{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:14:19Z","timestamp":1780614859885,"version":"3.54.1"},"reference-count":53,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T00:00:00Z","timestamp":1630368000000},"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>People with severe disabilities require assistance to perform their routine activities; a Human\u2013Machine Interface (HMI) will allow them to activate devices that respond according to their needs. In this work, an HMI based on electrooculography (EOG) is presented, the instrumentation is placed on portable glasses that have the task of acquiring both horizontal and vertical EOG signals. The registration of each eye movement is identified by a class and categorized using the one hot encoding technique to test precision and sensitivity of different machine learning classification algorithms capable of identifying new data from the eye registration; the algorithm allows to discriminate blinks in order not to disturb the acquisition of the eyeball position commands. The implementation of the classifier consists of the control of a three-wheeled omnidirectional robot to validate the response of the interface. This work proposes the classification of signals in real time and the customization of the interface, minimizing the user\u2019s learning curve. Preliminary results showed that it is possible to generate trajectories to control an omnidirectional robot to implement in the future assistance system to control position through gaze orientation.<\/jats:p>","DOI":"10.3390\/s21175882","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T22:58:15Z","timestamp":1630450695000},"page":"5882","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Human\u2013Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6967-9993","authenticated-orcid":false,"given":"Francisco David","family":"P\u00e9rez-Reynoso","sequence":"first","affiliation":[{"name":"Mechatronic Engineering, Universidad Polit\u00e9cnica de Pachuca (UPP), Zempoala 43830, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3587-0079","authenticated-orcid":false,"given":"Liliam","family":"Rodr\u00edguez-Guerrero","sequence":"additional","affiliation":[{"name":"Research Center on Technology of Information and Systems (CITIS), Electric and Control Academic Group, Universidad Aut\u00f3noma del Estado de Hidalgo (UAEH), Pachuca de Soto 42039, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1666-9924","authenticated-orcid":false,"given":"Julio C\u00e9sar","family":"Salgado-Ram\u00edrez","sequence":"additional","affiliation":[{"name":"Biomedical Engineering, Universidad Polit\u00e9cnica de Pachuca (UPP), Zempoala 43830, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1947-2169","authenticated-orcid":false,"given":"Roc\u00edo","family":"Ortega-Palacios","sequence":"additional","affiliation":[{"name":"Biomedical Engineering, Universidad Polit\u00e9cnica de Pachuca (UPP), Zempoala 43830, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,31]]},"reference":[{"key":"ref_1","first-page":"206","article-title":"Eye movement based electronic wheel chair for physically challenged persons","volume":"3","author":"Gautam","year":"2014","journal-title":"Int. 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