Abstract
Driver sleepiness has become one of the main reasons for traffic accidents. Previous studies have shown that two alpha-related phenomena - alpha blocking phenomenon and alpha wave attenuation-disappearance phenomenon - respectively represent two different sleepiness levels: the relaxed wakefulness and the sleep onset. Thus, we proposed a novel model to detect those two alpha-related phenomena based on EEG and EOG signals so as to determine sleepiness level. EOG and EEG signals inherently have temporal dependencies, and the sleepiness level transition is also a temporal process. Correspondingly, continuous wavelet transform represents physiological signals well, and LSTM is capable of handling long-term dependencies. Thus, our proposed dectection model utilized continuous wavelet transform and LSTM neural network for detecting driver sleepiness. The performance of our detection model are twofold: the recall and precision for detecting start and end points of alpha waves are generally high, and the LSTM classifier reaches a mean accuracy of 98.14%.
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Acknowledgments
This work was supported in part by the grants from the National Key Research and Development Program of China (Grant No. 2017YFB1002501), the National Natural Science Foundation of China (Grant No. 61673266), and the Fundamental Research Funds for the Central Universities.
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Deng, Y., Jiao, Y., Lu, BL. (2018). Driver Sleepiness Detection Using LSTM Neural Network. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_55
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DOI: https://doi.org/10.1007/978-3-030-04212-7_55
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