Health Monitoring Platform Research Articles

Katz and COVID-19: How a Pandemic Changed the Reasonable Expectation of Privacy

COVID-19 spread to 188 countries and infected tens of millions of people in the matter of months. Organizations, including governments and employers, turned to health surveillance technologies to slow the spread and combat the disease. Protected health information and personal information are required for the proper and effective functioning of the health surveillance technologies. The collection, use, and dissemination of protected health and personal information raised data privacy and security concerns. But under the current data privacy and security regime—based on the reasonable expectation of privacy standard—protected health and personal information is not protected to the extent that it needs to be. Unlike other scholarly work, this article presents deeper analysis into the technologies, the data that powers them, and the applicable legal standards. The objective is to provide a better understanding of (i) the data privacy and security risks, and (ii) whether the current data privacy and security regime in the United States provides sufficient protections for individuals. This article explores two health surveillance technologies (contact tracing applications and health monitoring platforms), presents three categories of data (user-inputted, queried, and autogenerated data), and describes the data supply chains that power technology and organizations. I discuss the benefits and risks of collecting the protected health and personal information in response to the pandemic. I explore the current legal standards and jurisprudence, and I propose the Privacy Continuum to explain how the pandemic shifted the reasonable expectation of privacy. I present a case study to synthesize the foregoing, and I conclude by proposing a new legal standard—the right to control—and other reforms to effectuate true data privacy and security protections. Only then can we reclaim our right to privacy.

A Smart Service Platform for Cost Efficient Cardiac Health Monitoring.

Aim: In this study we have investigated the problem of cost effective wireless heart health monitoring from a service design perspective. Subject and Methods: There is a great medical and economic need to support the diagnosis of a wide range of debilitating and indeed fatal non-communicable diseases, like Cardiovascular Disease (CVD), Atrial Fibrillation (AF), diabetes, and sleep disorders. To address this need, we put forward the idea that the combination of Heart Rate (HR) measurements, Internet of Things (IoT), and advanced Artificial Intelligence (AI), forms a Heart Health Monitoring Service Platform (HHMSP). This service platform can be used for multi-disease monitoring, where a distinct service meets the needs of patients having a specific disease. The service functionality is realized by combining common and distinct modules. This forms the technological basis which facilitates a hybrid diagnosis process where machines and practitioners work cooperatively to improve outcomes for patients. Results: Human checks and balances on independent machine decisions maintain safety and reliability of the diagnosis. Cost efficiency comes from efficient signal processing and replacing manual analysis with AI based machine classification. To show the practicality of the proposed service platform, we have implemented an AF monitoring service. Conclusion: Having common modules allows us to harvest the economies of scale. That is an advantage, because the fixed cost for the infrastructure is shared among a large group of customers. Distinct modules define which AI models are used and how the communication with practitioners, caregivers and patients is handled. That makes the proposed HHMSP agile enough to address safety, reliability and functionality needs from healthcare providers.

HomeSense: Design of an ambient home health and wellness monitoring platform for older adults

The unprecedented rise in the population of older adults and the number of seniors living with and managing chronic conditions are straining our institutional health care systems leading to reduced care quality and unmanageable cost increases. At the same time, an overwhelming majority of older adults express a strong desire to age in place in their communities. Ambient home sensing presents an opportunity to reduce healthcare costs by facilitating older adults’ ability to age-in-place in more familiar, less restrictive, and less expensive environments. Further, ambient home sensing tools have the potential to extend the health care work force and enhance health care quality/outcomes by facilitating remote patient monitoring as well as early intervention and prevention against adverse events – all while catering to older adults’ preference to live at home. Despite their potential, there is limited research at present about the benefits of ambient sensing systems installed in private homes, and older adults’ response to them. This paper describes a pilot ambient home sensing project, HomeSense, actively deployed in the homes of older adults residing in The Villages, Florida.

Metallicity-Dependent Ultrafast Water Transport in Carbon Nanotubes.

