{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T01:49:32Z","timestamp":1768700972772,"version":"3.49.0"},"reference-count":29,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2019,6,7]],"date-time":"2019-06-07T00:00:00Z","timestamp":1559865600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2019,6,7]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Concentration is the key to safer driving. Ideally, drivers should focus mainly on front views and side mirrors. Typical distractions are eating, drinking, cell phone use, using and searching things in car as well as looking at something outside the car. In this paper, distracted driving detection algorithm is targeting on nine scenarios nodding, head shaking, moving the head 45\u00b0 to upper left and back to position, moving the head 45\u00b0 to lower left and back to position, moving the head 45\u00b0 to upper right and back to position, moving the head 45\u00b0 to lower right and back to position, moving the head upward and back to position, head dropping down and blinking as fundamental elements for distracted events. The purpose of this paper is preliminary study these scenarios for the ideal distraction detection, the exact type of distraction.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The system consists of distraction detection module that processes video stream and compute motion coefficient to reinforce identification of distraction conditions of drivers. Motion coefficient of the video frames is computed which follows by the spike detection via statistical filtering.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The accuracy of head motion analyzer is given as 98.6 percent. With such satisfactory result, it is concluded that the distraction detection using light computation power algorithm is an appropriate direction and further work could be devoted on more scenarios as well as background light intensity and resolution of video frames.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The system aimed at detecting the distraction of the public transport driver. By providing instant response and timely warning, it can lower the road traffic accidents and casualties due to poor physical conditions. A low latency and lightweight head motion detector has been developed for online driver awareness monitoring.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/dta-09-2018-0086","type":"journal-article","created":{"date-parts":[[2019,6,7]],"date-time":"2019-06-07T06:23:02Z","timestamp":1559888582000},"page":"171-188","source":"Crossref","is-referenced-by-count":4,"title":["Head motion coefficient-based algorithm for distracted driving detection"],"prefix":"10.1108","volume":"53","author":[{"given":"Kwok Tai","family":"Chui","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wadee","family":"Alhalabi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryan Wen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2019,6,7]]},"reference":[{"key":"key2021041510004324400_ref001","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.measurement.2018.03.040","article-title":"Driver\u2019s stress detection using skin potential response signals","volume":"122","year":"2018","journal-title":"Measurement"},{"key":"key2021041510004324400_ref002","article-title":"Detection and evaluation of driver distraction using machine learning and fuzzy logic","year":"","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"9","key":"key2021041510004324400_ref003","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.3390\/s17091991","article-title":"A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability","volume":"17","year":"2017","journal-title":"Sensors"},{"key":"key2021041510004324400_ref004","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.aap.2015.08.015","article-title":"What drives technology-based distractions? 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