Comprehensive Survey on Recognition of Emotions from Body Gestures

Automatic emotion identification has emerged as a prominent area of research during the past decade, with applications in healthcare, human-computer interaction, and behavioral analysis. Although facial expressions and verbal communication have been thoroughly examined, the identification of emotions via body gestures is still inadequately investigated. Body gestures, an essential aspect of "body language" offer significant contextual indicators shaped by gender and culture variations. Recent breakthroughs in deep learning have facilitated the development of robust models capable of accurately capturing complex human movements, hence enhancing emotion recognition precision and adaptability. This study presents a thorough framework for the automatic recognition of emotional body gestures, encompassing essential elements such as individual detection, position estimation, and representation learning. High computational costs and the need for advanced algorithms to fuse multimodal data add to these hurdles. Recent advancements in deep learning, have shown great potential to overcome these issues and improve accuracy. This work highlights the applications, challenges, and future directions in emotion recognition from body gestures, emphasizing the need for scalable, robust, and real-world-ready systems that can enable emotionally intelligent technologies.