A Comprehensive Survey on Affective Computing: Challenges, Trends, Applications, and Future Directions

Abstract

Affective computing, as its name implies, focuses on the recognition of human emotions, sentiments, and feelings. This interdisciplinary field encompasses diverse areas such as languages, sociology, psychology, computer science, and physiology. However, there is a notable absence of research exploring the interaction between machine learning (ML) and mixed reality (XR) for affective computing. This paper aims to address this gap by discussing the importance of affective computing and delving into its concepts, methods, and outcomes. Drawing upon ML and XR approaches, we conduct a comprehensive survey of recent methodologies employed in affective computing. Additionally, we examine state-of-the-art approaches and available affective data resources. Furthermore, we explore various applications where affective computing has a significant impact, providing valuable insights for future scholars seeking to deepen their understanding of its relevance and practical implications.