Review on electromyography based intention for upper limb control using pattern recognition for human-machine interaction

Ali Asghar, Saad Jawaid Khan, Fahad Azim, Choudhary Sobhan Shakeel, Amatullah Hussain, Imran Khan Niazi

Publikation: Bidrag til tidsskriftReview (oversigtsartikel)peer review

18 Citationer (Scopus)

Abstract

Upper limb myoelectric prosthetic control is an essential topic in the field of rehabilitation. The technique controls prostheses using surface electromyogram (sEMG) and intramuscular EMG (iEMG) signals. EMG signals are extensively used in controlling prosthetic upper and lower limbs, virtual reality entertainment, and human-machine interface (HMI). EMG signals are vital parameters for machine learning and deep learning algorithms and help to give an insight into the human brain's function and mechanisms. Pattern recognition techniques pertaining to support vector machine (SVM), k-nearest neighbor (KNN) and Bayesian classifiers have been utilized to classify EMG signals. This paper presents a review on current EMG signal techniques, including electrode array utilization, signal acquisition, signal preprocessing and post-processing, feature selection and extraction, data dimensionality reduction, classification, and ultimate application to the community. The paper also discusses using alternatives to EMG signals, such as force sensors, to measure muscle activity with reliable results. Future implications for EMG classification include employing deep learning techniques such as artificial neural networks (ANN) and recurrent neural networks (RNN) for achieving robust results.

OriginalsprogEngelsk
TidsskriftProceedings of the Institution of mechanical engineers. Part H, journal of engineering in medicine
Vol/bind236
Udgave nummer5
Sider (fra-til)628-645
Antal sider18
ISSN0954-4119
DOI
StatusUdgivet - maj 2022

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