Journal of the Ergonomics Society of Korea (대한인간공학회지)

Ergonomics Society of Korea (대한인간공학회)
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Development of a Classification Model for Driver's Drowsiness and Waking Status Using Heart Rate Variability and Respiratory Features

  • Kim, Sungho (R.O.K. Air Force Academy, Department of Systems Engineering) ;
  • Choi, Booyong (R.O.K. Air Force Academy, Department of Basic Science) ;
  • Cho, Taehwan (R.O.K. Air Force Academy, Department of Electronics and Communications Engineering) ;
  • Lee, Yongkyun (R.O.K. Air Force Academy, Department of Basic Science) ;
  • Koo, Hyojin (R.O.K. Air Force Academy, Department of Basic Science) ;
  • Kim, Dongsoo (R.O.K. Air Force Academy, Department of Basic Science)
  • Received : 2016.06.29
  • Accepted : 2016.10.04
  • Published : 2016.10.31
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Abstract

Objective:This study aims to evaluate the features of heart rate variability (HRV) and respiratory signals as indices for a driver's drowsiness and waking status in order to develop the classification model for a driver's drowsiness and waking status using those features. Background: Driver's drowsiness is one of the major causal factors for traffic accidents. This study hypothesized that the application of combined bio-signals to monitor the alertness level of drivers would improve the effectiveness of the classification techniques of driver's drowsiness. Method: The features of three heart rate variability (HRV) measurements including low frequency (LF), high frequency (HF), and LF/HF ratio and two respiratory measurements including peak and rate were acquired by the monotonous car driving simulation experiments using the photoplethysmogram (PPG) and respiration sensors. The experiments were repeated a total of 50 times on five healthy male participants in their 20s to 50s. The classification model was developed by selecting the optimal measurements, applying a binary logistic regression method and performing 3-fold cross validation. Results: The power of LF, HF, and LF/HF ratio, and the respiration peak of drowsiness status were reduced by 38%, 22%, 31%, and 7%, compared to those of waking status, while respiration rate was increased by 3%. The classification sensitivity of the model using both HRV and respiratory features (91.4%) was improved, compared to that of the model using only HRV feature (89.8%) and that using only respiratory feature (83.6%). Conclusion: This study suggests that the classification of driver's drowsiness and waking status may be improved by utilizing a combination of HRV and respiratory features. Application: The results of this study can be applied to the development of driver's drowsiness prevention systems.

