{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:30:17Z","timestamp":1773772217488,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T00:00:00Z","timestamp":1716422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad de Las Americas (UDLA)","award":["IEA.WHP.21.02"],"award-info":[{"award-number":["IEA.WHP.21.02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>One of the biggest challenges of computers is collecting data from human behavior, such as interpreting human emotions. Traditionally, this process is carried out by computer vision or multichannel electroencephalograms. However, they comprise heavy computational resources, far from final users or where the dataset was made. On the other side, sensors can capture muscle reactions and respond on the spot, preserving information locally without using robust computers. Therefore, the research subject is the recognition of the six primary human emotions using electromyography sensors in a portable device. They are placed on specific facial muscles to detect happiness, anger, surprise, fear, sadness, and disgust. The experimental results showed that when working with the CortexM0 microcontroller, enough computational capabilities were achieved to store a deep learning model with a classification store of 92%. Furthermore, we demonstrate the necessity of collecting data from natural environments and how they need to be processed by a machine learning pipeline.<\/jats:p>","DOI":"10.3390\/s24113350","type":"journal-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T11:37:13Z","timestamp":1716464233000},"page":"3350","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Portable Facial Expression System Based on EMG Sensors and Machine Learning Models"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9699-4254","authenticated-orcid":false,"given":"Paola A.","family":"Sanipat\u00edn-D\u00edaz","sequence":"first","affiliation":[{"name":"SDAS Research Group, Hay Moulay Rachid, Ben Guerir 43150, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1995-400X","authenticated-orcid":false,"given":"Paul D.","family":"Rosero-Montalvo","sequence":"additional","affiliation":[{"name":"Computer Science Department, IT University of Copenhagen, 2300 Copenhagen, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4643-8377","authenticated-orcid":false,"given":"Wilmar","family":"Hernandez","sequence":"additional","affiliation":[{"name":"Carrera de Ingenieria Electronica y Automatizacion, Facultad de Ingenieria y Ciencias Aplicadas, Universidad de Las Americas, Quito 170124, Ecuador"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, M., Lee, W., Shu, L., Kim, Y.S., and Park, C.H. 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