Real-time Health Monitoring System Research Articles

Artificial intelligence enabled biodegradable all-textile sensor for smart monitoring and recognition

Artificial intelligence (AI) enabled electronic textiles inherit the advantages of traditional textiles, such as softness, flexibility, and wearable convenience, and demonstrate significant potential for wearable applications. However, the existence of metallic electrodes and polymer thin films sensitive/encapsulating layers in current textile- or fiber-based pressure sensors significantly reduces the unique advantages of textiles, particularly in terms of renewability and biodegradability. Here, an all-textile biodegradable AI enabled pressure sensor is demonstrated using tunable conductivity cotton as the electrode/sensitive layer, incorporating real-time deep learning-based data analysis. The metal electrode and polymer thin film widely adopted in smart textile-based pressure sensors are avoided, and the all-textile components allow the sensors to be freely cut and reassembled like Lego. Based on these Lego-like smart textiles, intelligence applications such as real-time health monitoring, game control, gait analysis, and authentication systems are demonstrated. After completing the functions, the whole device can be rapidly degraded in the cellulase solution and broken down into reducing sugars, thus effectively reducing the environmental pollution. This work provides a new strategy for creating renewable and sustainable textile pressure sensors with significant application potential in next-generation smart green electronics.

The Electrostatic Induction Characteristics of SiC/SiC Particles in Aero-Engine Exhaust Gases: A Simulated Experiment and Analysis

This study investigates the electrostatic induction characteristics of silicon carbide-fiber-reinforced silicon carbide (SiC/SiC) particles within aero-engine exhaust gases using a dedicated J20 turbojet engine experimental platform. Our comprehensive experiments explored the electrostatic properties of SiC/SiC particles under varying engine operational states—specifically focusing on different thermal conditions, particle mass concentrations, particle sizes, and exhaust gas velocities compared to those of common engine exhaust constituents like carbon (C) and iron (Fe) particles. The results demonstrate that SiC/SiC particles consistently maintain a stable positive charge across varied temperatures, significantly diverging from the behaviors of carbon (C) and iron (Fe) particles. Additionally, our findings reveal that higher mass concentrations of SiC/SiC particles, smaller particle sizes within a certain range, and greater exhaust gas velocities of the aero-engine all lead to increased particle charge and more pronounced electrostatic induction characteristics. This study highlights the potential of electrostatic sensors for the early detection and diagnosis of failures in aero-engines, offering crucial insights into the development of more resilient real-time aero-engine health monitoring systems.

IoT Based Monitoring and Maintenance of Highway Bridges using WSN

This paper presents a WSN-based IOT-based bridge monitoring system. The automated real-time bridge health monitoring system has been made possible by sophisticated modifications to sensor technology; this system will aid in disaster management. The technology used in this development is a wireless sensor network (WSN). This system uses a variety of sensors to continually monitor the state of the bridge. These sensors include an accelerometer to identify jerks in the bridge, a vibration sensor to identify vibrations occurring on the bridge, a flex sensor to identify bends in the bridge, and a water level sensor to identify the water level. To determine the weight on the bridge, use the load sensor. The barrier is detected by an IR sensor. Only the buzzer will activate when these are high. Additionally, a microcontroller processes the data from many sensors and transmits it to a server and management system so that real-time bridge condition monitoring via a mobile device using the GSM model is possible

Autonomous Navigated IOT Enabled Smart Wheel Chair - A Healthcare and Mobility solution

WHO disability statistics indicate that about 8 million people worldwide, or 1% of the total population, suffer from a mobility handicap. It is also known that the elderly residents in the area cannot move independently. Many hospital patients can force their hand with assistance but cannot move independently. We also understand that real-time health monitoring is necessary for patients, the elderly, and individuals with impairments. Therefore, this work aims to develop and provide a smart wheelchair that can be controlled with a single hand gesture and tracks many health indicators, such as blood pressure, body temperature, oxygen saturation, and pulse rate, using an Internet of Things-based real-time health monitoring system. Users can effortlessly maneuver the wheelchair using simple gesture control enabled by the Internet of Things (IoT), promoting independence and enhancing general well-being. This innovative approach to healthcare and mobility meets users' urgent requirements and establishes a new benchmark for assistive technology, a significant advancement in technology and healthcare. The benefit of this strategy is that the caregiver can view all of these health indicators and the wheelchair user's GPS location using a smartphone application. Taking care of oneself while employed by a firm might be easy.

A Review on Real Time Patient Health Monitoring System Using Cloud Technology

Abstract: The rising demand for advanced healthcare solutions has driven the development of innovative technologies aimed at improving patient care. This project introduces a Real-Time Patient Health Monitoring System, which utilizes cloud technology to its full potential. By incorporating cloud infrastructure, data science, and real-time analytics, the system offers continuous monitoring and analysis of crucial health metrics such as SpO2, temperature, and pulse rate. Designed with wearability in mind, the system's architecture ensures uninterrupted monitoring and instant feedback for both patients and healthcare professionals. Cloud technology enables seamless data storage, retrieval, and real-time analysis, thereby providing comprehensive insights into a patient's health status. The project's scalability guarantees its suitability for various healthcare environments, rendering it a versatile tool for hospitals, clinics, and remote healthcare scenarios.

