{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T10:15:26Z","timestamp":1783937726537,"version":"3.55.0"},"reference-count":43,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T00:00:00Z","timestamp":1652486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human\u2013computer interface that controls interaction through hand gestures. Although there is a gap between academic publications on the control of an upper-limb prosthesis developed in laboratories and its service in the natural environment, there are attempts to achieve easier control using multiple muscle signals. This work contributes to this, using a database and biomechanical simulation software, both open access, to seek simplicity in the classifiers, anticipating their implementation in microcontrollers and their execution in real time. Fifteen predefined finger movements of the hand were identified using classic classifiers such as Bayes, linear and quadratic discriminant analysis. The idealized movements of the database were modeled with Opensim for visualization. Combinations of two preprocessing methods\u2014the forward sequential selection method and the feature normalization method\u2014were evaluated to increase the efficiency of these classifiers. The statistical methods of cross-validation, analysis of variance (ANOVA) and Duncan were used to validate the results. Furthermore, the classifier with the best recognition result was redesigned into a new feature space using the sparse matrix algorithm to improve it, and to determine which features can be eliminated without degrading the classification. The classifiers yielded promising results\u2014the quadratic discriminant being the best, achieving an average recognition rate for each individual considered of 96.16%, and with 78.36% for the total sample group of the eight subjects, in an independent test dataset. The study ends with the visual analysis under Opensim of the classified movements, in which the usefulness of this simulation tool is appreciated by revealing the muscular participation, which can be useful during the design of a multifunctional prosthesis.<\/jats:p>","DOI":"10.3390\/s22103737","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"3737","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Applying Machine Learning to Finger Movements Using Electromyography and Visualization in Opensim"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8472-115X","authenticated-orcid":false,"given":"Jose","family":"Amezquita-Garcia","sequence":"first","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Baja California, Mexicali 21280, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1227-9726","authenticated-orcid":false,"given":"Miguel","family":"Bravo-Zanoguera","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Baja California, Mexicali 21280, Mexico"},{"name":"Ingenier\u00eda en Mecatr\u00f3nica, Universidad Polit\u00e9cnica de Baja California, Mexicali 21376, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9627-676X","authenticated-orcid":false,"given":"Felix F.","family":"Gonzalez-Navarro","sequence":"additional","affiliation":[{"name":"Instituto de Ingenier\u00eda, Universidad Aut\u00f3noma de Baja California, Mexicali 21280, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3615-6560","authenticated-orcid":false,"given":"Roberto","family":"Lopez-Avitia","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Baja California, Mexicali 21280, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. A.","family":"Reyna","sequence":"additional","affiliation":[{"name":"Instituto de Ingenier\u00eda, Universidad Aut\u00f3noma de Baja California, Mexicali 21280, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ghapanchizadeh, H., Ahmad, S.A., and Ishak, A.J. (2015, January 4). Recommended surface EMG electrode position for wrist extension and flexion. Proceedings of the ISSBES 2015\u2014IEEE Student Symposium in Biomedical Engineering and Sciences: By the Student for the Student, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ISSBES.2015.7435877"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Paleari, M., di Girolamo, M., Celadon, N., Favetto, A., and Ariano, P. (2015, January 25\u201329). On optimal electrode conguration to estimate hand movements from forearm surface electromyography. 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