Abstract
The objective is to detect activities taking place in a home for the purpose of creating models of behavior for the occupant. An array of sensors captures the status of appliances used in the home. Models for the occupant’s activities are built from the captured data using unsupervised learning techniques. Predictive models can be used in a number of ways: to enhance user experience, to maximize resource usage efficiency, for safety and for security. This work focuses on supporting independent living and enhancing quality of life for older persons. The goal is for the system to distinguish between normal and anomalous behavior. In this paper, we present the results of unsupervised classification techniques applied to the problem of modeling activity.
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Monekosso, D., Remagnino, P. (2008). Detecting Activities for Assisted Living. In: Mühlhäuser, M., Ferscha, A., Aitenbichler, E. (eds) Constructing Ambient Intelligence. AmI 2007. Communications in Computer and Information Science, vol 11. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85379-4_28
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DOI: https://doi.org/10.1007/978-3-540-85379-4_28
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