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RECAST: Recent & Context-Aware Sampling for Test-Time Adaptation in Streaming Biosignals

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

Streaming biosignals vary across subjects and drift over time, so population-trained models lose accuracy during long-term monitoring. Test-time adaptation (TTA) enables online personalization by updating the model on incoming samples. But in a stream, a basic question is left open: \emph{which samples should drive each update?} Using all buffered samples blurs the update with irrelevant segments. Using only the latest segment makes the update noisy and unstable. The most useful samples are recent, aligned with the current physiological state, and reliable enough to learn from. We propose \tex

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