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New guide details uncertainty quantification for ML models in PPG signal analysis

By PulseAugur Editorial · [1 sources] ·

A new guide offers best practices for quantifying uncertainties in machine learning models applied to photoplethysmography (PPG) signals from wearable devices. The work, conducted as part of the QUMPHY project, details model selection for regression and classification tasks, and outlines various uncertainty quantification techniques. It also includes six benchmark problems with associated datasets and discusses ethical considerations. AI

IMPACT Provides guidance for improving the reliability of ML models used in health monitoring devices.

RANK_REASON The cluster describes a published academic paper detailing a guide for machine learning model uncertainty quantification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New guide details uncertainty quantification for ML models in PPG signal analysis

COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · P. Harris, C. Bench, M. Rinkevi\v{c}ius, V. Marozas, L. Coquelin, A. Thompson, M. Nandi, U. Hackstein, P. J. Aston ·

    Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals

    arXiv:2607.19999v1 Announce Type: new Abstract: This Good Practice Guide presents work done in the QUMPHY project (Uncertainty quantification for machine learning models applied to photoplethysmography signals) that considered both machine learning and uncertainty quantification …

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