Health Monitoring Research Articles

A multimodal deep learning-based algorithm for specific fetal heart rate events detection.

This study aims to develop a multimodal deep learning-based algorithm for detecting specific fetal heart rate (FHR) events, to enhance automatic monitoring and intelligent assessment of fetal well-being. We analyzed FHR and uterine contraction signals by combining various feature extraction techniques, including morphological features, heart rate variability features, and nonlinear domain features, with deep learning algorithms. This approach enabled us to classify four specific FHR events (bradycardia, tachycardia, acceleration, and deceleration) as well as four distinct deceleration patterns (early, late, variable, and prolonged deceleration). We proposed a multi-model deep neural network and a pre-fusion deep learning model to accurately classify the multimodal parameters derived from Cardiotocography signals. These accuracy metrics were calculated based on expert-labeled data. The algorithm achieved a classification accuracy of 96.2 % for acceleration, 94.4 % for deceleration, 90.9 % for tachycardia, and 85.8 % for bradycardia. Additionally, it achieved 67.0 % accuracy in classifying the four distinct deceleration patterns, with 80.9 % accuracy for late deceleration and 98.9 % for prolonged deceleration. The proposed multimodal deep learning algorithm serves as a reliable decision support tool for clinicians, significantly improving the detection and assessment of specific FHR events, which are crucial for fetal health monitoring.

Leather-Based Shoe Soles for Real-Time Gait Recognition and Automatic Remote Assistance Using Machine Learning.

Real-time monitoring of gait characteristics is crucial for applications in health monitoring, patient rehabilitation feedback, and telemedicine. However, the effective and stable acquisition and automatic analysis of gait information remain significant challenges. In this study, we present a flexible sensor based on a carbon nanotube/graphene composite conductive leather (CGL), which uses collagen fiber with a three-dimensional network structure as the flexible substrate. The CGL-based sensor demonstrates a high dynamic range, with notable pressure responses ranging from 0.6 to 14.5 kPa and high sensitivity (S = 0.2465 kPa-1). We further developed a device incorporating the CGL-based sensor to collect foot characteristic signals from human motion and designed smart sports shoes to facilitate effective human-computer interaction. Machine learning was employed to collect and process gait characteristic information in various states, including standing, sitting, walking, and falling. For real-time monitoring of falls, we optimized the K-Nearest Time Series Classifier (KNTC) algorithm, achieving an accuracy of 0.99 and a prediction time of only 13 ms, which highlights the system's excellent intelligent response capabilities. The system maintained a gait recognition accuracy of 90% across diverse populations, with low false-positive (3.3%) and false-negative (3.3%) rates. This work demonstrates stable gait recognition capabilities and provides valuable methods and insights for plantar behavior monitoring and data analysis, contributing to the development of advanced real-time gait monitoring systems.

Safe drinking water: to what extent shallow wells are reliable?

Safe drinking water: to what extent shallow wells are reliable?

The Geysers Is a Unique Geothermal Resource Presenting Equally Unique Challenges to Drillers

_ For drillers, The Geysers is a unique sort of hell. It is one of the only places on Earth where a shallow well can reach a highly fractured reservoir so hot that it only produces steam. That steam powers 18 power plants generating 835 MW of electricity, making this 45-square-mile area the world’s most prolific geothermal power producer. High drilling costs there also stand out. Drilling a well requires 60 days or so and generates a lot of damaged drill bits and drillpipe. The drilling rigs have the size and power of modern onshore rigs, but a close look reveals there’s no topdrive. Below the rig floor there is a rotary table, known as a Kelly drive because it can spin drilling pipe without being in the line of fire of the steam flowing out when running slotted casing into the steam-production zone. When they get to that zone, they switch from drilling fluid and mud to injecting a high-pressure stream of air because of the enormous fluid losses anticipated when they reach that highly fractured level. A drill bit that lasts 500 ft in this harsh, highly fractured rock has had a long run. And the rugged terrain—mountains to a Texan but hills to a Californian—makes this a hard place to move a rig. “The Geysers is its own animal, for sure,” said Sam Noynaert, a Texas A&M professor who was a key member of a team of drilling experts and scientists that drilled a well in The Geysers to test if faster drilling methods from the oil business could work there. The two upper sections went pretty well, considering. As they went deeper, they repeatedly had to deal with severe fluid losses. Still, they had shaved nearly 6 days off the average drilling time. But when they reached the highly fractured steam-producing payzone and their drilling method changed to air drilling, things got worse with drill bits failing after runs as short as 21 ft. As a Geysers drilling paper presented at the Stanford Geothermal Workshop earlier this year delicately put it: “The air section proved to be more challenging.” The Blob The Geysers has a long history. The heat source is “a large blob of silica-rich magma (which) forced its way through Earth’s crust beneath the Coast Range,” according to the description of an image from NASA. That flow about 1.3 million years ago from deep within the earth heated the overburden, making the rock brittle and prone to fracturing. Over time, water seeped into those hot fissures, creating hot springs that were awed by Native Americans and later attracted tourists to a nearby resort hotel. By the 1960s drillers were tapping the steam in those payzones for commercial power generation, which peaked at 2,000 MW in 1987. It declined from there as steam production depleted the reservoir. Eventually, the output was stabilized at its current level by injecting water from sewage-treatment plants into the production zone.

