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Our novel unsupervised statistical concept drift detection approach uses variational autoencoders for estimating the parameters for a statistical hypothesis test for abnormal days. As feature, the Kullback\u2013Leibler divergence of activity probability maps derived from power and motion sensors were used. We showed the general feasibility (min. F<jats:sub>1<\/jats:sub>-Score of 91 %) on an artificial dataset of four concept drift types. Then we applied our new method to our real\u2013world dataset collected from the homes of 20 (pre\u2013)frail older adults (avg. age 84.75 y). 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