Sensory Perception & Interaction, Research Group

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±16.3% accuracy (2s windows; chance 25%, physiology-only models 64.1±16.9%), confirming the value of this unobtrusive channel. 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.