arXiv Computer Vision
WildPPG: A Real-World PPG Dataset of Long Continuous Recordings
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arXiv Machine Learning
Oct 12
PPG-Bench: A Unified Benchmark for PPG Foundation Models
PPG-Bench is an open‑source benchmark that evaluates five publicly available photoplethysmography (PPG) foundation models across nine datasets and 26 tasks, covering cardiovascular state estimation, activity, sleep, screening, and identification. The benchmark tests models under four downstream regimes—linear probing, random forests, frozen MLP probes, and end‑to‑end finetuning—while also comparing them to three controls: a random/constant baseline, a deterministic pulse‑rate‑variability feature set, and a generic time‑series foundation model (MOMENT). Results show that over a third of task‑regime combinations yield no advantage for any PPG foundation model over the baseline, and in most cases no PPG‑specific model outperforms the simple controls, indicating that current PPG foundation models lack generalised, transferable representations.
arXiv AI
Jun 8
A robust PPG foundation model using multimodal physiological supervision
arXiv:2606. 07365v1 Announce Type: cross Abstract: Photoplethysmography (PPG), a non-invasive measure of changes in blood volume, is widely used in both wearable devices and clinical settings.
arXiv Computation and Language
Sep 7
WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data
WearableQA is a new benchmark that tests AI systems on health reasoning using real-world wearable data from 200 users, each with up to 500 days of daily measurements. It contains 4,084 ten‑option multiple‑choice questions derived from wearable time series, blood biomarkers, and demographics, and is organized into 16 question types that distinguish data‑driven computation from physiological interpretation and single‑signal from cross‑signal reasoning. Evaluation of 14 large language models shows wide performance gaps, indicating that the benchmark remains challenging and useful for diagnosing model capabilities.
arXiv Machine Learning
Jul 23
Zero-Shot Heart Rate Variability Forecasting from Consumer Wearables Using Time Series Foundation Models
arXiv:2607. 20027v1 Announce Type: new Abstract: Short-term Heart Rate Variability (HRV) forecasting could provide clinicians with actionable lead time for detecting autonomic dysfunction and adverse cardiac events.
arXiv AI
Jul 28
Blood Pressure Estimation from PPG: A Comparative Study of Direct and ECG-Mediated Deep Learning Pipelines
arXiv:2607. 23406v1 Announce Type: cross Abstract: Continuous cuffless blood pressure (BP) monitoring is essential for connected health systems and wearable devices, enabling early detection, longitudinal tracking, and personalized management of cardiovascular disease.
arXiv Machine Learning
Oct 23
An Agent Architecture with Tools and Memory for Selective Correction of Wrist PPG Heart Rate Estimates
The paper introduces an agent architecture that uses tools and memory to selectively correct wrist photoplethysmography (PPG) heart‑rate estimates. By generating candidate corrections from pulse, motion, and historical data, and evaluating their predicted benefits and harms, the system decides whether to retain, correct, or reject each estimate. Across four public datasets, the approach reduced mean absolute error by up to 44.5% and demonstrated real‑time deployment on a wearable‑smartphone platform.