Carbon nanotubes (CNTs) with hydrophobic and atomically smooth inner channels are promising for building ultrahigh-flux nanofluidic platforms for energy harvesting, health monitoring, and water purification. Conventional wisdom is that nanoconfinement effects determine water transport in CNTs. Here, using full-atomistic molecular dynamics simulations, it is shown that water transport behavior in CNTs strongly correlates with the electronic properties of single-walled CNTs (metallic (met) vs semiconducting (s/c)), which is as dominant as the effect of nanoconfinement. Three pairs of CNTs (i.e., (8,8)met , 10.85 Å vs (9,7)s/c , 10.88 Å; (9,8)s/c , 11.53 Å vs (10,7)met , 11.59 Å; and (9,9)met , 12.20 Å vs (10,8)s/c , 12.23 Å) are used to investigate the roles of diameter and metallicity. Specifically, the (9,8)s/c can restrict the hydrogen-bonding-mediated structuring of water and give the highest reduction in carbon-water interaction energy, providing an extraordinarily high water flux, around 250 times that of the commercial reverse osmosis membranes and approximately fourfold higher than the flux of the state-of-the-art boron nitrate nanotubes. Further, the high performance of (9,8)s/c is also reproducible when embedded in lipid bilayers as synthetic high-water flux porins. Given the increasing availability of high-purity CNTs, these findings provide valuable guides for realizing novel CNT-enhanced nanofluidic systems.

Indicator Displacement Assay Inside Dye-Functionalized Covalent Organic Frameworks for Ultrasensitive Monitoring of Sialic Acid, an Ovarian Cancer Biomarker.

Identifying biomolecules for disease diagnosis requires simple, accurate, and reliable analytical techniques. Multiple signal transduction pathways have promoted the development of various biological analysis systems. However, most systems are largely limited by a single mechanism or model analysis, which can easily lead to false-positive/negative results. Herein, we report a covalent organic framework (COF) (TpPa-1) functionalized with a dye (fluorescein sodium) and design this hybrid material (TpPa-1@Dye) to fabricate hydrogels for subsequent analysis with the indicator displacement assay (IDA) method. Selecting a suitable metal cation (Cr3+) for the preparation of hydrogels can reduce the background fluorescence, improve the detection sensitivity, and increase the corresponding sensing selectivity. The TpPa-1@Dye functions as an indicator in the IDA-in-COF system, and Cr3+ is a receptor of the analyte (sialic acid (SA), a biomarker for ovarian cancer diagnosis). Based on the above studies, the integrative logic operations (AND + IMP) are further established, it helps in elucidating the design rules of the IDA-in-COF approach. This work represents the first effort in designing IDA-in-COF luminescent sensors with an On-Off-On mechanism to determine biomarkers and provides a new approach for developing hybrid COF luminescent materials as analysis platforms for human health monitoring.

Wireless Localization of Spallings in Switch-Rails With Guided Waves Based on a Time–Frequency Method

Guided waves have been studied for monitoring defects on switch-rails. However, few researchers study monitoring of spallings. Previous studies on switch-rail damage detection are not suitable for this kind of damage at the edge of the rail web. In addition, wired configurations have the challenges of power supply and more cost. This paper proposes a time-frequency analysis based algorithm that is used to locate spallings. The algorithm is verified through an improved wireless based structural health monitoring (SHM) platform. The excitation frequency of 108 kHz is chosen as a compromise between larger wave velocity differences and smaller detectable damage sizes. A total of 4 piezoelectric (PZT) devices make up one actuator-sensor array. 2 PZT devices are placed at 2 sides of the rail web and both of them have the same excitation direction. The other 2 devices are mounted at the top of the rail web to measure spalling induced waves according to the cloud charts. The arrival time, frequency, and wave velocity features of the main modes in transmission and reflection waves are extracted. Then these parameters are substituted in the proposed algorithm to predict the spalling location. Simulation and epoxy bonding based experimental results show that the proposed method can identify a 15 mm length spalling at different locations. Finally, attention is paid to the mode identification errors that influence spalling localization. The effects of curvature on guided waves in switch-rails are analyzed and considered to be the cause of the errors.

A Vendor-Independent Mobile Health Monitoring Platform for Digital Health Studies: Development and Usability Study.

