Blood pressure monitoring is an essential component of hypertension management and in the prediction of associated comorbidities. Blood pressure is a dynamic vital sign with frequent changes throughout a given day. Capturing blood pressure remotely and frequently (also known as ambulatory blood pressure monitoring) has traditionally been achieved by measuring blood pressure at discrete intervals using an inflatable cuff. However, there is growing interest in developing a cuffless ambulatory blood pressure monitoring system to measure blood pressure continuously. One such approach is by utilizing bioimpedance sensors to build regression models. A practical problem with this approach is that the amount of data required to confidently train such a regression model can be prohibitive. In this paper, we propose the application of the domain-adversarial training neural network (DANN) method on our multitask learning (MTL) blood pressure estimation model, allowing for knowledge transfer between subjects. Our proposed model obtains average root mean square error (RMSE) of $4.80 \pm 0.74$ mmHg for diastolic blood pressure and $7.34 \pm 1.88$ mmHg for systolic blood pressure when using three minutes of training data, $4.64 \pm 0.60$ mmHg and $7.10 \pm 1.79$ respectively when using four minutes of training data, and $4.48 \pm 0.57$ mmHg and $6.79 \pm 1.70$ respectively when using five minutes of training data. DANN improves training with minimal data in comparison to both directly training and to training with a pretrained model from another subject, decreasing RMSE by $0.19$ to $0.26$ mmHg (diastolic) and by $0.46$ to $0.67$ mmHg (systolic) in comparison to the best baseline models. We observe that four minutes of training data is the minimum requirement for our framework to exceed ISO standards within this cohort of patients.
LLM-Powered Prediction of Hyperglycemia and Discovery of Behavioral Treatment Pathways from Wearables and Diet
School of Computing and Augmented Intelligence, Arizona State University（计算与增强智能学院，亚利桑那州立大学）；College of Health Solutions, Arizona State University（健康解决方案学院，亚利桑那州立大学）
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"）
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）
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）
AI procedures joined with wearable gadgets can convey exact transient blood glucose level forecast models. Also, such models can learn customized glucose-insulin elements dependent on the sensor information gathered by observing a few parts of the physiological condition and every day movement of a person. Up to this point, the predominant methodology for creating information driven forecast models was to gather "however much information as could be expected" to help doctors and patients ideally change treatment. The goal of this work was to examine the base information assortment, volume, and…
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）
The advent of IoT has enabled the design of connected and integrated smart health monitoring systems. These smart health monitoring systems could be realized in a smart home context to render long-term care to the elderly population. In this paper, we present the design of a wearable health monitoring system suitable for older adults in a smart home context. The proposed system offers solutions to monitor the stress, blood pressure, and location of an individual within a smart home environment. The stress detection model proposed in this work uses Electrodermal Activity (EDA), Photoplethysmogr…
Mobile proactive tourist recommender systems can support tourists by recommending the best choice depending on different contexts related to herself and the environment. In this paper, we propose to utilize wearable sensors to gather health information about a tourist and use them for recommending tourist activities. We discuss a range of wearable devices, sensors to infer physiological conditions of the users, and exemplify the feasibility using a popular self-quantification mobile app. Our main contribution then comprises a data model to derive relations between the parameters measured by th…
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…
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）
Dublin City University（都柏林城市大学）；VNU-HCM, University of Science（越南胡志明市国家大学科学大学）
Stress is a complex issue with wide-ranging physical and psychological impacts on human daily performance. Specifically, acute stress detection is becoming a valuable application in contextual human understanding. Two common approaches to training a stress detection model are subject-dependent and subject-independent training methods. Although subject-dependent training methods have proven to be the most accurate approach to build stress detection models, subject-independent models are a more practical and cost-efficient method, as they allow for the deployment of stress level detection and ma…
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…
A photoplethysmography (PPG) is an uncomplicated and inexpensive optical technique widely used in the healthcare domain to extract valuable health-related information, e.g., heart rate variability, blood pressure, and respiration rate. PPG signals can easily be collected continuously and remotely using portable wearable devices. However, these measuring devices are vulnerable to motion artifacts caused by daily life activities. The most common ways to eliminate motion artifacts use extra accelerometer sensors, which suffer from two limitations: i) high power consumption and ii) the need to int…
People with type 1 diabetes (T1D) lack the ability to produce the insulin their bodies need. As a result, they must continually make decisions about how much insulin to self-administer to adequately control their blood glucose levels. Longitudinal data streams captured from wearables, like continuous glucose monitors, can help these individuals manage their health, but currently the majority of the decision burden remains on the user. To relieve this burden, researchers are working on closed-loop solutions that combine a continuous glucose monitor and an insulin pump with a control algorithm i…
Cuffless, calibration-free hemodynamic monitoring with physics-informed machine learning models
University of Utah（犹他大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；University of Utah Health（犹他大学健康中心）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Veterans Affairs Salt Lake City Health Care System（退伍军人事务部盐湖城医疗系统）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；University of Pittsburgh（匹兹堡大学）
LLM-Powered Personalized Glycemic Assessment in Type 2 Diabetes with Wearable Sensor Data
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校信息系统与网络安全系）；School of Engineering Medicine, Texas A&M University（德克萨斯农工大学工程医学院）；Department of Family and Community Medicine, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校家庭与社区医学系）
Healthcare Technology and Innovation Center (HTIC), Indian Institute of Technology (IIT-M)（印度理工学院马德拉斯分校医疗技术与创新中心）；Indian Institute of Technology, Madras (IITM)（印度理工学院马德拉斯分校）
Continuous monitoring of blood oxygen saturation levels is vital for patients with pulmonary disorders. Traditionally, SpO$_2$ monitoring has been carried out using transmittance pulse oximeters due to its dependability. However, SpO$_2$ measurement from transmittance pulse oximeters is limited to peripheral regions. This becomes a disadvantage at very low temperatures as blood perfusion to the peripherals decreases. On the other hand, reflectance pulse oximeters can be used at various sites like finger, wrist, chest and forehead. Additionally, reflectance pulse oximeters can be scaled down to…
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…
PMB-NN: Physiology-Centred Hybrid AI for Personalized Hemodynamic Monitoring from Photoplethysmography
Department of Biomedical Signals and Systems, University of Twente（乌得勒支理工大学生物医学信号与系统系）；Department of Cardiovascular and Respiratory Physiology, University of Twente（乌得勒支理工大学心血管与呼吸生理学系）；Department of Intensive Care, University Medical Center Utrecht（乌得勒支大学医学中心重症医学科）
Eindhoven University of Technology（埃因霍温理工大学）；RMIT University（皇家墨尔本理工大学）
Smartphones, wearables, and Internet of Things (IoT) devices produce a wealth of data that cannot be accumulated in a centralized repository for learning supervised models due to privacy, bandwidth limitations, and the prohibitive cost of annotations. Federated learning provides a compelling framework for learning models from decentralized data, but conventionally, it assumes the availability of labeled samples, whereas on-device data are generally either unlabeled or cannot be annotated readily through user interaction. To address these issues, we propose a self-supervised approach termed \te…