A complex network analysis of groundwater wells in and around the Doñana Natural Space, Spain

This paper proposes the use of Complex Network Analysis tools to study the spatial distribution and temporal evolution of groundwater abstraction wells within a region of interest. To that end a weighted undirected network connecting wells within a certain distance was built. The edge weights take into account the distance between the wells as well as their corresponding abstraction volumes. In addition to the conventional complex network metrics, two specific abstraction-based centrality indexes are proposed, based on the total impact of a groundwater abstraction well on its surrounding wells and on the total impact of surrounding wells on a specific location. The proposed approach has been applied to the area of the Doñana Natural Space, one of the most important wetlands in Europe. The topology of the network reveals a large number of small, disconnected components and a few large components. This is reflected also in the degree distribution, which follows a Power Law distribution. The network has a large average clustering coefficient, a small average path length and a low level of centralisation. Global and average local network efficiency are also high. The network is assortative in terms of degree-degree and strength-strength correlations. Eigenvector centrality of the nodes reveals the location of influential wells.

Employing cluster analysis in defining groundwater wells patterns in Rafah

Employing cluster analysis in defining groundwater wells patterns in Rafah

Single Method for Determination of Biocides Benzalkonium Chloride, Chloroxylenol and Chlorhexidine Gluconate in Cosmetics, Hygiene and Health Care Products by Using High Performance Liquid Chromatography.

Benzalkonium chloride (BKC), chloroxylenol (PCMX) and chlorhexidine gluconate (CHG) are widely recognized biocidal compounds that require precise control and monitoring in various applications, including cosmetics, hygiene, health and ophthalmic products. In this study, we present a straightforward and highly sensitive high-performance liquid chromatographic (HPLC) method for the detection of these substances. The HPLC method we developed allows for the detection of BKC at concentrations as low as 1.0ppm, PCMX at 1.0ppm and CHG at 2.0ppm, all with a signal-to-noise (S/N) ratio exceeding 3.0. Additionally, the method enables quantification up to 2.0ppm for BKC, 2.0ppm for PCMX and 5ppm for CHG, with an S/N ratio surpassing 10.0. This validated method serves as a versatile tool for the accurate quantification of BKC, PCMX and CHG in cosmetics, hygiene products, health products and ophthalmic products. Its applicability extends across a wide range of industries, ensuring the reliable assessment of these critical biocides.

Pavement health 4.0: a novel AI-enabled PavementVision approach for pavement health monitoring and classification

Pavement health 4.0: a novel AI-enabled PavementVision approach for pavement health monitoring and classification

Pasteables: A Flexible and Smart “Stick-and-Peel” Wearable Platform for Fitness and Athletics

Wearable technologies are becoming increasingly popular and transforming our fitness and sports industry. Monitoring health parameters and body activity can help achieve fitness goals, improve sports performance, and aid in physiotherapy. However, state-of-the-art wearable devices are often not flexible, expensive, hard to set up, or have privacy concerns. They are designed and optimized for a specific application with tight hardware-software integration and it is hard or impossible to re-purpose them for diverse use cases. In this paper, we propose a flexible, re-configurable human body movement and health monitoring platform, called "Pasteables". It is a stick-and-peel device (which acts like a band-aid) that attaches to the skin or clothing and can create an on-body network of smart wearables. Given an application, Pasteables can be easily adapted to its requirements while ensuring good performance and privacy. We present the overall architecture, system design steps, and the configuration process. We have designed a set of functional prototypes and configured them for performing a variety of camera-less pose and posture detection tasks, which are important for athletes, professional dancing, physiotherapy, and fitness workouts. We note that the Pasteables can perform pose/posture detection without external stationary reference at low cost and high accuracy.

