{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:30:19Z","timestamp":1778286619145,"version":"3.51.4"},"reference-count":27,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873043"],"award-info":[{"award-number":["61873043"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"publisher","award":["cstc2018jcyjAX0048"],"award-info":[{"award-number":["cstc2018jcyjAX0048"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2019,9]]},"abstract":"<jats:p> The study of the robust fatigue feature learning method for the driver\u2019s operational behavior is of great significance for improving the performance of the real-time detection system for driver\u2019s fatigue state. Aiming at how to extract more abstract and deep features in the driver\u2019s direction operation data in the robust feature learning, this article constructs a fuzzy recurrent neural network model, which includes input layer, fuzzy layer, hidden layer, and output layer. The steering-wheel direction sensing time series sends the time series to the input layer through a fixed time window. After the fuzzification process, it is sent to the hidden layer to share the weight of the hidden layer, realize the memorization of the fatigue feature, and improve the feature depth capability of the steering wheel angle time sequence. The experimental results show that the proposed model achieves an average recognition rate of 87.30% in the fatigue sample database of real vehicle conditions, which indicates that the model has strong robustness to different subjects under real driving conditions. The model proposed in this article has important theoretical and engineering significance for studying the prediction of fatigue driving under real driving conditions. <\/jats:p>","DOI":"10.1177\/1550147719872452","type":"journal-article","created":{"date-parts":[[2019,9,12]],"date-time":"2019-09-12T06:31:08Z","timestamp":1568269868000},"page":"155014771987245","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":28,"title":["A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data"],"prefix":"10.1177","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8154-3968","authenticated-orcid":false,"given":"Zuojin","family":"Li","sequence":"first","affiliation":[{"name":"College of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengfu","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liukui","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computing and Information Technology, Unitec Institute of Technology, Auckland, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,9,12]]},"reference":[{"issue":"9","key":"bibr2-1550147719872452","first-page":"803","volume":"35","author":"Qu X","year":"2013","journal-title":"Qiche Gongcheng\/Automot Eng"},{"key":"bibr3-1550147719872452","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2275192"},{"key":"bibr4-1550147719872452","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2011.2164242"},{"key":"bibr5-1550147719872452","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2012.0032"},{"key":"bibr6-1550147719872452","doi-asserted-by":"publisher","DOI":"10.1016\/j.medengphy.2013.07.011"},{"issue":"5","key":"bibr7-1550147719872452","first-page":"200","volume":"13","author":"Ma T","year":"2010","journal-title":"J Automot Saf Energy"},{"issue":"3","key":"bibr8-1550147719872452","first-page":"25","volume":"30","author":"Wu M","year":"2013","journal-title":"Computer Appl Softw"},{"issue":"7","key":"bibr9-1550147719872452","first-page":"176","volume":"14","author":"Lin SD","year":"2013","journal-title":"Int Symp Biom Secur Technol"},{"key":"bibr10-1550147719872452","volume-title":"Research on vehicle-based driver status\/performance monitoring; 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