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A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving
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 distrac...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679284/ https://www.ncbi.nlm.nih.gov/pubmed/31330929 http://dx.doi.org/10.3390/s19143174 |
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author | Torres, Renato Ohashi, Orlando Pessin, Gustavo |
author_facet | Torres, Renato Ohashi, Orlando Pessin, Gustavo |
author_sort | Torres, Renato |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6679284 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66792842019-08-19 A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving Torres, Renato Ohashi, Orlando Pessin, Gustavo Sensors (Basel) Article 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. MDPI 2019-07-19 /pmc/articles/PMC6679284/ /pubmed/31330929 http://dx.doi.org/10.3390/s19143174 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Torres, Renato Ohashi, Orlando Pessin, Gustavo A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving |
title | A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving |
title_full | A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving |
title_fullStr | A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving |
title_full_unstemmed | A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving |
title_short | A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving |
title_sort | machine-learning approach to distinguish passengers and drivers reading while driving |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679284/ https://www.ncbi.nlm.nih.gov/pubmed/31330929 http://dx.doi.org/10.3390/s19143174 |
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