A review of ECG data acquisition for driver drowsiness detection

Authors

  • Shahrudin NSN Department of Electrical and Computer Engineering, International Islamic University Malaysia, P.O. Box 10, Jalan Gombak, 50728 Kuala Lumpur
  • Sidek Department of Electrical and Computer Engineering, International Islamic University Malaysia, P.O. Box 10, Jalan Gombak, 50728 Kuala Lumpur

Keywords:

drowsiness, electrocardiogram, data acquisition, bio-signal, steering wheel, technology

Abstract

Over the years, cases related to road accidents and road fatalities keeps increasing. Both cases have potential to put life of a person at risk. One of the factors that leads to accidents are drowsiness. However, several lives can be saved with accurate and reliable drowsiness detection system. Thus, many researchers take this issue seriously by developing drowsiness detection mechanism in reducing cases related to driver drowsiness. As drowsiness is strongly correlated with the heart activities, hence bio-signal is the most preferable indicator to measure the drowsiness level. Reflection of electrical signal in the human body known as Electrocardiogram (ECG) are widely used in monitoring human action and reaction to prevent the occurrence of these devastating incidents. Thus, this paper will review the drowsiness detection technique focusing in ECG data acquisition for driver drowsiness detection. As the first step plays an important role for the whole system, this paper discussed on some open issues in drowsiness mechanism. We hope that this review will support and give some ideas to the future researchers in increasing the reliability of ECG measures towards driver drowsiness detection in reducing accident cases

References

Hall, Z., Zac, and Apple. 2019. How Apple Watch saved one man's life - and how it's empowering himafter his heart attack. 9to5Mac,

-Apr-2019. [Online].Available: https://9to5mac.com/ [Accessed: 13-Aug-2019].

Ishak, S.Z. 2020. MIROS & Its Role in ASEAN -Aseancap.Org. Towards Achieving Fatality Reductionin 2020. [Online]. Available: http://www.aseancap.org/ [Accessed: 08-Sep-2019].

Asia and the Pacific SDG progress report 2019. Bangkok: United Nations Economic and Social Commission for Asia and the Pacific.

Colic, A., Marques, O., & Furht, B, “Driver Drowsiness Detection Systems and Solutions (First)”, Springer, London, 2014.

Sahayadhas, A., Sundaraj, K. and Murugappan, M. 2012. Detecting driver drowsiness based on sensors: A review. Sensors (Switzerland). 12(12), 16937–16953.

Dong, Y., Hu, Z., Uchimura, K. and Murayama, N. 2011. Driver inattention monitoring system for intelligent vehicles: A review. IEEE Transactions on Intelligent Transportation Systems. 12(2), 596–614.

Zhenhai, G., DinhDat, L., Hongyu, H., Ziwen, Y. and Xinyu, W. 2017. Driver drowsiness detection based on time series analysis of steering wheel angular velocity. inProc. 9th Int. Conf. Measuring Technol. Mechatron. Automat. (ICMTMA). 99–101.

Li, Z., Li, S.E., Li, R., Cheng, B. and Shi, J. 2017. Online detection of driver fatigue using steering wheel angles for real driving conditions. Sensors. 17(3), p.495.

Yan, C., et al., 2016. Video-based classification of driving behavior using a hierarchical classification system with multiple features. Int.

J. Pattern Recognit. Artif. Intell. 30(5), Art. no. 1650010.

Rahman, A., Sirshar, M. and Khan, A. 2015. Real time drowsiness detection using eye blink monitoring. in Proc. Nat. Softw. Eng. Conf. (NSEC).1–7

Rahim, H.A., Dalimi, A. and Jaafar, H. 2015. Detecting Drowsy Driver Using Pulse Sensor. Jurnal Teknologi. 73(3).

Nor Shahrudin, N.S. and Sidek, K.A.2017. Development of a Driver Drowsiness Monitoring System using Electrocardiogram. Int. Conf.

on Communication and Computer Engineering (ICOCOE 2017). 10(1-6), 11–15.

