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Development of fall Detection System by Estimating the Amount of Impact and the Status of Torso Posture of the Elderly

노인 낙상 후 충격량 측정 및 기립여부 판단 시스템 구현

  • 김충현 (한국과학기술연구원(KIST) 의공학 연구소 바이오닉스연구단) ;
  • 이영재 (건국대학교 의료생명대학 의학공학부) ;
  • 이필재 (건국대학교 의료생명대학 의학공학과) ;
  • 이정환 (건국대학교 의료생명대학 의학공학부, 건국대 의공학 실용 기술 연구소)
  • Received : 2011.05.06
  • Accepted : 2011.05.19
  • Published : 2011.06.01

Abstract

In this study, we proposed the system that calculates the algorithm with an accelerometer signal and detects the fall shock and it's direction. In order to gather the activity patterns of fall status and attach on the subject's body without consciousness, the device needs to be small. With this aim, it is attached on the right side of subject's waist. With roll and pitch angle which represent the activity of upper body, the fall situation is determined and classified into the posture pattern. The impact is calculated by the vector magnitude of accelerometer signal. And in the case of the elderly keep the same posture after fall, it can distinguish the situation whether they can stand by themselves or not. Our experimental results showed that 95% successful detection rate of fall activity with 10 subjects. For further improvement of our system, it is necessary to include tasks-oriented classifying algorithm to diverse fall conditions.

Acknowledgement

Grant : 낙상케어 서비스용 요소기술 개발

Supported by : 한국과학기술연구원(KIST)

References

  1. 정낙수, 최규환, "노인낙상의 원인과 예방," 한국전문물리치료학회지 제 8권, 3 호, pp.107-111, 2001.
  2. Najafi, B, Aminian, K, Paraschiv-Ionescu, A, Loew, F, Bula, C. J, & Robert, P, "Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly," IEEE Transactions on Biomedical Engineering, vol. 50, no. 6, pp.711-723, 2003. https://doi.org/10.1109/TBME.2003.812189
  3. Mathie, M. J, Celler, B. G, Lovell, N. H, & Coster, A. C. F, "Classification of basic daily movements using a triaxial accelerometer," Medical & Biological Engineering & Computing, vol. 24, pp.679-687, 2004.
  4. Allen, F. R, Ambikairajah, E, & Lovell, N. H, "Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models," Physiological Measurement, vol. 27, pp.935-951, 2006. https://doi.org/10.1088/0967-3334/27/10/001
  5. Karantonis, D. M, Narayanan, M. R, Mathie, M, Lovell, N. H, & Celler, B. G, "Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring," IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 1, pp.156-167, 2006. https://doi.org/10.1109/TITB.2005.856864
  6. Noury. N, Fleury. A, Rumeau. P, Bourke. A. K, Laighin. G. O, Rialle. V, Lundy. J. E, "Fall detection - Principles and Methods," Engineering in Medicine and Biology Society, 2007. EMBS pp.1663 - 1666, Aug. 2007
  7. Bourke, A. K, O'Brienb, J. V, & Lyons, G. M, "Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm," Gait & Posture, vol. 26, pp.194-199, 2007. https://doi.org/10.1016/j.gaitpost.2006.09.012
  8. 김남섭, "유비쿼터스 헬스케어를 위한 효율적인 낙상감지 기법," 한국정보기술학회논문지, 제 8권, 제 8호, pp.133-140, 2010.
  9. Bourke. A. K, Lyons. G. M, "A threshold-based detection algorithm using a bi-axial gyroscope sensor," Med. Eng. Phys, vol. 30, pp.84-90, 2006

Cited by

  1. Hip Protector against the Impact by Fall Using Air-bag vol.21, pp.4, 2012, https://doi.org/10.7735/ksmte.2012.21.4.639