Monitoring Of Health Status Research Articles

AGRICULTURAL DROUGHT ANALYSIS BASED ON REMOTE SENSING: THE CASE OF AYDIN, TURKIYE

Agricultural drought is a phenomenon that arises when there is a deficiency of moisture in the soil, which has a detrimental impact on the productivity of agricultural crops. In Aydın, Turkey, particularly in the fertile Söke Plain region, agricultural drought is a major problem for farmers. The use of satellite data based on remote sensing and the indices derived from them allows for the timely and spatially detailed monitoring of vegetation health and moisture conditions over large areas. This enables the early detection and monitoring of agricultural drought. The present study evaluates the occurrence of agricultural drought in Aydın province between 1995 and 2020. For this purpose, satellite images captured by the Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) in August 1995 and 2020 and land cover maps produced by the European Space Agency (ESA) at the same dates were utilized. The Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) indices were produced using Landsat satellite images. Then, the Vegetation Temperature Condition Index (VTCI) was obtained to detect agricultural drought. Finally, the relationship between the VTCI and land cover (LC) was evaluated, as well as the changes in the VTCI index between 1995 and 2020. The study found that agricultural drought increased with rising land surface temperature and declining NDVI values in Aydın province between 1995 and 2020.

Recent Advances in Amperometric Biosensors for Medical Applications: A Mini-Review.

Amperometric biosensors have emerged as a cutting-edge technology in clinical diagnostics, thanks to their high level of sensitivity, rapid analytical results, compact size, and ability to monitor health parameters non-invasively and continuously using flexible and wearable sensors. This review explores the latest developments in the field of amperometric biosensing for medical applications. It discusses the materials used to construct these sensors and pays particular attention to biosensors designed to measure glucose, lactate, cholesterol, urea, and uric acid levels. The review also addresses the technological limitations and drawbacks of these devices. Furthermore, it presents the current status and identifies future trends in the development of flexible, wearable biosensors capable of providing continuous monitoring of a patient's health status.

A noise-enhanced machine fault diagnosis method with multi-channel information fusion

ABSTRACT Condition health monitoring and fault diagnosis is able to safeguard against potential threats to machines. Nevertheless, the acquired signals by using a plenty of sensors are often accompanied by noise and meanwhile the useful information contained in the signals is not both complete and reliable, thereby together reducing the accuracy and stability of the fault diagnosis. Therefore, this paper proposes a noise-enhanced intelligent fault diagnosis method with multi-channel information fusion. Here, it would inject the varying levels of Gaussian noise into multi-channel input data as well as intelligent diagnostic models, with a view to elucidating the benefits of noise. Furthermore, the proposed method is validated through three experiments. The experimental results demonstrate that the diagnostic accuracy of the hydraulic motor and the two different bearings reaches 99.85%, 99.89%, and 99.85% by using the proposed method, respectively, which are increased by 2.50%, 4.09%, and 2.50%, respectively, compared with one without the injection of noise. In addition, by comparing the diagnostic accuracy of ones with single-channel and multi-channel information fusion under the optimal noise level in both cases, it is found that one with multi-channel information fusion shows an average of 3.20% increase than the single-channel one.

Maternity care coordinator's experiences at the department of Veteran Affairs.

The Veterans Health Administration (VHA) provides maternity care by paying for Veterans to receive pregnancy-related care in community settings and by utilizing maternity care coordinators (MCCs) at each medical facility. The purpose of this qualitative descriptive study was to understand the MCC's experiences performing their role across VA facilities. Thirty MCCs were recruited and interviewed virtually using Microsoft Teams. Interviews were recorded and transcribed verbatim. Using thematic analysis, transcripts were coded, and themes were derived. MCC's roles include being a liaison, care coordinator, and supporter. MCCs improve Veterans' care during pregnancy and postpartum by education, monitoring health status, and connecting Veterans to providers within VA and the community. Across VA facilities, there was variation in how MCCs engaged with Veterans and in the services provided. A challenge shared was the lack of dedicated time to the role. In the VA, MCCs are valuable in ensuring high-quality care coordination of pregnant/postpartum Veterans despite the fragmentation of care between VA and community providers. To improve inconsistencies in how the MCC program is implemented, systematic strategies such as ensuring dedicated time are needed.

