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KERD
논문KCI 등재

노인을 위한 원격 낙상 검출 시스템

Telemonitoring System of Fall Detection for the Elderly

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참고문헌

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초록

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 %.

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 %.

참고문헌 (12)

  1. 김종민, "노인생애주기에 따른 낙상요인", 보건교육건강증진학회지, 27(1), pp.21~34 (2010), 한국보건교육건강증진학회
  2. S. S. Roach, "Introductory Gerontological Nursing" (1988), Lippincott Williams & Wilkins
  3. D. Haver, "Health promotion and aging: Practical applications for health professionals" (2007), Springer Publishing company Inc
  4. P. C. Fletcher, "Risk factors for falling among communitybased seniors", J. Patient Safety, 5(2), pp.61~66 (2009)
  5. 유인영, "RAI-HC를 이용한 노인의 다면적 재낙상 위험요인 분석-1회 낙상자와 재낙상자 비교-", Journal of Korean Academy of Nursing, 41(1), pp.119~128 (2011), 한국간호과학회
  6. R. Reyna, "Beyond accidental falls human fall detection using accelerometry", pp.44~48 (2009), Energy
  7. A. K. Bourke, "Evaluation of a threshold-based tri-axialaccelerometer fall detection algorithm", Gait andPosture, 26(2), pp.194~199 (2007)
  8. S. Srinivasan, "Towards automatic detection of falls using wirelesssensors", Proc. of the 29th Annual InternationalConf. IEEE EMBS, pp.1379~1382 (2007)
  9. A. Purwar, "Activity monitoring from real-time triaxial accelerometer data using sensor network", International Conf. on Control, Automation and System, pp.2402~2406 (2007)
  10. Q. Li, "Accurate,fast fall detection using gyroscopes and accelerometer-derived posture information", 6th International Workshop on Wearable and Implantable Body Sensor Networks, pp.138~143 (2009), USA
  11. M. N. Nyan, "Distinguishing fall activities from normal activities by angular rate characteristics and highspeed camera characterization", Medical Engineering and Physics, 8(28), pp.842~849 (2006)
  12. 윤길원, "Ubiquitous Health Monitoring System for Multiple Users Using a ZigBee and WLAN Dual-Network", TELEMEDICINE JOURNAL AND E-HEALTH, 15(9), pp.891~897

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