AI RESEARCH
Wearable Foundation Models Should Go Beyond Static Encoders
arXiv CS.LG
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ArXi:2603.19564v1 Announce Type: new Wearable foundation models (WFMs), trained on large volumes of data collected by affordable, always-on devices, have nstrated strong performance on short-term, well-defined health monitoring tasks, including activity recognition, fitness tracking, and cardiovascular signal assessment. However, most existing WFMs primarily map short temporal windows to predefined labels via static encoders, emphasizing retrospective prediction rather than reasoning over evolving personal history, context, and future risk trajectories.