Enhancing Prediction of Prosthetic Fingers Movement Based on sEMG using Mixtures of Features and Random Forest
Wafaa N. Al-Sharu1, Ali Mohammad Alqudah2
1Wafaa N. Al-Sharu, Department of Electrical Engineering, Hashemite University, Zarqa Jordan.
2Ali Mohammad Alqudah, Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid Jordan.

Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 289-294 | Volume-8 Issue-4, November 2019. | Retrieval Number: D6801118419/2019©BEIESP | DOI: 10.35940/ijrte.D6801.118419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: As the recent development of the prosthetic control systems, it is necessary to develop the sub-components such as myoelectric control system. This sub-system is used to acquire electromyogram (EMG) signals from a person’s muscles and convert it to movements to control prosthetic hands or fingers. Recently, researchers started focusing on providing feature extraction methods for both time domain and frequency domain for predicting either hand or finger movements. This paper proposes a new mixed feature set for both time and frequency domain for the classification of surface EMG (sEMG) into ten classes for controlling prosthetic finger movements. The features are based on enhanced statistical features extracted directly from a non-overlapped window with a predefined length which was selected carefully from the sEMG directly. Ten classes of individual and combined finger movements are to be recognized by using two sEMG channels from two electrodes are fixed on the human forearm to employ effective knowledge discovery and pattern recognition algorithms to enhance the recognition and classification accuracy. and the other features are statistical features extracted from Instantaneous frequency of the signal to utilize a suitable classifier helps detecting and recognizing the pattern from sEMG signals of different classes of the fingers movements either combined or single movement to enhance the classifier performances and to find the whole class of all extracted window ,a majority voting technique was applied.The method used the random forest as a classifier to build the classifier model which achieved an accuracy of 93.75% and sensitivity of 93.73% and specificity of 99.31%.
Keywords: sEMG, Statistical Features, Instantaneous Frequency, Mixed Features, Random Forest.
Scope of the Article: Health Monitoring and Life Prediction of Structures.