Reconsideration on evaluation of machine learning models in continuous monitoring using wearables
This addresses the need for better evaluation methods in health monitoring for researchers and practitioners, but it is incremental as it builds on existing work without introducing new methods.
The paper tackles the problem of evaluating machine learning models for continuous health monitoring with wearables, highlighting challenges like real-world variability and false notifications, and provides a comprehensive guideline based on insights from large-scale heart studies.
This paper explores the challenges in evaluating machine learning (ML) models for continuous health monitoring using wearable devices beyond conventional metrics. We state the complexities posed by real-world variability, disease dynamics, user-specific characteristics, and the prevalence of false notifications, necessitating novel evaluation strategies. Drawing insights from large-scale heart studies, the paper offers a comprehensive guideline for robust ML model evaluation on continuous health monitoring.