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Systemy umożliwiające identyfikację upadku z wysokości

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Warianty tytułu
EN
Systems for identifying falls from a height
Języki publikacji
PL
Abstrakty
PL
W artykule omówiono dwa rodzaje opracowanych w CIOP-PIB systemów umożliwiających identyfikację upadku człowieka z wysokości. Działanie pierwszego z nich jest oparte na akcelerometrycznym układzie pomiarowym, zaimplementowanym w pasie biodrowym, który stanowi element sprzętu zabezpieczającego przed upadkiem z wysokości. Działanie drugiego systemu polega na analizie zarejestrowanego obrazu wideo. Przedstawiono metody oraz wyniki badań kalibracyjnych i sprawdzających obu zaprojektowanych systemów, które wykorzystano do przeprowadzenia badań wstępnych. Podczas tych badań uczestnik wykonywał określone ćwiczenia: siadanie i symulowany upadek. Porównanie wyników badań wykazało dużą zbieżność pomiędzy wartościami zmierzonymi za pomocą czujników przyspieszeń oraz wartościami uzyskanymi na podstawie przeprowadzonej analizy obrazu. Opisane systemy mogą więc posłużyć do identyfikacji upadku oraz jako elementy inicjujące urządzenia do dynamicznego skracania drogi spadania, które zmniejszą siły działające na człowieka podczas powstrzymywania jego spadania z wysokości.
EN
The article discusses two types of systems developed at CIOP-PIB, which enable the identification of human falls from a height. The operation of the first system is based on an accelerometric measuring system implemented in the hip belt, which is an element of the equipment protecting against falls from a height. The operation of the second system is based on the analysis of the recorded video. The methods and results of calibration and verification tests of both designed systems, which were used for the preliminary tests, were presented. During these tests, the participant performed specific exercises: sitting down and simulating falling. The comparison of the test results showed a high convergence between the values measured with the acceleration sensors and the values obtained on the basis of the image analysis. The described systems can therefore be used for fall identification and as initiators of devices for dynamic shortening of the fall path, which will reduce the forces acting on a person during restraining his fall from a height.
Rocznik
Tom
Strony
17--21
Opis fizyczny
Bibliogr. 29 poz., rys.
Twórcy
  • Centralny Instytut Ochrony Pracy - Państwowy Instytut Badawczy
  • Centralny Instytut Ochrony Pracy - Państwowy Instytut Badawczy
Bibliografia
  • [1] NOURY, N., et al. A proposal for the classification and evaluation of fall detectors. IRBM. 2008, 29(6): 340-349.
  • [2] CHUNG, M.C., et al. Posttraumatic stress disorder in older people after a fall. International Journal of Geriatric Psychiatry. 2009, 24(9): 955-964.
  • [3] SADIGH, S., et al. Falls and fall-related injuries among the elderly: a survey of residential-care facilities in a Swedish municipality. Journal of Community Health. 2004, 29(2): 129-140.
  • [4] SHUMWAY-COOK, A., et al. Falls in the medicare population: incidence, associated factors, and on health care. Physical Therapy. 2009, 89(4): 324-332.
  • [5] ELLIOTT, S., PAINTER, J., HUDSON, S. Living alone and fall risk factors in community-dwelling middle age and older adults. Journal of Community Health. 2009, 34(4): 301-310.
  • [6] YU, X. Approaches and principles of fall detection for elderly and patient. In: Proceedings of the 10thIEEE International Conference on e-Health Networking, Applications and Service (HEALTHCOM’08). Singapore, July 2008, pp. 42-47.
  • [7] BOURKE, A.K., O’BRIEN, J.V., LYONS, G.M. Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait & Posture. 2007, 26(2): 194-199.
  • [8] KANGAS, M., et al. Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait & Posture. 2008, 28(2): 285-291.
