{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T09:21:18Z","timestamp":1771665678016,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,7]],"date-time":"2020-03-07T00:00:00Z","timestamp":1583539200000},"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>Driver drowsiness and stress are major causes of traffic deaths and injuries, which ultimately wreak havoc on world economic loss. Researchers are in full swing to develop various algorithms for both drowsiness and stress recognition. In contrast to existing works, this paper proposes a generic model using multiple-objective genetic algorithm optimized deep multiple kernel learning support vector machine that is capable to recognize both driver drowsiness and stress. This algorithm simplifies the research formulations and model complexity that one model fits two applications. Results reveal that the proposed algorithm achieves an average sensitivity of 99%, specificity of 98.3% and area under the receiver operating characteristic curve (AUC) of 97.1% for driver drowsiness recognition. For driver stress recognition, the best performance is yielded with average sensitivity of 98.7%, specificity of 98.4% and AUC of 96.9%. Analysis also indicates that the proposed algorithm using multiple-objective genetic algorithm has better performance compared to the grid search method. Multiple kernel learning enhances the performance significantly compared to single typical kernel. Compared with existing works, the proposed algorithm not only achieves higher accuracy but also addressing the typical issues of dataset in simulated environment, no cross-validation and unreliable measurement stability of input signals.<\/jats:p>","DOI":"10.3390\/s20051474","type":"journal-article","created":{"date-parts":[[2020,3,9]],"date-time":"2020-03-09T05:37:34Z","timestamp":1583732254000},"page":"1474","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7992-9901","authenticated-orcid":false,"given":"Kwok Tai","family":"Chui","sequence":"first","affiliation":[{"name":"Department of Technology, School of Science and Technology, The Open University of Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7281-5458","authenticated-orcid":false,"given":"Miltiadis D.","family":"Lytras","sequence":"additional","affiliation":[{"name":"School of Business &amp; Economics, Deree College\u2014The American College of Greece, 153-42 Athens, Greece"},{"name":"Effat College of Engineering, Effat University, Jeddah P.O. Box 34689, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1591-5583","authenticated-orcid":false,"given":"Ryan Wen","family":"Liu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,7]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2018). Global Status Report on Road Safety 2018, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1016\/j.trf.2018.07.008","article-title":"The exceptionists of Chinese roads: The effect of road situations and ethical positions on driver aggression","volume":"58","author":"Du","year":"2018","journal-title":"Transp. Res. Part F Traffic Psychol. 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