Probabilistic human daily activity recognition towards robot-assisted living

In this work, we present a human-centered robot application in the scope of daily activity recognition towards robot-assisted living. Our approach consists of a probabilistic ensemble of classifiers as a dynamic mixture model considering the Bayesian probability, where each base classifier contributes to the inference in proportion to its posterior belief. The classification model relies on the confidence obtained from an uncertainty measure that assigns a weight for each base classifier to counterbalance the joint posterior probability. Spatio-temporal 3D skeleton-based features extracted from RGB-D sensor data are modeled in order to characterize daily activities, including risk situations (e.g.: falling down, running or jumping in a room). To assess our proposed approach, challenging public datasets such as MSR-Action3D and MSR-Activity3D [1] [2] were used to compare the results with other recent methods. Reported results show that our proposed approach outperforms state-of-the-art methods in terms of overall accuracy. Moreover, we implemented our approach using Robot Operating System (ROS) environment to validate the DBMM running on-the-fly in a mobile robot with an RGB-D sensor onboard to identify daily activities for a robot-assisted living application.