A REVIEW OF CLASSIFICATION TECHNIQUES FOR ELECTROMYOGRAPHY SIGNALS
Keywords:Electromyography, Machine Learning, Classification
Electromyography (EMG) signals can be used in various sector such as medical, rehabilitation, robotics, and industrial fields. EMG measures muscle response or electrical activity in response to a nerve’s stimulation of the muscle. To detect neuromuscular abnormalities, these test is very useful. EMG can measures the electrical activity of muscle during rest, slight and forceful contraction. Normally, during rest our muscle tissue does not produce electrical signals. Machine Learning (ML) is an area of Artificial Intelligent (AI) with a concept that a computer program can learn and familiarize to new data without human intervention. ML is one of major branches of AI. Aim for this paper is to recover the latest scientific research on ML methods for EMG signal analysis. This paper focused on types of ML classifiers that are suitable for analysis the EMG signal in terms of accuracy. During the content review, we understood that ML performed for big and varied datasets. All of the ML classifiers have their own algorithm, special specification, pros and cons based on the available input. In this review revealed that Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA) are most popular algorithms in ML that used in diagnosis of EMG signal especially for upper limbs of our body because mostly the accuracy for the respective classifier shows that more than 80 to 90% accurate results. This article depicts the application of various ML algorithms used in EMG signal analysis till recently, but in the future, it will be used in more medical fields to improve the quality of diagnosis.
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