Noninvasive Heart Rate Variability Detection Device for Fatigue Driving Detection System

Abstract:

Monitor psychological parameters of driver to detect fatigue state is an effective approach to prevent traffic accident. Heart rate variability (HRV) has its particular advantage comparing with other methods, such as its accuracy and conveniences. ECG is a regular signal to obtain HRV, but during driving condition, electrodes and wires need to be placed on driver’s body and may disturb the driver’s normal driving behavior. Since ballistocardiogram (BCG) can reflect heart movement, so HRV can also be extracted from BCG. This paper gives a novel noninvasive method to detect driver’s BCG. Using PVDF sensor which is embedded in safety belt to get driver's BCG and designing hardware and software to amplify and convert it to digital signal for next processing. The result shows that the proposed device can obtain the driver’s BCG properly and the HRV of the driver can be calculated accurately and conveniently, so the design is reasonable.

• Periodical: Applied Mechanics and Materials (Volumes 246-247)
• Pages: 194-198
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.246-247.194
• Online since: December 2012
• Authors: Ning Ye, Yu Ge Sun, Dan Yang
• Keywords: BCG, Fatigue Driving, HRV, PVDF

References

[1] E. Michail, A. Kokonozi, and I. Chouvarda, 2008, EEG and HRV markers of sleepiness and loss of control during car driving, 30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada, pp.2566-2569.

DOI: 10.1109/iembs.2008.4649724

[2] L. M. Ai, Z. C. Yang, and J. F. An, 2010, EEG-based Method for Detecting Drowsy Driving State, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2010), pp.2164-2167.

DOI: 10.1109/fskd.2010.5569757

[3] I. C. Jeong, D. H. Lee, and S. W. Park, 2007, Automobile driver's stress index provision system that utilizes electrocardiogram , Proceedings of the 2007 IEEE Intelligent Vehicles Symposium Istanbul, Turkey, pp.652-656.

DOI: 10.1109/ivs.2007.4290190

[4] C. T. Lin, L.W. Ko, and J. C. Chiou, 2008, Noninvasive Neural Prostheses Using Mobile and Wireless EEG, Proceedings of the IEEE, Vol. 96, No. 7, PP. 1167-1183.

DOI: 10.1109/jproc.2008.922561

[5] E. Rogado, J. L. García, and R. Barea, 2009, Driver Fatigue Detection System, Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics Bangkok, Thailand, pp.1105-1110.

DOI: 10.1109/robio.2009.4913155

[6] J. Vicente, P. Laguna, and A. Bartra, 2011, Detection of Driver's Drowsiness by Means of HRV Analysis, Computing in Cardiology, vol. 38, pp.89-92.

[7] A. Akhbardeh, M. Koivuluoma, and T. Koivistoinen, 2005, BCG Data Discrimination using Daubechies compactly supported Wavelet Transform and Neural Networks towards Heart Disease Diagnosing, Proceedings of the 2005 IEEE International Symposium on Intelligent Control Limassol, Cyprus, pp.1452-1457.

DOI: 10.1109/.2005.1467228

[8] N. Gandhi, C. K. He, and D. Chung, 2011, Properties of Dry and Non-contact Electrodes for Wearable Physiological Sensors, 2011 International Conference on Body Sensor Networks, pp.107-112.

DOI: 10.1109/bsn.2011.39