Reconsideration on evaluation of machine learning models in continuous
  monitoring using wearables
Cheng Ding Zhicheng Guo Cynthia Rudin Ran Xiao Fadi B Nahab Xiao Hu
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.