Anticipatory Detection of Compulsive Body-focused Repetitive Behaviors with Wearables

TL;DR

This study develops a multi-sensory wearable system to detect body-focused repetitive behaviors (BFRBs) early, demonstrating high accuracy and emphasizing the importance of context-aware, timely interventions for prevention.

Contribution

It introduces a novel multi-modal sensing approach combining motion, orientation, and heart rate data for early detection of BFRBs, with extensive evaluation and insights on timing and context.

Findings

Models achieved AUC > 0.90 in detection accuracy.

Detection is more effective in 5-minute observation windows prior to behaviors.

Context-aware models are essential for effective just-in-time interventions.

Abstract

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.