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In this paper, we propose a non-intrusive technique that uses only data from smartphone sensors and machine learning to automatically distinguish between drivers and passengers while reading a message in a vehicle. We model and evaluate seven cutting-edge machine-learning techniques in different scenarios. The Convolutional Neural Network and Gradient Boosting were the models with the best results in our experiments. Results show accuracy, precision, recall, F1-score, and kappa metrics superior to 0.95.<\/jats:p>","DOI":"10.3390\/s19143174","type":"journal-article","created":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T03:14:41Z","timestamp":1563506081000},"page":"3174","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving"],"prefix":"10.3390","volume":"19","author":[{"given":"Renato","family":"Torres","sequence":"first","affiliation":[{"name":"Institute of Exact and Natural Sciences, Federal University of Par\u00e1 (UFPA), Bel\u00e9m 66-075-110 PA, Brazil"},{"name":"Informatics Department, Federal Institute of Par\u00e1, Vigia 68-780-000 PA, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Orlando","family":"Ohashi","sequence":"additional","affiliation":[{"name":"Cyberspace Institute, Federal Rural University of Amaz\u00f4nia, Bel\u00e9m 66-077-830 PA, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7411-9229","authenticated-orcid":false,"given":"Gustavo","family":"Pessin","sequence":"additional","affiliation":[{"name":"Robotics Lab, Instituto Tecnol\u00f3gico Vale, Ouro Preto 35-400-000 MG, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,19]]},"reference":[{"key":"ref_1","unstructured":"Chan, M., and World Health Organization (2018). 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