A Comprehensive Survey on Affective Computing; Challenges, Trends, Applications, and Future Directions
It provides a comprehensive overview for researchers and practitioners in affective computing, but is incremental as it synthesizes existing knowledge without new empirical results.
This paper surveys affective computing, focusing on the integration of machine learning and mixed reality to recognize human emotions, and discusses current methodologies, data resources, and applications.
As the name suggests, affective computing aims to recognize human emotions, sentiments, and feelings. There is a wide range of fields that study affective computing, including languages, sociology, psychology, computer science, and physiology. However, no research has ever been done to determine how machine learning (ML) and mixed reality (XR) interact together. This paper discusses the significance of affective computing, as well as its ideas, conceptions, methods, and outcomes. By using approaches of ML and XR, we survey and discuss recent methodologies in affective computing. We survey the state-of-the-art approaches along with current affective data resources. Further, we discuss various applications where affective computing has a significant impact, which will aid future scholars in gaining a better understanding of its significance and practical relevance.