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The evaluation of MW is of paramount importance for Advanced Driver-Assistance Systems, given its correlation with traffic accidents risk. In the present research, two cognitive tests (Digit Span Test\u2014DST and Ray Auditory Verbal Learning Test\u2014RAVLT) were administered to participants while driving in a simulated environment. The tests were chosen to investigate the drivers\u2019 response to predefined levels of cognitive load to categorize the classes of MW. Infrared (IR) thermal imaging concurrently with heart rate variability (HRV) were used to obtain features related to the psychophysiology of the subjects, in order to feed machine learning (ML) classifiers. Six categories of models have been compared basing on unimodal IR\/unimodal HRV\/multimodal IR + HRV features. The best classifier performances were reached by the multimodal IR + HRV features-based classifiers (DST: accuracy = 73.1%, sensitivity = 0.71, specificity = 0.69; RAVLT: accuracy = 75.0%, average sensitivity = 0.75, average specificity = 0.87). The unimodal IR features based classifiers revealed high performances as well (DST: accuracy = 73.1%, sensitivity = 0.73, specificity = 0.73; RAVLT: accuracy = 71.1%, average sensitivity = 0.71, average specificity = 0.85). These results demonstrated the possibility to assess drivers\u2019 MW levels with high accuracy, also using a completely non-contact and non-invasive technique alone, representing a key advancement with respect to the state of the art in traffic accident prevention.<\/jats:p>","DOI":"10.3390\/s22197300","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T03:30:37Z","timestamp":1664335837000},"page":"7300","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Classification of Drivers\u2019 Mental Workload Levels: Comparison of Machine Learning Methods Based on ECG and Infrared Thermal Signals"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1506-1995","authenticated-orcid":false,"given":"Daniela","family":"Cardone","sequence":"first","affiliation":[{"name":"Department of Engineering and Geology, University G. d\u2019Annunzio of Chieti-Pescara, 65127 Pescara, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1903-0501","authenticated-orcid":false,"given":"David","family":"Perpetuini","sequence":"additional","affiliation":[{"name":"Department of Neurosciences, Imaging and Clinical Sciences, University G. d\u2019Annunzio of Chieti-Pescara, 66100 Chieti, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2282-3537","authenticated-orcid":false,"given":"Chiara","family":"Filippini","sequence":"additional","affiliation":[{"name":"Department of Neurosciences, Imaging and Clinical Sciences, University G. d\u2019Annunzio of Chieti-Pescara, 66100 Chieti, Italy"}]},{"given":"Lorenza","family":"Mancini","sequence":"additional","affiliation":[{"name":"Next2U s.r.l., 65127 Pescara, Italy"}]},{"given":"Sergio","family":"Nocco","sequence":"additional","affiliation":[{"name":"Next2U s.r.l., 65127 Pescara, Italy"}]},{"given":"Michele","family":"Tritto","sequence":"additional","affiliation":[{"name":"Next2U s.r.l., 65127 Pescara, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9368-7088","authenticated-orcid":false,"given":"Sergio","family":"Rinella","sequence":"additional","affiliation":[{"name":"Physiology Section, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2565-8522","authenticated-orcid":false,"given":"Alberto","family":"Giacobbe","sequence":"additional","affiliation":[{"name":"Physiology Section, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy"}]},{"given":"Giorgio","family":"Fallica","sequence":"additional","affiliation":[{"name":"National Interuniversity Consortium of Science and Technology of Materials (INSTM), University of Messina, 98122 Messina, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1401-6623","authenticated-orcid":false,"given":"Fabrizio","family":"Ricci","sequence":"additional","affiliation":[{"name":"Department of Neurosciences, Imaging and Clinical Sciences, University G. d\u2019Annunzio of Chieti-Pescara, 66100 Chieti, Italy"}]},{"given":"Sabina","family":"Gallina","sequence":"additional","affiliation":[{"name":"Department of Neurosciences, Imaging and Clinical Sciences, University G. d\u2019Annunzio of Chieti-Pescara, 66100 Chieti, Italy"}]},{"given":"Arcangelo","family":"Merla","sequence":"additional","affiliation":[{"name":"Department of Engineering and Geology, University G. d\u2019Annunzio of Chieti-Pescara, 65127 Pescara, Italy"},{"name":"Next2U s.r.l., 65127 Pescara, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s12239-014-0007-9","article-title":"Evaluation of Driver\u2019s Mental Workload by Facial Temperature and Electrodermal Activity under Simulated Driving Conditions","volume":"15","author":"Kajiwara","year":"2014","journal-title":"Int. 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