@article{ref1,
title="Real-time driving distraction recognition through a wrist-mounted accelerometer",
journal="Human factors",
year="2021",
author="Xie, Ziyang and Li, Li and Xu, Xu",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="OBJECTIVE: We propose a method for recognizing driver distraction in real time using a wrist-worn inertial measurement unit (IMU). <br><br>BACKGROUND: Distracted driving results in thousands of fatal vehicle accidents every year. Recognizing distraction using body-worn sensors may help mitigate driver distraction and consequently improve road safety. <br><br>METHODS: Twenty participants performed common behaviors associated with distracted driving while operating a driving simulator. Acceleration data collected from an IMU secured to each driver's right wrist were used to detect potential manual distractions based on 2-s long streaming data. Three deep neural network-based classifiers were compared for their ability to recognize the type of distractive behavior using F1-scores, a measure of accuracy considering both recall and precision. <br><br>RESULTS: The results indicated that a convolutional long short-term memory (ConvLSTM) deep neural network outperformed a convolutional neural network (CNN) and recursive neural network with long short-term memory (LSTM) for recognizing distracted driving behaviors. The within-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.82, and 0.82, respectively. The between-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.76, and 0.85, respectively. <br><br>CONCLUSION: The results of this pilot study indicate that the proposed driving distraction mitigation system that uses a wrist-worn IMU and ConvLSTM deep neural network classifier may have potential for improving transportation safety.<p /> <p>Language: en</p>",
language="en",
issn="0018-7208",
doi="10.1177/0018720821995000",
url="http://dx.doi.org/10.1177/0018720821995000"
}