Ultrasensitive Ultrasoft Buckled Crack‐Based Sensor for Respiration Measurement and Enhanced Human–Machine Interface

Wearable strain sensors have transformed the real‐time monitoring of health conditions and human–machine interactions. However, recently developed wearable strain sensors exhibit several limitations. For example, when a sensor is designed with high sensitivity to detect strain, it struggles to accurately measuring the deformation of low‐stiffness materials like skin. Additionally, finding the optimal balance between sensitivity, durability, hysteresis, and strain range in sensor design is challenging. To address these challenges, a Buckled, Ultrasoft, Crack‐based, Large strain, EpiDermal (BUCKLED) sensor is developed. This sensor integrates the benefits of soft structure engineering with high sensitivity of crack‐based sensing mechanisms to ensure optimal skin deformation measurements. The BUCKLED sensor exhibits significant improvements in compliance (18 500 mm N−1), stretchability (100%), hysteresis (2%), durability (10 000 cycles with 100% strain), and force sensitivity () owing to its buckled shape, confirming its ability to detect subtle movements with enhanced accuracy. The sensor's high compliance allows it to accurately measure low‐stiffness objects, ensuring reliable performance. Furthermore, the sensor's tunability is demonstrating its effectiveness in applications such as respiratory monitoring, facial expression recognition, and silent speech interfaces. Consequently, the proposed sensor is versatile and holds great potential for a wide range of sensing applications.

Quantification measurement of hemoglobin with large dynamic range and low detection limit via an optical fiber optofluidic laser with enzyme-catalyzed reaction

Quantitative measurement of hemoglobin (Hb) levels is an essential part of routine medical examinations, disease diagnosis and health status monitoring. In this study, we introduced optical fiber optofluidic laser (FOFL) technology combined with catalytic oxidation reaction to design a laser sensor for sensitive Hb detection. For the H2O2-rhodamine B (RhB) oxidation system, radially emitting FOFL was achieved with a thin-walled hollow optical fiber (HOF) as an optical microcavity and amplified the concentration change of RhB during oxidation by H2O2. Hb was employed as peroxide-mimicking enzyme to catalyze the oxidation system, which sped up the reaction, resulting in an earlier laser extinction time. With the laser extinction time as sensing signal, the Hb sensor achieved a dynamic range of five orders of magnitude and a limit of detection (LOD) of 46.0 pM at an assay time of 40 min. The developed method provides ideas for the exploitation of FOFL biosensing based on catalytic oxidation reactions.

Automated Digital Sfigmomanometer Prototype Testing

Abstract Blood pressure is the pressure of blood flow in the arteries generated by the heart. Abnormal blood pressure can lead to hypertension and hypotension. Hypotension, or low blood pressure, has a prevalence of 5% to 34% among individuals aged 17 to 19, and over 50% in the elderly. Hypertension can cause death in patients with heart disease. About 39.7% of people suffer from hypertension, especially those over the age of 60. Blood pressure measurement needs to be done regularly because it has impact such as early detection of blood pressure health conditions and monitoring of health conditions for those already diagnosed with abnormal blood pressure. Unfortunately, only 59% of the population in Indonesia regularly measure their blood pressure. Accurate blood pressure measurement can be performed using an invasive method. Another method is non-invasive, which can be done using a sphygmomanometer that applies the oscillometric method. This method uses pressure oscillations in the cuff influenced by the pulse in the blood vessels. When using this method, several factors need to be considered, such as body position, cuff size, and the technique used during the measurement. In this study, a fully automatic digital sphygmomanometer has been developed with a cuff attached to the device, equipped with a thermal printer to print out the measurement results and classify blood pressure categories. The advantage of the developed prototype is that it has a smaller relative error value, with 1.03% for systolic blood pressure and 1.45% for diastolic blood pressure, and has already been integrated with a thermal printer to directly print the measurement results.

