@article{ref1,
title="Recognition of manual driving distraction through deep-learning and wearable sensing",
journal="Proceedings of the ... international driving symposium on human factors in driver assessment, training and vehicle design",
year="2019",
author="Li, Li and Xie, Ziyang and Xu, Xu",
volume="2019",
number="",
pages="22-28",
abstract="The goal of this study is to design a novel framework incorporating deep-learning techniques and wearable sensors to recognize manual distractions during driving. Manual distraction is defined as hands off the wheel for any reason (e.g. trying to get a cell phone). In this preliminary study, participants were tasked to drive in city street and highway scenarios in a driving simulator. Verbal instructions prompted participants to perform various manual distraction tasks. The motion of driver's right wrist during driving was recorded by a wearable inertial measurement unit. A deep-learning technique called convolutional neural network (CNN) was then constructed and trained based on 72% of the experiment trials, and evaluated by the remaining 28% of trials. The results indicated that the convolutional neural network is able to recognize the type of manual distraction task based on the right wrist motion with 87.0% accuracy and F1-score of 0.87. The results indicated that there is a good potential to apply deep-learning techniques and wearable sensing to monitor driver's inattention status.  Available: https://drivingassessment.uiowa.edu/sites/drivingassessment.uiowa.edu/files/da2019_05_xuxu_final.pdf  <p /> <p>Language: en</p>",
language="en",
issn="",
doi="",
url="http://dx.doi.org/"
}