PaperarXiv:2306.07730
BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation
UAI 2023 (PMLR v216, pp. 173–183)
Open code
TL;DR
Models HR evolution as discrete-time HMM; NN emits distribution over HR for each PPG window; belief propagation incorporates HR-change statistics for temporal refinement → calibrated quantized predictive distribution (uncertainty). Evaluated on eight public datasets with three CV protocols; claims SOTA accuracy including OOD (arXiv/PMLR abstract). Contact PPG (wearable benchmarks) — not camera.
- Form factor: wrist
Abstract
We present a novel learning-based method that achieves state-of-the-art performance on several heart rate estimation benchmarks extracted from photoplethysmography signals (PPG). We consider the evolution of the heart rate in the context of a discrete-time stochastic process that we represent as a hidden Markov model. We derive a distribution over possible heart rate values for a given PPG signal window through a trained neural network. Using belief propagation, we incorporate the statistical distribution of heart rate changes to refine these estimates in a temporal context. From this, we obtain a quantized probability distribution over the range of possible heart rate values that captures a meaningful and well-calibrated estimate of the inherent predictive uncertainty. We show the robustness of our method on eight public datasets with three different cross-validation experiments.
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APA
Bieri, V., Streli, P., Demirel, B. U., & Holz, C. (2023). BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation. UAI 2023. https://arxiv.org/abs/2306.07730
BibTeX
@article{bieri2023beliefppg,
title = {{BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation}},
author = {Bieri, Valentin and Streli, Paul and Demirel, Berken Utku and Holz, Christian},
year = {2023},
journal = {UAI 2023},
eprint = {2306.07730},
archivePrefix = {arXiv}
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