Study on Fault Prediction Method of Hydropower Equipment Based on Equipment Health State Detection

Abstract With the continuous development of China’s electric power industry, the installed capacity of power plants is increasing, and hydropower units are developing in the direction of large capacity and high efficiency. As the complexity of the equipment is getting higher and higher, different equipment are more and more different, showing different characteristics when faults occur, which puts forward higher requirements for the safe and stable operation of hydropower units. In response to the above problems, this paper develops a set of human-machine closed-loop remote fault diagnosis reasoning machine system with self-learning function and a set of equipment health state monitoring and prediction system. This remote fault diagnosis system reasoning machine system can reduce the cost of unit equipment fault diagnosis, improve the diagnosis efficiency, and ensure the safe, stable and economic operation of the unit.

On the measurement of piezoelectric d33 coefficient of soft thin films under weak mechanical loads: A rapid and affordable method

Thanks to their intrinsic flexibility, energy efficiency and high portability, soft piezoelectric thin films represent the most effective technological approach for wearable devices to monitor health conditions. In order to improve effectiveness and applicability, more and more innovative and high-performing soft piezoelectric materials are being developed and benchmarked through their piezoelectric d33 coefficient. However, most existing methods to measure the d33 were developed for ceramic or bulk materials and cannot be applied to soft materials because high force/pressure can deform and damage the material structure. This work introduces a simple, effective, and fast method to accurately measure the d33 of soft and thin piezoelectric films by applying weak sinusoidal forces to avoid any damage to the sample, and simultaneously measuring the charges produced by the direct piezoelectric effect. The approach is versatile as it can be used for different types of materials and sizes of the active area. This method represents an effective solution to speed up the process of material optimization, paving the way for the rapid development of novel wearable piezoelectric devices.

Tracking and Behavior Analysis of Group-Housed Pigs Based on a Multi-Object Tracking Approach

Smart farming technologies to track and analyze pig behaviors in natural environments are critical for monitoring the health status and welfare of pigs. This study aimed to develop a robust multi-object tracking (MOT) approach named YOLOv8 + OC-SORT(V8-Sort) for the automatic monitoring of the different behaviors of group-housed pigs. We addressed common challenges such as variable lighting, occlusion, and clustering between pigs, which often lead to significant errors in long-term behavioral monitoring. Our approach offers a reliable solution for real-time behavior tracking, contributing to improved health and welfare management in smart farming systems. First, the YOLOv8 is employed for the real-time detection and behavior classification of pigs under variable light and occlusion scenes. Second, the OC-SORT is utilized to track each pig to reduce the impact of pigs clustering together and occlusion on tracking. And, when a target is lost during tracking, the OC-SORT can recover the lost trajectory and re-track the target. Finally, to implement the automatic long-time monitoring of behaviors for each pig, we created an automatic behavior analysis algorithm that integrates the behavioral information from detection and the tracking results from OC-SORT. On the one-minute video datasets for pig tracking, the proposed MOT method outperforms JDE, Trackformer, and TransTrack, achieving the highest HOTA, MOTA, and IDF1 scores of 82.0%, 96.3%, and 96.8%, respectively. And, it achieved scores of 69.0% for HOTA, 99.7% for MOTA, and 75.1% for IDF1 on sixty-minute video datasets. In terms of pig behavior analysis, the proposed automatic behavior analysis algorithm can record the duration of four types of behaviors for each pig in each pen based on behavior classification and ID information to represent the pigs’ health status and welfare. These results demonstrate that the proposed method exhibits excellent performance in behavior recognition and tracking, providing technical support for prompt anomaly detection and health status monitoring for pig farming managers.

