Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Telemonitoring System of Fall Detection for the Elderly

노인을 위한 원격 낙상 검출 시스템

  • Lee, Yong-Gyu (Department of Electronic and Information Engineering, Seoul National University of Science and Technology) ;
  • Cheon, Dae-Jin (Department of Electronic and Information Engineering, Seoul National University of Science and Technology) ;
  • Yoon, Gil-Won (Department of Electronic and Information Engineering, Seoul National University of Science and Technology)
  • 이용규 (서울과학기술대학교 전자정보공학과) ;
  • 천대진 (서울과학기술대학교 전자정보공학과) ;
  • 윤길원 (서울과학기술대학교 전자정보공학과)
  • Received : 2011.08.29
  • Accepted : 2011.11.08
  • Published : 2011.11.30
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Abstract

The population of elderly people increases rapidly as our society moves towards the aged one. Healthcare for the elderly becomes an important issue and falling down is one of the critical problems although not well recognized. In this study, a fall detection system was developed using a 3-axis accelerometer. Analyzing fall patterns, we took into account the degree of impact, posture angle, the repetitions of similar movements and the activities after a potential fall and proposed an algorithm of fall detection. Information of the fall sensor was sent to a remote healthcare server through the wireless networks of Zigbee and WLAN. Our system was designed to monitor multiples users. 12 persons participated in experiment and each one performed 24 different movements. Our proposed algorithm was compared with other reported ones. Our method produced the excellent results having a sensitivity of 96.4 % and a specificity of 100 % whereas other methods had a sensitivity range between 87.5 % and 94.8 % and a specificity range between 63.5 % and 83.3 %.

Keywords

References

  1. J. M. Kim and H. K. Suh, "Risk factors for falls in the elderly by life-cycle", Korean J. Health Education and Promotion., vol. 27, no. 1, pp. 21-34, 2010.
  2. S. S. Roach, Introductory Gerontological Nursing, Lippincott Williams & Wilkins, Philadelphia, 1988.
  3. D. Haver, Health promotion and aging: Practical applications for health professionals, Springer Publishing company Inc., New York, 2007.
  4. P. C. Fletcher, K. Berg, D. M. Dalby, and J. P. Hirdes, "Risk factors for falling among communitybased seniors", J. Patient Safety., vol. 5, no. 2, pp. 61-66, 2009. https://doi.org/10.1097/PTS.0b013e3181a551ed
  5. I. Y. Yoo, "Analysis of multivariate recurrent fall risk factors in elderly people using residential assessment instrument-homecare-comparisons between single and recurrent fallers", J. Korean Acad Nurs., vol. 41, no. 1, pp. 119-128, 2011. https://doi.org/10.4040/jkan.2011.41.1.119
  6. R. Reyna and K. Tuck, "Beyond accidental falls human fall detection using accelerometry", Energy., pp. 44-48, 2009.
  7. A. K. Bourke, J. V. O'Brien, and G. M. Lyons, "Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm", Gait and Posture., vol. 26, no. 2, pp. 194-199, 2007. https://doi.org/10.1016/j.gaitpost.2006.09.012
  8. S. Srinivasan, J. Han, D. Lal, and A. Gacic, "Towards automatic detection of falls using wireless sensors", Proc. of the 29th Annual International Conf. IEEE EMBS, pp. 1379-1382, Lyon, France, 2007.
  9. A. Purwar, D. U. Jeong, and W. Y. Chung, "Activity monitoring from real-time triaxial accelerometer data using sensor network", International Conf. on Control, Automation and System, pp. 2402-2406, Seoul, Korea, 2007.
  10. Q. Li, J. A. Stankovic, M. Hanson, A. Barth, J. Lach, and G. Zhou, "Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information", 6th International Workshop on Wearable and Implantable Body Sensor Networks, pp. 138-143, Berkeley, USA, 2009.
  11. M. N. Nyan, F. E. H. Tay, A. W. Y. Tan, and K. H. W. Seah, "Distinguishing fall activities from normal activities by angular rate characteristics and highspeed camera characterization", Medical Engineering and Physics., vol. 8, no. 28, pp. 842-849, 2006.
  12. Y. D. Cha and G. Yoon, "Ubiquitous health monitoring system using Zigbee and wireless LAN dual-network", J. Telemed. and e-Health., vol. 15, no. 9, pp. 891-897, 2009. https://doi.org/10.1089/tmj.2009.0049

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