HealthGuard: Real-Time Health Monitoring and Emergency Alert System

Abstract: In today's fast-paced world, the need for continuous health monitoring and rapid response to emergent medical situations is paramount. "HealthGuard" is a cutting-edge real-time health monitoring and emergency alert system designed to address this critical need. Leveraging the power of IoT sensors and advanced data analytics, HealthGuard offers a comprehensive solution for tracking and responding to individual's health conditions, ensuring timely intervention when necessary.

Design of a real-time health monitoring and prediction system for table tennis players based on optical detection sensor networks

Design of a real-time health monitoring and prediction system for table tennis players based on optical detection sensor networks

Array-Designed Triboelectric Nanogenerator for Healthcare Diagnostics: Current Progress and Future Perspectives

Array-designed triboelectric nanogenerators (AD-TENGs) have firmly established themselves as state-of-the-art technologies for adeptly converting mechanical interactions into electrical signals. Central to the AD-TENG’s prowess is its inherent modularity and the multifaceted, grid-like design that pave the way to robust and adaptable detection platforms for wearables and real-time health monitoring systems. In this review, we aim to elucidate the quintessential role of array design in AD-TENGs for healthcare detection, emphasizing its ability to heighten sensitivity, spatial resolution, and dynamic monitoring while ensuring redundancy and simultaneous multi-detection. We begin from the fundamental aspects, such as working principles and design basis, then venture into methodologies for optimizing AD-TENGs that ensure the capture of intricate physiological changes, from nuanced muscle movements to sensitive electronic skin. After this, our exploration extends to the possible cutting-edge electronic systems that are built with specific advantages in filtering noise, magnifying signal-to-noise ratios, and interpreting complex real-time datasets on the basis of AD-TENGs. Culminating our discourse, we highlight the challenges and prospective pathways in the evolution of array-designed AD-TENGs, stressing the necessity to refine their sensitivity, adaptability, and reliability to perfectly align with the exacting demands of contemporary healthcare diagnostics.

Advancing structural health monitoring: A vibration-based IoT approach for remote real-time systems

In the modern world, civil engineering structures are of paramount importance. Early damage detection through continuous health monitoring, instead of conventional periodic inspection, is the promising way forward for healthy structures with the least maintenance requirement. The damage is identified at the initial stage, so the cost associated with its repair can be reduced. Considering the immense scale of civil infrastructure, currently, available structural health monitoring systems are not feasible for mass application as they are prohibitively expensive. Hence, it is the need of the hour to develop a remote real-time Structural Health Monitoring System (SHMS). In the current study, a system is proposed that utilizes a vibration-based structural health monitoring approach and extracts dynamic parameters from acceleration data. The system consists of a node tasked with data collection and transmission and a server assigned with data post-processing and results display. The node comprises the following main hardware components: ESP32 as a microcontroller unit and ADXL345 as an accelerometer. The system was developed with the capability of multiple node integration, onboard data storage to eliminate sampling rate inconsistencies, dual trigger modes to achieve a high signal-to-noise ratio (SNR), power usage optimization to prolong stand-by time, and wireless data transmission along with automated post-processing. To minimize the cost of the system, Internet of Things (IoT) technology was adopted to develop the system.

Real-time Health Monitoring and Speedy Personal Alert Support System using Medical IoT-Cloud Infrastructures

Health care monitoring technology is evolving day by day with the help of computational techniques and intelligent diagnosis models. Compared to conventional approaches and clinical observations, recent health care methodologies are heavily depending on more accurate computational platforms to diagnose various diseases. On the stream, computational principles and intelligent diagnosis model are proposed for monitoring and classifying the biosensor signal data. The existing diagnosis procedures that are using crucial computational models have been trained to observe the medical symptoms of particular disease. The contributions of recent medical support systems and computational models are closely made of Internet of Medical Things (IoMT) environment. However, the existing techniques are applied to obtain accurate decisions only rather than speedy response properties. Against the issue, the proposed Real-time Gated Recurrent Unit (GRU) based Health Monitoring (RGHM) system has been initiated to build more reliable, fast and accurate health assistance solutions. Most of the recent works are taking more complex data processing techniques that are producing higher delay and resources. The proposed RGHM works against these issues and attains effective results compared to current techniques such as Smart Artificial Intelligence based Monitoring Technique (SAIMT), Patient Monitoring with IoT model (PM-IoT) and Patient Centric Cloud Health Monitoring system (PCCHM). The performance of proposed RGHM is illustrated in result section that shows the RGHM has attained 11% to 14% of better performance than existing techniques.

