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Citation |
Li L, Xie Z, Xu X. Proc. Int. Driv. Symp. Hum. Factors Driv. Assess. Train. Veh. Des. 2019; 2019: 22-28. |
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Copyright |
(Copyright © 2019, University of Iowa Public Policy Center) |
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DOI |
unavailable |
PMID |
unavailable |
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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. Language: en |