A conductive ionogel with Stretchability, low hysteresis and adjustable adhesion for Air/Underwater mechanosensing

Conductive ionogels based on deep eutectic solvents (DESs) have great application potential in the field of flexible sensors. However, the development of conductive ionogels that are stable under various environmental conditions (such as high and low temperatures, air, and water) remains extremely challenging. In this work, the hydrophobic conductive ionogel BxHyIG was prepared via a one-step photoinitiated copolymerization of butyl acrylate and N-hydroxyethylacrylamide in a hydrophobic DES. The good chemical compatibility between the polymer and DES, as well as the dense and reversible ion–dipole interactions, dipole–dipole interactions and hydrogen bond interactions, endow BxHyIG with satisfactory transparency (80 %), adhesion (polystyrene: 74.56 kPa), mechanical properties (elongation at break: 578 %, tensile strength: 171.25 kPa), a low degree of hysteresis (DH=1.9 %) and conductivity (3.95 × 10-3 S m−1). Strain/pressure sensors and bioelectrodes prepared from ionogels have the characteristics of high sensitivity, fast response and excellent stability and can be used for real-time monitoring of human activities and health status in the atmosphere. In addition, the inherent hydrophobicity of BxHyIG (water contact angle > 110°) can effectively prevent BxHyIG from being eroded and swollen by water, thereby achieving reliable underwater sensing. More importantly, by directly exposing BxHyIG to air or storing it in nitrogen, the surface adhesion of conductive ionogels can be effectively regulated to obtain double-sided, single-sided or even nonadhesive ionogels. Therefore, this study provides new ideas for the development of conductive ionogels that can be applied under various environmental conditions.

Application of noncontact sensors for cardiopulmonary physiology and body weight monitoring at home: A narrative review.

Monitoring health status at home has garnered increasing interest. Therefore, this study investigated the potential feasibility of using noncontact sensors in actual home settings. We searched PubMed for relevant studies published until February 19, 2024, using the keywords "home-based," "home," "monitoring," "sensor," and "noncontact." The studies included in this review involved the installation of noncontact sensors in actual home settings and the evaluation of their performance for health status monitoring. Among the 3 included studies, 2 monitored respiratory status during sleep and 1 monitored body weight and cardiopulmonary physiology. Measurements such as heart rate, respiratory rate, and body weight obtained with noncontact sensors were compared with the results obtained from polysomnography, polygraphy, and commercial scales. All included studies demonstrated that noncontact sensors produced results comparable to those of standard measurement tools, confirming their excellent capability for biometric measurements. Overall, noncontact sensors have sufficient potential for monitoring health status at home.

Secured Public Health data Management Framework using the Internet of Things in a Smart City Environment

The Internet of Things (IoT) is being embraced and impacts various aspects of lives. Integrating IoT technology in the healthcare sector paves the way for continuous remote monitoring of individuals' health status. The healthcare (HC) sector has traditionally produced substantial data. Due to the implementation of the IoT, this quantity has significantly increased. Ensuring proper HC storage is imperative to derive meaningful insights from the data. This study presents a public health (PH) framework that utilizes cloudlets and IoT technology. The primary objective of this PH architecture is to facilitate the retrieval of real-time data by using cloudlets. The research provides and executes an HC data management system to store vast HC data and handle consumer queries to retrieve HC information. The efficacy of the proposed model has been evaluated in terms of data transmission time, energy usage, query response time, and data packet loss. The proposed model outperforms existing PH systems by comparing the assessment findings of the proposed approach with the performance of typical PH systems.

Flexible Temperature Sensor with High Reproducibility and Wireless Closed-Loop System for Decoupled Multimodal Health Monitoring and Personalized Thermoregulation.

