Probabilistic human daily activity recognition towards robot-assisted living

Abstract

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. Spatiotemporal 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.

Dynamic Bayesian Network for Time-Dependent Classification Problems in Robotics

“…The first one regarding the semantic place classification and the second one based on activity classification. In both formulations, the DBN was used as basis to compose the DBMM [6,8,18], a more complex structure used to handle more complex scenarios. In both applications, the DBMM has shown to be a powerful choice in modelling of time-dependent scenarios.…”

“…[8,18]. The CAD-60 dataset comprises video sequences and skeleton data of human daily activities acquired from a RGB-D sensor.…”

“…In this section, we have shown the DBMM [8,18] performance using an offline dataset. Additionally, further tests using a mobile platform with an RGB-D sensor on-board running on-the-fly in an assisted living context was also successfully validated with accuracy above Figure 7.…”

“…More details about the DBMM using a mobile robot for activity recognition and a video showing the classification performance can be found in Ref. [18].…”

Automatic detection of human interactions from RGB-D data for social activity classification

“…In [6], the authors have applied a 3D extension of the Qualitative Trajectory Calculus (QTC) to model movements of the body joints, which have been analysed with HMMs. Faria et al [7], [8] have introduced the Dynamic Bayesian Mixture Model (DBMM). It is a probability based ensemble which combines a set of classifiers through their temporal entropy.…”