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This paper explores the challenges in evaluating machine learning (ML) modelsfor continuous health monitoring using wearable devices beyond conventionalmetrics. We state the complexities posed by real-world variability, diseasedynamics, user-specific characteristics, and the prevalence of falsenotifications, necessitating novel evaluation strategies. Drawing insights fromlarge-scale heart studies, the paper offers a comprehensive guideline forrobust ML model evaluation on continuous health monitoring.

Reconsideration on evaluation of machine learning models in continuous monitoring using wearables