Reliability-Controlled Head Adaptation for Gait Prediction with Wearable Devices Under Unreliable Calibration

When a wearable gait predictor is personalized to a new user, a short calibration session may contain missing channels, sensor-placement changes, electrode shift, noise, fatigue, or other non-ideal effects. Treating all calibration samples as equally trustworthy can overpersonalize the prediction head and induce harmful drift away from the source model. We study calibration quality as a control signal for new-user personalization and propose Dynamic Trust-Region Head Adaptation (DTR-HA), a head-only adaptation rule that combines reliability-weighted calibration loss with a reliability-scaled source-head anchor. The temporal encoder is frozen, and lower reliability strengthens a soft source-head penalty. The Bilateral Lower-Limb Neuromechanical Signals dataset (BLISS) defines the target early gait-phase prediction task with 21-subject leave-one-subject-out evaluation; complete-bout calibration/test separation; and 0, 100, and 200 ms horizons. K2MUSE supplies real non-ideal calibration conditions for mechanism-level stress testing. In K2MUSE 75% bad-calibration tests with a held-out blind context-based reliability scorer, DTR-HA reduced empirical risk by 20–23% relative to plain head adaptation, improved macro-F1 by 0.018–0.022, and achieved 25/28 paired subject–condition wins with fewer negative-adaptation cases. In a secondary BLISS calibration-pollution check, DTR-HA kept macro-F1 within 0.003 of plain head adaptation while reducing calibration-induced head drift across all horizons. These results support calibration quality as an adaptation-stage control signal for stable, lightweight wearable gait personalization under unreliable calibration.