Keywords

References

  1. Bergasa, L.M., Nuevo, J., Sotelo, M.A., Barea, R. and Lopez, M.E., Real-time system for monitoring driver vigilance, IEEE Transactions on Intelligent Transportation Systems, 7(1), 63-77, 2006. https://doi.org/10.1109/TITS.2006.869598
  2. Bonjyotsna, A. and Roy, S., Correlation of drowsiness with electrocardiogram: A review, International Journal of Advanced Research in Electronical, Electronics and Instrumentation Engineering, 3(5), 9538-9544, 2014.
  3. Fairclough, S.H. and Graham, R., Impairment of driving performance caused by sleep deprivation or alcohol: A comparative study, Journal of Human Factors and Ergonomics, 41, 118-128, 1999. https://doi.org/10.1518/001872099779577336
  4. Fu, C.L., Li, W.K., Chun, H.C., Tung, P.S. and Chin, T.L., Generalized EEG-Based Drowsiness Prediction System by Using a Self- Organizing Neural Fuzzy System, IEEE Transactions on Circuits and Systems, 59(9), 2044-2055, 2012. https://doi.org/10.1109/TCSI.2012.2185290
  5. Heo, Y.S., Lee, J.C. and Kim, Y.N., Analysis and processing of driver's biological signal of workload, Journal of the Korea Society of Industrial Information Systems, 20(3), 87-93, 2015. https://doi.org/10.9723/jksiis.2015.20.3.087
  6. Hong, W.G., Lee, W.S., Jung, K.H., Lee, B.H., Park, J.W., Park, S.W., Park, Y.S., Son, J.W., Park, S.K. and You, H.C., Development of an evaluation method for a driver's cognitive workload using ECG signal, Journal of the Korean Institute of Industrial Engineers, 40(3), 325-332, 2014. https://doi.org/10.7232/JKIIE.2014.40.3.325
  7. Inger, M., Akerstedt, T., Peters, B., Anund, A. and Kecklund, G., Subjective sleepiness, simulated driving performance and blink duration: Examining individual differences, Journal of Sleep Research, 15, 47-53, 2006.
  8. Ju, J.H., Park, Y.J. and Park, J.H., Lee, B.G., Lee, J.C. and Lee, J.Y., Real-time driver's biological signal monitoring system, Sensors and Materials, 27(1), 51-59, 2015.
  9. Jun, C.H., Techniques and application of data mining, Hannarae, Korea, 2012.
  10. Kang, H.B., Various approaches for driver and driving behavior monitoring: A review, In Proceedings of IEEE International Conference on Computer Vision Workshops, 616-623, 2013.
  11. Karlen, W., Mattiussi, C. and Floreano, D., Sleep and wake classification with ECG and respiratory effort signals, IEEE Transactions on Circuits and Systems, 3(2), 71-78, 2009. https://doi.org/10.1109/TBCAS.2008.2008817
  12. Kim, B.S. and Min, J.A., Application and interpretation of HRV in stress clinic, Panmun education, Korea, 2015.
  13. Kim, C.J., Whang, M.C., Kim, J.H., Woo, J.C., Kim, Y.W. and Kim, J.H., A study on evaluation of human arousal level using PPG analysis, Journal of the Ergonomics Society of Korea, 29(1), 113-120, 2010. https://doi.org/10.5143/JESK.2010.29.1.113
  14. Kim, M.S., Kim, Y.N. and Heo, Y.S., Characteristics of Heart Rate Variability Derived from ECG during the Driver's Wake and Sleep States, Transaction of the Korean Society of Automotive Engineers, 22(3), 136-142, 2014. https://doi.org/10.7467/KSAE.2014.22.3.136
  15. Korea Road Traffic Authority, Statistics of Drowsy Driving Accident, 2011.
  16. Korea Transportation Safety Authority, Survey of Drowsy Driving, 2015.
  17. Korean Expressway Corporation, Current State of Car Accident, 2015.
  18. Lewicke, A.T., Sazonov, E.S., Corwin, M.J. and Schuckers, S.A.C., Reliable determination of sleep versus wake from heart rate variability using neural networks, In Proceedings of International Joint Conference on Neural Networks, 2394-2399, 2005.
  19. Lee, W.S., Park, J.W., Kim, S.J., Yoon, S.H., Yang, X., Lee, Y.T., Son, J.W., Kim, M.H. and You, H.C., Development of an analysis system for biosignal and driving performance measurements, Journal of the Ergonomics Society of Korea, 29(1), 47-53, 2010. https://doi.org/10.5143/JESK.2010.29.1.047
  20. Liu, J. and Zhang, C., EEG-based estimation of mental fatigue by using KPCA-HMM and complexity parameters, Biomedical Signal Processing and Control, 5(2), 124-130, 2010. https://doi.org/10.1016/j.bspc.2010.01.001
  21. Miyaji, M., Method of drowsy state detection for driver monitoring function, International Journal of Information and Electronics Engineering, 4(4), 264-268, 2014.
  22. Moon, K.S., Hwang, K.I., Choi, E.J. and Oah, S.Z., Study on prevention of drowsiness driving using electrocardiography (LF/HF) index, Journal of the Korean Society of Safety, 30(2), 56-62, 2015. https://doi.org/10.14346/JKOSOS.2015.30.2.56
  23. Murphy-Chutorian, E. and Trivedi, M.M., Head pose estimation and augmented reality tracking: An integrated system and evaluation for monitoring driver awareness, IEEE Transactions on Intelligent Transportation Systems, 11(2), 300-311, 2010. https://doi.org/10.1109/TITS.2010.2044241
  24. Parikh, A. and Patel, H., Drowsy driving detection based on RR cycle of ECG, International Journal of Innovative and Emerging Research in Engineering, 1(1), 11-14, 2014.
  25. Park, J.H., Kim, J.W. and Lee, J.C., Real Time Driver's Respiration Monitoring, Journal of Sensor Science and Technology, 23(2), 142-147, 2014. https://doi.org/10.5369/JSST.2014.23.2.142
  26. Patel, M., Lal, S.K.L., Kavanagh, D. and Rossiter, P., Applying neural network analysis on heart rate variability data to assess driver fatigue, Expert Systems with Applications, 38(6), 7235-7242, 2011. https://doi.org/10.1016/j.eswa.2010.12.028
  27. Rogado, E., Garcia, J.L., Barea, R. and Bergasa, L.M., Driver fatigue detection system, In Proceedings of IEEE International Conference on Robotics and Biomimetics, 1105-1110, 2009.
  28. Sahayadhas, A., Sundaraj, K. and Murugappan, M., Detecting Driver Drowsiness Based on Sensors: A Review, Sensors, 12, 16937-16953, 2012. https://doi.org/10.3390/s121216937
  29. Saini, V. and Saini, R., Driver drowsiness detection system and techniques: A review, International Journal of Computer Science and Information Technologies, 5(3), 4245-4249, 2014.
  30. Shin, H.S., Jung, S.J., Seo, Y.S. and Chung, W.Y., Real-time intelligent health and attention monitoring system for car driver, Journal of the Korea Institute of Information and Communication Engineering, 14(5), 1303-1310, 2010. https://doi.org/10.6109/jkiice.2010.14.5.1303
  31. Sukanesh, R. and Vijayprasath, S., Certain investigations on drowsiness alert system based on heart rate variability using LabVIEW, WSEAS Transactions on Information Science and Applications, 10(11), 368-379, 2013.
  32. Tjolleng, A., Jung, K.H., Hong, W.G., Lee, W.S., Lee, B.H., You, H.C., Son, J.W. and Park, S.K., Classification of Driver's Cognitive Workload Levels using Artificial Neural Network on ECG, In Proceedings of KIIE Conference, 1484-1506, 2015.
  33. Vicente, J., Laguna, P., Bartra, A. and Bailon, R., Detection of Driver's Drowsiness by means of HRV analysis, Computing in Cardiology, 38, 89-92, 2011.
  34. Werteni, H., Yacoub, S. and Ellouze, N., An automatic sleep-wake classifier using ECG signals, International Journal of Computer Science Issues, 11(4), 84-93, 2014.

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