Machine learning techniques applied in surface emg detection- a systematic review

Sidra Abid Syed; Shahzad Nasim; Hira Zahid; Bullo Saifullah; Sarmad Shams; Sania Tanvir; Syed Jamal Haider Zaidi

doi:10.34016/pjbt.2023.20.02.804

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Machine learning techniques applied in surface emg detection- a systematic review

Author(s)

Sidra Abid Syed Sir Syed University of Engineering and Technology, Biomedical Engineering Department,Karachi,Pakistan

Shahzad Nasim Department of Management Science & Technology, The Begum Nusrat Bhutto Women University Sukkur 65170, Pakistan

Hira Zahid Ziauddin University, Biomedical Engineering Department,Karachi,Pakistan

Bullo Saifullah Department of Human & Rehabilitation Sciences, The Begum Nusrat Bhutto Women University Sukkur 65170, Pakistan

Sarmad Shams Department of Biomedical Engineering IBET, LUMHS Jamshoro, Pakistan

Sania Tanvir Sir Syed University of Engineering and Technology, Biomedical Engineering Department,Karachi,Pakistan

Syed Jamal Haider Zaidi Department of Computer Science, IQRA University, Karachi / The Begum Nusrat Bhutto Women University Sukkur, Pakistan

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, we outline potential future directions that can guide further advancements in this critical area of research.

Publication Details

Page(s) 225-237

DOI 10.34016/pjbt.2023.20.02.804

Published Journal: Pakistan Journal of Biotechnology, Volume: 20, Issue: 2, Year: 2023

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Keywords

Machine learning, Healthcare, Artificial Neural Network, Muscles, Electromyograph, Surface EMG detection

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