Temperature and pulse waves are two fundamental indicators of body health. Specifically, thermoresistive flexible temperature sensors are one of the most applied sensors. However, they suffer from poor reproducibility of resistivity; and decoupling temperature from pressure/strain is still challenging. Besides, autonomous thermoregulation by wearable sensory systems is in high demand, but conventional commercial apparatuses are cumbersome and not suitable for long-term portable use. Here, a material-design strategy is developed to overcome the problem of poor reproducibility of resistivity by tuning the thermal expansion coefficient to nearly zero, precluding the detriment caused by shape expansion/shrinkage with temperature variation and achieving high reproducibility. The strategy also obtains more reliable sensitivity and higher stability, and the designed thermoresistive fiber has strain-insensitive sensing performance and fast response/recovery time. A smart textile woven by the thermoresistive fiber can decouple temperature and pulse without crosstalk; and a flexible wireless closed-loop system comprising the smart textile, a heating textile, a flexible diminutive control patch, and a smartphone is designed and constructed to monitor health status in real-time and autonomously regulate body temperature. This work offers a new route to circumvent temperature-sensitive effects for flexible sensors and new insights for personalized thermoregulation.

Transparent, Flexible Nanoporous Sensors with Humidity and Pressure Sensing Capabilities for Sports Health Status Monitoring

Transparent, Flexible Nanoporous Sensors with Humidity and Pressure Sensing Capabilities for Sports Health Status Monitoring

Analyzing the role of social media in addressing public health development in india through a multi-criteria decision-making approach

Nowadays, social media plays a vital role in managing public health developments. This research study has analyzed the factors of social media enhancing public health development. The analytic hierarchy process (AHP) is used to rank the factors, and the Decision-making trial and evaluation laboratory (DEMATEL) is used to find out the network relationship map of the role of social media in public health development. Results of AHP revealed that social media enhances public engagement and participation, collaboration of health professionals worldwide, and helps in remote monitoring of patients. Results of DEMATEL show that the development of health policies, repository of public health-related information, and prevention of health-related misinformation and fake news are classified as the topmost causal factors. Real-time monitoring of health conditions is the factor with the most significant impact on other factors. Policymakers and stakeholders should focus on improving public health through social media. Social media enhances collaboration among health professionals worldwide, and various stakeholders use it to address public health issues in the case of COVID-19.

Nanoplasmonic biosensors for environmental sustainability and human health.

Monitoring the health conditions of the environment and humans is essential for ensuring human well-being, promoting global health, and achieving sustainability. Innovative biosensors are crucial in accurately monitoring health conditions, uncovering the hidden connections between the environment and human well-being, and understanding how environmental factors trigger autoimmune diseases, neurodegenerative diseases, and infectious diseases. This review evaluates the use of nanoplasmonic biosensors that can monitor environmental health and human diseases according to target analytes of different sizes and scales, providing valuable insights for preventive medicine. We begin by explaining the fundamental principles and mechanisms of nanoplasmonic biosensors. We investigate the potential of nanoplasmonic techniques for detecting various biological molecules, extracellular vesicles (EVs), pathogens, and cells. We also explore the possibility of wearable nanoplasmonic biosensors to monitor the physiological network and healthy connectivity of humans, animals, plants, and organisms. This review will guide the design of next-generation nanoplasmonic biosensors to advance sustainable global healthcare for humans, the environment, and the planet.

Diaper-based wearable SERS sensing system with a silver nano dual-structure composite hydrogel for the detection of biomarkers and pH in urine

Wearable sensors are essential for continuous monitoring of an individual’s health status to enable personalized health management. Urine as a typical non-invasive biological fluid is rich in biological information. Although diaper-based biosensors have been developed, the development of wearable sensors for multiple biomarkers in urine remains a major challenge to be explored. In this paper, a hydrogel SERS sensor consisting of two metal nanostructures of silver nanowires (Ag NWs) and silver nanocubes (Ag NCs) was developed and adhered to nappies to form a wearable nappy-based SERS sensor for the rapid and sensitive detection of bio markers (creatinine, uric acid and bilirubin) and pH in urine. The hydrogel’s excellent adsorption and water-locking properties greatly simplify the collection of urine samples. At the same time, its encapsulation effect can effectively maintain the freshness of the urine sample and ensure the accuracy of the test. The introduction of silver nano dual-structure improves the antimicrobial properties of the hydrogel and reduces the risk of infection when the sensor comes into contact with the skin. Moreover, the silver nano dual-structure triggers abundant electromagnetic “hot spots” around intersecting nanojunctions and gaps, which greatly improves SERS sensitivity. For the detection of creatinine, uric acid and bilirubin, the LODs were as low as 0.59 μM, 69 nM, 89 nM. Finally, the urine pH was accurately detected using a functionalized Ag NWs-Ag NCs/CCNF/Gel/PVA hydrogel. This wearable diaper sensor is expected to be used for personal health condition monitoring and holds great promise for effective care of infants, the elderly, and those with limited mobility.

