Social Behaviour Understanding using Deep Neural Networks: Development of Social Intelligence Systems

TL;DR

This paper proposes a deep neural network framework for understanding social behaviors, integrating multiple components to develop systems for depression detection, activity recognition, and cognitive impairment screening, advancing social intelligence in AI.

Contribution

It introduces a novel social behavior understanding framework using deep neural networks, integrating various modules for social and behavioral analysis, and demonstrates its application in health-related systems.

Findings

Effective social behavior analysis through deep learning

Development of systems for depression detection, activity recognition, and cognitive screening

Contributes to social computing and health informatics advancements

Abstract

With the rapid development in artificial intelligence, social computing has evolved beyond social informatics toward the birth of social intelligence systems. This paper, therefore, takes initiatives to propose a social behaviour understanding framework with the use of deep neural networks for social and behavioural analysis. The integration of information fusion, person and object detection, social signal understanding, behaviour understanding, and context understanding plays a harmonious role to elicit social behaviours. Three systems, including depression detection, activity recognition and cognitive impairment screening, are developed to evidently demonstrate the importance of social intelligence. The study considerably contributes to the cumulative development of social computing and health informatics. It also provides a number of implications for academic bodies, healthcare practitioners, and developers of socially intelligent agents.