Wearable Galvanic Skin Response for Characterization and Identification of Distraction During Naturalistic Driving

  • Omid DehzangiEmail author
  • Vikas Rajendra
Conference paper
Part of the Internet of Things book series (ITTCC)


Fatalities due to road accidents are mainly caused by distracted driving. Driving demands continuous attention of the driver. Certain levels of distraction while driving can cause the driver lose his/her attention which might lead to a fatal accident. Thus, early detection of distraction will help reduce the number of accidents. Several researches have been conducted for automatic detection of driver distraction. Many previous approaches have employed camera based techniques. However, these methods might detect the distraction rather late to warn the drivers. Although neurophysiological signals using Electroencephalography (EEG) have shown to be another reliable indicator of distraction, EEG signals are very complex and the technology is intrusive to the drivers, which creates serious doubt for its implementation. In this study we investigate Galvanic Skin Responses (GSR) using a wrist band wearable and conduct an empirical characterization of driver GSR signals during a naturalistic driving experiment. We explored time and frequency domain to extract relevant features to capture the changes/patterns at the physiological level. Due to the fact that feature extraction is a manual process and to normalize the feature space toward the identification task, we then transform the feature space using linear discriminant dimensionality reduction to discover discriminative bases of the GSR multivariate feature space that identify distraction. That would eliminate both the computational complexity and the redundancies in the manually generated feature space. Due to multi-class nature of the identification task, there might be biases between the distracted and non-distracted categories that can bias the estimation of between- and within-class scatter matrices. Therefore, we incorporated a class dependent weight to calculate the within class scatter matrices. The proposed weight aims to increase the flexibility of the discriminative bases vectors to capture the factors that focus on eliminating the overlap between distracted versus non-distracted in the generalization phase. Our experimental results demonstrated high cross validation accuracies of distraction identification using GSR signals (i.e. 85.19%). Conducting dimensionality reduction using LDA resulted in slight improvement in accuracy (i.e. 85.94%) using only two discriminant bases. The generalization accuracy was further improved by applying our proposed weighting mechanism (i.e. 88.92%).


GSR driver distraction Distraction detection with skin conductance Distraction detection 


  1. 1.
    Dawson, D., Searle, A.K., Paterson, J.L.: Look before you (s)leep: evaluating the use of fatigue detection technologies within a fatigue risk management system for the road transport industry. Sleep Med, Rev (2014)Google Scholar
  2. 2.
    Metz, B., Schmig, N., Krger, H.P.: Attention during visual secondary tasks in driving: adaptation to the demands of the driving task. Transp. Res. Part F Traffic Psychol. Behav. 14(5), 369–380 (2011)CrossRefGoogle Scholar
  3. 3.
    Young, K.L., Lenn, M.G., Williamson, A.R.: Sensitivity of the lane change test as a measure of in-vehicle system demand. Appl. Ergon. 42(4), 611–618 (2011)CrossRefGoogle Scholar
  4. 4.
    Wege, C., Will, S., Victor, T.: Eye movement and brake reactions to real world brake-capacity forward collision warnings–a naturalistic driving study. Accid. Anal. Prev. 58, 259–270 (2013)CrossRefGoogle Scholar
  5. 5.
    Wang, S., Zhang, Y., Wu, C., Darvas, F., Chaovalitwongse, W.A.: Online prediction of driver distraction based on brain activity patterns. IEEE Trans. Intell. Transp. Syst. 16(1), 136–150 (2015)CrossRefGoogle Scholar
  6. 6.
    Almahasneh, H., Chooi, W.T., Kamel, N., Malik, A.S.: Deep in thought while driving: an EEG study on drivers cognitive distraction. Transp. Res. Part F Traffic Psychol. Behav. 26, no. PA, 218–226 (2014)Google Scholar
  7. 7.
    Nourbakhsh, N., Wang, Y., Chen, F.: GSR and blink features for cognitive load classification. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) LNCS, vol. 8117, no. PART 1, pp. 159–166 (2013)CrossRefGoogle Scholar
  8. 8.
    Nourbakhsh, N., Wang, Y., Chen, F., Calvo, R.A.: Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks. In: Proceedings of 24th Conference on Australian Computer Interaction OzCHI 12, pp. 420–423 (2012)Google Scholar
  9. 9.
    Lew, R., Dyre, B.P., Werner, S., Wotring, B.: Exploring the potential of short-time fourier transforms for analyzing skin conductance and pupillometry in real-time applications. In: Proceedings of the Human Factors and Ergonomics Society 52th Annual Meeting, vol. 52, no. 3, pp. 34–38 (2008)CrossRefGoogle Scholar
  10. 10.
    Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40–51 (2007)CrossRefGoogle Scholar
  11. 11.
    Farooq, M., Dehzangi, O.: High accuracy wearable SSVEP detection using feature profiling and dimensionality reduction. In: 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 161–164 (2017)Google Scholar
  12. 12.
    Vapnik, V.: Support vector machine. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  13. 13.
    van der Maaten, L.J.P., Hinton, G.E.: Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Rockefeller Neuroscience Institute, West Virginia UniversityMorgantownUSA
  2. 2.University of Michigan DearbornDearbornUSA

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