{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:51:00Z","timestamp":1780102260471,"version":"3.54.0"},"reference-count":56,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":365,"URL":"http:\/\/www.sagepub.com\/licence-information-for-chorus"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1822477"],"award-info":[{"award-number":["1822477"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Hum Factors"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We propose a method for recognizing driver distraction in real time using a wrist-worn inertial measurement unit (IMU).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Distracted driving results in thousands of fatal vehicle accidents every year. Recognizing distraction using body-worn sensors may help mitigate driver distraction and consequently improve road safety.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Twenty participants performed common behaviors associated with distracted driving while operating a driving simulator. Acceleration data collected from an IMU secured to each driver\u2019s right wrist were used to detect potential manual distractions based on 2-s long streaming data. Three deep neural network-based classifiers were compared for their ability to recognize the type of distractive behavior using F1-scores, a measure of accuracy considering both recall and precision.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The results indicated that a convolutional long short-term memory (ConvLSTM) deep neural network outperformed a convolutional neural network (CNN) and recursive neural network with long short-term memory (LSTM) for recognizing distracted driving behaviors. The within-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.82, and 0.82, respectively. The between-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.76, and 0.85, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The results of this pilot study indicate that the proposed driving distraction mitigation system that uses a wrist-worn IMU and ConvLSTM deep neural network classifier may have potential for improving transportation safety.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/0018720821995000","type":"journal-article","created":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T12:14:35Z","timestamp":1614168875000},"page":"1412-1428","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":18,"title":["Real-Time Driving Distraction Recognition Through a Wrist-Mounted Accelerometer"],"prefix":"10.1177","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1160-3822","authenticated-orcid":false,"given":"Ziyang","family":"Xie","sequence":"first","affiliation":[{"name":"North Carolina State University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"North Carolina State University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8790-3103","authenticated-orcid":false,"given":"Xu","family":"Xu","sequence":"additional","affiliation":[{"name":"North Carolina State University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2021,2,24]]},"reference":[{"key":"bibr1-0018720821995000","doi-asserted-by":"publisher","DOI":"10.1109\/IPIN.2014.7275474"},{"key":"bibr2-0018720821995000","doi-asserted-by":"publisher","DOI":"10.1016\/j.trf.2018.06.027"},{"key":"bibr3-0018720821995000","doi-asserted-by":"publisher","DOI":"10.1145\/3342197.3344529"},{"key":"bibr4-0018720821995000","doi-asserted-by":"publisher","DOI":"10.1016\/S0926-6410(00)00044-6"},{"key":"bibr5-0018720821995000","doi-asserted-by":"publisher","DOI":"10.1145\/3054977.3054979"},{"key":"bibr6-0018720821995000","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2016.7738077"},{"key":"bibr7-0018720821995000","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS45731.2020.9180826"},{"key":"bibr8-0018720821995000","first-page":"1502.00250","author":"Craye C.","year":"2015","journal-title":"ArXiv Preprint Ar"},{"key":"bibr9-0018720821995000","doi-asserted-by":"publisher","DOI":"10.1093\/iwc\/iws011"},{"key":"bibr10-0018720821995000","doi-asserted-by":"publisher","DOI":"10.1038\/sj.ijo.0800741"},{"key":"bibr11-0018720821995000","first-page":"967","volume-title":"[Conference session]. 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