Non-Invasive Vital Sign Monitoring

Latest papers

• Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches

  Mohammad Moulaeifard, Loic Coquelin, Mantas Rinkevi\v{c}ius, Andrius Solo\v{s}enko, Oskar Pfeffer, Ciaran Bench, Nando Hegemann, Sara Vardanega, Manasi Nandi, Jordi Alastruey, Christian Heiss, Vaidotas Marozas, Andrew Thompson, Philip J. Aston, Peter H. Charlton, Nils Strodthoff · 3 March 2026

  Photoplethysmography (PPG) is a widely used non-invasive physiological sensing technique, suitable for various clinical applications. Such clinical applications are increasingly supported by machine learning methods, raising the question of the most appropriate input representation and model choice.…

• Generalizable deep learning for photoplethysmography-based blood pressure estimation -- A Benchmarking Study

  Mohammad Moulaeifard, Peter H. Charlton, Nils Strodthoff · 3 March 2026

  Photoplethysmography (PPG)-based blood pressure (BP) estimation represents a promising alternative to cuff-based BP measurements. Recently, an increasing number of deep learning models have been proposed to infer BP from the raw PPG waveform. However, these models have been predominantly evaluated o…

• Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling

  Zhengdong Huang, Zicheng Xie, Wentao Tian, Jingyu Liu, Lunhong Dong, Peng Yang · 25 February 2026

  Heart rate prediction is vital for personalized health monitoring and fitness, while it frequently faces a critical challenge in real-world deployment: data heterogeneity. We classify it in two key dimensions: source heterogeneity from fragmented device markets with varying feature sets, and user he…

• Non-Invasive Anemia Detection: A Multichannel PPG-Based Hemoglobin Estimation with Explainable Artificial Intelligence

  Garima Sahu, Poorva Verma, Nachiket Tapas · 20 February 2026

  Anemia is a prevalent hematological disorder that requires frequent hemoglobin monitoring for early diagnosis and effective management. Conventional hemoglobin assessment relies on invasive blood sampling, limiting its suitability for large-scale or continuous screening. This paper presents a non-in…

• Task- and Metric-Specific Signal Quality Indices for Medical Time Series

  Jad Haidamous, Christoph Hoog Antink · 16 February 2026

  Medical time series such as electrocardiograms (ECGs) and photoplethysmograms (PPGs) are frequently affected by measurement artifacts due to challenging acquisition environments, such as in ambulances and during routine daily activities. Since automated algorithms for analyzing such signals increasi…

• Towards Affordable, Non-Invasive Real-Time Hypoglycemia Detection Using Wearable Sensor Signals

  Lawrence Obiuwevwi, Krzysztof J. Rechowicz, Vikas Ashok, Sampath Jayarathna · 12 February 2026

  Accurately detecting hypoglycemia without invasive glucose sensors remains a critical challenge in diabetes management, particularly in regions where continuous glucose monitoring (CGM) is prohibitively expensive or clinically inaccessible. This extended study introduces a comprehensive, multimodal …

• In-Hospital Stroke Prediction from PPG-Derived Hemodynamic Features

  Jiaming Liu, Cheng Ding, Daoqiang Zhang · 11 February 2026

  The absence of pre-hospital physiological data in standard clinical datasets fundamentally constrains the early prediction of stroke, as patients typically present only after stroke has occurred, leaving the predictive value of continuous monitoring signals such as photoplethysmography (PPG) unvalid…

• Adaptive Attribute-Decoupled Encryption for Trusted Respiratory Monitoring in Resource-Limited Consumer Healthcare

  Xinyu Li, Jinyang Huang, Feng-Qi Cui, Meng Wang, Peng Zhao, Meng Li, Dan Guo, Meng Wang · 6 February 2026

  Respiratory monitoring is an extremely important task in modern medical services. Due to its significant advantages, e.g., non-contact, radar-based respiratory monitoring has attracted widespread attention from both academia and industry. Unfortunately, though it can achieve high monitoring accuracy…

• PENGUIN: General Vital Sign Reconstruction from PPG with Flow Matching State Space Model

  Shuntaro Suzuki, Shuitsu Koyama, Shinnosuke Hirano, Shunya Nagashima · 5 February 2026

  Photoplethysmography (PPG) plays a crucial role in continuous cardiovascular health monitoring as a non-invasive and cost-effective modality. However, PPG signals are susceptible to motion artifacts and noise, making accurate estimation of vital signs such as arterial blood pressure (ABP) challengin…

