2. A Smart System for Driver’s Fatigue Detection, Remote Notification & Semi-Automatic Parking of Vehicles To Prevent Road Accidents
Presenter
Faisal Bin Kashem
Co-Authors
Alamgir Hossan, Md. Mehedi Hasan, Sabkiun Naher, Md. Ismail Rahman
Department of Applied Physics, Electronics & Communication Engineering
University of Chittagong

3. Outline Abstract Motivation Brain Waves Methodology
Drowsiness Detection
Notification & Control
Conclusion

4. Abstract
Drowsy driving is one of the main reasons of road accidents. Different techniques have been reported in literature to detect driver’s drowsiness, but almost all the prevailing systems only alert the driver if drowsiness is detected. Consequently, the drowsy driver continues driving, with a high risk of devastating accident. In this paper, we proposed and verified an EEG based system which not only alerts the driver by alarm, but also puts the vehicle in semiautomatic parking mode by controlling fuel supply if drowsiness is detected. At the same time, it reports nearby police station by SMS which contains necessary information to take essential steps locating the vehicle. Stored EEG signals, obtained with wireless wearable headsets from numerous subjects in different conditions by different research groups, were used in this work. Power spectrum analyses were carried out in MATLAB to determine the dominant frequency components in the brain signals. The slow wave to fast wave ratios of EEG activities were assessed for a number of epochs to determine driver’s drowsiness. GPS and GSM modules were used with Arduino MEGA for tracking, remote notification and servomotor control. Performance of the proposed system was evaluated by stored data which confirmed its feasibility and reliability.

5. Motivation
SMASH

6. Brain Waves Theta Frequency 4-8 Hz Alpha Frequency 8-13 Hz Beta
Gamma
Frequency
22-30 Hz & Above
Delta
Frequency
0.5-4 Hz

7. Methodology

8. Drowsiness Detection Start Receive EEG Data from Wireless EEG Headset
Input EEG Data into MATLAB Workspace
Perform Spectral Analysis Using fft Algorithm
Find (θ+ δ)/(α+β) Ratio
YES
Above
Threshold Value? NO
Send “HIGH” Value to Microcontroller
Send “LOW” Value to Microcontroller

9. Drowsiness Detection (Contd.)

10. Notification & Control
FUEL CHAMBER
ENGINE
FUEL CHAMBER
ENGINE

11. Conclusion
We developed a driver’s drowsiness detection, alarm and a novel semi-automatic parking system to prevent road crashes. In addition, we extended the work to report the nearby police station of the occurrence. We used slow waves to fast waves ratio for detecting drowsiness as it is more reliable to detect drowsiness than any single wave. We processed the signals in MATLAB platform and implemented a model of the rest of the system in real time which worked successfully. If this proposed system is implemented with the legislation of proper traffic rules, it would hopefully reduce the rate of devastating road crashes.

12. ?
Any Questions?

13. Thank You & Drive Safe

14. EEG Specs EEG Emotiv 128 samples/second 5 channels 2 references
Channels are AF3, AF4, T7, T8, Pz

15. Acquired Signal
PhysioNet
BCI 2000

16. Nearest Police Station
From Server via GPRS
SD card database

17. Used Dataset
PhysioNet
BCI 2000