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.
Keywords:
array electrodes / delicate movements / EMG maps / Finger Movements Recognition / image processing / spatial and temporal model
Source:
Journal of Electromyography and Kinesiology, 2019, 49, 102364-
TY - JOUR
AU - Topalović, Ivan
AU - Graovac, Stevica
AU - Popović, Dejan B.
PY - 2019
UR - http://www.sciencedirect.com/science/article/pii/S1050641119302160
UR - http://dais.sanu.ac.rs/123456789/6904
AB - 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.
PB - Elsevier
T2 - Journal of Electromyography and Kinesiology
T1 - EMG map image processing for recognition of fingers movement
SP - 102364
VL - 49
DO - 10.1016/j.jelekin.2019.102364
ER -
@article{
author = "Topalović, Ivan and Graovac, Stevica and Popović, Dejan B.",
year = "2019",
url = "http://www.sciencedirect.com/science/article/pii/S1050641119302160, http://dais.sanu.ac.rs/123456789/6904",
abstract = "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.",
publisher = "Elsevier",
journal = "Journal of Electromyography and Kinesiology",
title = "EMG map image processing for recognition of fingers movement",
pages = "102364",
volume = "49",
doi = "10.1016/j.jelekin.2019.102364"
}
Topalović I, Graovac S, Popović DB. EMG map image processing for recognition of fingers movement. Journal of Electromyography and Kinesiology. 2019;49:102364
Topalović, I., Graovac, S.,& Popović, D. B. (2019). EMG map image processing for recognition of fingers movement.
Journal of Electromyography and KinesiologyElsevier., 49, 102364.
https://doi.org/10.1016/j.jelekin.2019.102364
Topalović Ivan, Graovac Stevica, Popović Dejan B., "EMG map image processing for recognition of fingers movement" 49 (2019):102364,
https://doi.org/10.1016/j.jelekin.2019.102364 .
