  Body-focused repetitive behaviors (BFRBs), like face-touching or
skin-picking, are hand-driven behaviors which can damage one's appearance, if
not identified early and treated. Technology for automatic detection is still
under-explored, with few previous works being limited to wearables with single
modalities (e.g., motion). Here, we propose a multi-sensory approach combining
motion, orientation, and heart rate sensors to detect BFRBs. We conducted a
feasibility study in which participants (N=10) were exposed to BFRBs-inducing
tasks, and analyzed 380 mins of signals under an extensive evaluation of
sensing modalities, cross-validation methods, and observation windows. Our
models achieved an AUC > 0.90 in distinguishing BFRBs, which were more evident
in observation windows 5 mins prior to the behavior as opposed to 1-min ones.
In a follow-up qualitative survey, we found that not only the timing of
detection matters but also models need to be context-aware, when designing
just-in-time interventions to prevent BFRBs.
