Naturalistic human touch expression can be an emotionally potent modality, and promising as an informative but unobtrusive input into machine-learning models of affect. However, application-ready emotion-aware technologies must be trained on labeled samples of authentic (felt) emotion of a significant range of intensity and valence. These may come at significant, even traumatic, personal cost for any modality. We examined (a) performance of the novel touch modality, and (b) how a care-centered protocol might manage personal burden. Participants (N=5; 3 team members), shared autobiographical stories that elicited powerful emotional dynamics, in 1-3 sessions each (total 10). During storytelling, incidental touch was captured on a pillow-mounted custom flexible 10×10-taxel pressure sensor, alongside physiological signals; then labeled with multiple passes of rich, multimodal self-reports. Protocol, study and analysis design prioritized reflexivity and participant experience.Accuracy:Participant-specific, touch-only models predicted emotion direction (trajectory slope) with 65.2$\pm$16.3% accuracy (2s windows; chance 25%, physiology-only models 64.1$\pm$16.9%), confirming the value of this unobtrusivechannel.Personal cost:Qualitative analysis contributed an extensive picture of the emotional toll of generating such data, but also some benefits. We offer recommendations for sustainable ethical sourcing of affective data which balance personalization, performance, therapeutic insight and participant care.

Computational emotion recognition relies on observable expressions. However, negative situations can evoke regulation mechanisms that obscure and mask emotional experiences, often by smiling. As smiles are typically associated with positive emotions, this mismatch of emotional experience and expression may lead to misinterpretations by most current algorithmic affective computing approaches. To improve computational modeling of real-life experiences and expressions in negative social situations, we explore connections between smile appearance and function, incorporating participants' rich personal self-reports into ground truth labels for their expressions. We present an empirically grounded smile corpus of 199 smiles that is based on a) recordings of N = 30 participants in negative social situations that are analyzed regarding smile morphology and b) a category system of smile functions based on participants‘ self-reports. In a computational model, we used cleaned corpus data of 183 unique smile instances to classify five smile function categories based on observable nonverbal signals, with results benchmarked at above chance. Applying a theory- and data-driven approach, our analyses confirm a complex relationship between internal smile functions and observable signals. Finally, we discuss smile functions in negative social situations, including ‘despising’, ‘provoking’, and 'kiss my ass'-smiles.

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.

Many emotion classification and prediction approaches focus on emotion state, defined as static and single-valued. In contrast, our in-body experience is of sensations that can quickly evolve, consistent with scientific evidence of physiological regulation mechanisms. Can we reframe classification to estimate dynamic emotion parameters at interactive rates? For insight into dynamic emotion characteristics, we developed a multipass labelling protocol to capture controlled yet genuine emotion evolution elicited as 16 participants played a tense video game. We analyze and align multiple self-report outputs, inspect the signals for emotion dynamics, and consider label metaphors of position and angle — “where I am” vs. “where I'm going”. Finally, we reflect on the benefits and drawbacks of such a protocol for developing models of fast-evolving emotion.