Cuffless Blood Pressure Estimation from Six Wearable Sensor Modalities in Multi-Motion-State Scenarios
Faculty of Frontier Sciences, Harbin Institute of Technology, Shenzhen（前沿科学学院，哈尔滨工业大学深圳校区）；University of Melbourne（墨尔本大学）；University of New South Wales（新南威尔士大学）
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 cuffless BP estimation methods use only photoplethysmography (PPG) and electrocardiography (ECG) signals, alone or in combination. These models are typically developed under resting or quasi-static conditions and struggle to maintain robust accuracy in multi-motion-state scenarios. In this study, we propose a six-modal BP estimation framework that jointly leverages ECG, multi-channel PPG, attachment pressure, sensor temperature, and triaxial acceleration and angular velocity. Each modality is processed by a lightweight branch encoder, contrastive learning enforces cross-modal semantic alignment, and a mixture-of-experts (MoE) regression head adaptively maps the fused features to BP across motion states. Comprehensive experiments on the public Pulse Transit Time PPG Dataset, which includes running, walking, and sitting data from 22 subjects, show that the proposed method achieves mean absolute errors (MAE) of 3.60 mmHg for systolic BP (SBP) and 3.01 mmHg for diastolic BP (DBP). From a clinical perspective, it attains Grade A for SBP, DBP, and mean arterial pressure (MAP) according to the British Hypertension Society (BHS) protocol and meets the numerical criteria of the Association for the Advancement of Medical Instrumentation (AAMI) standard for mean error (ME) and standard deviation of error (SDE).
This study explores extended feature space that is indicative of blood pressure (BP) changes for better estimation of continuous BP in an unobtrusive way. A total of 222 features were extracted from noninvasively acquired electrocardiogram (ECG) and photoplethysmogram (PPG) signals with the subject undergoing coronary angiography and/or percutaneous coronary intervention, during which intra-arterial BP was recorded simultaneously with the subject at rest and while administering drugs to induce BP variations. The association between the extracted features and the BP components, i.e. systolic BP…
End-to-end Automated Deep Neural Network Optimization for PPG-based Blood Pressure Estimation on Wearables
Interuniversity Department of Regional and Urban Studies and Planning at Politecnico di Torino（理工学院区域与城市研究与规划联合大学部门）；Department of Control and Computer Engineering at Politecnico di Torino（理工学院控制与计算机工程系）；Department of Information Technology and Electrical Engineering at ETH Zurich（苏黎世联邦理工学院信息技术与电气工程系）；Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" at University of Bologna（博洛尼亚大学电子、电子与信息工程系"Guglielmo Marconi"）
Benchmarking and Enhancing PPG-Based Cuffless Blood Pressure Estimation Methods
Department of Engineering Technology, University of Houston, Texas, USA（工程科技系，休斯顿大学，德克萨斯州，美国）；Department of Electrical and Computer Engineering, University of Houston, Texas, USA（电气与计算机工程系，休斯顿大学，德克萨斯州，美国）；Department of Information Science Technology, University of Houston, Texas, USA（信息科学科技系，休斯顿大学，德克萨斯州，美国）；Department of Cardiovascular Surgery, Houston Methodist Hospital, Texas, USA（心血管外科系，休斯顿方法医院，德克萨斯州，美国）；Department of Biomedical Engineering, University of Houston, Texas, USA（生物医学工程系，休斯顿大学，德克萨斯州，美国）
Large Language Models for Cuffless Blood Pressure Measurement From Wearable Biosignals
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong Polytechnic University（香港理工大学）；Shenzhen Polytechnic University（深圳职业技术大学）
Goal: Although photoplethysmogram (PPG) and electrocardiogram (ECG) signals can be used to estimate blood pressure (BP) by extracting various features, the changes in morphological contours of both PPG and ECG signals due to various diseases of circulatory system and interaction of other physiological systems make the extraction of such features very difficult. Methods: In this work, we propose a waveform-based hierarchical Artificial Neural Network - Long Short Term Memory (ANN-LSTM) model for BP estimation. The model consists of two hierarchy levels, where the lower hierarchy level uses ANNs…
SSN College of Engineering（SSN工程学院）；College of Engineering, Guindy（Guindy工程学院）；Solarillion Foundation（Solarillion基金会）
