Detection of human presence in a vehicle by vibration analysis

Detection of human presence in a vehicle by vibration analysis

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The aim of this study is to propose a complete instrumentation and signal processing method able to detect the presence of a person seated on the rear bench of a vehicle. The sensor is based on a piezoelectric film (EMFI sensor), designed to detect mechanical vibrations. In order to avoid confusion between humans and heavy objects or empty seats, the authors focused on the extraction of a biological signature from the acquired signals. This physiological pattern was extracted using an original wavelet denoising algorithm and was used further as a matched filter, in order to detect human presence in the vibration signals. Physiologically significant features were extracted from the output of the (on-line) filtering process and fed further-on into a classical Bayes-based classifier. After training, the proposed method yielded very promising results, the output of the classifier being almost error-free for different acquisition conditions (stopped and on-road vehicle, new and artificially aged sensor).


    1. 1)
      • , : `Traffic safety facts research note: Seat belt use in rear seats in 2008, DOT HS 811 133', Technical report, 2009.
    2. 2)
      • , : `Traffic safety facts, 2009 data, DOT HS 811 392', Technical report, 2010.
    3. 3)
      • National Highway Traffic Safety Administration (NHTSA): ‘Fatality analysis reporting system’. Available at: pt.
    4. 4)
      • Null, J.: ‘Fact sheet’, Department of Geosciences, San Francisco State University. Available at: pt.
    5. 5)
      • Fischer, C., Tibken, B., Fischer, T.: `Left behind occupant recognition in parked cars based on acceleration and pressure information using ', IEEE Intelligent Vehicles Symp. (IV), 2010, p. 1242–1247.
    6. 6)
      • Fischer, C., Fischer, T., Tibken, B.: ‘The autonomous recognition of left behind passengers in parked vehicles. SAE International’. 2011, Available at: pt.
    7. 7)
      • Yu, X., Gong, D., Li, S., Xu, Y.: `Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem', Knowledge-Based Intelligent Information and Engineering Systems, 2003, p. 646–652, (LNCS, 2773).
    8. 8)
      • Postolache, O., Silva Girão, P., Postolache, G., Pereira, M.: `Vital signs monitoring system based on emfi sensors and wavelet analysis', Instrumentation and Measurement Technology Conf. – IMTC 2007, 1–3 May 2007, Warsaw, Poland.
    9. 9)
      • Junnila, S., Akhbardeh, A., Barna, L., Defee, I., Varri, A.: `A wireless ballistocardiographic chair', 28thIEEE EMBS Ann. Int. Conf., 30 August–3 September 2006, New York City, USA.
    10. 10)
      • Akhbardeh, A., Junnila, S., Koivistoinen, T., Varri, A.: `Applying novel supervised fuzzy adaptive resonance theory (sfart) neural network and biorthogonal wavelets for ballistocardiogram diagnosis', IEEE Int. Symp. on Intelligent Control, October 2006, Munich, Germany, p. 143–148.
    11. 11)
    12. 12)
      • Sullivan, P., Cheung, K., Sullivan, C., Pernambuco-Wise, P.: `Passive physiological monitoring (P2M) system', US, 6984207 B1, 2006.
    13. 13)
      • IEE International Electronics & Engineering S.A.R.L., Orlewski, P.: ‘Occupant detection system for an automotive vehicle’. European Patent EP 2017139 A1, 2009.
    14. 14)
      • S. Mallat . (1998) A wavelet tour of signal processing.
    15. 15)
      • A. Antoniadis , J. Bigot , T. Sapatinas . Wavelet estimators in nonparametric regression: a comparative simulation study. J. Stat. Softw. , 6 , 1 - 83
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)

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