Ramzan, M., Khan, H.U., Awan, S.M., Ismail, A.,Ilyas, M. and Mahmood, A. 2019. A Survey on State-of-the-Art Drowsiness Detection Techniques. IEEE Access. 7, 61904–61919.

Babaeian, M. and Mozumdar, M. 2019. Driver drowsinessdetection algorithms using electrocardiogram data analysis. 2019 IEEE 9th

Annu. Comput. Commun.Work. Conf. CCWC 2019, 1–6.

Attarodi, G., Nikooei, S.M.,Dabanloo, N.J., Pourmasoumi, P. and Tareh, A. 2018. Detection of Driver‟sDrowsiness Using New Features Extracted from HRVSignal. In 2018 Computing in Cardiology Conference (CinC). 45, 1-4.

Vicente, J., Laguna, P.,Bartra, A. andBailón, R. 2016.Drowsiness detection using heart rate variability. Med. Biol. Eng. Comput. 54, 927–937.

Warwick, B., Symons, N., Chen, X. and Xiong, K. 2015. Detecting driver drowsiness using wireless wearables. in Proc. 12th Int. Conf.

Mobile Ad Hoc Sensor Syst. (MASS), 585–588.

Choi, Y., Shin, H. and Lee, J. 2014. Smart steering wheelsystem for driver‟s emergency situation usingphysiological sensors and smart

phone. INISTA 2014 -IEEE Int. Symp. Innov. Intell. Syst. Appl. Proc.,281–286.

Lourenço, A. Alves, A.P. Carreiras, C. Duarte,R.P. and Fred, A. 2015. CardioWheel: ECG Biometrics on the Steering Wheel. Machine Learning and KnowledgeDiscovery in Databases Lecture Notes in ComputerScience, 267–270.

Silva, H., Louren¸co, A., Canento, F., Fred, A. andRaposo, N. 2013. ECG biometrics: Principles and applications. InProc. of the 6th Conf. on Bio-Inspired Systems and Signal Processing (BIOSIGNALS).

Jung, S.J., Shin, H.S. and Chung, W.Y. 2014. Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel. IET Intell.Transp. Syst. 8(1), 43–50.

Awais, M., Badruddin, N. and Drieberg, M.2017. A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and Wearability. Sensors (Switzerland. 17(9), 1–16.

Joysly L. and Tamilselvi, R. 2015. Abnormality recognition during drowsy state from ECG and EEG. 2015International Conference on

Innovations in Information, Embedded and Communication Systems (ICIIECS).

Massoz, Q.,Langohr, T., Francois, C. and Verly, J.G. 2016. The ULg multimodality drowsiness database (calledDROZY) and examples of

use. 2016 IEEE WinterConf. Appl. Comput. Vision, WACV.

Nor Shahrudin, N.S. and Sidek, K.A. 2019. Driver DrowsinessDetection using Different Classification Algorithms. International Conference on Telecommunication. Electronic and Computer Engineering.

Koo, C.H.,Zhu, H., Tsang, Y.T.,Yu, T.T., Tsang,K.F. and Lai,L.L. 2018. A Humans‟ Status Detection Scheme forIndustrial Safety. IEEE Int. Symp. Ind. Electron. 2018(June), 1291–1295.

Oliveira, L., Cardoso, J.S.,Lourenco, A. and Ahlstrom, C. 2018. Driver drowsiness detection: a comparison between intrusive and non-intrusive signal acquisition methods. 2018 7th European Workshop on Visual Information Processing (EUVIP).

Raimundo, D., Lourenco, A. and Abrantes, A. 2018. Driving simulator for performance monitoring withphysiological sensors. 2018 19th IEEE Mediterranean Electrotechnical Conference (MELECON).

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Published

2024-02-26

How to Cite

Shahrudin, N. S. N., & Sidek, K. A. (2024). A review of ECG data acquisition for driver drowsiness detection. COMPUSOFT: An International Journal of Advanced Computer Technology, 9(07), 3749–3754. Retrieved from https://ijact.in/index.php/j/article/view/578

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Section

Original Research Article

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