Non-Invasive Vital Sign Monitoring

Latest papers

• Learning to Assess Heartbeat Observability for mmWave Heart-Rate Sensing

  Yuxuan Hu, Shilin Shan, Jianfei Yang, Feng Xu · 5 October 2026

  Contactless heart-rate sensing with millimeter-wave (mmWave) radar requires assessing whether individual measurements support reliable estimation. We study learning to assess heartbeat observability, defined as the readability of the heartbeat component in an acquired phase spectrum, for selective h…

• FLOW: Feature-Level Optimal Warping for Generalized Remote Physiological Measurement

  Bo Zhao, Junzhe Cao, Dan Guo, Dongmin Huang, Wenjin Wang, Tao Tan, Yue Sun, Zitong YU · 1 October 2026

  Remote photoplethysmography (rPPG) enables non-contact physiological measurement but remains vulnerable to domain shifts from illumination, motion, and sensors. We propose \textbf{FLOW (Feature-Level Optimal Warping)}, an \emph{optimal transport--driven} framework for domain-generalized rPPG. FLOW i…

• PPG-LM: A Photoplethysmography-Language Model with Multi-Level Clinical Alignment

  Xiaoda Wang, Minxiao Wang, Maxwell A Xu, Patrick Langer, Kaiqiao Han, Defu Cao, Xiao Luo, Yuzhe Yang, Yan Liu, Xiao Hu, Yizhou Sun, Wei Wang, Carl Yang · 29 September 2026

  Photoplethysmography (PPG) is widely recorded by clinical monitors and consumer wearables, providing a scalable source of continuous physiological information. These recordings offer an opportunity for physiological assessment at scale, but realizing this potential requires models to learn from both…

• $\unicode{x1F493}$Heartian: Physiology-Aware Relightable Gaussian Head Avatar

  Xiaoyue Fan, Jose Echevarria, Akshay Paruchuri, Kaan Ak\c{s}it · 25 September 2026

  Gaussian head avatars typically model intrinsic facial appearance as temporally static, omitting subtle cardiac-induced skin-color variation. We propose $\unicode{x1F493}$Heartian, a physiology-aware modulation framework that learns cardiac-cycle-dependent per-frame albedo modulation of facial skin-…

• Robust Photoplethysmography Signal Denoising via Mamba Networks

  I Chiu, Yu-Tung Liu, Kuan-Chen Wang, Hung-Yu Wei, Yu Tsao · 23 September 2026

  Photoplethysmography (PPG) is widely used in wearable health monitoring, but its reliability is often degraded by noise and motion artifacts, limiting downstream applications such as heart rate (HR) estimation. This paper presents a deep learning framework for PPG denoising with an emphasis on prese…

• Impact of Multiple Non-Invasive Biosignals on Cardiovascular Biomarker Estimation via Simulation-Based Inference

  Shusaku Maeda, Masahiro Nakano, Tomoharu Iwata, Kenji Komiya, Ryo Nishikimi, Kunio Kashino · 14 September 2026

  As the population ages, the number of patients with cardiovascular diseases continues to increase, highlighting the need for early detection before progression to severe and irreversible functional decline. Consequently, estimating cardiovascular biomarkers from non-invasive biosignals, such as phot…

• SIFPBPNet: A Dual-Path Network for Wearable and Cuffless Blood Pressure Estimation via Individualized Steady-state Representation

  Shuailong Tang, Xiaoyu Li, Donglin Xie, Wei Chen, Guangpu Zhu, Yelei Li, Yali Zheng · 14 September 2026

  Continuous and cuffless blood pressure (BP) monitoring using photoplethysmography (PPG) is of great interest for low-cost and personalized cardiovascular health management. However, significant population heterogeneity and the "one-to-many mapping" problem, where similar waveforms across individuals…

• Beyond Ambiguous Visual Cues: Studying Physiological Disruptions and Cross-Modal Inconsistencies in Deepfake Videos

  Chenxi Yang, Yassine Ouzar, Larbi Boubchir · 14 September 2026

  Recent deepfake detection studies increasingly suggest remote photoplethysmography (rPPG) signals as an authenticity cue. However, existing benchmarks lack physiological ground truth, and current detectors underexplore the cross-level relationship between facial features and physiological dynamics, …

