{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T00:57:08Z","timestamp":1790384228968,"version":"4.1.0"},"reference-count":38,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T00:00:00Z","timestamp":1636588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"publisher","award":["Sinergia project # 410160837 MeganePro"],"award-info":[{"award-number":["Sinergia project # 410160837 MeganePro"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>One major challenge limiting the use of dexterous robotic hand prostheses controlled via electromyography and pattern recognition relates to the important efforts required to train complex models from scratch. To overcome this problem, several studies in recent years proposed to use transfer learning, combining pre-trained models (obtained from prior subjects) with training sessions performed on a specific user. Although a few promising results were reported in the past, it was recently shown that the use of conventional transfer learning algorithms does not increase performance if proper hyperparameter optimization is performed on the standard approach that does not exploit transfer learning. The objective of this paper is to introduce novel analyses on this topic by using a random forest classifier without hyperparameter optimization and to extend them with experiments performed on data recorded from the same patient, but in different data acquisition sessions. Two domain adaptation techniques were tested on the random forest classifier, allowing us to conduct experiments on healthy subjects and amputees. Differently from several previous papers, our results show that there are no appreciable improvements in terms of accuracy, regardless of the transfer learning techniques tested. The lack of adaptive learning is also demonstrated for the first time in an intra-subject experimental setting when using as a source ten data acquisitions recorded from the same subject but on five different days.<\/jats:p>","DOI":"10.3390\/s21227500","type":"journal-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T23:04:46Z","timestamp":1636671886000},"page":"7500","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Questioning Domain Adaptation in Myoelectric Hand Prostheses Control: An Inter- and Intra-Subject Study"],"prefix":"10.3390","volume":"21","author":[{"given":"Giulio","family":"Marano","sequence":"first","affiliation":[{"name":"Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), 3960 Sierre, Switzerland"},{"name":"Department of Computer, Control, and Management Engineering, La Sapienza University, 00185 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina","family":"Brambilla","sequence":"additional","affiliation":[{"name":"UOS STIIMA Lecco-Human-Centered, Smart & Safe, Living Environment, Italian National Research Council (CNR), 23900 Lecco, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert Mihai","family":"Mira","sequence":"additional","affiliation":[{"name":"UOS STIIMA Lecco-Human-Centered, Smart & Safe, Living Environment, Italian National Research Council (CNR), 23900 Lecco, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1896-9325","authenticated-orcid":false,"given":"Alessandro","family":"Scano","sequence":"additional","affiliation":[{"name":"UOS STIIMA Lecco-Human-Centered, Smart & Safe, Living Environment, Italian National Research Council (CNR), 23900 Lecco, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6800-9878","authenticated-orcid":false,"given":"Henning","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), 3960 Sierre, Switzerland"},{"name":"Department of Radiology, Medical Faculty, University of Geneva, 1211 Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5397-2063","authenticated-orcid":false,"given":"Manfredo","family":"Atzori","sequence":"additional","affiliation":[{"name":"Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), 3960 Sierre, Switzerland"},{"name":"Department of Neuroscience, University of Padua, 35122 Padua, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1109\/TBME.2011.2159216","article-title":"The effects of electrode size and orientation on the sensitivity of myoelectric pattern recognition systems to electrode shift","volume":"58","author":"Young","year":"2011","journal-title":"IEEE Trans. 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