Researchers Establish Link Between Human Emotions and Artificial Intelligence

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Affective computing is changing the way machines grasp and interact with human emotions, merging computer science, psychology, and neuroscience. With progress in emotion recognition, ethical practices, and immersive technologies, this area is poised to transform interactions across healthcare, customer service, and virtual reality. Credit:

The field of affective computing is comprehensively reviewed, encompassing both advancements and future directions.

Envision a world where your smartphone can sense your mood simply by the way you compose a message or the intonation of your voice. Envision a vehicle that adapts its music playlist based on your stress levels while stuck in rush hour traffic. These scenarios are not just futuristic daydreams. They are sneak peeks into the swiftly evolving realm of affective computing.

Affective computing is a multidisciplinary domain integrating computer science, engineering, psychology, neuroscience, and other related fields. A recent comprehensive evaluation on affective computing was published in the periodical Intelligent Computing. It delineates recent progressions, obstacles, and forthcoming trends.

The Extent and Utilization of Affective Computing

Affective computing enables machines to sense, recognize, comprehend, and react to human emotions. It has diverse uses across various industries, such as education, healthcare, business services, and the convergence of science and art. Emotional intelligence plays a substantial part in human-machine interactions, and affective computing holds the potential to significantly enrich these interactions.

Per the evaluation, inquiries in this sphere cover five primary facets: fundamental theory of emotion, gathering of emotional signals, sentiment analysis, multimodal fusion, and production and expression of emotions.

Exploration Approaches and Expansion

To refine the overall understanding of the theory, technical techniques, and applications of affective computing, researchers conducted a statistical assessment using a bibliometric method. Bibliometrics employs quantitative techniques like mathematics and statistics to the literature of a scientific or any other field and processes statistical data grounded on information science theory.

Five Research Themes in Affective Computing Graphic

The keywords ascribed to papers by authors in the domain of affective computing were assessed for frequency and co-occurrence, and the central keywords among them were grouped to acquire five clusters. Credit: Guanxiong Pei et al.

According to the compiled data in the bibliometric study, the quantity of articles released in the field of affective computing has surged notably since 1997, with a consistent rise in publications until 2009, succeeded by swift growth from 2010 to 2019 due to progress in deep learning. However, post-2019, the growth has leveled off, perhaps due to a slowdown in deep learning innovation and the repercussions of the

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