• Beyond Contact Sensors: Deep learning with Pseudo-Labeling for remote Photoplethysmography

  Bhargav Acharya, Barbara Hammer, Hanna Drimalla · 10 September 2026

  Heart rate is a critical biomarker of health, and remote photoplethysmography (rPPG) enables its contactless estimation from video data for telemedicine applications. Recent advancements in deep learning based rPPG methods achieve state-of-the-art results, outperforming classical signal-processing m…

• Analysis of Respiratory Sinus Arrhythmia with Neural Networks

  Julian Szymanski, Patryk Orkisz, Higinio Mora · 9 September 2026

  The paper introduces a neural network-based approach for analyzing ECG signals to estimate respiratory rate by leveraging the phe- nomenon of Respiratory Sinus Arrhythmia (RSA). Our method employs a deep learning model trained to predict respiratory waveforms directly from ECG input data. To achieve…

• A Multidimensional Data-Driven Hybrid Transformer Framework for Non-invasive Continuous Blood Pressure Prediction

  Yuexin Ma, Jingqi Hou, Yuxuan Kang, Zhaoying Liu · 4 September 2026

  Objective. To develop and evaluate a cuffless continuous blood pressure (BP) estimator using temporal physiological and demographic features. We propose a hybrid Transformer framework to estimate diastolic and systolic BP from ECG/PPG-derived feature sequences. Approach. Rather than raw waveforms, t…

• Cross-Dataset Transfer and Reliability of Explainable Artificial Intelligence for RhythmFormer Remote Photoplethysmography

  Louis Chen, Torbj\"orn E. M. Nordling · 4 September 2026

  Background. Remote photoplethysmography estimates the cardiovascular pulse from facial video, and its explanations have rested on inspecting heatmaps rather than on quantitative evidence about where a model reads it. We quantified the explanations and asked whether such explanations transfer between…

• Physiological Information Reliability: Cross-Layer Adaptive Resource Allocation for Cardiovascular Sensing

  Navaneeth Krishnan Kamalakannan, Janakiraman Kamalakannan, Harinisri Velmurugan · 2 September 2026

  Cardiovascular sensing systems must preserve clinically useful information despite signal degradation, wireless losses, energy constraints, and edge-computation latency. We introduce Physiological Information Reliability (PIR), a cross-layer framework that represents physiological information value …

• Property-Specific Recoverability from Contact PPG to Camera rPPG under Heterogeneous Observation Conditions

  Timothy Oladunni, Farouk Ganiyu-Adewumi · 28 August 2026

  Camera-derived remote photoplethysmography (rPPG) is commonly validated through endpoint accuracy, but endpoint performance does not establish whether other physiological properties of source contact photoplethysmography (PPG) remain preserved recording by recording. We evaluated property-specific P…

• Physics-Constrained Deep Learning Model for Contactless Blood Pressure Monitoring from Triaxial Bodyseismography

  Yuanyuan Zhang, Yida Zhang, Jiahui Li, Yuyan Wu, Fei Dou, Xiao Yin, Zhenlin An, Hae Young Noh, Wenzhan Song · 25 August 2026

  Ballistocardiography (BCG) is promising for unobtrusive long-term blood pressure (BP) monitoring in laboratory settings, but traditional BCG signals are vulnerable to the variations in body-bed interaction with shifted fiducial points in temporal or amplitude axis, and BP varies with personal hemody…

• Fuzzy Accuracy Compensates for Label Subjectivity in Classification of Skin Tone Using Wearable Photoplethysmography Signals

  Padmini Krishnadas, Urs Hackstein, Alen Bosnjakovic, Philip J. Aston · 20 August 2026

  We consider the problem of classification of skin tone using photoplethysmography (PPG) signals with labels of the ordinal six-class Fitzpatrick skin tones. A typical accuracy for this task is a poor 40-55 %. However, the labels are subjectively determined by comparing the skin with a colour chart, …

• Change Point--Aware Evaluation and Re-Calibration of PPG-Based Blood Pressure Estimation

