HD-sEMG gestures recognition by SVM classifier for controlling prosthesis
Joint Authors
Jabir, Hanadi Abbas
Rashid, Mufid Turki
Source
Iraqi Journal of Computer, Communications and Control Engineering
Issue
Vol. 19, Issue 1 (31 Jan. 2019), pp.10-19, 10 p.
Publisher
Publication Date
2019-01-31
Country of Publication
Iraq
No. of Pages
10
Main Subjects
Abstract EN
Electromyography signals (EMG) are an important source to infer motion intention.
It has been broadly applied in human-machine interfacing to control the neurorehabilitation devices such as prosthesis and rehabilitation robot.
HD-sEMG is a muscle's activity recorded at the delimited area of the skin using 2D array electrode.
This strategy permits the analysis of sEMG signals in both temporal and spatial domain.
Recent studies display that the spatial distribution of HD-EMG maps improves the recognition of tasks.
This work investigates the use of HD-EMG recording to control upper limb prosthesis.
The classification of eight hand gestures of able-bodied subjects was developed.
Three feature sets were presented in this work.
HOG features, time domain features(TD) and the combination of HOG and average intensity features (AIH).
Combination of features possibly improved the performance of the classifier.
Results show that the combined of intensity features and HOG features achieved higher performance of classifier than other features (Acc=99.37%, P=98.375%, S=97.5%).
American Psychological Association (APA)
Jabir, Hanadi Abbas& Rashid, Mufid Turki. 2019. HD-sEMG gestures recognition by SVM classifier for controlling prosthesis. Iraqi Journal of Computer, Communications and Control Engineering،Vol. 19, no. 1, pp.10-19.
https://search.emarefa.net/detail/BIM-896205
Modern Language Association (MLA)
Jabir, Hanadi Abbas& Rashid, Mufid Turki. HD-sEMG gestures recognition by SVM classifier for controlling prosthesis. Iraqi Journal of Computer, Communications and Control Engineering Vol. 19, no. 1 (Jan. 2019), pp.10-19.
https://search.emarefa.net/detail/BIM-896205
American Medical Association (AMA)
Jabir, Hanadi Abbas& Rashid, Mufid Turki. HD-sEMG gestures recognition by SVM classifier for controlling prosthesis. Iraqi Journal of Computer, Communications and Control Engineering. 2019. Vol. 19, no. 1, pp.10-19.
https://search.emarefa.net/detail/BIM-896205
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 18-19
Record ID
BIM-896205