Development of a Driver Drowsiness Monitoring System Using Electrocardiogram

Authors

  • N. S. Nor Shahrudin Department of Electrical and Computer Engineering, International Islamic University Malaysia, P.O. Box 10, Jalan Gombak, 50728 Kuala Lumpur.
  • K. A. Sidek Department of Electrical and Computer Engineering, International Islamic University Malaysia, P.O. Box 10, Jalan Gombak, 50728 Kuala Lumpur.
  • A.F. Ismail Department of Electrical and Computer Engineering, International Islamic University Malaysia, P.O. Box 10, Jalan Gombak, 50728 Kuala Lumpur.

Keywords:

Cardioid, Drowsiness, Electrocardiogram, R peak, RR Interval,

Abstract

Driver drowsiness has become a common issue that leads to road accidents and death. Accidents not only affect the physical body of the driver, but it also affects people in the surrounding, physical road conditions, and environments. It is proven in previous studies that biological signal are closely related to a person’s reaction. Electrocardiogram (ECG), which is an electrical indicator of the heart, provides such criteria as it reflects the heart activity. Morphological signal of the heart is strongly correlated to our actions which relates to our emotions and reactions. Thus, this study proposed a non-intrusive detector to detect driver drowsiness by using the ECG. A total of 10 subjects were obtained from The Cyclic Alternating Pattern (CAP) Sleep database. The signals are later processed using low pass Butterworth filter with 0.1 cutoff frequency. Then, QRS complexes are extracted from the acquired ECG signal. Classification techniques such as RR interval and different of amplitude at R peak were used in order to differentiate between normal and drowsy ECG signal. Cardioid based graph was used to support the argument made in analyzing area and circumference of both normal and drowsy graph. The result shows that RR Interval of a drowsy state increased almost 22% rather than in normal state. The percentage different of amplitude difference at R peak between normal and drowsy state can reach up to 36.33%. In terms of cardioid, area, perimeter and Euclidean distance of the centroid are always higher than drowsy. Thus, from the outcomes that been suggested for drowsiness detection using RR interval and amplitude of R are able to become as the most efficient drowsiness detection.

References

The Star Online, "Risk of road accidents increases during festive seasons, says Nangka rep", Retrieved on 3rd April 2016, p. 1, 2014.

Road Safety Department of Malaysia, Message from the Director General, http://www.jkjr.gov.my/en/about-us/message-from-thedirector-general.html, Retrieved on 5th April 2016.

Wang W.F.; Wang N.T.; Shih S.J., “Design of a Health Management System with Physiological Signal Acquisition”, Circuits and Systems (APCCAS), 2010 IEEE Asia Pacific Conference, pp. 456-459, 2010.

Shin H.S.; Jung S.J.; Kim J.; Chung W.Y, “Real Time Car Driver’s Condition Monitoring System” SENSORS, 2010 IEEE Conference, pp. 951-954, 2010.

Warwick B.; Symons N.; Chen X.; Xiong K., “Detecting Driver Drowsiness using Wireless Wearables”, Mobile Ad Hoc and Sensor Systems (MASS), 2015 IEEE 12th International Conference, pp. 585- 588, 2015.

Omidyeganeh,M.; Javadtalab,A.; Shirmohammadi,S., “Intelligent Driver Drowsiness Detection through Fusion of Yawning and Eye Closure”, 2011 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems Proceedings, pp. 1-6, 2011.

Ingre M.; Akerstedt T.; Peters B.; Anund A.; Kecklund G., “Subjective Sleepiness, Simulated Driving Performance and Blink Duration: Examining Individual Differences”, 2006 European Sleep Research Society, pp. 47-53, 2005.

Otmani S.; Pebayle T.; Roge J.; Muzet A., “Effect of Driving Duration and Partial Sleep Deprivation on Subsequent Alertness and Performance of Car Drivers”, Physiology & Behavior 84 (2005), pp. 715-724, 2005.

Healey J.A.; Picard R.W., “Detecting Stress during Real-World Driving Tasks Using Physiological Sensors”, IEEE Transactions on Intelligent Transportation Systems, Vol. 6, NO. 2, pp. 156-166, 2005.

Physionet: Stress Recognition in Automobile Drivers, (Accessed on10th April 2016), from https://physionet.org/physiobank/database/drivedb/

Jalil B.; Laligant O.; Fauvet E.; Beya O., “Detection of QRS Complex in ECG Signal Based on Classification Approach”, Proceedings of 2010 IEEE 17th International Conference on Image Processing, pp. 345-348, 2010.

Karlsson M.; Hornsten R.; Rydber A.; Wiklund U.,“Automatic filtering of outliers in RR intervals before analysis of heart rate variability in Holter recordings: a comparison with carefully edited data” BioMedical Engineering Online, pp. 1-6, 2012.

Mohd Azam S.N.A; Zainal N.I and Sidek K.A., “Development of Cardioid Based Graph ECG Heart Abnormalities Classification Technique”, ARPN Journal of Engineering and Applied Sciences, Vol.10, pp. 9759 – 9765, 2015.

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Published

2018-02-05

How to Cite

Nor Shahrudin, N. S., Sidek, K. A., & Ismail, A. (2018). Development of a Driver Drowsiness Monitoring System Using Electrocardiogram. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-6), 11–15. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3655