VideoPulse is a neonatal dataset and end‑to‑end pipeline that estimates heart rate and peripheral capillary oxygen saturation (SpO2) from facial video without contact. The dataset contains 157 recordings from 52 neonates, and the pipeline uses face alignment, artifact‑aware supervision, and 3D CNN backbones to produce predictions every 2 seconds. On the NBHR dataset the model achieves a heart‑rate MAE of 2.97 bpm and SpO2 MAE of 1.69 %.
"whyItMatters":"The results show that short, unaligned neonatal video segments can accurately estimate vital signs, offering a low‑cost, non‑invasive monitoring option for neonatal intensive care."
By Deependra Dewagiri, Kamesh Anuradha, Pabadhi Liyanage, Helitha Kulatunga, Pamuditha Somarathne, Udaya S. K. P. Miriya Thanthrige, Nishani Lucas, Anusha Withana, Joshua P. Kulasingham
EgoHRV is a method that estimates heart rate variability (HRV) and heart rate (HR) from the gaze cameras in egocentric headsets. It uses a 3D backbone and a low–high decomposition module to extract the blood volume pulse signal from gaze video, and aligns frequency‑domain representations of contact‑based and camera‑derived signals through cross‑domain pretraining. The approach achieves state‑of‑the‑art accuracy for HR and HRV estimation and, when integrated into EgoExo4D’s proficiency estimator, improves accuracy by 17.8%.
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:2606. 12378v1 Announce Type: cross Abstract: Physiological awareness is important for service, social, and assistive robots that interact with humans in everyday environments.
By Zhi Wei Xu, Torbj\"orn E. M. Nordling
arXiv:2606. 15927v1 Announce Type: new Abstract: Diabetes and extreme blood sugar levels are some of the major health problems faced by humans today across the world.
By Ruhani Bhatia, Vijval Ekbote
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
arXiv:2508. 11664v2 Announce Type: replace-cross Abstract: Sleep stage classification is critical for diagnosing and managing disorders like sleep apnea and insomnia.
By Zahra Mohammadi, Parnian Fazel, Siamak Mohammadi
Remote photoplethysmography (rPPG) estimates the blood volume pulse (BVP) signal from facial videos, enabling contact-free health monitoring. Conventional clip-wise approaches, which use video clips as input, require capturing over one hundred frames before inference, thus introducing several seconds of delay and hindering real-time use.
The paper introduces DPNet, a Mamba-based deep learning framework for denoising photoplethysmography (PPG) signals while preserving physiological information. It incorporates a scale‑invariant signal‑to‑distortion ratio loss and an auxiliary heart‑rate predictor to enhance waveform fidelity and maintain heart‑rate accuracy. Experiments on the BIDMC dataset show that DPNet outperforms conventional filtering and existing neural models in robustness against synthetic noise and real‑world motion artifacts, making it suitable for wearable healthcare systems.
By I Chiu, Yu-Tung Liu, Kuan-Chen Wang, Hung-Yu Wei, Yu Tsao
The paper introduces Heartian, a physiology‑aware framework that augments Gaussian head avatars with cardiac‑cycle‑dependent albedo modulation, enabling the encoding of remote photoplethysmography (rPPG) signals. By supervising with synchronized contact PPG, the method models the cardiac waveform as a sum of two Gaussians and learns per‑frame spatial residuals via a lightweight MLP. Experiments on 152 stationary recordings from UBFC‑rPPG, PURE, and MMPD show heart‑rate estimation errors as low as 0.29 bpm MAE and 0.38 % MAPE, while preserving reconstruction quality with negligible PSNR loss.
By Xiaoyue Fan, Jose Echevarria, Akshay Paruchuri, Kaan Ak\c{s}it
arXiv:2609.24814v1 Announce Type: cross
Abstract: Advances in processing power, camera technologies, and mobile image analysis have made smartphones and other mobile devices, such as laptops, increas...
By Syed Muhammad Ibne Zulfiker, Tanzima Hashem, Fariha Tabassum Islam, Md Sultanul Arifin, Khandker Aftarul Islam, Nishat Anjum Bristy, Faria Huq, Priyeta Saha, Syeda Nahida Akter, Arpita Saha
The study introduces TRACER, a Transformer-based model that uses contrastive event representation to predict timelines leading to heart failure hospitalizations from low-resolution, irregularly sampled telemonitoring data. TRACER incorporates time-aware embeddings, contrastive pre‑training for anomaly detection, and independent binary classifiers, and was evaluated on biomarker sequences from 276 heart failure patients. The model achieved 66.7% accuracy in predicting hospitalization timelines with a 7.9% overestimation, outperforming other tested models by reformulating training as an event detection problem.
By Erik Aerts, Yinan Yu, Annika Rosengren, Michael Fu, Martin Lindgren, Falk Dippel, Martin Adiels, Helen Sj\"oland