Can you use movement, temperature, and proximity sensor data to differentiate between body-focused repetitive behaviors (BFRBs), like hair pulling, from non-BFRB everyday gestures, like adjusting glasses? The goal of this competition is to develop a predictive model that distinguishes BFRB-like and non-BFRB-like activity using data from a variety of sensors collected via a wrist-worn device. Successfully disentangling these behaviors will improve the design and accuracy of wearable BFRB-detection devices, which are relevant to a wide range of mental illnesses, ultimately strengthening the tools available to support their treatment.