ARM Controller and EEG based Drowsiness Tracking and Controlling during Driving

Naresh, B.; Rambabu, S.; Khalandar Basha, D. · 2018 · Crossref

DOI: 10.11591/ijres.v6.i3.pp127-132

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Summary

This paper addresses the critical safety issue of driver drowsiness and distraction, which significantly increases the risk of accidents. The authors propose a real-time embedded system that utilizes Brain-Computer Interface (BCI) technology to monitor a driver’s mental state and control vehicle alerts. The primary motivation is to detect loss of attention and fatigue by analyzing electroencephalogram (EEG) signals, offering a non-invasive method to assess driver alertness during both day and night driving conditions. The system architecture integrates an ARM-based LPC2148 microcontroller with a MindWave MW001 brainwave sensor, ultrasonic sensors, and RFID technology. The MW001 sensor captures raw EEG data from the driver’s forehead at a sampling frequency of 512 Hz, filtering out ambient noise using dry electrodes. This data is transmitted via Bluetooth to a MATLAB environment, where the ThinkGear library processes the signals to estimate attention levels, meditation states, and blink detection. The MATLAB interface serves as the Level Analyzer Unit, comparing signal values against user-defined thresholds. Concurrently, the hardware layer uses ultrasonic sensors to detect nearby obstacles and RFID technology to identify traffic signal conditions. The ARM controller processes these inputs to trigger specific responses, such as activating a voice chip for alerts or controlling a DC motor for vehicle adjustments. The experimental design focuses on the communication protocol between the MATLAB software and the hardware, utilizing a serial connection at 9600 bps to minimize transmission errors. The system logic dictates that if the driver’s attention drops or if three consecutive blinks are detected—indicating drowsiness—the system generates an interrupt. This interrupt triggers a voice alert to wake the driver. Additionally, if the ultrasonic sensors detect a nearby vehicle, a separate voice alert is issued to warn of potential collisions. The paper presents graphical representations of brain signal identification and drowsiness detection within the MATLAB environment, demonstrating the system's ability to distinguish between different cognitive states based on frequency bands ranging from 0.5 Hz to 30 Hz. The significance of this work lies in its demonstration of a functional, low-cost BCI system for automotive safety applications. By combining EEG-based mental state monitoring with traditional proximity sensors, the system provides a multi-layered approach to accident prevention. The authors conclude that this non-invasive method effectively captures cognitive states like attention and eye-blinking, allowing for real-time intervention. While the conclusion briefly mentions applications for physically challenged individuals, the core contribution is the robust real-time monitoring of driver fatigue, suggesting that EEG-based methods are highly authentic for estimating drowsiness and can be integrated into embedded platforms to enhance driving safety.

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Topics

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• drowsiness detection algorithms

Information type

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• Empirical Findings: physiological data
• Methodological Resource: tool software