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Article

A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving

by 1,2,*,†, 3,*,† and 4,*,†
1
Institute of Exact and Natural Sciences, Federal University of Pará (UFPA), Belém 66-075-110 PA, Brazil
2
Informatics Department, Federal Institute of Pará, Vigia 68-780-000 PA, Brazil
3
Cyberspace Institute, Federal Rural University of Amazônia, Belém 66-077-830 PA, Brazil
4
Robotics Lab, Instituto Tecnológico Vale, Ouro Preto 35-400-000 MG, Brazil
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(14), 3174; https://doi.org/10.3390/s19143174
Received: 24 April 2019 / Revised: 29 June 2019 / Accepted: 2 July 2019 / Published: 19 July 2019
Driver distraction is one of the major causes of traffic accidents. In recent years, given the advance in connectivity and social networks, the use of smartphones while driving has become more frequent and a serious problem for safety. Texting, calling, and reading while driving are types of distractions caused by the use of smartphones. 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. View Full-Text
Keywords: driver distraction; reading while driving; machine learning; deep learning driver distraction; reading while driving; machine learning; deep learning
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MDPI and ACS Style

Torres, R.; Ohashi, O.; Pessin, G. A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving. Sensors 2019, 19, 3174. https://doi.org/10.3390/s19143174

AMA Style

Torres R, Ohashi O, Pessin G. A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving. Sensors. 2019; 19(14):3174. https://doi.org/10.3390/s19143174

Chicago/Turabian Style

Torres, Renato, Orlando Ohashi, and Gustavo Pessin. 2019. "A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving" Sensors 19, no. 14: 3174. https://doi.org/10.3390/s19143174

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