{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T17:44:28Z","timestamp":1792691068990,"version":"4.5.0"},"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:00:00Z","timestamp":1792627200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>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\u2013CNN) 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.<\/jats:p>","DOI":"10.1371\/journal.pone.0361050","type":"journal-article","created":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T17:32:24Z","timestamp":1792690344000},"page":"e0361050","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning\u2013driven fine-resolution finger movement recognition from surface EMG signals"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2966-6442","authenticated-orcid":true,"given":"Hiam","family":"Alquran","sequence":"first","affiliation":[{"name":"Yarmouk University","place":["Irbid, Jordan"],"department":["Biomedical Systems and Informatics Engineering 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