Amelie Bonde
ABSTRACT
Continuous heart rate variability (HRV) monitoring in cars can allow ambient health monitoring and help track driver stress and fatigue. Current approaches that involve wearable or externally mounted sensors are accurate but inconvenient for the user. In particular, prior approaches often fail when noise from human motion or car noise are present.
In this paper, we present VVRRM, an ambient heartbeat monitoring system in an automobile which uses a set of accelerometers in a car seat to monitor a subject's heart RR-intervals. The system removes high energy motion noise and periodic noise using a combination of peak detection with extracted wavelet coefficients. Furthermore, it tracks human heart locations through sensor selection to maximize the heart signal energy. We tested the system with both a manufactured heartbeat signal and experiments with human subjects. Overall our mean absolute error for RR-interval estimation was 54 ms across all human subjects, and 3 ms with our manufactured heartbeat signal.
- M. Burke and M. Nasor. Ecg analysis using the mexican-hat wavelet. In Int. Conf. Multirate Systems & Wavelet Analysis, pages 1--6, 2012.Google Scholar
- S. Y. Chekmenev, A. A. Farag, W. M. Miller, E. A. Essock, and A. Bhatnagar. Multiresolution approach for noncontact measurements of arterial pulse using thermal imaging. In Augmented vision perception in infrared, pages 87--112. Springer, 2009.Google ScholarCross Ref
- H. Cohen, M. Kotler, M. A. Matar, Z. Kaplan, U. Loewenthal, H. Miodownik, and Y. Cassuto. Analysis of heart rate variability in posttraumatic stress disorder patients in response to a trauma-related reminder. Biological psychiatry, 44(10):1054--1059, 1998.Google ScholarCross Ref
- N. Egelund. Spectral analysis of heart rate variability as an indicator of driver fatigue. Ergonomics, 25(7):663--672, 1982.Google ScholarCross Ref
- J. A. Healey and R. W. Picard. Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on intelligent transportation systems, 6(2):156--166, 2005. Google ScholarDigital Library
- K. J. Heilman, M. Handelman, G. Lewis, and S. W. Porges. Accuracy of the stresseraser® in the detection of cardiac rhythms. Applied psychophysiology and biofeedback, 33(2):83--89, 2008.Google Scholar
- Z. Jia, M. Alaziz, X. Chi, R. E. Howard, Y. Zhang, P. Zhang, W. Trappe, A. Sivasubramaniam, and N. An. Hb-phone: a bed-mounted geophone-based heartbeat monitoring system. In Information Processing in Sensor Networks (IPSN), 2016 15th ACM/IEEE International Conference on, 2016. Google ScholarDigital Library
- B.-G. Lee and W.-Y. Chung. A smartphone-based driver safety monitoring system using data fusion. Sensors, 12(12):17536--17552, 2012.Google ScholarCross Ref
- N. Munla, M. Khalil, A. Shahin, and A. Mourad. Driver stress level detection using hrv analysis. In Advances in Biomedical Engineering (ICABME), 2015 International Conference on, pages 61--64. IEEE, 2015.Google ScholarCross Ref
- T. C. O'Haver. Peak finding and measurement. 2008.Google Scholar
- M. Pagani, R. Furlan, P. Pizzinelli, W. Crivellaro, S. Cerutti, and A. Malliani. Spectral analysis of rr and arterial pressure variabilities to assess sympatho-vagal interaction during mental stress in humans. Journal of hypertension. Supplement: official journal of the International Society of Hypertension, 7(6):S14--5, 1989.Google Scholar
- P. Piezotronics. W354c03_010g10 datasheet. http://www.pcb.com/Products.aspx?m=W354C03_010G10.Google Scholar
- M. L. Selzer and A. Vinokur. Life events, subjective stress, and traffic accidents. American Journal of Psychiatry, 131(8):903--906, 1974.Google ScholarCross Ref
- Y. Sun and X. B. Yu. An innovative nonintrusive driver assistance system for vital signal monitoring. IEEE journal of biomedical and health informatics, 18(6):1932--1939, 2014.Google Scholar
- Z. P. Systems. Zephyr biomodule device. https://www.zephyranywhere.com/benefits/physiological-biomechanical.Google Scholar
- A. H. Taylor and L. Dorn. Stress, fatigue, health, and risk of road traffic accidents among professional drivers: the contribution of physical inactivity. Annu. Rev. Public Health, 27:371--391, 2006.Google ScholarCross Ref
- J. Vicente, P. Laguna, A. Bartra, and R. Bailón. Drowsiness detection using heart rate variability. Medical & Biological Engineering & Computing, 54(6):927--937, Jun 2016.Google ScholarCross Ref
- Q. Zhang, G.-q. Xu, M. Wang, Y. Zhou, and W. Feng. Webcam based non-contact real-time monitoring for the physiological parameters of drivers. In Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2014 IEEE 4th Annual International Conference on.Google Scholar
Index Terms
- VVRRM: Vehicular Vibration-Based Heart RR-Interval Monitoring System
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