Scientists link human emotions to AI.

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Affective computing is changing the way machines understand and interact with human emotions, integrating computer science, psychology and neuroscience. With advances in emotion recognition, ethical practices, and immersive technologies, the field is poised to revolutionize interactions in healthcare, customer service, and virtual reality. Credit:

A comprehensive review covers both recent developments and future trends in the field of affective computing.

Imagine a world where your smartphone can detect your mood by the way you type a message or the tone of your voice. Picture a car that adjusts its music playlist based on your stress level during rush hour traffic. These scenarios are not just visions of the future. They are a glimpse into the rapidly emerging field of impulsive computing.

Affective computing is a multidisciplinary field that integrates computer science, engineering, psychology, neuroscience and other related fields. A new and comprehensive review on affective computing was recently published in the journal Intelligent computing. It outlines recent developments, challenges and future trends.

Scope and application of efficient computing

Effective computing enables machines to understand, recognize, understand and respond to human emotions. It has various applications in various fields, such as education, healthcare, business services and the integration of science and art. Emotional intelligence plays an important 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 areas: basic theory of emotion, collection of emotional signals, analysis of emotion, multimodal fusion, and generation and expression of emotion.

Research methods and growth

To improve the overall understanding of the theory, technical methods, and applications of affective computing, the researchers conducted 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.

Keywords assigned by authors to papers in the field of affective computing were analyzed for frequency and coherence, and the main keywords among them were clustered to obtain five clusters. Credit: Guanxiong Pei et al.

According to data collected in a bibliometric study, the number of articles published in the field of affective computing has increased significantly since 1997, with a steady increase in publications until 2009, followed by deep learning from 2010 to 2019. I grew rapidly because of growth. . However, after 2019, growth has slowed, possibly due to a slowdown in deep learning innovation and its impact. COVID 19 China leads the world in publication volume on academia, followed by the US, India, the UK and Germany.

Field research is published in a wide range of journals, including high-profile journals such as IEEE Transactions on Efficient Computing, Expert systems with applications, And Knowledge based systems Being favored by scholars in the field of impressive computing.

Interdisciplinary research and innovation

Effective computing research spans disciplines including computer science, engineering, neurosciences, and other disciplines. Major research topics in this field, as identified by keyword frequency analysis, are natural language processing, facial expression recognition, human-computer interaction, affective disorder analysis, and multimodal emotion analysis. .

Among the leading institutions in this field are Nanyang Technological University, Imperial College London, and Tsinghua University. International cooperation is extensive, especially between China and the United States. Scholars in the field are primarily concentrated in Asia and North America.

Ethical considerations and future trends

Affective computing, a field that connects technology to the subtleties of human emotion, is experiencing an increase in innovation and related ethical concerns. Innovations identified in the review include emoticon techniques that enhance the natural ability of human-computer interaction by increasing the realism of facial expressions and body movements of avatars and robots.

The development of sophisticated emotion classification models is also progressing, allowing a better understanding of the mixed emotions encountered in everyday life. Cognitive neuroscience is providing valuable insight into the neural basis of emotion, which is important for designing systems that can authentically simulate emotional responses.

Meanwhile, the field is reportedly shifting its focus from individual to group-level emotions, recognizing the importance of collective affect in settings such as consumer behavior and workplaces. In addition, researchers are addressing the challenge of cultural diversity by developing models that can adapt to different emotional expressions in different cultures. Such research includes addressing ethical and privacy concerns and advocating for the establishment of international standards to govern the use of emotional data.

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 toward collecting and analyzing data that reflect natural emotional expression. Multimodal fusion technology is being refined to effectively integrate information from different sources, such as facial expressions, voice and text. correction Recognition of emotions. Additionally, the field is moving toward a knowledge-based approach, combining expert knowledge to compensate for data limitations and increase computational efficiency.

Applications and future prospects

Efficient computing is being applied to political speeches, music, drama and the visual arts to predict emotional reactions and enhance emotional expression. This interdisciplinary approach is not only advancing impressive computing capabilities, but also opening up new avenues for research and application.

Future applications of affective computing in various domains show great promise, particularly with advances in affective brain–computer interfaces, empathic human-computer dialogue, emotion-assisted decision-making, and emotional virtual reality. . Effective brain-computer interfaces, which use neural signals, play an important role in measuring and regulating emotional states, benefiting medical diagnosis, therapy, and military applications. Empathic human-computer interaction seeks to create more human-like interactions by leveraging multifaceted emotional cues, offering the potential for elder companionship and intelligent customer service.

Emotion-assisted decision-making incorporates emotional variables into machine decision-making processes, potentially improving outcomes in areas such as product safety and assisted driving. Affective virtual reality plays an important role in building immersive metaverses, using emotionally rich avatars to engage users, thereby facilitating virtual socialization, anchors and applications in marketing.

Citation: “Effective Computing: Recent Developments, Challenges, and Future Trends” by Guanxiong Pei, Haiying Li, Yandi Lu, Yanlei Wang, Shizhen Hua and Taihao Li, 5 January 2024, Intelligent computing.
DOI: 10.34133/icomputing.0076

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