Discussion on Structural Health Monitoring of Urban Underground Road Tunnel

Discussion on Structural Health Monitoring of Urban Underground Road Tunnel

Lessons to learn for better safeguarding of genetic resources during tree pandemics: The case of ash dieback in Europe

Ash dieback (ADB) has been threatening populations of European ash (Fraxinus excelsior & F. angustifolia) for more than three decades. Although much knowledge has been gathered in the recent past, practical conservation measures have been mostly implemented at local scale. Since range contraction in both ash species is likely to be exacerbated already in the near future by westward expansion of the emerald ash borer and climate change, systematic conservation frameworks need to be developed to avoid long-term population-genetic consequences and depletion of genomic diversity. In this article, we address the advantages and obstacles of conservation approaches aiming to conserve genetic diversity in situ or ex situ during tree pandemics. We are reviewing 47 studies which were published on ash dieback to unravel three important dimensions of ongoing conservation approaches or perceived conservation problems: i) conservation philosophy (i.e. natural selection, resistance breeding or genetic conservation), ii) the spatial scale (ecosystem, country, continent), and iii) the integration of genetic safety margins in conservation planning. Although nearly equal proportions of the reviewed studies mention breeding or active conservation as possible long-term solutions, only 17 % consider that additional threats exist which may further reduce genetic diversity in both ash species. We also identify and discuss several knowledge gaps and limitations which may have limited the initiation of conservation projects at national and international level so far. Finally, we demonstrate that there is not much time left for filling these gaps, because European-wide forest health monitoring data indicates a significant decline of ash populations in the last 5 years.

An intelligent system for determining the degree of tree bark beetle damage based on the use of generative‐adversarial neural networks

AbstractThe increasing prevalence of tree bark beetle infestations poses a significant threat to forest ecosystems, leading to detrimental impacts on biodiversity, carbon storage, and timber resources. This study addresses the pressing need for innovative solutions to enhance forest management practices through early detection and assessment of tree health. We developed an intelligent system leveraging multispectral images and generative‐adversarial networks (GANs) to accurately determine the extent of bark beetle damage. Recognizing the challenges posed by traditional neural networks, which require vast amounts of labeled training data, we proposed a novel approach that utilizes a GAN architecture. In this system, a discriminator functions as a classifier, effectively trained on both real and synthetically generated data. Our methodology not only reduces the dependency on extensive labeled datasets but also enhances the robustness of the classification process. The results indicate a classification accuracy of 87.5%, demonstrating significant promise for improving detection capabilities even with limited training resources. The implications of this research are profound, offering potential benefits for the forestry industry through optimized management strategies and economic gains. Furthermore, our findings contribute to the preservation of critical ecosystem services by providing a means to monitor forest health effectively. However, the responsible implementation of this technology is paramount. Continuous refinement of the model, integration with traditional ecological knowledge, and ensuring transparency and equitable access to the developed system are essential for maximizing societal benefits. Additionally, addressing risks such as misinterpretation of data, overreliance on technology, and privacy concerns is crucial to minimize unintended consequences. Overall, this research presents a significant advancement in the field of forest health monitoring and establishes a foundation for future developments in intelligent ecological management systems.

An Unsupervised Learning framework for health diagnosis by incorporating Multiscale Data

Integrating real-time evaluations of health states with Structural Health Monitoring (SHM) data and environmental observations is crucial, particularly under large datasets. Not only is the volume of real-time data analysis substantial, but traditional algorithms like wavelet analysis often require extensive prior work and significant manual input, limiting the application of SHM and big data technologies in structural analysis. To overcome this limitation, this study introduces an unsupervised analytical framework coupling a Denoising Sparse Wavelet Network (DeSpaWN) and a dynamic-inner Principal Component Analysis (DiPCA) algorithm. The DeSpaWN is designed to extract sparse representation from multiscale sensor signals, and the features are subsequently analyzed using the DiPCA to assess health diagnoses. The DeSpaWN is specifically tailored for monitoring high-frequency time series dynamically and adaptively, enabling reconstruction and extraction of signals across various frequency domains. Additionally, the DiPCA effectively handles large-scale time-series data, providing precise evaluations by accounting for dynamic latent correlations. To validate the method, a 5-Degree of Freedom (DoF) structure model is established, equipped with five virtual sensors at each DoF and an anemometer at the top DoF. The sensor signals, obtained under different health states and subjected to artificial excitations, are input into the proposed framework. Validation results demonstrate the effectiveness in synthesizing multi-sensor data and assessing structural integrity. The framework's practical utility is showcased through a real-world case study that assesses the structural integrity of a wind turbine. By integrating real and virtual sensor data collected over a year and cross-referencing this with the fault log, the study validates the algorithm's efficacy in processing multisource heterogeneous data and its precision in structural health diagnosis. The study substantiates real-time tracking of health states for SHM-equipped structures, underscoring its potential to enhance safety and reliability in civil infrastructure.

