Emotion Recognition Using Affective Touch: A Survey

Abstract

Touch Emotion Recognition (TER) is an emerging field in affective computing that focuses on interpreting human emotions through touch interactions. This survey provides a comprehensive review of the haptic technologies, methodologies, and applications of TER, emphasizing its role as a rich but underexplored modality for emotion recognition. A new taxonomy for TER is proposed, categorizing research based on recognition entities, recognition approaches, and touch gesture types, offering a structured perspective for advancing TER studies. The survey highlights the importance of affective touch gesture datasets, evaluates the hardware technologies underpinning TER systems, and reviews key methods for data processing and classification. Applications across domains such as emotional AI, social robotics, virtual reality, and healthcare are examined, showcasing the potential of TER in enhancing the emotional intelligence of artificial agents and robotic systems. Finally, challenges including dataset scarcity, device standardization, and cultural variability are discussed, alongside future directions for creating more robust and inclusive TER systems.