{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T10:24:23Z","timestamp":1788258263650,"version":"build-2803163510"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,10,12]],"date-time":"2021-10-12T00:00:00Z","timestamp":1633996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:p>In recent years, myoelectric interfaces using surface electromyogram (EMG) signals have been developed for assisting people with physical disabilities. Especially, in the myoelectric interfaces for robotic hands or arms, decoding the user\u2019s upper-limb movement intentions is cardinal to properly control the prosthesis. However, because previous experiments were implemented with only healthy subjects, the possibility of classifying reaching-to-grasping based on the EMG signals from the residual limb without the below-elbow muscles was not investigated yet. Therefore, we aimed to investigate the possibility of classifying reaching-to-grasping tasks using the EMG from the upper arm and upper body without considering wrist muscles for prosthetic users. In our study, seven healthy subjects, one <jats:italic>trans<\/jats:italic>-radial amputee, and one wrist amputee were participated and performed 10 repeatable 12 reaching-to-grasping tasks based on the Southampton Hand Assessment Procedure (SHAP) with 12 different weighted (light and heavy) objects. The acquired EMG was processed using the principal component analysis (PCA) and convolutional neural network (CNN) to decode the tasks. The PCA\u2013CNN method showed that the average accuracies of the healthy subjects were 69.4 \u00b1 11.4%, using only the EMG signals by the upper arm and upper body. The result with the PCA\u2013CNN method showed 8% significantly higher accuracies than the result with the widely used time domain and auto-regressive-support vector machine (TDAR\u2013SVM) method as 61.6 \u00b1 13.7%. However, in the cases of the amputees, the PCA\u2013CNN showed slightly lower performance. In addition, in the aspects of assistant daily living, because grip force is also important when grasping an object after reaching, the possibility of classifying the two light and heavy objects in each reaching-to-grasping task was also investigated. Consequently, the PCA\u2013CNN method showed higher accuracy at 70.1 \u00b1 9.8%. Based on our results, the PCA\u2013CNN method can help to improve the performance of classifying reaching-to-grasping tasks without wrist EMG signals. Our findings and decoding method can be implemented to further develop a practical human\u2013machine interface using EMG signals.<\/jats:p>","DOI":"10.3389\/fnins.2021.733359","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T17:35:16Z","timestamp":1634146516000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["Upper-Limb Electromyogram Classification of Reaching-to-Grasping Tasks Based on Convolutional Neural Networks for Control of a Prosthetic Hand"],"prefix":"10.3389","volume":"15","author":[{"given":"Keun-Tae","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangsoo","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tae-Hyun","family":"Lim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song Joo","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,10,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8030292","article-title":"A state-of-the-art survey on deep learning theory and architectures.","volume":"8","author":"Alom","year":"2019","journal-title":"Electronics"},{"key":"B2","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1109\/tnsre.2015.2445634","article-title":"Improving the performance against force variation of EMG controlled multifunctional upper-limb prostheses for transradial amputees.","volume":"24","author":"Al-Timemy","year":"2015","journal-title":"IEEE Trans. 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