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.
By Xu Wang, Shuhan Yi, Zhen Cheng
arXiv:2412.17540v2 Announce Type: replace
Abstract: Reflective photoplethysmography (PPG) has become the default sensing technique in wearable devices to monitor cardiac activity via a person's heart...
By Manuel Meier, Berken Utku Demirel, Christian Holz
arXiv:2605.10871v3 Announce Type: replace-cross
Abstract: We present Attractor-Vascular Coupling Theory (AVCT), a theory-guided framework for deriving interpretable blood pressure (BP) predictors fro...
By Timothy Oladunni, Farouk Ganiyu Adewumi
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.
By Arvasu Kulkarni, Mahir Jain, Aditya Ray Mishra, Sandeep Singh, Siddharth Panwar
The study evaluates how acquisition context and photoplethysmography (PPG) signals contribute to session‑calibrated blood pressure tracking using a wearable‑state model on 653 AuroraBP participants. Adding contextual information reduced mean absolute errors (MAEs) for systolic and diastolic BP by 0.436 mmHg and 0.126 mmHg, respectively, while PPG alone provided modest improvements. Exploratory follow‑ups with sensor‑history ensembles and pulse‑rate fusion yielded only slight gains, and a separate intraoperative model (VitalDB) showed limited PPG benefit.
By Farouk Ganiyu Adewumi, Timothy Oladunni
arXiv:2607. 27076v1 Announce Type: new Abstract: Continuous cuffless blood pressure (BP) monitoring remains challenging due to motion artifacts, physiological variability, and the limited robustness of conventional pulse transit time (PTT) models under dynamic conditions.
By Kindeep K. Dhatt, Tengyue Wu, Hanbang Hua, Yayun Du
The paper introduces SpectrumAudit, a label‑sealed auditing method for wearable human‑activity recognition models that uses phase‑randomized full‑window stimuli to probe sensor biases. By replaying the DC component and a zero‑mean residual on held‑out subjects, the audit demonstrates significant accuracy drops across 27 victim models, with DC perturbations proving more harmful than AC in most cases. The study also shows that the audit can distinguish between persistent sensor offsets and zero‑mean variations under a fixed peak‑budget.
By Qingyu Wu, Yuan Wei, Renju Liu, Hua Cheng
arXiv:2608.24555v1 Announce Type: cross
Abstract: Prehospital stroke assessment aims to accurately identify stroke symptoms and make rapid decisions through standardized procedures within an extremel...
By Wentao Yang, Zhenye Xu, Ruoyi Li, Musen Zhang, Yao Guo
arXiv:2608. 07759v1 Announce Type: cross Abstract: Cardiovascular AI models can classify clean elec- trocardiogram (ECG) signals, but real wearable signals change because of motion, breathing, posture, sensor contact, and true clinical deterioration.
By Farouk Ganiyu Adewumi, Timothy Oladunni, Rochak Ghimire, Kosisochukwu Ogbuanya, Sanaa Reeves, Sandy Akoy
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.
By Luukas Per\"akyl\"a, Fahad Sohrab, Ville Hautam\"aki, Merja Hein\"aniemi, Sui Huang, Pekka Abrahamsson
arXiv:2602.15042v2 Announce Type: replace-cross
Abstract: Reliable sleep staging remains challenging for lightweight wearable devices such as single-channel electroencephalography (scEEG) or photople...
By Jiawei Wang, Liang Xu, Shuntian Zheng, Yu Guan, Kaichen Wang, Ziqing Zhang, Chen Chen, Laurence T. Yang, Sai Gu, M. Jamal Deen
The paper introduces EpiCoach, an agentic system that treats a single athlete as the subject of accountable N‑of‑1 science. It uses a mechanistic digital twin—comprising a lagged heart‑rate model, a fitness‑fatigue model, and a latent skill state—to generate probabilistic predictions for future training sessions, which are logged in an evidence‑gated hypothesis registry. Over eighteen months, the system’s predictions were evaluated both retrospectively and prospectively, showing improved heart‑rate forecasting and a transparent, auditable loop for personalized training guidance.
By Behnam Moradi