Deep learning–driven fine-resolution finger movement recognition from surface EMG signals
7 affiliations
- 1Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid, Jordan.
- 2Hunter Medical Research Institute, Mark Hughes Foundation Centre for Brain Cancer Research, University of Newcastle, Newcastle, NSW, Australia.
- 3The Institute of Biomedical Technology, King Hussein Medical Center, Royal Jordanian Medical Service, Amman, Jordan.
- 4The Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid, Jordan.
- 5Faculty of Electrical Engineering & Technology, University of Malaysia Perlis, UniCITI Alam Campus, Arau, Perlis, Malaysia.
- 6Advanced Computing (AdvCOMP), Centre of Excellence, Universiti Malaysia Perlis (UniMAP), Arau, Perlis, Malaysia.
- 7Department of Diagnostic Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
Abstract
Surface electromyography (sEMG) has emerged as an impactful tool for controlling advanced prosthetic devices by detecting and recording muscle electrical activity during motion. This study presents a unique technique for recognizing nine distinct classes of finger movements by utilizing the Ninapro database. To address an underexplored domain of upper-limb prosthetic control, this research targets fine-resolution individual finger movements rather than the broad range of hand gestures. A custom-made one-dimensional convolutional neural network (1D–CNN) was developed and validated using a within-subject evaluation strategy and signals. The proposed model achieved an overall classification accuracy of 90.1%. An in-depth sensitivity/specificity analysis for each finger movement class was administered to gain key insights into current recognition capabilities. The findings presented in this study demonstrate the viability of fine-resolution finger-movement recognition, offering foundational insights that may inform the future development and refinement of control systems in the prosthetic field.
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BibTeX
@article{alquran2026deep,
title = {{Deep learning–driven fine-resolution finger movement recognition from surface EMG signals}},
author = {Hiam Alquran and Hassan Alawneh and Mohammed Alsalatie and Ateka Khader and Wan Azani Mustafa and Khaled Zayed Alawneh},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0361050},
url = {https://doi.org/10.1371/journal.pone.0361050},
note = {CC BY},
}
RIS
TY - JOUR
TI - Deep learning–driven fine-resolution finger movement recognition from surface EMG signals
AU - Hiam Alquran
AU - Hassan Alawneh
AU - Mohammed Alsalatie
AU - Ateka Khader
AU - Wan Azani Mustafa
AU - Khaled Zayed Alawneh
PY - 2026
DA - 2026/10/22
JO - PLOS One
DO - 10.1371/journal.pone.0361050
UR - https://doi.org/10.1371/journal.pone.0361050
AB - Surface electromyography (sEMG) has emerged as an impactful tool for controlling advanced prosthetic devices by detecting and recording muscle electrical activity during motion. This study presents a unique technique for recognizing nine distinct classes of finger movements by utilizing the Ninapro database. To address an underexplored domain of upper-limb prosthetic control, this research targets fine-resolution individual finger movements rather than the broad range of hand gestures. A custom-made one-dimensional convolutional neural network (1D–CNN) was developed and validated using a within-subject evaluation strategy and signals. The proposed model achieved an overall classification accuracy of 90.1%. An in-depth sensitivity/specificity analysis for each finger movement class was administered to gain key insights into current recognition capabilities. The findings presented in this study demonstrate the viability of fine-resolution finger-movement recognition, offering foundational insights that may inform the future development and refinement of control systems in the prosthetic field.
SN - 1932-6203
ER -