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:2609.10303v1 Announce Type: new
Abstract: Face recognition (FR) is a widely used modality for biometric authentication, but conventional models rely on visible-spectrum imagery and degrade when...
By Anjith George, Adam Unal, Sebastien Marcel
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:2607. 06598v1 Announce Type: cross Abstract: Heart rate measurement is one of the key requirements for real-time health monitoring, in particular for health caring of elderly people.
By Kelly Li, Fulu Li
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
The paper introduces the Columbia University Palm‑vein (CUP) dataset, the first public video‑based palm‑vein dataset that captures palms under four surface conditions—clean, warm, wet, and dirty—along with physiological and demographic metadata. Twenty‑one recognizers are benchmarked on CUP, revealing that models performing well on clean palms lose most accuracy on dirty palms, with mean EER roughly quadrupling. The authors propose a lightweight design that fuses global cosine similarity with a saliency‑steered region‑level optimal transport, achieving state‑of‑the‑art performance across all surfaces while reducing parameters and computational cost, and they identify demographic gaps in warm‑condition performance.
By Xiaofeng Yan, Kechen Liu, Abhilash Venkatesh, Cathy Zhang, Xia Zhou, Salvatore Stolfo