A Comparative Study Investigating Machine Learning Methods for EMG Data Classification in Post-Stroke Rehabilitation

Abstract

Physiotherapy is essential for boosting recovery and enhancing quality of life after a stroke. Individualized therapies are required for stroke rehabilitation. This work explores machine learning techniques for electromyography (EMG) data classification in the context of post-stroke rehabilitation, which is important for comprehending and improving motor function. Our analysis covers a wide range of methods, such as Gradient Boosting (GB), Histogram-Based Gradient Boosting, Cat Boost, K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Linear Discriminant Analysis (LDA), and LDA. We measure their performance using criteria like accuracy, precision, recall, and F1-score through a thorough evaluation procedure. Our results show that DT and RF perform consistently better than other algorithms, proving their reliability and effectiveness in the classification of EMG data. But KNN is equally promising, with relatively lesser precision than LDA. The work emphasizes how decision-based algorithms and ensemble methods may effectively classify EMG data, which has ramifications for improving stroke rehabilitation techniques. These discoveries can be used by scholars and professionals to further the creation of machine learning-based instruments for accurate gesture recognition in post-stroke rehabilitation.