EEG-based drowsiness alert system with IoT

Din, Nur Munirah (2024) EEG-based drowsiness alert system with IoT. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

Driver drowsiness and fatigue are significant issues that can lead to accidents on the road. Drowsy driving weaken a driver's ability to focus, make quick decisions, and react to potential hazards. In extreme cases, drowsy drivers may experience microsleeps, which the driver is essentially unconscious, and if it occurs while driving, it can result in serious accidents. Electroencephalogram (EEG) are commonly employed to measure neural activity, serving as a widely utilized physiological indicator for detecting driver drowsiness. This paper presents the solution to detect drowsiness at the early stage using EEG signal and an alert system to trigger the driver by an audible alarm, status display on LCD, and a light indicator. The input is acquired using the MindLink Neuro Sensor, a wireless, wearable dry EEG headset that is connected to the microcontroller via bluetooth. The data will be send and stored on Internet of Thing (IoT) which is via Thing speak platform, which includes features like collecting data in real-time, processing data, creating visual representations, and using various applications and plugins. In this project, analyzing the power spectral density of the driver's EEG and assessing the duration of theta wave persistence enables the identification of whether the driver is entering a drowsy state or not. Furthermore, using decreasing in attention and increasing in meditation is also the reason for a driver in a drowsy state.

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