Finger Movement Discrimination of EMG Signals Towards Improved Prosthetic Control using TFD

Prosthetic is an artificially made as a substitute or replacement for missing part of a body. The function of the missing body part can be replaced by using the prosthesis and it can help disabled people do their activities easily. A myoelectric control system is a fundamental part of modern prosthe...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2020, Vol.11 (9)
Main Authors: Shair, E. F., Jamaluddin, N.A., Abdullah, A.R.
Format: Article
Language:English
Subjects:
Algorithms
Body parts
Classification
Classifiers
Control systems
Electromyography
Feature extraction
Frequency distribution
Machine learning
Muscles
Myoelectric control
Myoelectricity
People with disabilities
Prostheses
Support vector machines
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Summary:Prosthetic is an artificially made as a substitute or replacement for missing part of a body. The function of the missing body part can be replaced by using the prosthesis and it can help disabled people do their activities easily. A myoelectric control system is a fundamental part of modern prostheses. The electromyogram (EMG) signals are used in this system to control the prosthesis movements by taking it from a person’s muscle. The problem for the myoelectric control system is when it did not receive the same attention to control fingers due to more dexterous of individual and combined finger control in a signal. Thus, a method to solve the problem of the myoelectric control system by using time-frequency distribution (TFD) is proposed in this paper. The EMG features of the individual and combine finger movements for ten subjects and ten different movements is extracted using TFD, ie. spectrogram. Three machine learning algorithms which are Support Vector Machine (SVM), k-Nearest Neighbor (KNN) and Ensemble Classifier are then used to classify the individuals and combine finger movement based on the extracted EMG feature from the spectrogram. The performance of the proposed method is then verified using classification accuracy. Based on the results, the overall accuracy for the classification is 90% (SVM), 100% (KNN) and 100% (Ensemble Classifier), respectively. The finding of the study could serve as an insight to improve the conventional prosthetic control strategies.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2020.0110928