Social Behaviour Understanding using Deep Neural Networks: Development of Social Intelligence Systems
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2021
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| _version_ | 1866909541477646336 |
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| author | Feng, Ethan Lim Ding Neo, Zhi-Wei De Silva, Aaron William Sim, Kellie Tan, Hong-Ray Nguyen, Thi-Thanh Koh, Karen Wei Ling Wang, Wenru Nguyen, Hoang D. |
| author_facet | Feng, Ethan Lim Ding Neo, Zhi-Wei De Silva, Aaron William Sim, Kellie Tan, Hong-Ray Nguyen, Thi-Thanh Koh, Karen Wei Ling Wang, Wenru Nguyen, Hoang D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2105_09489 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Social Behaviour Understanding using Deep Neural Networks: Development of Social Intelligence Systems Feng, Ethan Lim Ding Neo, Zhi-Wei De Silva, Aaron William Sim, Kellie Tan, Hong-Ray Nguyen, Thi-Thanh Koh, Karen Wei Ling Wang, Wenru Nguyen, Hoang D. Artificial Intelligence 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. |
| title | Social Behaviour Understanding using Deep Neural Networks: Development of Social Intelligence Systems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2105.09489 |