Abstract
Among the numerous factors that are responsible for increasing road accidents, the second most common cause is drowsiness. In an attempt to reduce the rate of accidents, we propose a system which would efficiently handle the timely detection of drowsiness and would accordingly curb the speed of the vehicle being driven. As a proof of concept of the proposed method, we have trained the SVM classifier on the EEG (electroencephalogram) waves derived from “Analysis of a sleep-dependent neuronal feedback loop: the slow-wave micro continuity of the EEG” by Kemp et al. [1, 2]. The data is obtained from a wireless EEG headset. The classification results will determine whether the EEG data corresponds to drowsiness or alertness. This level of drowsiness is then used to determine the maximum speed limit. As the work in [3] has stated, there is a strong correlation between the number of accidents and the speed limit. Hence altogether, the proposed system integrates EEG waves for sleep level detection, and speed lock as a preventive measure to reduce the number of plausible accidents.
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References
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Ghube, C., Kulkarni, A., Bankar, C., Bedekar, M. (2019). BMI Application: Accident Reduction Using Drowsiness Detection. In: Abraham, A., Gandhi, N., Pant, M. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2018. Advances in Intelligent Systems and Computing, vol 939. Springer, Cham. https://doi.org/10.1007/978-3-030-16681-6_7
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DOI: https://doi.org/10.1007/978-3-030-16681-6_7
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