Internet based highly secure data transmission system in health care monitoring system

<span>The health care systems in our contemporary countries are advancing rapidly in terms of maturity and professionalism. In an effort to alleviate the current burden on the public health system and boost the popularity of regular health self-checks, this method has been developed for producing prediagnoses that are easier to use, quicker, and more accurate. To ascertain how well the heart is circulating oxygen throughout the body, a pulse test, a painless examination that measures an individual's degree of oxygen saturation, is used. It can be used to evaluate the state of any patient with a disease, particularly those with pulmonary problems. Diseases in these patients could need ongoing observation and care. Our system comes to the rescue in order to resolve this problem. This portable system is simple to use and may be taken anywhere by the subject. The internet of things (IoT) will update the pertinent parameters. This health monitoring system's controller is made up of an adaptor, a saturation of peripheral oxygen (SPO2 ) sensor (a blood oxygen meter), a temperature sensor, a heart rate sensor, a WiFi module, and a liquid crystal display (LCD).</span>

CMOS Pulse Oximeter

Abstract: This paper presents the design and implementation of a non-invasive pulse oximeter based on Complementary MetalOxide-Semiconductor (CMOS) technology, aimed at enhancing the accuracy and accessibility of blood oxygen saturation measurements. Pulse oximetry is a critical tool in clinical and personal health monitoring, providing vital information about respiratory function. The integration of CMOS technology allows for miniaturization and energy efficiency, facilitating the development of compact, wearable devices capable of continuous monitoring. The proposed system utilizes advanced signal processing techniques, including lock-in detection and dual-wavelength illumination, to improve the reliability of photoplethysmographic (PPG) signal acquisition under various conditions. Experimental validation demonstrates the device's capability to deliver accurate SpO2 readings even in the presence of motion artifacts and ambient light interference. Furthermore, the calibration methods employed ensure that measurements are consistent and precise across different users. By leveraging the advantages of CMOS technology, this study not only highlights the potential for improved patient comfort and compliance but also opens new avenues for the application of pulse oximetry in diverse environments. The findings underscore the effectiveness of CMOS-based pulse oximeters in advancing non-invasive health monitoring solutions, ultimately contributing to enhanced patient care and outcomes.

PulseGuard: Health Monitoring and Analysis System

Abstract: The use of wearable devices such as smart watches has led to a major change in the way a person is able to manage their health. In this paper, health monitoring system construction based on a smartwatch is designed which is capable of Health parameters like heartbeat monitoring, counting steps and their physical activity levels at continuous intervals. The system is built on modern web technologies and cloud services for data processing in a real-time manner, thus ensuring that alert messages are delivered immediately in case health parameters go beyond the normal ranges. The goal is to improve personal health management by providing expansion, dependability and availability to the users in areas where there is poor or no health care provision. The paper discusses system architecture, implementation, and the prospect of the system in enhancing preventive medicine and its practice for better health.

Effectiveness of King's Theory of Goal Attainment in Blood Glucose Management for Newly Diagnosed Patients With Type 2 Diabetes: Randomized Controlled Trial.

