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Tytuł:
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EMG map image processing for recognition of fingers movement.
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Autorzy:
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Topalović I; Institute of Technical Sciences of SASA, Knez Mihailova 35/IV, Belgrade, Serbia. Electronic address: .
Graovac S; Faculty of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, Belgrade, Serbia.
Popović DB; Serbian Academy of Sciences and Arts (SASA), Knez Mihailova 35, Belgrade, Serbia; Aalborg University, Fredrik Bajers Vej 7, Aalborg, Denmark.
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Źródło:
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Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology [J Electromyogr Kinesiol] 2019 Dec; Vol. 49, pp. 102364. Date of Electronic Publication: 2019 Oct 11.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Publication: <1995- >: Oxford : Elsevier
Original Publication: New York, N.Y. : Raven Press, 1991-
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MeSH Terms:
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Electromyography/*methods
Fingers/*physiology
Image Processing, Computer-Assisted/*methods
Adult ; Electromyography/standards ; Humans ; Image Processing, Computer-Assisted/standards ; Male ; Movement ; Muscle, Skeletal/physiology
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Contributed Indexing:
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Keywords: Array Electrodes; Delicate movements; EMG maps; Finger Movements Recognition; Image processing; Spatial and temporal model
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Entry Date(s):
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Date Created: 20191027 Date Completed: 20200210 Latest Revision: 20200210
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Update Code:
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20240104
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DOI:
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10.1016/j.jelekin.2019.102364
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PMID:
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31654842
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Electromyography (EMG) is the conventional noninvasive method for the estimation of muscle activities. We developed a new image processing method for the recognition of individual finger movements based on EMG maps. The maps were formed from the EMG recordings via an array electrode with 24 contacts connected to a multichannel wireless miniature digital amplifier. The task was to detect and quantify the high activity regions in the EMG maps in persons with no known motor impairment. The results show the temporal and spatial patterns within the images during well-defined finger movements. The average accuracy of the automatic recognition compared with the recognition by an expert clinician in persons involved in the tests was 97.87 ± 0.92%. The application of the technique is foreseen for control for an assistive system (hand prosthesis and exoskeleton) since the interface is wearable and the processing can be implemented on a microcomputer.
(Copyright © 2019 Elsevier Ltd. All rights reserved.)