  Yunwon Tae, Minje Park, Gyunho Rho, Dongjoon Yoo, Sunghoon Joo · 20 August 2026

  Non-invasive continuous blood pressure (BP) monitoring using photoplethysmography (PPG) is a promising alternative to cuff-based measurements. However, existing PPG-based BP estimation studies predominantly rely on aggregated performance metrics (e.g., mean absolute error) computed over entire evalu…

• CardiacMamba: Fair and Robust RGB-RF Fusion for Remote Heart Rate Estimation via State Space Modeling

  Bo Zhao, Zheng Wu, Yiping Xie, Zitong YU · 18 August 2026

  Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) monitoring from facial videos, but RGB-only methods are vulnerable to illumination changes, motion artifacts, and skin-tone-dependent optical reflectance. We propose CardiacMamba, a fair and robust RGB-RF fusion framework that in…

• Take it Personally: The Limits of General SSL Representations for Real-Life PPG Emotion Detection

  Dominika Kunc, Przemys{\l}aw Kazienko, Stanis{\l}aw Saganowski · 18 August 2026

  While Self-Supervised Learning (SSL) effectively extracts general representations from noisy, unconstrained physiological signals such as photoplethysmography (PPG), its suitability for highly subjective tasks remains unproven. In this work, we evaluate the efficacy of PPG-based SSL for real-life in…

• P2E-VQ: ECG-linked representation augmentation for PPG via discrete patch retrieval

  Zhongli Wu, Zhuangzhi Gao, He Zhao, Feixiang Zhou, Fu Wang, Jinru Ding, Yuankai Wang, Hongyi Qin, Gregory Y. H. Lip, Bil Kirmani, Yalin Zheng · 18 August 2026

  Photoplethysmography (PPG) is widely used in consumer wearables because of its low cost and ease of acquisition. However, unlike electrocardiography (ECG), PPG measures peripheral pulse dynamics rather than cardiac electrical activity, limiting its ability to predict cardiac conditions that rely on …

• Attractor Image-Based Deep Learning of Arterial Pulse Waves for Age Classification

  Sara Vardanega, Patrick Segers, Philip Aston, Ernst Rietzschel, Jordi Alastruey, Manasi Nandi · 13 August 2026

  Arterial pulse waveform morphology evolves with age, reflecting structural and functional changes in the cardiovascular system. Thus, vascular age is a valuable surrogate marker of cardiovascular health, and premature vascular ageing can indicate increased disease risk. Pulse wave analysis could sup…

• A Low-Power Wearable Respiratory Sensor for Non-Invasive Stress Monitoring

  Mohammad Hosseini, Hamed Khatounabadi, Mohammad Fakharzadeh · 7 August 2026

  Respiration provides a continuously available window into physiological state and behavior. However, monitoring it outside controlled settings remains challenging because a wearable system must capture small body deformations while remaining comfortable, low power, and robust to changes in posture a…

• Prototype-based Self-Supervised Multimodal Learning for PPG and Accelerometry Signals

  Wanting Mao, Maxwell A Xu, Harish Haresamudram, Mithun Saha, Santosh Kumar, James Matthew Rehg · 6 August 2026

  Modeling multi-modal time-series data is critical for capturing system-level dynamics, particularly in biosignals where modalities such as ECG, PPG, EDA, and accelerometry provide complementary perspectives on interconnected physiological processes. While recent self-supervised learning (SSL) advanc…

• Reassessing the Feasibility of PPG-Based Non-Invasive Blood Glucose Level Estimation

  Supraja Ramesh, Markus Neufeld, Michael K\"uttner, Tobias R\"oddiger, Michael Beigl · 4 August 2026

  Non-invasive blood glucose level (BGL) estimation from photoplethysmography (PPG) holds great promise for wearable health monitoring, but results across studies are hard to compare due to inconsistent datasets, data leakage, and non-standardized evaluation metrics. We present the first reproducible,…

• PhysAgent: A Multi-Agent Framework for Reliable Remote Heart Rate Estimation

  Yehui Yang, Bo Zhao, Junzhe Cao, Hui Ma, Yue Sun, Wenjin Wang, Zitong Yu · 30 July 2026

  Remote photoplethysmography (rPPG) enables non-contact heart-rate estimation from facial videos, but its weak physiological signal is easily corrupted by motion, illumination changes, occlusion, skin-appearance variation, and device noise. Existing rPPG methods typically rely on a single model to di…