• Benchmarking and Enhancing PPG-Based Cuffless Blood Pressure Estimation Methods

  Neville Mathew, Yidan Shen, Renjie Hu, Maham Rahimi, George Zouridakis · 5 February 2026

  Cuffless blood pressure screening based on easily acquired photoplethysmography (PPG) signals offers a practical pathway toward scalable cardiovascular health assessment. Despite rapid progress, existing PPG-based blood pressure estimation models have not consistently achieved the established clinic…

• Weighted Temporal Decay Loss for Learning Wearable PPG Data with Sparse Clinical Labels

  Yunsung Chung, Keum San Chun, Migyeong Gwak, Han Feng, Yingshuo Liu, Chanho Lim, Viswam Nathan, Nassir Marrouche, Sharanya Arcot Desai · 4 February 2026

  Advances in wearable computing and AI have increased interest in leveraging PPG for health monitoring over the past decade. One of the biggest challenges in developing health algorithms based on such biosignals is the sparsity of clinical labels, which makes biosignals temporally distant from lab dr…

• SIGMA-PPG: Statistical-prior Informed Generative Masking Architecture for PPG Foundation Model

  Zongheng Guo, Tao Chen, Yang Jiao, Yi Pan, Xiao Hu, Manuela Ferrario · 30 January 2026

  Current foundation model for photoplethysmography (PPG) signals is challenged by the intrinsic redundancy and noise of the signal. Standard masked modeling often yields trivial solutions while contrastive methods lack morphological precision. To address these limitations, we propose a Statistical-pr…

• Collaborative Learning-Enhanced Lightweight Models for Predicting Arterial Blood Pressure Waveform in a Large-scale Perioperative Dataset

  Wentao Li, Yonghu He, Zirong Yu, Kun Gao, Qing Liu, Yali Zheng · 27 January 2026

  Noninvasive arterial blood pressure (ABP) monitoring is essential for patient management in critical care and perioperative settings, providing continuous assessment of cardiovascular hemodynamics with minimal risks. Numerous deep learning models have developed to reconstruct ABP waveform from nonin…

• A New Paradigm for Trusted Respiratory Monitoring Via Consumer Electronics-grade Radar Signals

  Xinyu Li, Jinyang Huang, Feng-Qi Cui, Meng Wang, Peng Zhao, Meng Li, Dan Guo, Meng Wang · 26 January 2026

  Respiratory monitoring is an extremely important task in modern medical services. Due to its significant advantages, e.g., non-contact, radar-based respiratory monitoring has attracted widespread attention from both academia and industry. Unfortunately, though it can achieve high monitoring accuracy…

• SCG With Your Phone: Diagnosis of Rhythmic Spectrum Disorders in Field Conditions

  Peter Golenderov, Yaroslav Matushenko, Anastasia Tushina, Michal Barodkin · 21 January 2026

  Aortic valve opening (AO) events are crucial for detecting frequency and rhythm disorders, especially in real-world settings where seismocardiography (SCG) signals collected via consumer smartphones are subject to noise, motion artifacts, and variability caused by device heterogeneity. In this work,…

• Wavelet-Driven Masked Multiscale Reconstruction for PPG Foundation Models

  Megha Thukral, Cyrus Tanade, Simon A. Lee, Juhyeon Lee, Hao Zhou, Keum San Chun, Migyeong Gwak, Viswam Nathan, Md Mahbubur Rahman, Li Zhu, Mehrab Bin Morshed, Subramaniam Venkatraman, Sharanya Arcot Desai · 21 January 2026

  Wearable foundation models have the potential to transform digital health by learning transferable representations from large-scale biosignals collected in everyday settings. While recent progress has been made in large-scale pretraining, most approaches overlook the spectral structure of photopleth…

• Physiological-model-based neural network for modeling the metabolic-heart rate relationship during physical activities

  Yaowen Zhang, Libera Fresiello, Peter H. Veltink, Dirk W. Donker, Ying Wang · 19 January 2026

  Heart failure (HF) poses a significant global health challenge, with early detection offering opportunities for improved outcomes. Abnormalities in heart rate (HR), particularly during daily activities, may serve as early indicators of HF risk. However, existing HR monitoring tools for HF detection …

• EvoMorph: Counterfactual Explanations for Continuous Time-Series Extrinsic Regression Applied to Photoplethysmography

