Abstract
Abnormal inactivity indicates situations, where elderly need assistance. Systems detecting the need for help models the amount of inactivity using inactivity profiles. Depending on the analysis of the profiles, events (e.g. falls) or long-term changes (decrease of mobility) are detected. Until now, inactivity profiles are only used to detect abnormal behavior on the short-term (e.g. fall, illness), but not on the long-term. Hence, this work introduces an approach to detect significant changes on mobility using long-term inactivity profiles, since these changes indicate enhanced or decreased mobility of elderly. Preliminary results are obtained by the analysis of the motion data of an elderly couple over the duration of 100 days and illustrates the feasibility of this approach.
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References
Wild, D., Nayak, U.S., Isaacs, B.: How dangerous are falls in old people at home? British Medical Journal (Clinical Research Ed.) 282(6260), 266–268 (1981)
Farquhar, M.: Elderly people’s definitions of quality of life. Social Science & Medicine 41(10), 1439–1446 (1995)
Anderson, D., Luke, R.H., Keller, J.M., Skubic, M., Rantz, M., Aud, M.: Linguistic Summarization of Video for Fall Detection Using Voxel Person and Fuzzy Logic. Computer Vision and Image Understanding 113(1), 80–89 (2009)
Mastorakis, G., Makris, D.: Fall detection system using Kinects infrared sensor. Journal of Real-Time Image Processing (March 2012)
Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Fall Detection from Human Shape and Motion History Using Video Surveillance. In: Advanced Information Networking and Applications Workshops, pp. 875–880. IEEE, Niagara Falls (2007)
Cuddihy, P., Weisenberg, J., Graichen, C., Ganesh, M.: Algorithm to automatically detect abnormally long periods of inactivity in a home. In: Proc. of the International Workshop on Systems and Networking Support for Healthcare and Assisted Living Environments HealthNet 2007, pp. 89–94. ACM, NY (2007)
Floeck, M., Litz, L.: Activity- and Inactivity-Based Approaches to Analyze an Assisted Living Environment. In: Second International Conference on Emerging Security Information, Systems and Technologies, pp. 311–316 (2008)
Nait-Charif, H., McKenna, S.: Activity summarisation and fall detection in a supportive home environment. In: Proc. of the International Conference on Pattern Recognition (ICPR), vol. 4, pp. 323–326. IEEE (2004)
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© 2013 Springer International Publishing Switzerland
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Planinc, R., Kampel, M. (2013). Detecting Changes in Elderly’s Mobility Using Inactivity Profiles. In: Nugent, C., Coronato, A., Bravo, J. (eds) Ambient Assisted Living and Active Aging. IWAAL 2013. Lecture Notes in Computer Science, vol 8277. Springer, Cham. https://doi.org/10.1007/978-3-319-03092-0_15
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DOI: https://doi.org/10.1007/978-3-319-03092-0_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03091-3
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