Affective Computing: Enhancing Human-Machine Interactions

A recent review highlights the growth and potential of affective computing in various domains.


Affective Computing: An Interdisciplinary Approach

Affective computing is a rapidly evolving field that integrates multiple disciplines, including computer science, engineering, psychology, and neuroscience. Its goal is to improve machine interaction with human emotions.

With advancements in deep learning, affective computing has experienced significant growth in research since 1997, particularly from 2010 to 2019. Leading contributors to the field include China, the United States, and India.

Future Trends in Affective Computing

The future of affective computing holds promise in creating multimodal datasets, refining multimodal fusion technology, and applying affective computing in various domains.

These developments pave the way for technology to become more emotionally intelligent, allowing for enhanced human-machine interactions in areas such as virtual reality and empathic dialogue.

Advancements and Innovations in Affective Computing

Affective computing has brought about innovations such as enhanced emotion generation techniques for avatars and robots, fine-grained sentiment classification models, and insights from cognitive neuroscience.

Research in affective computing also focuses on group-level emotions and addressing cultural diversity in emotional expressions. Furthermore, ethical considerations and international standards for emotional data usage are being discussed.