Adaptive data selection improves wearable prediction under low baseline performance

arXiv:2606.00141v1 Announce Type: new Abstract: Adaptive sensing strategies that selectively sample data are increasingly used in wearable health systems to improve prediction performance under limited data budgets, yet their benefits across individuals remain poorly understood. Here, we evaluate adaptive selection of time windows for model training under fixed measurement budgets across multiple sensing modalities, including heart rate, activity, and ecological momentary assessment (EMA), in a longitudinal wearable dataset. We quantify performance gains relative to random sampling using both

Source: arXiv cs.LG — read the full report at the original publisher.

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