Applying machine learning methods in scenarios involving smart homes is a complex task. The many possible variations of sensors, feature representations, machine learning algorithms, middle-ware architectures, reasoning/decision schemes, and interactive strategies make research and development tasks non-trivial to solve.In this paper, the use of a portable, flexible and holistic smart home demonstrator is proposed to facilitate iterative development and the acquisition of feedback when testing in regard to the above-mentioned issues. Specifically, the focus in this paper is on scenarios involving anomaly detection and response. First a model for anomaly detection is trained with simulated data representing a priori knowledge pertaining to a person living in an apartment. Then a reasoning mechanism uses the trained model to infer and plan a reaction to deviating activities. Reactions are carried out by a mobile interactive robot to investigate if a detected anomaly constitutes a true emergency. The implemented demonstrator was able to detect and respond properly in 18 of 20 trials featuring normal and deviating activity patterns, suggesting the feasibility of the proposed approach for such scenarios. © IEEE 2015