Non-Invasive Vital Sign Monitoring

Neueste Paper

• HRVConformer: Neonatal Hypoxic-Ischemic Encephalopathy Classification from the Heart Rate signals

  Shuwen Yu, William P Marnane, Geraldine B. Boylan, Gordon Lightbody · 27. Mai 2026

  This paper presents the HRVConformer, a novel deep learning architecture for the classification of hypoxic-ischemic encephalopathy (HIE) using the instantaneous heart rate (HR) signal. Unlike conventional approaches that rely on handcrafted features, HRVConformer directly processes raw HR signals in…

• VAMP-Diff: VampPrior Latent Diffusion for Photoplethysmography Modeling

  Fatemeh Ghasemi Balouei, Nathan Willemsen, Mahesh Banavar, Bahman Moraffah · 25. Mai 2026

  Photoplethysmography (PPG) has become a ubiquitous physiological signal; however, current generative models still struggle to preserve realistic waveform morphology and learn a latent structure that captures cardiac and respiratory physiology. PPG generators trained with adversarial losses can produ…

• Cross-View Attention Fusion Net: A Prior-Guided Dual-View Representation Learning for Cardiac Output Estimation from Short-Term PPG Signals

  Yaowen Zhang, Bo Cui, Libera Fresiello, Peter H. Veltink, Dirk W. Donker, Ying Wang · 20. Mai 2026

  Accurate cardiac output (CO) estimation from photoplethysmography (PPG) is promising for unobtrusive hemodynamic monitoring, but remains difficult since CO is jointly determined by cardiac function and vascular tone. Conventional feature-based models use physiologically meaningful PPG descriptors, y…

• A Nonlinear Complexity Index for Wearable PPG Cardiovascular Stability: Multiscale Validation, Systematic Evaluation Correction, and Bayesian Parameter Optimization

  Timothy Oladunni, Farouk Ganiyu Adewumi · 20. Mai 2026

  Cardiovascular stability estimation from wearable photoplethysmography (PPG) requires a principled nonlinear framework, yet major gaps persist in heuristic parameter selection and evaluation protocols that inflate reported performance. We introduce a Stability-Constrained Cardiovascular Stability In…

• Multi-site PPG: An In-the-Wild Physiological Dataset from Emerging Multi-site Wearables

  Jiayi Shao (Shirley), Jiaying Ye (Shirley), Shengyao Liu (Shirley), Zachary Englhardt (Shirley), Girish Narayanswamy (Shirley), Vikram Iyer (Shirley), Qiuyue (Shirley), Xue · 19. Mai 2026

  Wearables are widely used for mobile health monitoring, and photoplethysmography (PPG) is a key sensing modality for heart rate and related physiological measurements. However, public in-the-wild PPG datasets remain largely wrist-centric or limited to short, controlled studies, constraining research…

• Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography

  Mohammad Moulaeifard, Ciaran Bench, Philip J. Aston, Nils Strodthoff · 19. Mai 2026

  Uncertainty quantification (UQ) is critical for safety-critical domains like healthcare, yet it is rarely evaluated under realistic out-of-distribution (OOD) conditions. Here, we assessed predictive performance and uncertainty reliability for deep learning-based blood pressure (BP) estimation from p…

• Attractor-Vascular Coupling Theory: Formal Grounding and Empirical Validation for AAMI-Standard Cuffless Blood Pressure Estimation from Smartphone Photoplethysmography

  Timothy Oladunni, Farouk Ganiyu Adewumi · 19. Mai 2026

  This work proposes Attractor-Vascular Coupling Theory (AVCT), a mathematical framework showing that cardiac attractor geometry encodes blood pressure (BP) information sufficient for AAMI-standard estimation, and validates the theory through a calibrated cuffless BP model using photoplethysmography (…

• Compact Latent Manifold Translation: A Parameter-Efficient Foundation Model for Cross-Modal and Cross-Frequency Physiological Signal Synthesis

  Bo Cui, Xiaowen Song, Yaowen Zhang, Shunzhe Zhang, B. J. F. van Beijnum, Monique Tabak, Ying Wang · 14. Mai 2026

  The analysis of physiological time series, such as electrocardiograms (ECG) and photoplethysmograms (PPG), is persistently hindered by modality and frequency gaps stemming from heterogeneous recording devices. Existing foundation models typically rely on continuous latent spaces, which frequently su…

• WavesFM: Hierarchical Representation Learning for Longitudinal Wearable Sensor Waveforms

