{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T09:46:52Z","timestamp":1775900812617,"version":"3.50.1"},"reference-count":224,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,15]],"date-time":"2021-10-15T00:00:00Z","timestamp":1634256000000},"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>As a definition, Human\u2013Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs\u2019 complexity, so their usefulness should be carefully evaluated for the specific application.<\/jats:p>","DOI":"10.3390\/s21206863","type":"journal-article","created":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T23:25:15Z","timestamp":1634513115000},"page":"6863","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Biosignal-Based Human\u2013Machine Interfaces for Assistance and Rehabilitation: A Survey"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0716-8431","authenticated-orcid":false,"given":"Daniele","family":"Esposito","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples \u201cFederico II\u201d, 80125 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3422-8727","authenticated-orcid":false,"given":"Jessica","family":"Centracchio","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples \u201cFederico II\u201d, 80125 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4829-3941","authenticated-orcid":false,"given":"Emilio","family":"Andreozzi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples \u201cFederico II\u201d, 80125 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1790-9838","authenticated-orcid":false,"given":"Gaetano D.","family":"Gargiulo","sequence":"additional","affiliation":[{"name":"School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2747, Australia"},{"name":"The MARCS Institute, Western Sydney University, Penrith, NSW 2751, Australia"}]},{"given":"Ganesh R.","family":"Naik","sequence":"additional","affiliation":[{"name":"School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2747, Australia"},{"name":"The Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA 5042, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9585-971X","authenticated-orcid":false,"given":"Paolo","family":"Bifulco","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples \u201cFederico II\u201d, 80125 Naples, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1080\/03091902.2021.1936237","article-title":"Developments in the Human Machine Interface Technologies and Their Applications: A Review","volume":"45","author":"Singh","year":"2021","journal-title":"J. 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