Affective Computing: Scientists Connect Human Emotions With AI

Affective computing is transforming the way machines understand and interact with human emotions, integrating computer science, psychology, and neuroscience. With advancements in emotion recognition, ethical practices, and immersive technologies, this field is set to revolutionize interactions across healthcare, customer service, and virtual reality.


The Scope and Application of Affective Computing

Affective computing enables machines to perceive, recognize, understand, and respond to human emotions. It has various applications across different sectors, such as education, healthcare, business services, and the integration of science and art. Emotional intelligence plays a significant role in human-machine interactions, and affective computing has the potential to significantly enhance these interactions.

According to the review, research in this field covers five main aspects: basic theory of emotion, collection of emotional signals, sentiment analysis, multimodal fusion, and generation and expression of emotions.

Research Methods and Growth

To improve the overall understanding of the theory, technical methods, and applications of affective computing, researchers performed a statistical analysis using a bibliometric method. Bibliometrics applies quantitative methods such as mathematics and statistics to the literature of a scientific or other field and processes statistical data based on information science theory.

According to the data collected in the bibliometric study, the number of articles published in the field of affective computing has grown considerably since 1997, with a steady increase in publications until 2009, followed by rapid growth from 2010 to 2019 due to advancements in deep learning. However, post-2019, the growth has plateaued, possibly due to a slowdown in deep learning innovation and the impact of the COVID-19 pandemic on academia. China is leading the world in publication volume, followed by the United States, India, the UK, and Germany.

Interdisciplinary Research and Innovation

Affective computing research spans disciplines including computer science, engineering, neurosciences, and other disciplines. The main research themes in this field, as identified through keyword frequency analysis, are 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. International collaborations are widespread, particularly between China and the US. The field’s scholars are primarily concentrated in Asia and North America.


Ethical Considerations and Future Trends

Affective computing, a field that combines technology with the nuanced understanding of human emotions, is experiencing surges in innovation and related ethical considerations. Innovations identified in the review include emotion-generation techniques that enhance the naturalness of human-computer interactions by increasing the realism of the facial expressions and body movements of avatars and robots.

The researchers identified several current trends that are likely to continue. For example, the construction of large-scale, multimodal datasets is essential for training robust affective computing systems, and there is a growing trend towards collecting and analyzing data that reflects natural emotional expressions. Multimodal fusion technology is being refined to effectively integrate information from various sources, such as facial expressions, voice, and text, to improve the accuracy of emotion recognition. Additionally, the field is moving towards a knowledge-driven approach, integrating expert knowledge to compensate for data limitations and enhance computational efficiency.