. Through the use of machine learning techniques, this study enables patients to conduct rehabilitation exercises remotely and to track their progress in real-time with therapists.