A Context-aware Hybrid Framework for Human Behavior Analysis

Abstract

In Ambient Assisted Living (AAL) systems and personal assistive robots, human behavior analysis is essential to provide intelligent services intended to improve people's quality of lives in terms of autonomy, well-being, and safety. Human behavior analysis allows discovering people's preferences, activities, and habits. While human behavior analysis has been explored in several domains, there are still some challenges for developing efficient approaches dealing with the limitations of data-driven approach to analyze human behaviors. In this paper, a framework is proposed to better characterize the human context by inferring new knowledge about his/her behaviors using commonsense reasoning and exploiting contextual information. Human activities are firstly recognized using a CNN-LSTM model. Different contexts of human activities are then extracted to analyze human behaviors. The obtained activity contexts are mapped to an ontology, called Human AcTivity (HAT) ontology, conceptualizing the human activities and their contexts. Answer Set Programming (ASP), a high-level expressive logic-based formalism, is then used to represent human behaviors and carry out commonsense reasoning to infer new knowledge about these behaviors. The proposed framework is evaluated using the Orange4Home dataset. Moreover, two quantitative experiments are carried out to demonstrate the ability of the proposed framework to better characterize human behaviors.