Abstract
Smart meters offer a low-cost, simple, and unobtrusive method to design monitoring systems for the detection of emergency situations in homes. The aim of this paper is to automatically detect the activity (Activity of Daily Living, ADL) of occupants based on such data. The approach trains a Semi-Markov-Model that describes the daily use of appliances, such as domestic appliances, sanitation, and heating system. Human habits are detected within the Semi Markov Model and rated by a similarity measure that calculates the probabilities for the ADL executed by the occupant based on the appliances he uses. This rating influences the probability for the ADL and permits model adaptation to individual user behavior. Considering the timeline of appliance usage, the probability estimations of each model state allow individualized and situation depending conclusions about the ongoing activity of the occupant.
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Clement, J., Ploennigs, J., Kabitzsch, K. (2012). Smart Meter: Detect and Individualize ADLs. In: Wichert, R., Eberhardt, B. (eds) Ambient Assisted Living. Advanced Technologies and Societal Change. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27491-6_8
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DOI: https://doi.org/10.1007/978-3-642-27491-6_8
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