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
The proliferation of wearable health technologies and advancements in AI/ML enable more sophisticated data interpretation and strategic data selection.
Improving prediction accuracy with limited data in wearable health systems can lead to more effective personalized health interventions and resource optimization.
The understanding of adaptive sensing strategies' benefits and limitations across individuals and sensing modalities is expanded.
- · Wearable health system developers
- · Personalized medicine providers
- · AI/ML researchers in health applications
- · Patients with chronic conditions
- · Traditional 'always-on' sensing approaches
- · Healthcare systems reliant on infrequent, manual data collection
More efficient and accurate wearable health predictions become possible, particularly in low-performance scenarios.
This efficiency could lead to wider adoption of wearables for health monitoring and proactive intervention tailored to individual needs.
The increased data utility may spur demand for advanced AI agents capable of interpreting diverse sensor data for predictive health management.
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