Abstract
Elderly suffers from injuries or disabilities through falls every year. With a high likelihood of falls causing serious injury or death, falling can be extremely dangerous, especially when the victim is home-alone and is unable to seek timely medical assistance. Our fall detection systems aims to solve this problem by automatically detecting falls and notify healthcare services or the victim’s caregivers so as to provide help. In this paper, development of a fall detection system based on Kinect sensor is introduced. Current fall detection algorithms were surveyed and we developed a novel posture recognition algorithm to improve the specificity of the system. Data obtained through trial testing with human subjects showed a 26.5% increase in fall detection compared to control algorithms. With our novel detection algorithm, the system conducted in a simulated ward scenario can achieve up to 90% fall detection rate.
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References
George, F.F.: Falls in the elderly. Am Fam Physician 61(7), 2159–2168 (2000)
Wild, D., Nayak, U.S., Isaacs, B.: How dangerous are falls in old people at home? British Medical Journal (Clinical research ed.)Â 282(6260), 266 (1981)
Perry, J.T., Kellog, S., Vaidya, S.M., Youn, J.H., Ali, H., Sharif, H.: Survey and evaluation of real-time fall detection approaches. In: 2009 6th International Symposium on High-Capacity Optical Networks and Enabling Technologies (HONET), pp. 158–164. IEEE (December 2009)
Rimminen, H., Lindstrom, J., Linnavuo, M., Sepponen, R.: Detection of falls among the elderly by a floor sensor using the electric near field. IEEE Transactions on Information Technology in Biomedicine 14(6), 1475–1476 (2010)
Yu, X.: Approaches and principles of fall detection for elderly and patient. In: 10th International Conference on e-Health Networking, Applications and Services, HealthCom 2008, pp. 42–47. IEEE (July 2008)
Gadre, K.R.: Fall Detection System Using Low Cost Computing (Doctoral dissertation, University of Missouri) (2012)
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Lee, C.K., Lee, V.Y. (2013). Fall Detection System Based on Kinect Sensor Using Novel Detection and Posture Recognition Algorithm. In: Biswas, J., Kobayashi, H., Wong, L., Abdulrazak, B., Mokhtari, M. (eds) Inclusive Society: Health and Wellbeing in the Community, and Care at Home. ICOST 2013. Lecture Notes in Computer Science, vol 7910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39470-6_30
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DOI: https://doi.org/10.1007/978-3-642-39470-6_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39469-0
Online ISBN: 978-3-642-39470-6
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