{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:42:35Z","timestamp":1764175355407,"version":"build-2065373602"},"reference-count":70,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"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>Surface electromyography (sEMG) is the acquisition, from the skin, of the electrical signal produced by muscle activation. Usually, sEMG is measured through electrodes with electrolytic gel, which often causes skin irritation. Capacitive contactless electrodes have been developed to overcome this limitation. However, contactless EMG devices are still sensitive to motion artifacts and often not comfortable for long monitoring. In this study, a non-invasive contactless method to estimate parameters indicative of muscular activity and fatigue, as they are assessed by EMG, through infrared thermal imaging (IRI) and cross-validated machine learning (ML) approaches is described. Particularly, 10 healthy participants underwent five series of bodyweight squats until exhaustion interspersed by 1 min of rest. During exercising, the vastus medialis activity and its temperature were measured through sEMG and IRI, respectively. The EMG average rectified value (ARV) and the median frequency of the power spectral density (MDF) of each series were estimated through several ML approaches applied to IRI features, obtaining good estimation performances (r = 0.886, p &lt; 0.001 for ARV, and r = 0.661, p &lt; 0.001 for MDF). Although EMG and IRI measure physiological processes of a different nature and are not interchangeable, these results suggest a potential link between skin temperature and muscle activity and fatigue, fostering the employment of contactless methods to deliver metrics of muscular activity in a non-invasive and comfortable manner in sports and clinical applications.<\/jats:p>","DOI":"10.3390\/s23020832","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T04:59:58Z","timestamp":1673413198000},"page":"832","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Can Data-Driven Supervised Machine Learning Approaches Applied to Infrared Thermal Imaging Data Estimate Muscular Activity and Fatigue?"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1903-0501","authenticated-orcid":false,"given":"David","family":"Perpetuini","sequence":"first","affiliation":[{"name":"Department of Neurosciences, Imaging and Clinical Sciences, University \u201cG. d\u2019Annunzio\u201d of Chieti-Pescara, 66100 Chieti, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2941-937X","authenticated-orcid":false,"given":"Damiano","family":"Formenti","sequence":"additional","affiliation":[{"name":"Department of Biotechnology and Life Sciences (DBSV), University of Insubria, Via Dunant, 3, 21100 Varese, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1506-1995","authenticated-orcid":false,"given":"Daniela","family":"Cardone","sequence":"additional","affiliation":[{"name":"Department of Engineering and Geology, University \u201cG. d\u2019Annunzio\u201d of Chieti-Pescara, 65127 Pescara, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6080-5260","authenticated-orcid":false,"given":"Athos","family":"Trecroci","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences for Health, University of Milan, 20129 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6400-5914","authenticated-orcid":false,"given":"Alessio","family":"Rossi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Pisa, 56127 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8388-9305","authenticated-orcid":false,"given":"Andrea","family":"Di Credico","sequence":"additional","affiliation":[{"name":"Department of Medicine and Aging Sciences, University \u201cG. d\u2019Annunzio\u201d of Chieti-Pescara, 66100 Chieti, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1389-7596","authenticated-orcid":false,"given":"Giampiero","family":"Merati","sequence":"additional","affiliation":[{"name":"Department of Biotechnology and Life Sciences (DBSV), University of Insubria, Via Dunant, 3, 21100 Varese, Italy"},{"name":"IRCCS Fondazione Don Carlo Gnocchi, 20148 Milano, Italy"}]},{"given":"Giampietro","family":"Alberti","sequence":"additional","affiliation":[{"name":"University of Milan, 20122 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4473-4909","authenticated-orcid":false,"given":"Angela","family":"Di Baldassarre","sequence":"additional","affiliation":[{"name":"Department of Medicine and Aging Sciences, University \u201cG. d\u2019Annunzio\u201d of Chieti-Pescara, 66100 Chieti, Italy"}]},{"given":"Arcangelo","family":"Merla","sequence":"additional","affiliation":[{"name":"Department of Engineering and Geology, University \u201cG. d\u2019Annunzio\u201d of Chieti-Pescara, 65127 Pescara, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7792","DOI":"10.1109\/ACCESS.2019.2963881","article-title":"Myoelectric Interfaces and Related Applications: Current State of EMG Signal Processing\u2013A Systematic Review","volume":"8","author":"Soto","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TNSRE.2015.2454503","article-title":"Single-Channel EMG Classification With Ensemble-Empirical-Mode-Decomposition-Based ICA for Diagnosing Neuromuscular Disorders","volume":"24","author":"Naik","year":"2016","journal-title":"IEEE Trans. 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