  Peng Cao, Zhijian Yang, Tennison Liu, Jonathan Wang, Jiang Wu, Magdalena Proszewska, Arvind Pillai, Mingwu Gao, Amir Farjadian, Lawrence Cai, Emily Blanchard, Daniel McDuff, Pramod Rudrapatna, Matthew Thompson, Anupam Pathak, Mark Malhotra, Shwetak Patel, Dina Katabi, Paolo Di Achille, Ming-Zher Poh · 12. Mai 2026

  Wearable sensors enable the continuous acquisition of high-resolution physiological waveforms, such as photoplethysmography and accelerometry, under free-living conditions. However, inferring health-related phenotypes from these signals presents significant challenges due to high sampling frequencie…

• Emergent Symbolic Structure in Health Foundation Models: Extraction, Alignment, and Cross-Modal Transfer

  Gajendra Katuwal, Advait Koparkar, Salar Abbaspourazad, Anshuman Mishra, Sarvesh Kirthivasan · 11. Mai 2026

  Health foundation models (FMs) learn useful representations from wearable sensors, but interpreting what they encode and transferring that knowledge across modalities after training remains difficult. We present a post-training framework that decomposes frozen embeddings into interpretable direction…

• A Scoping Review of Deep Learning Methods for Photoplethysmography Data

  Guangkun Nie, Jiabao Zhu, Gongzheng Tang, Deyun Zhang, Shijia Geng, Qinghao Zhao, Shenda Hong · 6. Mai 2026

  Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information, with broad deployment in both clinical monitoring systems and wearable devices. In recent years, the integration of deep learning has substantially advanced PPG signal a…

• A Hybrid Windkessel-Neural Approach for Improved Noninvasive Blood Pressure Monitoring

  Vaibhav Gollapalli, Aniruth Ananthanarayanan · 5. Mai 2026

  Owing to the recent advancements in wearable devices for health care, the importance of BP estimation without cuffs increases. Cuff technologies are inappropriate for continuous BP measurement due to their inconvenient usage, invasive character, necessity of calibration, large size, and inability to…

• Cardiac Stability Theory: An Axiomatically Grounded Framework for Continuous Cardiac Health Monitoring via Smartphone Photoplethysmography

  Timothy Oladunni, Farouk Ganiyu Adewumi · 28. April 2026

  We present Cardiac Stability Theory (CST), an axiomatically grounded framework formally defining cardiovascular health as a stability margin around a cardiac dynamical attractor. From four axioms we derive the Cardiac Stability Index (CSI), a composite scalar in [0,1] integrating the largest Lyapuno…

• A General Framework for Generative Self-supervised Learning in Non-invasive Estimation of Physiological Parameters Using Photoplethysmography

  Zexing Zhang, Huimin Lu, Songzhe Ma, Jianzhong Peng, Chenglin Lin, Niya Li, Bingwang Dong · 28. April 2026

  Aligning physiological parameter labels with large-scale photoplethysmographic (PPG) data for deep learning is challenging and resource-intensive. While self-supervised representation learning (SSRL) can handle limited annotated data, the challenge lies in learning robust shared representations from…

• Trustworthy deep domain adaptation for wearable photoplethysmography signal analysis with decision-theoretic uncertainty quantification

  Ciaran Bench · 21. April 2026

  In principle, deep generative models can be used to perform domain adaptation; i.e. align the input feature representations of test data with that of a separate discriminative model's training data. This can help improve the discriminative model's performance on the test data. However, generative mo…

• End-to-end Automated Deep Neural Network Optimization for PPG-based Blood Pressure Estimation on Wearables

  Francesco Carlucci, Giovanni Pollo, Xiaying Wang, Massimo Poncino, Enrico Macii, Luca Benini, Sara Vinco, Alessio Burrello, Daniele Jahier Pagliari · 14. April 2026

  Photoplethysmography (PPG)-based blood pressure (BP) estimation is a challenging task, particularly on resource-constrained wearable devices. However, fully on-board processing is desirable to ensure user data confidentiality. Recent deep neural networks (DNNs) have achieved high BP estimation accur…

• Sense Less, Infer More: Agentic Multimodal Transformers for Edge Medical Intelligence

  Chengwei Zhou, Zhaoyan Jia, Haotian Yu, Xuming Chen, Brandon Lee, Christopher Pulliam, Steve Majerus, Massoud Pedram, Gourav Datta · 14. April 2026