BackgroundMedical smartphone apps and mobile health devices are rapidly entering mainstream use because of the rising number of smartphone users. Consequently, a large amount of consumer-generated data is being collected. Technological advances in innovative sensory systems have enabled data connectivity and aggregation to become cornerstones in developing workable solutions for remote monitoring systems in clinical practice. However, few systems are currently available to handle such data, especially for clinical use.ObjectiveThe aim of this study was to develop and implement the digital health research platform for mobile health (DHARMA) that combines data saved in different formats from a variety of sources into a single integrated digital platform suitable for mobile remote monitoring studies.MethodsDHARMA comprises a smartphone app, a Web-based platform, and custom middleware and has been developed to collect, store, process, and visualize data from different vendor-specific sensors. The middleware is a component-based system with independent building blocks for user authentication, study and patient administration, data handling, questionnaire management, patient files, and reporting.ResultsA prototype version of the research platform has been tested and deployed in multiple clinical studies. In this study, we used the platform for the follow-up of pregnant women at risk of developing pre-eclampsia. The patients’ blood pressure, weight, and activity were semi-automatically captured at home using different devices. DHARMA automatically collected and stored data from each source and enabled data processing for the end users in terms of study-specific parameters, thresholds, and visualization.ConclusionsThe increasing use of mobile health apps and connected medical devices is leading to a large amount of data for collection. There has been limited investment in handling and aggregating data from different sources for use in academic and clinical research focusing on remote monitoring studies. In this study, we created a modular mobile health research platform to collect and integrate data from a variety of third-party devices in several patient populations. The functionality of the platform was demonstrated in a real-life setting among women with high-risk pregnancies.

Numerical and experimental study of aircraft structural health

The purpose of this work is to monitor the structural health of the Portuguese Air Force (PoAF) EPSILON TB30 fleet by measuring the dynamic behaviour of aircraft’s critical components towards the optimization of maintenance programme scheduling. The mission load spectrum and the structural response are simultaneously measured using a combination of accelerometers and strain-gauges, enabling the calculation of individual transference functions for each critical component monitored. The EPSILON TB30 fleet was divided into two different types of aircrafts: a) IMA-Highly Instrumented Aircrafts which are able to capture vertical accelerations and strain at critical locations and b) LIA-Less Instrumented Aircrafts that make use of the transference functions for mapping the captured acceleration data to stress at pre-selected structural hotspots. The fleet operation is continuously analysed to calculate the fatigue damage of each component and predict the operation limit for each aircraft. In this context, finite element models of critical components are used to define the critical locations of sensors used for monitoring stress in those locations. The maximum principal stress zones of the 2nd bulkhead beam was obtained and are consistent with the manufacturer’s reports that place fatigue crack initiation near the bottom filleted joint of the innermost rib, during real scale fatigue test. Mission is analysed using the structural health monitoring platform PRODDIA™ Aero which uses time and frequency-based fatigue models to predict the structural health of the entire fleet.

A Real-Time Decision Platform for the Management of Structures and Infrastructures

Natural disasters and the poor management of civil engineering structures and infrastructures require timely action and new tools such as specially designed structural health monitoring platforms. This paper proposes an innovative platform based on a network of wirelessly connected, low-power, and renewable-energy-fed sensor units. The platform is a multipurpose tool for diagnostics, maintenance, and supervision, capable of simultaneously carrying out damage detection, localization, identification, and “multiclass” and “multi-material” level quantification of different types of failures. In addition, it works as a decision support tool for emergency management and post-disaster assessment, here tailored for an Italian theme park. The platform uses innovative algorithms based on the concept of the vibro-acoustic signature of the asset monitored. The vibro-acoustic signatures of the monitored assets are gathered by the microphones and accelerometers of the platform’s sensor units. Then, almost simultaneously, they are analyzed using specifically designed wavelet-based and convolutional-neural-network-based algorithms, which are able to extract crucial information about the structural and environmental conditions of both the asset and the areas of the thematic park. In addition, the platform shows escape routes during an emergency, indicating meeting points and helping people to proceed safely along a recognizable escape route to a safe place, as demonstrated by the simulations.

OpenHealth: Open-Source Platform for Wearable Health Monitoring

This article presents an open-source platform for wearable health monitoring. It aims to design a standard set of hardware/software and wearable devices that can enable autonomous collection of clinically relevant data. It provides reference implementations of human activity and gesture recognition applications within this platform.

