A Two-Step Framework for Novelty Detection in Activities of Daily Living

The ability to recognize and model human Activities of Daily Living (ADL) and to detect possible deviations from regular patterns, or anomalies, constitutes an enabling technology for developing effective Socially Assistive Robots. Traditional approaches aim at recognizing an anomaly behavior by means of machine-learning techniques trained on anomalies' dataset, like subject's falls. The main problem with these approaches lies in the difficulty to generate these dataset. In this work, we present a two-step framework implementing a new strategy for the detection of ADL anomalies. Indeed, rather than detecting anomaly behaviors, we aim at identifying those that are divergent from normal ones. This is achieved by a first step, where a deep learning technique determine the most probable ADL class related to the action performed by the subject. In a second step, a Gaussian Mixture Model is used to compute the likelihood that the action is normal or not, within that class. We performed an experimental validation of the proposed framework on a public dataset. Results are very close to the best traditional approaches, while at the same time offering the significant advantage that it is much easier to create dataset of normal ADL.