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Emergency Monitoring System Based on a Newly-Developed Fall Detection Algorithm

  • Yi, Yun Jae (Department of Electrical, Electronic and Control Engineering, Hankyong National University) ;
  • Yu, Yun Seop (Department of Electrical, Electronic and Control Engineering, Hankyong National University)
  • Received : 2013.02.28
  • Accepted : 2013.04.15
  • Published : 2013.09.30

Abstract

An emergency monitoring system for the elderly, which uses acceleration data measured with an accelerometer, angular velocity data measured with a gyroscope, and heart rate measured with an electrocardiogram, is proposed. The proposed fall detection algorithm uses multiple parameter combinations in which all parameters, calculated using tri-axial accelerations and bi-axial angular velocities, are above a certain threshold within a time period. Further, we propose an emergency detection algorithm that monitors the movements of the fallen elderly person, after a fall is detected. The results show that the proposed algorithms can distinguish various types of falls from activities of daily living with 100% sensitivity and 98.75% specificity. In addition, when falls are detected, the emergency detection rate is 100%. This suggests that the presented fall and emergency detection method provides an effective automatic fall detection and emergency alarm system. The proposed algorithms are simple enough to be implemented into an embedded system such as 8051-based microcontroller with 128 kbyte ROM.

Keywords

References

  1. I. Neild, D. J. T. Heatley, R. S. Kalawsky, and P. A. Bowman, "Sensor networks for continuous health monitoring," BT Technology Journal, vol. 22, no. 3, pp. 130-139, 2004. https://doi.org/10.1023/B:BTTJ.0000047127.01462.49
  2. M. H. Park, J. C. Ha, I. H. Shin, H. G. Kim, S. Y. Lee, J. H. Cho, H. R. Kim, E. J. Kim, J. S. Kim, M. H. Park, J. M. Lee, E. J. Kim, Y. M. Yim, G. R. Hong, and J. A. Song, Senior Survey 2008: Life and Welfare Service Needs of the Elderly in Korea. Seoul: Ministry for Health and Welfare, 2009.
  3. L. Z. Rubenstein and K. R. Josephson, "Falls and their prevention in elderly people: what does the evidence show?," Medical Clinics of North America, vol. 90, no. 5, pp. 807-824, 2006. https://doi.org/10.1016/j.mcna.2006.05.013
  4. B. J. Vellas, S. J. Wayne, L. J. Romero, R. N. Baumgartner, and P. J. Garry, "Fear of falling and reduction of mobility in elderly fallers," Age and Ageing, vol. 26, no. 3, pp. 189-193, 1997. https://doi.org/10.1093/ageing/26.3.189
  5. E. J. Porter, "Wearing and using personal emergency response system buttons," Journal of Gerontological Nursing, vol. 31, no. 10, pp. 26-33, 2005.
  6. A. K. Bourke, J. V. O'Brien, and G. M. Lyons, "Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm," Gait & Posture, vol. 26, no. 2, pp. 194-199, 2007. https://doi.org/10.1016/j.gaitpost.2006.09.012
  7. M. Kangas, A. Konttila, I. Winblad, and T. Jamsa, "Determination of simple thresholds for accelerometry-based parameters for fall detection," in Proceeding of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, pp. 1367-1370, 2007.
  8. 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, no. 2, pp. 285-291, 2008. https://doi.org/10.1016/j.gaitpost.2008.01.003
  9. P. K. Chao, H. L. Chan, F. T. Tang, Y. C. Chen, and M. K. Wong, "A comparison of automatic fall detection by the cross-product and magnitude of tri-axial acceleration," Physiological Measurement, vol. 30, no. 10, pp. 1027-1037, 2009. https://doi.org/10.1088/0967-3334/30/10/004
  10. M. Kangas, I. Vikman, J. Wiklander, P. Lindgren, L. Nyberg, and T. Jamsa, "Sensitivity and specificity of fall detection in people aged 40 years and over," Gait & Posture, vol. 29, no. 4, pp. 571-574, 2009. https://doi.org/10.1016/j.gaitpost.2008.12.008
  11. A. Weiss, I. Shimkin, N. Giladi, and J. M. Hausdorff, "Automated detection of near falls: algorithm development and preliminary results," BMC Research Notes, vol. 3, p. 62, 2010. https://doi.org/10.1186/1756-0500-3-62
  12. A. K. Bourke and G. M. Lyons, "A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor," Medical Engineering & Physics, vol. 30, no. 1, pp. 84-90, 2008. https://doi.org/10.1016/j.medengphy.2006.12.001
  13. Q. Li, J. A. Stankovic, M. A. Hanson, A. T. Barth, J. Lach, and G. Zhou, "Accurate, fast fall detection using gyroscopes and acelerometer-derived posture information," in Proceeding of the 6th International Workshop Wearable and Implantable Body Sensor Networks, Berkeley: CA, pp. 138-143, 2009.
  14. M. L. Lehrman, M. D. Halleck, and E. L. Massman, "System and method for detecting motion of a body motion," Patent US 7095331, 2006.
  15. M. A. Clifford, R. L. Borras, and L. Gomez, "System and method for human body fall detect," Patent US 7248172, 2007.
  16. LabVIEW [Internet], Availble: http://www.ni.com/labview/.
  17. M. Raju, "Heart-rate and EKG monitor using the msp430fg439," Texas Instruments, Dallas, TX, Application Report SLAA280A, 2007.
  18. K. Tuck, "Implementing auto-zero calibration technique for accelerometers," Freescale Semiconductor, Austin, TX, AN3447, 2007.
  19. K. Tuck, "Tilt sensing using linear accelerometers," Freescale Semiconductor, Austin, TX, AN3461, 2007.

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