Abstract 028: Remote Health Monitoring Platform Reduces Blood Pressure and Crisis Hypertension

Introduction: We assessed the efficacy of mobile health hypertension monitoring for patients enrolled in Medicare’s Remote Physiologic Monitoring program. Hypothesis: Uncontrolled hypertension is an increasing epidemic associated with cardiovascular disease. Despite many available treatments, the average time to blood pressure control is slow. We hypothesized that mobile health monitoring and communication with clinicians in a Medicare cohort would decrease the hypertension burden and mitigate crisis blood pressure in patients. Methods: 337 patients who had contributed ≥ 20 blood pressure readings in a remote monitoring program were included in the study population, spanning clinics in Florida, Tennessee, and Arizona. Eligible patients carried a diagnosis of hypertension and had been seen by their doctor within the year they were referred. The mobile health platform was utilized to aggregate blood pressure data, which was analyzed by a remote care team and provided to clinicians on a monthly basis. Patients’ doctors and their teams reviewed and managed the patients based on the data provided by the mobile-cloud platform. The remote monitoring program provided alerts to clinic staff for patients who had blood pressures greater than 180mm Hg systolic (crisis hypertension) for expedited decision making. Results: 337 patients who provided >20 BP readings from October 2018 to April 2019 were included in the study. A total of 51,125 blood pressure readings were included in this analysis. Patient readings were stratified by epoch chronologically. The first epoch (E1), represented the first 25% of readings in the remote monitoring system, and the fourth epoch (E4) represented the final 25% of readings. From E1 to E4, patients saw an average decrease of 4.7 mmHg in systolic blood pressure (133.0 vs. 128.3; p<0.001). The proportion of readings in crisis hypertension range decreased from 3.3% to 1.9% (p<0.001), while the amount of hypotensive readings did not substantially change (1.4 vs. 1.6%; p=0.14). Conclusions: In agreement with our hypothesis, patients in a Medicare cohort achieved a significant reduction in blood pressure via remote monitoring as well as a significant reduction in crisis blood pressure readings.

Concurrent Modulation of Competitive Mechanisms to Design Stimuli‐Responsive Ln‐MOFs: A Light‐Operated Dual‐Mode Assay for Oxidative DNA Damage

AbstractThe identification of biomolecules for disease diagnosis requires facile analytical technologies with high precision and reliability. Several signal transduction pathways have inspired the development of various bioanalytical systems. However, most systems are greatly limited by a single‐mechanism/mode assay, which easily results in false‐positive/negative results. Herein, a multiple‐mechanism‐driven optical biosensor for 8‐oxo‐2′‐deoxyguanosine (8‐oxo‐dG), an early pathological signature of DNA lesions and various diseases, is designed by assembling adenine as a recognition element, mellitic acid as energy donors and Eu3+ as signal reporters into one metal‐organic framework (MOF) system. Significantly, by regulating the delicate competition between the different mechanisms, the fabricated single platform (Eu‐ade‐MOF) concurrently provides two switchable approaches for rapid qualitative (30 s and 4 min) and quantitive (ppb level) recognition of 8‐oxo‐dG in both complex artificial and real human urine environments. Compared with those single‐mechanism/mode‐driven detections, this light‐operated dual‐mode analysis system can inherently boost the analysis reliability and largely minimize the chances of false negatives/positives for a non‐invasive diagnosis of DNA damage and related diseases. This work represents the first effort in designing a luminescent sensor coupling multiple mechanisms in a single interface to determine DNA damage degree and provides a new approach for developing multimode analysis platforms for human health monitoring.

Nightingale V2: Low-power Compact-sized Multi-Sensor Platform for Wearable Health Monitoring.

Nightingale V2, a new wearable multi-sensor platform, is introduced in this paper. It can measure various bio-signals including ECG, BioZ, PPG, motion and heart sounds simultaneously at very low power consumption. Patient safety with optical and electrical sensors were carefully investigated. Preliminary data collection results as well as the sensor characterization results reveal that the Nightingale V2 would be suitable for wearable health monitoring applications.

SmartHerd management: A microservices-based fog computing-assisted IoT platform towards data-driven smart dairy farming.

