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

2023/12/04 by Cheng Ding, Ding, Cheng, Zhicheng Guo +9
Computer Science · Environmental Science · Health Professions · #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #FOS: Electrical engineering #Health, Environment, Cognitive Aging #Machine Learning (cs.LG) #Mobile Health and mHealth Applications #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2312.02300

openalex publication_date 2023/12/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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