  Mesut Ceylan, Alexis Tabin, Patrick Langer, Elgar Fleisch, Filipe Barata · 16 January 2026

  Wearable devices enable continuous, population-scale monitoring of physiological signals, such as photoplethysmography (PPG), creating new opportunities for data-driven clinical assessment. Time-series extrinsic regression (TSER) models increasingly leverage PPG signals to estimate clinically releva…

• Using Legacy Polysomnography Data to Train a Radar System to Quantify Sleep in Older Adults and People living with Dementia

  M. Yin, K. G. Ravindran, C. Hadjipanayi, A. Bannon, A. Rapeaux, C. Della Monica, T. S. Lande, Derk-Jan Dijk, T. G. Constandinou · 8 January 2026

  Objective: Ultra-wideband radar technology offers a promising solution for unobtrusive and cost-effective in-home sleep monitoring. However, the limited availability of radar sleep data poses challenges in building robust models that generalize across diverse cohorts and environments. This study pro…

• Cuffless, calibration-free hemodynamic monitoring with physics-informed machine learning models

  Henry Crandall, Tyler Schuessler, Filip B\v{e}l\'ik, Albert Fabregas, Barry M. Stults, Alexandra Boyadzhiev, Huanan Zhang, Jim S. Wu, Aylin R. Rodan, Stephen P. Juraschek, Ramakrishna Mukkamala, Alfred K. Cheung, Stavros G. Drakos, Christel Hohenegger, Braxton Osting, Benjamin Sanchez · 5 January 2026

  Wearable technologies have the potential to transform ambulatory and at-home hemodynamic monitoring by providing continuous assessments of cardiovascular health metrics and guiding clinical management. However, existing cuffless wearable devices for blood pressure (BP) monitoring often rely on metho…

• Wrist Photoplethysmography Predicts Dietary Information

  Kyle Verrier, Achille Nazaret, Joseph Futoma, Andrew C. Miller, Guillermo Sapiro · 19 December 2025

  Whether wearable photoplethysmography (PPG) contains dietary information remains unknown. We trained a language model on 1.1M meals to predict meal descriptions from PPG, aligning PPG to text. PPG nontrivially predicts meal content; predictability decreases for PPGs farther from meals. This transfer…

• PMB-NN: Physiology-Centred Hybrid AI for Personalized Hemodynamic Monitoring from Photoplethysmography

  Yaowen Zhang, Libera Fresiello, Peter H. Veltink, Dirk W. Donker, Ying Wang · 12 December 2025

  Continuous monitoring of blood pressure (BP) and hemodynamic parameters such as peripheral resistance (R) and arterial compliance (C) are critical for early vascular dysfunction detection. While photoplethysmography (PPG) wearables has gained popularity, existing data-driven methods for BP estimatio…

• SweetDeep: A Wearable AI Solution for Real-Time Non-Invasive Diabetes Screening

  Ian Henriques, Lynda Elhassar, Sarvesh Relekar, Denis Walrave, Shayan Hassantabar, Vishu Ghanakota, Adel Laoui, Mahmoud Aich, Rafia Tir, Mohamed Zerguine, Samir Louafi, Moncef Kimouche, Emmanuel Cosson, Niraj K Jha · 4 December 2025

  The global rise in type 2 diabetes underscores the need for scalable and cost-effective screening methods. Current diagnosis requires biochemical assays, which are invasive and costly. Advances in consumer wearables have enabled early explorations of machine learning-based disease detection, but pri…

• Cuffless Blood Pressure Estimation from Six Wearable Sensor Modalities in Multi-Motion-State Scenarios

  Yiqiao Chen, Fazheng Xu, Zijian Huang, Juchi He, Zhenghui Feng · 2 December 2025

  Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and sustained hypertension is an often silent risk factor, making cuffless continuous blood pressure (BP) monitoring with wearable devices important for early screening and long-term management. Most existing cuffl…

• Adaptive Parameter Optimization for Robust Remote Photoplethysmography

  Cecilia G. Morales, Fanurs Chi En Teh, Kai Li, Pushpak Agrawal, Artur Dubrawski · 1 December 2025

  Remote photoplethysmography (rPPG) enables contactless vital sign monitoring using standard RGB cameras. However, existing methods rely on fixed parameters optimized for particular lighting conditions and camera setups, limiting adaptability to diverse deployment environments. This paper introduces …