SummaryInternet of Things (IoT), fog computing, cloud computing, and data‐driven techniques together offer a great opportunity for verticals such as dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. In this paper, we present SmartHerd, a fog computing–assisted end‐to‐end IoT platform for animal behavior analysis and health monitoring in a dairy farming scenario. The platform follows a microservices‐oriented design to assist the distributed computing paradigm and addresses the major issue of constrained Internet connectivity in remote farm locations. We present the implementation of the designed software system in a 6‐month mature real‐world deployment, wherein the data from wearables on cows is sent to a fog‐based platform for data classification and analysis, which includes decision‐making capabilities and provides actionable insights to farmer towards the welfare of animals. With fog‐based computational assistance in the SmartHerd setup, we see an 84% reduction in amount of data transferred to the cloud as compared with the conventional cloud‐based approach.

A pulse sensor interface design for FPGA based multisensor health monitoring platform

A pulse sensor interface design for FPGA based multisensor health monitoring platform

Recent Patient Health Monitoring Platforms Incorporating Internet of Things-Enabled Smart Devices.

This corrects the article "Recent Patient Health Monitoring Platforms Incorporating Internet of Things-Enabled Smart Devices" on page S76.

E-Health Biosensor Platform for Noninvasive Health Monitoring for the Elderly in Low Resource Setting

E-Health Biosensor Platform for Noninvasive Health Monitoring for the Elderly in Low Resource Setting

Recent Patient Health Monitoring Platforms Incorporating Internet of Things-Enabled Smart Devices.

Synergistic integration of the Internet of Things (IoT), cloud computing, and big data technologies in healthcare have led to the notion of “smart health.” Smart health is an emerging concept that refers to the provision of healthcare services for prevention, diagnosis, treatment, and follow-up management at any time or any place by connecting information technologies and healthcare. As a significant breakthrough in smart healthcare development, IoT-enabled smart devices allow medical centers to carry out preventive care, diagnosis, and treatment more competently. This review focuses on recently developed patient health monitoring platforms based on IoT-enabled smart devices that can collect real-time patient data and transfer information for assessment by healthcare providers, including doctors, hospitals, and clinics, or for self-management. We aimed to summarize the available information about recently approved devices and state-of-the-art developments through a comprehensive, systematic literature review. In this review, we also discuss possible future directions for the integration of cloud computing and blockchain, which may offer unprecedented breakthroughs in on-demand medical services. The combination of IoT with real-time, remote patient monitoring empowers patients to assert more control over their care, thereby allowing them to actively monitor their particular health conditions.

Prediction of Physiological Response over Varying Forecast Lengths with a Wearable Health Monitoring Platform.

The goal of this study is to characterize the accuracy of prediction of physiological responses for varying forecast lengths using multi-modal data streams from wearable health monitoring platforms. We specifically focus on predicting breathing rate due to its significance in medical and exercise physiology research. We implement a nonlinear support vector machine regression model for accurate prediction of future values of these physiological signals with forecast windows of up to one minute long. We explore the effects of heart rate and various other sensing modalities in prediction of breathing rate. Results reveal that including other physiological responses and activity information captured by inertial measurements in the regression model improves the breathing rate prediction accuracy. We carried out experiments by collecting and analyzing physiological and activity data outside the lab using a wearable platform composed of various off-the-shelf sensors.

Machine Learning and Mobile Health Monitoring Platforms: A Case Study on Research and Implementation Challenges.

Machine learning-based patient monitoring systems are generally deployed on remote servers for analyzing heterogeneous data. While recent advances in mobile technology provide new opportunities to deploy such systems directly on mobile devices, the development and deployment challenges are not being extensively studied by the research community. In this paper, we systematically investigate challenges associated with each stage of the development and deployment of a machine learning-based patient monitoring system on a mobile device. For each class of challenges, we provide a number of recommendations that can be used by the researchers, system designers, and developers working on mobile-based predictive and monitoring systems. The results of our investigation show that when developers are dealing with mobile platforms, they must evaluate the predictive systems based on its classification and computational performance. Accordingly, we propose a new machine learning training and deployment methodology specifically tailored for mobile platforms that incorporates metrics beyond traditional classifier performance.