MACHINE LEARNING TECHNIQUES APPLIED IN SURFACE EMG DETECTION- A SYSTEMATIC REVIEW

Abstract

Surface electromyography (EMG) has emerged as a promising clisnical decision support system, enabling the extraction of muscles' electrical activity through non-invasive devices placed on the body. This study focuses on the application of machine learning (ML) techniques to preprocess and analyze EMG signals for the detection of muscle abnormalities. Notably, state-of-the-art ML algorithms, including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Random Forests (RF), and Naive Bayes (NB), have been harnessed by researchers in the biomedical sciences to achieve accurate surface EMG signal detection. Within this paper, we present a meticulously conducted systematic review, employing the PRISMA method to select relevant research papers. Various databases were thoroughly searched, and multiple pertinent studies were identified for detailed examination, weighing their respective merits and drawbacks. Our survey comprehensively elucidates the latest ML techniques used in surface EMG detection, offering valuable insights for researchers in this domain. Additionally