Department of Computer, Control and Management Engineering

Human activities represent a major source of information for smart home automation. While performing their daily activities, humans trigger sensors producing measurements that flow into a sensor log. Vast majority of techniques to recognize and exploit the occurrences of human activities are supervised, requiring the log to be manually labeled in correspondence of the onset and the end of each activity repetition. This task requires a considerable effort by the final user, resulting in imprecise labeling tampering the performance of algorithms. In this paper, we propose an unsupervised technique allowing to automatically segment smart home logs containing position sensor measurements. The proposed technique exploits information about the position of the human to automatically extract basic actions, which are then segmented on a temporal basis and clustered. The approach is evaluated against a state-of-the-art dataset.