{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T15:07:32Z","timestamp":1764688052797,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T00:00:00Z","timestamp":1688688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoa de N\u00edvel Superior\u2014Brasil (CAPES)","award":["001","315298\/2020-0","306448\/2021-1","51497"],"award-info":[{"award-number":["001","315298\/2020-0","306448\/2021-1","51497"]}]},{"DOI":"10.13039\/501100003593","name":"Brazilian National Council for Scientific and Technological Development (CNPq)","doi-asserted-by":"publisher","award":["001","315298\/2020-0","306448\/2021-1","51497"],"award-info":[{"award-number":["001","315298\/2020-0","306448\/2021-1","51497"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004612","name":"Arauc\u00e1ria Foundation","doi-asserted-by":"publisher","award":["001","315298\/2020-0","306448\/2021-1","51497"],"award-info":[{"award-number":["001","315298\/2020-0","306448\/2021-1","51497"]}],"id":[{"id":"10.13039\/501100004612","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surgical Instrument Signaling (SIS) is compounded by specific hand gestures used by the communication between the surgeon and surgical instrumentator. With SIS, the surgeon executes signals representing determined instruments in order to avoid error and communication failures. This work presented the feasibility of an SIS gesture recognition system using surface electromyographic (sEMG) signals acquired from the Myo armband, aiming to build a processing routine that aids telesurgery or robotic surgery applications. Unlike other works that use up to 10 gestures to represent and classify SIS gestures, a database with 14 selected gestures for SIS was recorded from 10 volunteers, with 30 repetitions per user. Segmentation, feature extraction, feature selection, and classification were performed, and several parameters were evaluated. These steps were performed by taking into account a wearable application, for which the complexity of pattern recognition algorithms is crucial. The system was tested offline and verified as to its contribution for all databases and each volunteer individually. An automatic segmentation algorithm was applied to identify the muscle activation; thus, 13 feature sets and 6 classifiers were tested. Moreover, 2 ensemble techniques aided in separating the sEMG signals into the 14 SIS gestures. Accuracy of 76% was obtained for the Support Vector Machine classifier for all databases and 88% for analyzing the volunteers individually. The system was demonstrated to be suitable for SIS gesture recognition using sEMG signals for wearable applications.<\/jats:p>","DOI":"10.3390\/s23136233","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T01:02:50Z","timestamp":1688950970000},"page":"6233","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Surgical Instrument Signaling Gesture Recognition Using Surface Electromyography Signals"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4726-3425","authenticated-orcid":false,"given":"Melissa La Banca","family":"Freitas","sequence":"first","affiliation":[{"name":"Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology\u2013Paran\u00e1 (UTFPR), Ponta Grossa 84017-220, PR, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5578-7734","authenticated-orcid":false,"suffix":"Jr.","given":"Jos\u00e9 Jair Alves","family":"Mendes","sequence":"additional","affiliation":[{"name":"Graduate Program in Electrical Engineering and Industrial Informatics (CPGEI), Federal University of Technology\u2013Paran\u00e1 (UTFPR), Curitiba 80230-901, PR, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6538-4127","authenticated-orcid":false,"given":"Thiago Sim\u00f5es","family":"Dias","sequence":"additional","affiliation":[{"name":"Graduate Program in Electrical Engineering and Industrial Informatics (CPGEI), Federal University of Technology\u2013Paran\u00e1 (UTFPR), Curitiba 80230-901, PR, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1278-4602","authenticated-orcid":false,"given":"Hugo Valadares","family":"Siqueira","sequence":"additional","affiliation":[{"name":"Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology\u2013Paran\u00e1 (UTFPR), Ponta Grossa 84017-220, PR, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4783-5350","authenticated-orcid":false,"suffix":"Jr.","given":"Sergio Luiz","family":"Stevan","sequence":"additional","affiliation":[{"name":"Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology\u2013Paran\u00e1 (UTFPR), Ponta Grossa 84017-220, PR, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"131902","DOI":"10.1016\/j.snb.2022.131902","article-title":"Characterization of signal kinetics in real time surgical tissue classification system","volume":"365","author":"Karjalainen","year":"2022","journal-title":"Sens. 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