Wearable Device for Drowsy User Detection

This paper proposes a wearable acquisition device for noninvasive brain–computer interface-based drowsy driving detection, which is a major cause of automobile accidents. The device detects changes in brain activity and eye movements that indicate drowsiness by monitoring electrical signals obtained using electroencephalography, electrooculography, and muscle activity. This hardware device is intended to be low-cost and robust. The acquired signals (electroencephalography, electrooculography, and muscle activity) are accurate and noise-free, allowing the detection of electrical activity fluctuations when blinking, moving the head, or biting the teeth. This device can prevent car accidents caused by drowsy driving by providing real-time alerts to sleepy drivers. The proposed solution is low-cost. It is based on machine learning and monitors electroencephalography signals. It presents a promising approach to improving road safety. Further research and development in this area are warranted.