Spatio-Temporal Convolutional Networks and N-Ary Ontologies for Human Activity-Aware Robotic System

Abstract

Endowing a companion robot with cognitive abilities to recognize human daily activities, in particular from body skeletons information, is a significant challenge, which needs complex and novel approaches. Recently, most of the proposed approaches exploit the hand-crafted features or the predefined traversal rules techniques to recognize human daily activities from skeleton information, which often lead to the deficit of robustness and generalization. In this work, a novel hybrid framework for human activity-aware robotic system is proposed. In the low-level, a novel Spatio-Temporal Joint based Convolutional Neural Network (STJ-CNN) is proposed to recognize human daily activities in the ambient environments. In the high-level, novel representation and inference services based on Narrative Knowledge Representation Language (NKRL) are proposed to represent and combine the detected human activities with the ambient events, and to infer the semantic context of the detected activity. Empirical experiments on real-world datasets have been conducted, besides an online demonstration created to validate the proposed approach. The final results show that the proposed approach outperforms the baseline models with a significant improvement up to 24% in terms of $F$-score on DAHLIA dataset.