Abstract
Various methods have been explored to develop an effective drowsiness detection system to give drivers a warning of impending drowsiness. The present work has successfully developed an electrooculagraphy-based wearable drowsiness detection system in the form of a visor cap by implementing an artificial neural network (ANN) into an Arduino LilyPadUSB microcontroller. As a result, a stand-alone and wearable system that does not require a computer was achieved. The performance of the system for drowsiness detection has an overall accuracy of 90.00 %, precision of 88.00 %, sensitivity of 91.67 % and a training mean squared error (MSE) of 2.70 × 10−3.
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Acknowledgment
The authors would like to thank the Engineering Research and Development for Technology (ERDT) scholarship program of the Department of Science and Technology-Science Education Institute (DOST-SEI) for funding this study.
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Tabal, K.M.R., Caluyo, F.S., Ibarra, J.B.G. (2016). Microcontroller-Implemented Artificial Neural Network for Electrooculography-Based Wearable Drowsiness Detection System. In: Sulaiman, H., Othman, M., Othman, M., Rahim, Y., Pee, N. (eds) Advanced Computer and Communication Engineering Technology. Lecture Notes in Electrical Engineering, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-319-24584-3_39
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