It's all about habits: Exploiting multi-task clustering for activities of daily living analysis

Motivated by applications in areas such as patient monitoring, tele-rehabilitation and ambient assisted living, analyzing activities of daily living is an active research topic in computer vision and image processing. In this paper we address the problem of everyday activity recognition from unlabeled data proposing a novel multi-task clustering (MTC) approach. Our intuition is that, when analyzing activities of daily living, we can take advantage of the fact that people tend to perform the same actions in the same environment (e.g. people working in an office environment use to read and write documents). Thus, even if labels are not available, information about typical activities can be exploited in the learning process. Arguing that the tasks of recognizing activities of specific individuals are related, we resort on multi-task learning and rather than clustering the data of each individual separately, we also look for clustering results which are coherent among related tasks. Extensive experimental results show that our method outperforms several state-of-the-art approaches by up to 11% on the Rochester activities of daily living dataset.