  • [9] NOURY, N., et al. Fall detection – principles and methods. In: Proceedings of the 29th Annual International Conference on IEEE Engineering in Medicine and Biology Society. 2007, pp. 1663-1666.
  • [10] BOURKE, A.K., LYONS, G.M. A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Medical Engineering & Physics. 2008, 30(1): 84-90.
  • [11] KARANTONIS, D.M., et al. Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Transactions on Information Technology in Biomedicine. 2006, 10(1): 156-167.
  • [12] HWANG, J.Y., et al. Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly. In: Proceedings of the 26thAnnual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’04). San Francisco, USA, September 2004, pp. 2204-2207.
  • [13] LUINGE, H.J., VELTINK, P.H. Inclination measurement of human movement using a 3-D accelerometer with autocalibration. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2004 12(1): 112-121.
  • [14] ANANIA, G., et al. Development of a novel algorithm for human fall detection using wearable sensors. In: Proceedings of the IEEE Sensors (SENSORS’08). Lecce, Italy, October 2008, pp. 1336-1339.
  • [15] MATHIE, M.J., et al. Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological Measurement. 2004, 25(2): R1-20.
  • [16] WILLIAMS, G., et al. A smart fall and activity monitor for telecare applications. In: Proceedings of the 20thAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. 1998.
  • [17] DOUGHTY, K., LEWIS, R., Mc INTOSH, A. The design of a practical and reliable fall detector for community and institutional telecare. Journal of Telemedicine and Telecare. 2000, 6(suppl. 1):150-154.
  • [18] MATHIE, M., BASILAKIS, J., CELLER, B.G. A system for monitoring posture and physical activity using accelerometers. In: Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol. 4. 2001.
  • [19] SALLEH, R., et al. Low power tri-axial ambulatory falls monitor. In: Proceedings of the 10th International Conference on Biomedical Engineering. 2000.
  • [20] SIXSMITH, A., JOHNSON, N. A smart sensor to detect the falls of elderly. IEEE Pervasive Computing. 2004, 3: 42-47.
  • [21] FU, Z., et al. Fall detection using an address-event temporal contrast vision sensor. In: Proceedings of the IEEE International Symposium on Circuits and Systems. Seattle, Washington, USA, 2008, pp. 424-427.
  • [22] JANSEN, B., DEKLERCK, R. Context aware inactivity recognition for visual fall detection. In: Proceedings of the Pervasive Health Conference and Workshops. Innsbruck, Austria, 2006, pp. 1-4.
  • [23] MIAOU, S., SUNG, P., HUANG, C. A customized human fall detection system using omni-camera images and personal information. Proceedings of the 1stDistributed Diagnosis and Home Healthcare Conference. Arlington, Virginia, 2006, pp. 39-42.
  • [24] ANDERSON, D., et al. Recognizing falls from silhouettes. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2006, pp. 6388-6391.
  • [25] ROUGIER, C. et al. Monocular 3D head tracking to detect falls of elderly people. In: Proceedings of the Annual International Conference of the IEEE
  • Engineering in Medicine and Biology Society. 2006, pp. 6384-6387.
  • [26] CUCCHIARA, R., PRATI, A., VEZZANI, R. A multi-camera vision system for fall detection and alarm generation. Expert Systems. 2007, 24(5): 334-345.
  • [27] MIAOU, S.-G., SUNG, P.-H., HUANG, C.-Y. A customized human fall detection system using omni-camera images and personal information. Distributed Diagnosis and Home Healthcare. 2006, pp. 39-42.
  • [28] JANSEN, B., DEKLERCK, R. Context aware inactivity recognition for visual fall detection. In: Proceedings of the IEEE Pervasive Health Conference and Workshops. 2006, pp. 1-4.
  • [29] WAGNER, J. Regularised differentiation of measurement data in systems for healthcare-oriented monitoring of elderly persons. Rozprawa doktorska. Wrocław, 2020
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-09e11614-3a17-4b7d-9da2-a4e372aac7b9
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