  Edge-based multimodal medical monitoring requires models that balance diagnostic accuracy with severe energy constraints. Continuous acquisition of ECG, PPG, EMG, and IMU streams rapidly drains wearable batteries, often limiting operation to under 10 hours, while existing systems overlook the high t…

• PULSE: Privileged Knowledge Transfer from Rich to Deployable Sensors for Embodied Multi-Sensory Learning

  Zihan Zhao, Kaushik Pendiyala, Masood Mortazavi, Ning Yan · 9. April 2026

  Multi-sensory systems for embodied intelligence, from wearable body-sensor networks to instrumented robotic platforms, routinely face a sensor-asymmetry problem: the richest modality available during laboratory data collection is absent or impractical at deployment time due to cost, fragility, or in…

• Benchmark Problems and Benchmark Datasets for the evaluation of Machine and Deep Learning methods on Photoplethysmography signals: the D4 report from the QUMPHY project

  Urs Hackstein, Jordi Alastruey, Philip Aston, Ciaran Bench, Peter H. Charlton, Loic Coquelin, Nando Hegemann, Vaidotas Marozas, Mohammad Moulaeifard, Manasi Nandi, Andrius Petrenas, Oskar Pfeffer, Mantas Rinkevicius, Andrius Solosenko, Nils Strodthoff, Sara Vardanega · 3. April 2026

  This report is part of the Qumphy project (22HLT01 Qumphy) that is funded by the European Union and is dedicated to the development of measures to quantify the uncertainties associated with Machine Learning algorithms applied to medical problems, in particular the analysis and processing of Photople…

• EMPD: An Event-based Multimodal Physiological Dataset for Remote Pulse Wave Detection

  Qian Feng, Pengfei Li, Rongshan Gao, Jiale Xu, Rui Gong, Yidi Li · 31. März 2026

  Remote photoplethysmography (rPPG) based on traditional frame-based cameras often struggles with motion artifacts and limited temporal resolution. To address these limitations, we introduce EMPD (Event-based Multimodal Physiological Dataset), the first benchmark dataset specifically designed for non…

• Deriving Health Metrics from the Photoplethysmogram: Benchmarks and Insights from MIMIC-III-Ext-PPG

  Mohammad Moulaeifard, Philip J. Aston, Peter H. Charlton, Nils Strodthoff · 24. März 2026

  Photoplethysmography (PPG) is one of the most widely captured biosignals for clinical prediction tasks, yet PPG-based algorithms are typically trained on small-scale datasets of uncertain quality, which hinders meaningful algorithm comparisons. We present a comprehensive benchmark for PPG-based clin…

• mmWave-Diffusion:A Novel Framework for Respiration Sensing Using Observation-Anchored Conditional Diffusion Model

  Yong Wang, Qifan Shen, Bao Zhang, Zijun Huang, Chengbo Zhu, Shuai Yao, Qisong Wu · 24. März 2026

  Millimeter-wave (mmWave) radar enables contactless respiratory sensing,yet fine-grained monitoring is often degraded by nonstationary interference from body micromotions.To achieve micromotion interference removal,we propose mmWave-Diffusion,an observation-anchored conditional diffusion framework th…

• A Review of Deep Learning Methods for Photoplethysmography Data

  Guangkun Nie, Jiabao Zhu, Gongzheng Tang, Deyun Zhang, Shijia Geng, Qinghao Zhao, Shenda Hong · 17. März 2026

  Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information and is extensively deployed in both clinical monitoring systems and wearable devices. In recent years, the integration of deep learning has substantially advanced PPG sig…

• Vib2ECG: A Paired Chest-Lead SCG-ECG Dataset and Benchmark for ECG Reconstruction

  Guorui Lu, Xiaohui Cai, Todor Stefanov, Qinyu Chen · 17. März 2026

  Twelve-lead electrocardiography (ECG) is essential for cardiovascular diagnosis, but its long-term acquisition in daily life is constrained by complex and costly hardware. Recent efforts have explored reconstructing ECG from low-cost cardiac vibrational signals such as seismocardiography (SCG), howe…

• PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning

  Hung Manh Pham, Jinyang Wu, Xiao Ma, Yiming Zhang, Yixin Xu, Aaqib Saeed, Bin Zhu, Zhou Pan, Dong Ma · 5. März 2026

  Photoplethysmography (PPG) is a widely used non-invasive sensing modality for continuous cardiovascular and physiological monitoring across clinical, laboratory, and wearable settings. While existing PPG datasets support a broad range of downstream tasks, they typically provide supervision in the fo…