{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T06:02:50Z","timestamp":1780984970643,"version":"3.54.1"},"reference-count":63,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T00:00:00Z","timestamp":1616112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008256","name":"Hyundai Motor Group","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008256","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"publisher","award":["20003519"],"award-info":[{"award-number":["20003519"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["2021R1A2B5B01005433"],"award-info":[{"award-number":["2021R1A2B5B01005433"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In intelligent vehicles, it is essential to monitor the driver\u2019s condition; however, recognizing the driver\u2019s emotional state is one of the most challenging and important tasks. Most previous studies focused on facial expression recognition to monitor the driver\u2019s emotional state. However, while driving, many factors are preventing the drivers from revealing the emotions on their faces. To address this problem, we propose a deep learning-based driver\u2019s real emotion recognizer (DRER), which is a deep learning-based algorithm to recognize the drivers\u2019 real emotions that cannot be completely identified based on their facial expressions. The proposed algorithm comprises of two models: (i) facial expression recognition model, which refers to the state-of-the-art convolutional neural network structure; and (ii) sensor fusion emotion recognition model, which fuses the recognized state of facial expressions with electrodermal activity, a bio-physiological signal representing electrical characteristics of the skin, in recognizing even the driver\u2019s real emotional state. Hence, we categorized the driver\u2019s emotion and conducted human-in-the-loop experiments to acquire the data. Experimental results show that the proposed fusing approach achieves 114% increase in accuracy compared to using only the facial expressions and 146% increase in accuracy compare to using only the electrodermal activity. In conclusion, our proposed method achieves 86.8% recognition accuracy in recognizing the driver\u2019s induced emotion while driving situation.<\/jats:p>","DOI":"10.3390\/s21062166","type":"journal-article","created":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T23:47:41Z","timestamp":1616370461000},"page":"2166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["DRER: Deep Learning\u2013Based Driver\u2019s Real Emotion Recognizer"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0498-5631","authenticated-orcid":false,"given":"Geesung","family":"Oh","sequence":"first","affiliation":[{"name":"Graduate School of Automotive Engineering, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 02707, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junghwan","family":"Ryu","sequence":"additional","affiliation":[{"name":"Graduate School of Automotive Engineering, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 02707, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Euiseok","family":"Jeong","sequence":"additional","affiliation":[{"name":"Graduate School of Automotive Engineering, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 02707, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8349-6931","authenticated-orcid":false,"given":"Ji Hyun","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Automobile and IT Convergence, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 02707, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungwook","family":"Hwang","sequence":"additional","affiliation":[{"name":"Chassis System Control Research Lab, Hyundai Motor Group, Hwaseong 18280, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangho","family":"Lee","sequence":"additional","affiliation":[{"name":"Chassis System Control Research Lab, Hyundai Motor Group, Hwaseong 18280, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1917-699X","authenticated-orcid":false,"given":"Sejoon","family":"Lim","sequence":"additional","affiliation":[{"name":"Department of Automobile and IT Convergence, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 02707, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/S1369-8478(99)00006-6","article-title":"Anger while driving","volume":"2","author":"Underwood","year":"1999","journal-title":"Transp. 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