Abstract
One of the most contributing factors to the accidents on the roadways is distracted driving. While in-vehicle, driver may get distracted by variety of ways such as talking on the cellphone, conversing with the accompanying passengers, texting while driving, etc. In order to reduce potential chances of road-accidents, it is highly essential to characterize and identify distracted situations in real-time. In this paper, we investigate Electrocardiogram (ECG) signals as the physiological measure to characterize driver distraction. We aim to provide an empirical guideline for accurate and in real-time analysis irrespective of the body physic. ECG-based driver distraction identification has significant advantages in practice such as being easy to capture, minimally intrusive, and reliable in biometric patterns. ECG dynamics encompass multiple descriptors that we examine in this investigation for efficient characterization of driver state toward real-time identification of distracted driving. In this effort, six drivers were actively participated in our naturalistic driving experiments, where the distraction is introduced by the cellphone conversation and the conversation with the passenger. Our study mainly focuses on the efficient characterization of distraction by localizing R-R interval based on temporal features as well as spectral features. In addition to this, we further investigated the real-time predictive ability of the extracted features through state of the art predictive algorithms. Our experimental results demonstrated ∼83% average predictive accuracy of driver distraction identification in near real-time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
National Highway Traffic Safety Administration: Traffic safety facts 2011 data–pedestrians. Ann. Emerg. Med. 62(6), 612 (2013)
Nakayama, O., Futami, T., Nakamura, T., Boer, E.R.: Development of a steering entropy method for evaluating driver workload (1999)
Rongben, W., Lie, G., Bingliang, T., Lisheng, J.: Monitoring mouth movement for driver fatigue or distraction with one camer. In: Proceedings the 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749), pp. 314–319
You, C., et al.: CarSafe app: alerting drowsy and distracted drivers using dual cameras on smartphones categories and subject descriptors. In: Mobisys’13, pp. 1–14 (2012)
FernĂ¡ndez, A., Usamentiaga, R., CarĂºs, J., Casado, R.: Driver distraction using visual-based sensors and algorithms. Sensors 16(12), 1805 (2016)
Lin, C.-T., Chen, S.-A., Chiu, T.-T., Lin, H.-Z., Ko, L.-W.: Spatial and temporal EEG dynamics of dual-task driving performance. J. Neuroeng. Rehabil. 8(1), 11 (2011)
Kim, J.Y., Jeong, C.H., Jung, M.J., Park, J.H., Jung, D.H.: Highly reliable driving workload analysis using driver electroencephalogram (EEG) activities during driving. Int. J. Automot. Technol. 14(6), 965–970 (2013)
Yu, L., Sun, X., Zhang, K.: Driving distraction analysis by ECG signals: an entropy analysis. In: Lecture Notes in Computer Science (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 6775 LNCS, pp. 258–264 (2011)
Mahachandra, M., Yassierli, Sutalaksana, I.Z., Suryadi, K.: Sensitivity of heart rate variability as indicator of driver sleepiness. In: 2012 Southeast Asian Network Ergonomics Societies Conference Ergonomics Innovations Leveraging User Experience Sustainable SEANES 2012, pp. 0–5 (2012)
Deshmukh, S.V., Dehzangi, O.: ECG-based driver distraction identification using wavelet packet transform and discriminative kernel-based features. In: 2017 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–7 (2017)
Deshmukh, S., Dehzangi, O.: Identification of real-time driver distraction using optimal subband detection powered by Wavelet Packet Transform. In: 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 9–12 (2017)
Taherisadr, M., Dehzangi, O., Parsaei, H.: Single channel EEG artifact identification using two-dimensional multi-resolution analysis. Sensors 17(12), 2895 (2017)
Alizadeh, V., Dehzangi, O.: The impact of secondary tasks on drivers during naturalistic driving: analysis of EEG dynamics. In: IEEE Conference on Intelligent Transportation Systems Proceedings, ITSC, pp. 2493–2499 (2016)
Chernenko, S., ECG processing— R-peaks detection— Librow— Software. Available from: http://www.librow.com/cases/case-2
Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding Sixth International Conference on Machine Learning, p. 10 (1999)
Liaw, A., Wiener, M.: Classification and regression by randomForest. R news 2(3), 18–22 (2002)
Keller, J.M., Gray, M.R.: A fuzzy K-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. SMC-15(4), 580–585 (1985)
Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20(3), 273–297 (1995)
da Silva, F.P.: Mental workload, task demand and driving performance: what relation? Procedia Soc. Behav. Sci. 162, 310–319 (2014)
Hancock, P.A., Desmond, P.A.: Stress, Workload, and Fatigue (2001)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software. ACM SIGKDD Explor. Newsl. 11(1), 10 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Deshmukh, S.V., Dehzangi, O. (2019). Characterization and Identification of Driver Distraction During Naturalistic Driving: An Analysis of ECG Dynamics. In: Fortino, G., Wang, Z. (eds) Advances in Body Area Networks I. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-02819-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-02819-0_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02818-3
Online ISBN: 978-3-030-02819-0
eBook Packages: EngineeringEngineering (R0)