by In a world where technology continues to advance at a rapid pace, the concept of affective computing is gaining traction. This interdisciplinary field integrates computer science, engineering, psychology, neuroscience, and other related disciplines to enable machines to perceive, recognize, understand, and respond to human emotions. A recent comprehensive review published in Intelligent Computing sheds light on the recent advancements, challenges, and future trends in affective computing.
Affective computing has diverse applications across various sectors, such as education, healthcare, business services, and the integration of art and science. It plays a significant role in enhancing human-machine interactions by incorporating emotional intelligence. The review highlights five main aspects of research in the field: the basic theory of emotion, collection of emotional signals, sentiment analysis, multimodal fusion, and generation and expression of emotions.
A bibliometric analysis revealed a significant growth in the number of articles published in affective computing since 1997, with a notable surge in publications from 2010 to 2019 driven by advancements in deep learning. However, growth has plateaued post-2019, possibly influenced by the slowdown in deep learning innovation and the impact of the COVID-19 pandemic on academia. Leading countries in publication volume include China, the United States, India, the UK, and Germany.
Key research themes in affective computing identified through keyword frequency analysis encompass natural language processing, facial expression recognition, human-computer interaction, affective disorder analysis, and multimodal emotion analysis. Top institutions in this field include Nanyang Technological University, Imperial College London, and Tsinghua University, with a significant focus on international collaborations, particularly between China and the US.
Innovations in affective computing include emotion-generation techniques to enhance human-computer interactions, fine-grained sentiment classification models, and the exploration of group-level emotions and cultural diversity. The field is evolving towards the development of large-scale multimodal datasets and refining multimodal fusion technology to improve emotion recognition accuracy.
Further applications of affective computing across various domains show promising potential, including affective brain-computer interfaces, empathic human-computer dialogue, emotion-assisted decision-making, and affective virtual reality. These applications aim to optimize outcomes in fields like clinical diagnostics, therapy, production safety, assisted driving, virtual socialization, anchors, and marketing.
The future of affective computing holds exciting possibilities as technological advancements continue to shape the landscape of empathetic AI. By integrating human emotions with computing, affective computing is paving the way for more nuanced and impactful human-machine interactions. The evolution of this field not only promises enhanced user experiences but also opens up new avenues for research and application in diverse domains.
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1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
