WearableBridge: Task-Guided Latent Transport for Free-Living Activity Recognition

Wrist-worn accelerometers make activity monitoring feasible at scale, but incidental arm motion, device orientation, and shifting context blur the signal that separates one activity from another. Calibration studies supply higher-quality reference measurements such as concurrent thigh recordings or supervised laboratory wrist sessions, but they are expensive, cover few participants, and are rarely available at deployment. How can limited reference data improve recognition for participants who wear only a wrist device? We propose WearableBridge, an algorithm that learns a task-guided transport map from the wrist-feature distribution toward the reference-feature distribution. WearableBridge holds the reference encoder and classifier fixed and learns a map, the bridge, that carries each wrist feature into the region the classifier associates with its activity. Reference features or the reference classifier's predictions indicate the destination, and a classification loss penalizes wrist features that land in the region of another activity. We instantiate WearableBridge with three bridges that differ in how the reference guides the map: Mapper regresses onto reference features, Distillation matches the reference classifier's class probabilities, and MeanFlow learns a one-step flow from wrist toward reference features. On 40 held-out free-living PAAWS participants, all three bridges improve macro-F1 in every training run, and the strongest, MeanFlow, raises mean macro-F1 from 35.1% to 56.0% with concurrent thigh references (Thigh2Wrist), and from 54.0% to 63.5% with group-matched laboratory wrist references (Lab2Freeliving). Fitted only on PAAWS, MeanFlow also raises macro-F1 on the CAPTURE-24 cohort from 42.8% to 54.7% without using its labels. The gains replicate on SELFback, hold across four reference encoders, and grow further with temporal context. Reference recordings from a calibration subset can therefore improve wrist-only recognition for new participants while the reference model stays unchanged.