Blood pressure (BP) is one of the most influential bio-markers for cardiovascular diseases and stroke; therefore, it needs to be regularly monitored to diagnose and prevent any advent of medical complications. Current cuffless approaches to continuous BP monitoring, though non-invasive and unobtrusive, involve explicit feature engineering surrounding fingertip Photoplethysmogram (PPG) signals. To circumvent this, we present an end-to-end deep learning solution, BP-Net, that uses PPG waveform to estimate Systolic BP (SBP), Mean Average Pressure (MAP), and Diastolic BP (DBP) through intermediate…
Introducing WARM-VR: Benchmark Dataset for Multimodal Wearable Affect Recognition in Virtual Reality
School of Electrical Engineering and Computer Science, University of Ottawa（Ottawa 大学电气工程与计算机科学学院）；Computer Science, University of Ottawa（Ottawa 大学计算机科学系）；Computer Vision Department, Mohamed bin Zayed University of Artificial Intelligence（摩根·bin·扎耶德人工智能大学计算机视觉部门）
The paper proposes accurate Blood Pressure Monitoring (BPM) based on a single-site Photoplethysmographic (PPG) sensor and provides an energy-efficient solution on edge cuffless wearable devices. Continuous PPG signal preprocessed and used as input of the Artificial Neural Network (ANN), and outputs systolic BP (SBP), diastolic BP (DBP), and mean arterial BP (MAP) values for each heartbeat. The improvement of the BPM accuracy is obtained by removing outliers in the preprocessing step and the whole-based inputs compared to parameter-based inputs extracted from the PPG signal. Performance obtaine…
A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy Sensors
Aston University（阿斯顿大学）；University of Toronto（多伦多大学）；University Health Network（大学健康网络）；Florida Institute of Technology（佛罗里达理工学院）；Hai Duong Central College of Pharmacy（海洞中央药学院）；Stanford University（斯坦福大学）；Industrial University of Ho Chi Minh City（胡志明市工业大学）；The University of Alabama at Birmingham（伯明翰大学）；Pukyong National University（浦项国立大学）
There has been a recent tremendous interest in label-free detection of biomarkers which is a critical enabler of point-of-need diagnostics. A low-power, small form factor, multiplexed wearable system is proposed for continuous detection of glucose in passively expressed sweat using electrochemical impedance spectroscopy (EIS) measurement. The wearable EIS system consists of a sensing analog front end integrated with low-volume (1-5 $μ$L) ultra-sensitive flexible biosensors. A passive sweat sensor was designed to integrate a glucose oxidase electrochemical system on active semiconducting materi…
Photoplethysmogram (PPG) signal-based blood pressure (BP) estimation is a promising candidate for modern BP measurements, as PPG signals can be easily obtained from wearable devices in a non-invasive manner, allowing quick BP measurement. However, the performance of existing machine learning-based BP measuring methods still fall behind some BP measurement guidelines and most of them provide only point estimates of systolic blood pressure (SBP) and diastolic blood pressure (DBP). In this paper, we present a cutting-edge method which is capable of continuously monitoring BP from the PPG signal a…
The general public and medical professionals recognized the importance of accurately measuring and storing blood oxygen levels and heart rate during the COVID-19 pandemic. The demand for accurate contact-less devices was motivated by the need for cross-infection reduction and the shortage of conventional oximeters, partially due to the global supply chain issue. This paper evaluated a contact-less mini-program HealthyPai's heart rate (HR) and oxygen saturation (SpO2) measurements compared with other wearable devices. In the HR study of 185 samples (81 in the laboratory environment, 104 in the …
Rapid Adaptation of SpO2 Estimation to Wearable Devices via Transfer Learning on Low-Sampling-Rate PPG
Department of Computer Science, University of California, Davis（加州大学戴维斯分校计算机科学系）；Department of Electrical and Computer Engineering, University of California, Davis（加州大学戴维斯分校电气与计算机工程系）
Infant Research Centre（婴儿研究中心）；School of Computing（计算机学院）；Dublin City University（都柏林城市大学）；Department of Electrical Engineering（电气工程系）；KU Leuven（鲁汶大学）；Cork University Maternity Hospital（科克大学妇产医院）；Insight SFI Research Centre（Insight SFI研究中心）
