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Study on Vertical Velocity-Based Pre-Impact Fall Detection

수직속도 기반 충격전 낙상 감지에 관한 연구

  • Lee, Jung Keun (Department of Mechanical Engineering, Hankyong National University)
  • 이정근 (한경대학교 기계공학과)
  • Received : 2014.07.14
  • Accepted : 2014.07.27
  • Published : 2014.07.31

Abstract

While the feasibility of vertical velocity as a threshold parameter for pre-impact fall detection has been verified, effects of sensor attachment locations and methods calculating vertical acceleration and velocity on the detection performance have not been studied yet. Regarding the vertical velocity-based pre-impact fall detection, this paper investigates detection accuracies of eight different cases depending on sensor locations (waist vs. sternum), vertical accelerations (accurate acceleration based on both accelerometer and gyroscope vs. approximated acceleration based on only accelerometer), and vertical velocities (velocity with attenuation vs. velocity difference). Test results show that the selection of waist-attached sensor, accurate acceleration, and velocity with attenuation based on accelerometer and gyroscope signals is the best in overall in terms of sensitivity and specificity of the detection as well as lead time.

Keywords

References

  1. S. N. Robinovitch, F. Feldman, Y. Yang, R. Schonnop, P. M. Leung, T. Sarraf, J. Sims-Gould, and M. Loughin, "Video capture of the circumstances of falls in elderly people residing in long-term care: an observational study", Lancet, Vol. 381, pp. 47-54, 2013. https://doi.org/10.1016/S0140-6736(12)61263-X
  2. P. Bonato, "Advances in wearable technology and applications in physical medicine and rehabilitation", J. Neuroeng. Rehabil., Vol. 2, No. 2, 2005.
  3. J. K. Lee and E. J. Park, "3D spinal motion analysis during staircase walking using an ambulatory inertial and magnetic sensing system", Med. Biol. Eng. Comput., Vol. 49, No. 7, pp. 755-764, 2011. https://doi.org/10.1007/s11517-011-0738-y
  4. G. C. Park, A. Y. Jeon, S.H. Lee, J. M. Son, M. C. Kim, and G. R. Jeon, "Implementation of a falls recognition system using acceleration and angular velocity signals", J. Sensor Sci. & Tech., vol. 22, no. 1, pp. 54-64, 2013. https://doi.org/10.5369/JSST.2013.22.1.54
  5. Y. G. Lee, D. J. Cheon, and G. Yoon, "Telemonitoring system of fall detection for the elderly", J. Sensor Sci. & Tech., Vol. 20, No. 6, pp. 420-427, 2011. https://doi.org/10.5369/JSST.2011.20.6.420
  6. A. K. Bourke, P. van de Ven, M. Gamble, R. O'Connor, K. Murphy, E. Bogan, E. McQuade, P. Finucane, G. Olaighin, and J. Nelson, "Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities", J. Biomech., Vol. 43, pp. 3051-3057, 2010. https://doi.org/10.1016/j.jbiomech.2010.07.005
  7. T. Tamura, T. Yoshimura, M. Sekine, M. Uchida, and O. Tanaka, "A wearable airbag to prevent fall injuries", IEEE Trans. Inf. Technol. Biomed., Vol. 13, No. 6, pp. 910-914, 2009. https://doi.org/10.1109/TITB.2009.2033673
  8. G. Shi, C. S. Chan, W. J. Li, K. Leung, Y. Zou, and Y. Jin, "Mobile human airbag system for fall protection using MEMS sensors and embedded SVM classifier", IEEE Sensors J., Vol. 9, No. 5, pp. 495-503, 2009. https://doi.org/10.1109/JSEN.2008.2012212
  9. G. Wu and S. Xue, "Portable preimpact fall detector with inertial sensors", IEEE Trans. Neural. Syst. Rehabil. Eng., Vol. 16, No. 2, pp. 178-183, 2008. https://doi.org/10.1109/TNSRE.2007.916282
  10. A. K. Bourke, K. J. ODonovan, and G. OLaighin, "The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls", Med. Eng. Phys., Vol. 30, pp. 937-946, 2008. https://doi.org/10.1016/j.medengphy.2007.12.003
  11. J. K. Lee, E. J. Park, and S. N. Robinovitch, "Estimation of attitude and external acceleration using inertial sensor measurement during various dynamic conditions", IEEE Trans. Instrum. Meas., Vol. 61, No. 8, pp. 2262-2273, 2012. https://doi.org/10.1109/TIM.2012.2187245
  12. T. Degen, H. Jaeckel, M. Rufer, and S. Wyss, "SPEEDY: a fall detector in a wrist watch", in Proc. Seventh IEEE Int. Symposium on Wearable Computing, pp. 184-187, 2003.
  13. A. K. Bourke, K. J. O'Donovan, J. Nelson. and G. M. OLaighin, "Fall-detection through vertical velocity thresholding using a tri-axial accelerometer characterized using an optical motion-capture system", in Proc. IEEE 30th Annu. Int. Conf. Eng. Med. Biol. Soc., Vancouver, BC, pp. 2832-2835, 2008.
  14. A. K. Bourke, P. van de Ven, M. Gamble, R. O'Connor, K. Murphy, E. Bogan, E. McQuade, P. Finucane, G. Olaighin, and J. Nelson, "Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities", J. Biomech., vol. 43, pp. 3051-3057, 2010. https://doi.org/10.1016/j.jbiomech.2010.07.005
  15. M. Kangas, A. Konttila, P. Lindgren, I. Winblad, and T. Jamsa, "Comparison of low-complexity fall detection algorithms for body attached accelerometers", Gait & Posture, Vol. 28, pp. 285-291, 2008. https://doi.org/10.1016/j.gaitpost.2008.01.003
  16. T. Fawcett, ROC Graphs: Notes and Practical Considerations for Researchers, HP Laboratories, Technical Report HPL-2003-4, USA, 2003.

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  1. Determination of Fall Direction Before Impact Using Support Vector Machine vol.24, pp.1, 2015, https://doi.org/10.5369/JSST.2015.24.1.47