Diabetes poses a significant public health challenge in China and globally, with the number of patients expected to reach 592 million by 2035, notably in Asia. In China alone, an estimated 140 million individuals are living with diabetes, and a significant portion is nonadherent to medications, underscoring the urgency of effective management strategies. Recognizing the necessity of early and comprehensive management for newly diagnosed patients with type 2 diabetes, this study leverages an online teach-back method and "Internet + Nursing" platform based on King's Theory of Goal Attainment. The approach aims to enhance glycemic control and reduce fear and misconceptions about the disease, addressing both the educational and emotional needs of the patients. The primary aim of this study was to assess the effectiveness of King's Goal Attainment Theory in the management of newly diagnosed patients with type 2 diabetes. This research sought to develop a collaborative model for blood glucose management, integrating the expertise and roles of physicians, nurses, and patients. The model is designed to enhance the synergy in health care provision, ensuring a comprehensive approach to diabetes management. In this study conducted at Changzhou Traditional Chinese Medicine Hospital between January 2022 and February 2023, eligible patients were randomized into a control group or an online feedback group. The control group received standard care, while the online feedback group participated in a King's Theory of Goal Attainment-based online teach-back program, enhanced by "Internet + Nursing" strategies. This included an interactive platform for goal planning, video content sharing, comprehension assessment, misconception correction, and patient-driven recaps of disease information. Health monitoring was facilitated through the "Internet + Nursing" platform. The study focused on comparing changes in glucose metabolism and emotional disorder symptoms between the groups to evaluate the intervention's effectiveness. Following a 24-week intervention, we observed significant differences in key metrics between the online feedback group and the control group, each comprising 60 participants. The online feedback group demonstrated significant reductions in fasting plasma glucose, 2-hour postprandial glucose, and hemoglobin A1c (P<.05). Additionally, there was a notable decrease in hypoglycemia-related anxiety and alexithymia within this group. Conversely, the control group maintained relatively higher values for these metrics at the same time point (P<.05). These findings underscore the efficacy of online feedback in managing glycemic control and reducing psychological distress associated with hypoglycemia. The online teaching-back method, guided by King's Theory of Goal Attainment, effectively enhances glycemic control, reducing fasting plasma glucose, 2-hour postprandial glucose, and hemoglobin A1c levels in newly diagnosed patients with type 2 diabetes. Simultaneously, it alleviates hypoglycemia-related anxiety and mitigates alexithymia. This approach merits widespread promotion and implementation in clinical settings. Chinese Clinical Trial Registry ChiCTR2400079547; https://www.chictr.org.cn/showproj.html?proj=208223.

Nanodomain-Enhanced Stable and Multifunctional Probes with Near 100% Quantum Yield for Versatile Biosensing.

The preparation of high quantum yield, stable, and multifunctional fluorescent probes is of great significance in the fields of biomedicine and photoelectric sensing. Here, a triphenylamine-based D-π-A fluorescent molecule (TPA-CN) was designed and prepared, demonstrating a fluorescence quantum yield of 88.84%. With a polystyrene nanosphere as the carrier, TPA-CN was encapsulated inside the nanosphere to form intra-nanosphere confining domains. These nanodomain-enhanced fluorescent nanospheres exhibited a fluorescence quantum yield of 98.21%. Using antigen-antibody specificity and the selective catalytic activity of a bioenzyme, with chloramphenicol as a model target, a dual-signal readout biosensor (in fluorescence and colorimetric modes) was designed for ultrasensitive and instrument-free determination. The detection limit was 24 pg/mL within 30 min in fluorescence mode, 38-fold more sensitive and 10-fold faster than that of enzyme linked immunosorbent assays. The nanodomain-enhanced fluorescent probes and dynamic biosensor provide a robust and versatile solution for public health and environmental monitoring needs.

Framework for Identification of Bridges in Need of Monitoring Based on Maintenance History

Recently, advanced Internet-of-Things (IoT) technology has been implemented in structural health monitoring to help increase safety of infrastructure and the reliability of maintenance decisions. In this context, preliminary studies are required to identify infrastructure in need of monitoring. This study establishes a basis for the identification of bridges that require monitoring and selects the bridges for measurement in order to build an efficient IoT-based monitoring system for small and medium bridges. The bridges selected based on various maintenance histories and risk factors are expected to be utilized in comprehensively assessing the characteristics of the overall bridge network.

Numerical Analysis of Steel Frame Using Electro-Mechanical Impedance Technique

Nowadays, piezoelectric materials are most widely used in structural health monitoring (SHM) system for nondestructive evaluation. In this paper the damage study using electro-mechanical impedance (EMI) technique, which is nondestructive method of SHM. This paper presents a numerical analysis of four-story steel frame and multiple damages were induced in the steel frame by utilizing smart material like Lead Zirconate Titanate (PZT). A PZT-4 piezoelectric material was embedded in the beam of the frame. The type of damping used in steel frame model was Rayleigh damping. The numerical analysis was done in COMSOL MULTIPHYSICS 6.2 and frequency domain analysis was done, from that admittance values were find out for 30-300KHz frequency range. The real part of the admittance gives conductance values. Then, conductance verses frequency graph was created that will indicate the structural health and performance. Moreover, root mean square deviation (RMSD) index was found out for each case that provides a quantitative measure of deviation.