Ischemic heart disease is the highest cause of mortality globally each year. This not only puts a massive strain on the lives of those affected but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart doctors commonly use electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, in particular when continuous arterial blood pressure (ABP) readings are taken and not to mention very costly. Using machine learning methods we seek to develop a framework that is capable of inferring ABP from a single optical phot…
Cardiac abnormalities affecting heart rate and rhythm are commonly observed in both healthy and acutely unwell people. Although many of these are benign, they can sometimes indicate a serious health risk. ECG monitors are typically used to detect these events in electrical heart activity, however they are impractical for continuous long-term use. In contrast, current-generation wearables with optical photoplethysmography (PPG) have gained popularity with their low-cost, lack of wires and tiny size. Many cardiac abnormalities such as ectopic beats and AF can manifest as both obvious and subtle …
Non-invasive ambulatory estimation of blood oxygen saturation has emerged as an important clinical requirement to detect hypoxemia in the delayed post-infective phase of COVID-19, where dangerous hypoxia may occur in the absence of subjective breathlessness. This immediate clinical driver, combined with the general quest for more personalised health data, means that pulse oximetry measurement of capillary oxygen saturation (SpO2) will likely expand into both the clinical and consumer market of wearable health technology in the near future. In this study, we set out to establish the feasibility…
Ingenuity Labs Research Institute（Ingenuity Labs 研究院）
Electrocardiogram (ECG) is the electrical measurement of cardiac activity, whereas Photoplethysmogram (PPG) is the optical measurement of volumetric changes in blood circulation. While both signals are used for heart rate monitoring, from a medical perspective, ECG is more useful as it carries additional cardiac information. Despite many attempts toward incorporating ECG sensing in smartwatches or similar wearable devices for continuous and reliable cardiac monitoring, PPG sensors are the main feasible sensing solution available. In order to tackle this problem, we propose CardioGAN, an advers…
Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, personalized treatments. While recent work has convincingly demonstrated that seizure risk assessment is possible, these early approaches relied largely on complex, often invasive setups including intracranial electrocorticography, implanted devices and multi-channel EEG, which limits translation of these methods to broad clinical application. To facilitate broader adaptation of seizure forecasting in clinical practice, non-invasive, easily applicable techni…
Detection of Autonomic Dysreflexia in Individuals With Spinal Cord Injury Using Multimodal Wearable Sensors
SCAI-Lab, Department of Health Sciences and Technology (D-HEST), ETH Zurich（SCAI实验室，健康科学与技术系（D-HEST），苏黎世联邦理工学院）；School of Computation, Information and Technology, Technical University of Munich（计算、信息与技术学院，慕尼黑技术大学）；Swiss Paraplegic Research (SPF)（瑞士瘫痪研究机构（SPF））；Institute of Advanced Production Technologies, University of Valladolid（先进生产技术研究所，瓦尔达利德大学）；Swiss Paraplegic Centre (SPZ)（瑞士瘫痪中心（SPZ））；Balgrist University Hospital（巴尔格里斯大学医院）
In the first half of the 20th century, a first pulse oximeter was available to measure blood flow changes in the peripheral vascular net. However, it was not until recent times the PhotoPlethysmoGraphic (PPG) signal used to monitor many physiological parameters in clinical environments. Over the last decade, its use has extended to the area of biometrics, with different methods that allow the extraction of characteristic features of each individual from the PPG signal morphology, highly varying with time and the physical states of the subject. In this paper, we present a novel PPG-based biomet…
Smartwatch Photoplethysmography-Derived Heart Age via ECG-Guided Cross-Modal Pretraining as a Digital Biomarker of Vascular Aging
National Institute of Health Data Science, Peking University（北京大学健康医疗大数据科学国家研究院）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医药技术学院）；OPPO Health Lab（OPPO健康实验室）；School of Intelligence Science and Technology, Peking University（北京大学智能科学学院）；Xidian University（西安电子科技大学）；Shenzhen Technology University（深圳职业技术大学）
