Humans physically express emotion by modulating parameters that register on mammalian skin mechanoreceptors, but are unavailable in current touch-sensing technology. Greater sensory richness combined with data on affect-expression composition is a prerequisite to estimating affect from touch, with applications including physical human-robot interaction. To examine shear alongside more easily captured normal stresses, we tailored recent capacitive technology to attain performance suitable for affective touch, creating a flexible, reconfigurable and soft 36-taxel array that detects multitouch normal and 2-dimensional shear at ranges of 1.5kPa-43kPa and ± 0.3-3.8kPa respectively, wirelessly at 43Hz (1548 taxels/s). In a deep-learning classification of 9 gestures (N=16), inclusion of shear data improved accuracy to 88%, compared to 80% with normal stress data alone, confirming shear stress’s expressive centrality. Using this rich data, we analyse the interplay of sensed-touch features, gesture attributes and individual differences, propose affective-touch sensing requirements, and share technical considerations for performance and practicality.

The global pandemic exposed serious drawbacks in relying on communication modalities in which social touch, however important, is absent. Considerable research has explored haptic technologies for sensing or displaying social touch and influencing affective state, for wellness, social communication, emotion regulation, and affect therapy. However, this Affective Haptic System design (AHSD) work varies widely in purpose and origin discipline, making it difficult to perceive overall progress and identify primary obstacles to practical deployment. We conducted a scoping review and conceptual analysis with a design lens, identifying 110 papers from the last decade in 11 ACM and IEEE venues that regularly attract AHSD work. Our analysis identified 38 dimensions within 8 facets: demographic, theoretical grounding, impact, system specification, usage specification, ethical consideration, technology, and evaluation. We visualize trends, disciplinary mixing, and topical focus over time, and highlight major advances while pinning down crucial gaps that can be addressed in the future.