Unsupervised Recognition of Multi-Resident Activities in Smart-Homes

Several methods have been proposed in the last two decades to recognize human activities based on sensor data acquired in smart-homes. While most existing methods assume the presence of a single inhabitant, a few techniques tackle the challenging issue of multi-resident activity recognition. To the best of our knowledge, all existing methods for multi-inhabitant activity recognition require the acquisition of a labeled training set of activities and sensor events. Unfortunately, activity labeling is costly and may disrupt the users' privacy. In this article, we introduce a novel technique to recognize multi-inhabitant activities without the need of labeled datasets. Our technique relies on an unlabeled sensor data stream acquired from a single resident, and on ontological reasoning to extract probabilistic associations among sensor events and activities. Extensive experiments with a large dataset of multi-inhabitant activities show that our technique achieves an average accuracy very close to the one of state-of-the-art supervised methods, without requiring the acquisition of labeled data.