Abstract
In recent years, driver’s drowsiness is one of the main causes of traffic accidents, which can result in severe physical injury and serious economic loss. Fatigue of the driver is an important factor in road accidents, and fatigue detection has a significant influence on traffic safety. This article describes a drowsiness detection approach based on the combination of various multi-sensors. The present study proposed a method to detect the driver’s drowsiness that combines features of electrocardiography (ECG) and environmental factors, such as vehicle temperature and humidity, to improve detection performance. The activity of the autonomic nervous system which can be measured in heart rate variability (HRV) signals obtained from surface ECG, indicates changes during stress, extreme fatigue, and episodes of drowsiness. The combination of the multi-sensors feature of drowsiness is significant factors in determining the driver’s fatigue state and can use this information to transportation drowsy driving control center if necessary.
Keywords
- Sensors data fusion
- Driver drowsiness detection
- Biosensors
- Environmental sensors
This is a preview of subscription content, access via your institution.
Buying options
References
Seugnet, L., Boero, J., Gottschalk, L., Duntley, S.P., Snaw, P.J.: Identification of a biomarker for sleep drive in flies and human. Proc. Natl. Acad. Sci. 103(52), 19913–19918 (2006)
http://www.police.go.kr/portal/bbs/list.do?bbsId=B0000011&menuNo=200488
Husar, P.: Eyetracker Warns against Momentary Driver Drowsiness. http://www.fraunhofer.de/en/press/research-news/2010/10/eye-tracker-driver-drowsiness.html. Accessed 27 July 2012
Kosmadopoulos, A., Sargent, C., Zhou, X., Darwent, D., Matthews, R.W., Dawson, D., et al.: The efficacy of objective and subjective predictors of driving performance during sleep restriction and circadian misalignment. Accid. Anal. Prev. 99, 445–451 (2015)
Schmidt, E., Decke, R., Rasshofer, R.: Correlation between subjective driver state measures and psychophysiological and vehicular data in simulated driving. In: Intelligent Vehicles Symposium (IV), pp. 1380–1385. IEEE (2016)
Chen, C., Li, K., Wu, Q., Wang, H., Qian, Z., Sudlow, G.: EEG-based detection and evaluation of fatigue caused by watching 3DTV. Displays 34(2), 81–88 (2013)
Wierwille, W.W., Ellsworth, L.A., Wreggit, S.S., Fairbanks, R.J., Kirn, C.L.: Research on vehicle-based driver status/performance monitoring development, validation, and refinement of algorithms for detection of driver drowsiness. Technical report, DOT HS 808 247, Office of Crash Avoidance Research National Highway Traffic Safety (1994)
Forsman, P.M., Vila, B.J., Short, R.A., Mott, C.G., van Dongen, H.P.A.: Efficient driver drowsiness detection at moderate levels of drowsiness. Accid. Anal. Prevent. 50, 341–350 (2012)
Sahayadhas, A., Sundaraj, K., Murugappan, M.: Detecting driver drowsiness based on sensors: a review. Sensors 12(12), 16937–16953. https://doi.org/10.3390/s121216937, PMID: 23223151 (2012)
Zhang, Z., Zhang, J.: A new real-time eye tracking based on nonlinear unscented Kalman filter for monitoring driver fatigue. J. Control Theor. Appl. 2010(8), 181–188 (2010)
Jo, J., Lee, S.J., Park, K.R., Kim, I.-J., Kim, J.: Detecting driver drowsiness using feature-level fusion and user specific classification. Expert Syst. Appl. 41(4), 1139–1152 (2014)
Jianfeng, H., Zhendong, M., Ping, W.: Multi-feature authentication system based on event evoked electroencephalogram. J. Med. Imaging Health Inform. 5(4), 862–870 (2015)
Wang, H.: Detection and alleviation of driving fatigue based on EMG and EMS/EEG using wearable sensor. In: 2015 Proceedings of the 5th EAI International Conference on Wireless Mobile Comm. and Healthcare. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 155–157 (2015)
Task Force of ESC and NASPE: Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 93(5), 1043–1065 (1996)
Patel, M., Lal, S.K.L., Kavanagh, D., Rossiter, P.: Applying neural network analysis on heart rate variability data to assess driver fatigue. Exp. Syst. Appl. 38, 7235–7242 (2011)
Acknowledgments
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funder by the Korea government (MSIT) (2017-0-00336, Platform Development of Multi-log based Multi-Modal Data Convergence Analysis and Situational Response.
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2018R1D1A1B07047112).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kim, S., Park, H., Lee, YT., Yoon, Y. (2020). Detecting Driver Drowsiness Based Fusion Multi-sensors Method. In: Park, J., Park, DS., Jeong, YS., Pan, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2018 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-9341-9_79
Download citation
DOI: https://doi.org/10.1007/978-981-13-9341-9_79
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9340-2
Online ISBN: 978-981-13-9341-9
eBook Packages: EngineeringEngineering (R0)