Abstract
Due to the ageing of the world population, a growing number of elderly people remain in their homes, requiring different levels of care. Our formative user studies show that the main concern of elderly people and their families is “fall detection and safe movement in the house”, while eschewing intrusive monitoring devices. This paper introduces a statistical model based on non-intrusive sensor observations that posits whether a person is not safe by identifying unusually long periods of inactivity within different regions in the home. Evaluation on two real-life datasets shows that our system outperforms a state-of-the-art system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pollack, M.E.: Intelligent technology for an aging population: The use of AI to assist elders with cognitive impairment. AI Magazine 26(2), 9–24 (2005)
Mynatt, E., Rogers, W.: Developing technology to support the functional independence of older adults. Ageing International 27, 24–41 (2001)
Kasteren, T.L., Englebienne, G., Kröse, B.J.: An activity monitoring system for elderly care using generative and discriminative models. Personal Ubiquitous Computing 14(6), 489–498 (2010)
Cook, D.: Learning setting-generalized activity models for smart spaces. IEEE Intelligent Systems 27(1), 32–38 (2012)
Courtney, K., Demiris, G., Rantz, M.: Needing smart home technologies: The perspective of older adults in continuing care retirement communities. Informatics in Primary Care 16(3), 195–201 (2008)
Larizza, M., Zukerman, I., Bohnert, F., Russell, R.A., Busija, L., Albrecht, D.W., Rees, G.: Studies to determine user requirements regarding in-home monitoring systems. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 139–150. Springer, Heidelberg (2012)
Cuddihy, P., Weisenberg, J., Graichen, C., Ganesh, M.: Algorithm to automatically detect abnormally long periods of inactivity in a home. In: HealthNet 2007 – Proceedings of the 1st ACM International Workshop on Systems and Networking Support for Healthcare and Assisted Living Environments, San Juan, Puerto Rico, pp. 89–94 (2007)
Weisenberg, J., Cuddihy, P., Rajiv, V.: Augmenting motion sensing to improve detection of periods of unusual inactivity. In: HealthNet 2008 – Proceedings of the 2nd ACM International Workshop on Systems and Networking Support for Healthcare and Assisted Living Environments, Breckenridge, Colorado, pp. 2:1–2:6 (2008)
Yu, X.: Approaches and principles of fall detection for elderly and patient. In: IEEE HealthCom 2008 – Proceedings of the International Conference on e-Health Networking, Applications and Services, Singapore, pp. 42–47 (2008)
Nait-Charif, H., McKenna, S.: Activity summarisation and fall detection in a supportive home environment. In: ICPR 2004 – Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, England, vol. 4, pp. 323–326 (2004)
Haigh, K., Kiff, L., Myers, J., Guralnik, V., Geib, C., Phelps, J., Wagner, T.: The independent lifestyle assistant (I.L.S.A.): AI lessons learned. In: IAAI 2004 –Proceedings of the 16th Conference on Innovative Applications of Artificial Intelligence, San Jose, California, pp. 25–29 (2004)
Moshtaghi, M., Zukerman, I., Russell, R.A., Albrecht, D.: Towards detecting inactivity using an in-home monitoring system. In: IEEE Conference on Industrial Electronics and Applications, Melbourne, Australia (2013)
Feldmann, A., Whitt, W.: Fitting mixtures of exponentials to long-tail distributions to analyze network performance models. Performance Evaluation, 245–279 (1998)
Riska, A., Diev, V., Smirni, E.: Efficient fitting of long-tailed data sets into hyperexponential distributions. In: GLOBECOM 2002 – Proceedings of the IEEE Global Telecommunications Conference, Taipei, Taiwan, vol. 3, pp. 2513–2517 (2002)
Moore, D., McCabe, G., Craig, B.: Introduction to the Practice of Statistics. Macmillan Higher Education (2009)
Massey, F.: The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association 46(253), 68–78 (1951)
Davison, B., Hirsh, H.: Predicting sequences of user actions. In: Notes of the AAAI/ICML 1998 Workshop on Predicting the Future: AI Approaches to Time-Series Analysis, Madison, Wisconsin (1998)
Ljung, L.: System Identification: Theory for the User, 2nd edn. Prentice Hall (1999)
Deaton, A.: The Analysis of Household Surveys: A Microeconomic Approach to Development Policy. World Bank Publications (1997)
Schultze, V., Pawlitschko, J.: The identification of outliers in exponential samples. Statistica Neerlandica 56(1), 41–57 (2002)
Davies, L., Gather, U.: The identification of multiple outliers. Journal of the American Statistical Association 88(423), 782–792 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moshtaghi, M., Zukerman, I., Albrecht, D., Russell, R.A. (2013). Monitoring Personal Safety by Unobtrusively Detecting Unusual Periods of Inactivity. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38844-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-38844-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38843-9
Online ISBN: 978-3-642-38844-6
eBook Packages: Computer ScienceComputer Science (R0)