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

arXiv: https://arxiv.org/abs/2312.02300 · HTML: https://mlsanity.com/paper/2312.02300/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 23 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Built on by (most cited first, 1 of 1)

- [Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting](https://mlsanity.com/paper/2606.09809/) (2026)

## Abstract (arXiv)

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.