A New Approach to Recording Rumination Behavior in Dairy Cows.

Rumination behavior in cattle can provide valuable information for monitoring health status and animal welfare, but continuous monitoring is essential to detect changes in rumination behavior. In a previous study validating the use of a respiration rate sensor equipped with a triaxial accelerometer, the regurgitation process was also clearly visible in the pressure and accelerometer data. The aim of the present study, therefore, was to measure the individual lengths of rumination cycles and to validate whether the sensor data showed the same number of regurgitations as those counted visually (video or direct observation). For this purpose, 19 Holstein Friesian cows equipped with a respiration rate sensor were observed for two years, with a focus on rumination behavior. The results showed a mean duration of 59.27 ± 9.01 s (mean ± SD) per rumination cycle and good agreement (sensitivity: 99.1-100%, specificity: 87.8-95%) between the two methods (sensor and visual observations). However, the frequency of data streaming (continuously or every 30 s) from the sensor to the data storage system strongly influenced the classification performance. In the future, an algorithm and a data cache will be integrated into the sensor to provide rumination time as an additional output.

Feasibility characteristics of wrist-worn fitness trackers in health status monitoring for post-COVID patients in remote and rural areas.

Common, consumer-grade biosensors mounted on fitness trackers and smartwatches can measure an array of biometrics that have potential utility in post-discharge medical monitoring, especially in remote/rural communities. The feasibility characteristics for wrist-worn biosensors are poorly described for post-COVID conditions and rural populations. We prospectively recruited patients in rural communities who were enrolled in an at-home rehabilitation program for post-COVID conditions. They were asked to wear a FitBit Charge 2 device and biosensor parameters were analyzed [e.g. heart rate, sleep, and activity]. Electronic patient reported outcome measures [E-PROMS] for mental [bi-weekly] and physical [daily] symptoms were collected using SMS text or email [per patient preference]. Exit surveys and interviews evaluated the patient experience. Ten patients were observed for an average of 58 days and half [N = 5] were monitored for 8 weeks or more. Five patients [50%] had been hospitalized with COVID [mean stay = 41 days] and 4 [36%] had required mechanical ventilation. As baseline, patients had moderate to severe levels of anxiety, depression, and stress; fatigue and shortness of breath were the most prevalent physical symptoms. Four patients [40%] already owned a smartwatch. In total, 575 patient days of patient monitoring occurred across 10 patients. Biosensor data was usable for 91.3% of study hours and surveys were completed 82.1% and 78.7% of the time for physical and mental symptoms, respectively. Positive correlations were observed between stress and resting heart rate [r = 0.360, p<0.01], stress and daily steps [r = 0.335, p<0.01], and anxiety and daily steps [r = 0.289, p<0.01]. There was a trend toward negative correlation between sleep time and physical symptom burden [r = -0.211, p = 0.05]. Patients reported an overall positive experience and identified the potential for wearable devices to improve medical safety and access to care. Concerns around data privacy/security were infrequent. We report excellent feasibility characteristics for wrist-worn biosensors and e-PROMS as a possible substrate for multi-modal disease tracking in post-COVID conditions. Adapting consumer-grade wearables for medical use and scalable remote patient monitoring holds great potential.