Neighborhood component analysis to leverage the class label information during feature selection to enhance the damage classification performance

In the health monitoring of large structures data from multiple sensors are fused to get reliable and robust results. Using data from multiple sensors leads to high dimensional feature vectors which result in computational inefficiency and overfitting. Therefore, it is necessary to reduce the dimension of these feature vectors, especially in the case of real-time health monitoring systems. Principal component analysis has become a go-to algorithm for dimensionality reduction in the structural health monitoring literature. However, it reduces the dimension by maximum variance criterion without considering the class labels of these initial feature vectors, this implies that the reduced dimensional feature vectors do not guarantee best class separability in damage classification problems. Using linear discriminant analysis to solve this issue poses singularity problems in the damage classification problems with small datasets having high dimensional feature vectors. This paper proposes the use of neighborhood component analysis for reducing the dimensionality of the feature vectors. As in the case of damage type identification problems the input signals have to be labeled, neighborhood component analysis makes use of the class label information and reduces the dimension by considering the classification performance. The algorithm’s performance has been validated by solving a damage classification problem by combining it with a K-nearest neighbor classifier. An experimental benchmark dataset from the American Society of Civil Engineers is utilized for this purpose. A comparison with the Principal Component Analysis on the same dataset depicts that the ability of Neighborhood Component Analysis to include the class label information indeed helps in enhancing the damage classification performance.

Retraction Note: A fuzzy model of wearable network real-time health monitoring system on pharmaceutical industry

Retraction Note: A fuzzy model of wearable network real-time health monitoring system on pharmaceutical industry

Animal Vocal Recognition-Based Breeding Tracking and Disease Warning

<p>In this study, we investigate the application of a deep learning framework for the recognition of pig vocalizations. This innovative approach aims to actively monitor and evaluate the diverse states of pigs, with an overarching objective to improve the efficiency of pig farming through prompt identification and resolution of issues. In our comprehensive data collection effort, we carefully gathered a vast assortment of vocal samples from 50 pigs, representative of four distinct states: normal, frightened, coughing, and sneezing. We then meticulously analyzed this vocal data using Mel Frequency Cepstral Coefficients (MFCC). For accurate recognition of pig vocalizations, we devised a fusion model that combines the strengths of Residual Networks (ResNet) and Long Short-Term Memory Networks (LSTM). This model was subsequently tailored, trained, and optimized to meet our specific requirements. Upon rigorous evaluation, we found our model to exhibit exceptional performance in pig vocal recognition tasks, thereby reinforcing the potential of deep learning methodologies in revolutionizing the livestock industry. This research notably underscores the potential of deploying efficient real-time health monitoring systems, offering a promising avenue towards modernizing livestock management practices.</p> <p> </p>

Review on Analysis and Optimization of Gait Stroke Detection using Variable Sensors with Machine Learning Approach

Abstract: Gait monitoring is considered an important marker of impairment, disability and gait symmetry. This research aims to develop a real-time consumer health monitoring system based on IoT sensors and machine learning technology to detect health disorders such as the onset of stroke. The proposed consumer stroke prediction system consists of IoT-based gait monitoring sensors, real-time vital signs monitoring, and a machine learning-based disease prediction model that predicts impaired and healthy gait. This study is useful for post-stroke walking coordination rehabilitation and consumer health monitoring service.

Inter-Story Drift Ratio Detection of High-Rise Buildings Based on Ambient Noise Recordings

The inter-story drift ratio (IDR) is a crucial parameter in structural health monitoring to judge the safety, stability, and serviceability of buildings. Real-time, continuous, and widely applicable detection on IDRs is essential. However, the current methods present some challenges in conducting such detection, including adverse effects from weather, the requirement for large amounts of space, and fragile instruments. This study proposes an alternative method to overcome these defects to measure IDRs and evaluate the structural conditions using ambient noise recordings. Ambient noise is a random and continuous wave signal with various sources and is modified by its propagating medium. Taking the Zhonghe Building on the campus of Tongji University, Shanghai, China, as an example, 24 three-component seismometers were deployed to capture and record the ambient noise continuously from 20 November 2021 to 9 December 2021. Using analysis of the polarization parameters of ambient noise during the building’s most dangerous time and ordinary time, a deflection curve, IDRs, and harmful IDRs of the Zhonghe Building during the most dangerous time were calculated. The computed maximum drift was 0.06 m, the maximum IDR was 1.140×10−3, and the maximum harmful IDR was −2.573×10−4. These results were compared with the relevant specifications in China, and it was found that the structure was in good condition. The study proposes an alternative method to measure IDRs with high applicability and continuity in real time and underscores the need for further research to achieve a localized and real-time structural health monitoring system.

Internet-based centralized remote real-time long-span bridge health monitoring system

The safe operation of bridges is a major issue related to the national economy and people’s livelihood. With the rapid development of the water transport industry, the problems of ship yaw and bridge collision avoidance occur frequently, which cause serious harm to the bridge structure and safety. However, the current bridge health monitoring system is difficult to feed back the monitored information in time, and in the process of collecting information by wireless sensors, information errors or missing problems easily occur, which hinders the normal monitoring of bridge health. With the rapid development of the Internet and the gradual maturity of wireless sensor technology, how to achieve complete and effective collection and feedback of monitoring information has become a hot research topic and an urgent problem. Therefore, on the basis of Internet technology, this study perceives, collects and processes the ship information in the bridge monitoring area covered by the network through the wireless sensor network, uses the embedded wavelet neural network model to denoise the monitoring data, and finally transmits it to the data processing center, thus establishing a centralized remote real-time Long-span bridge health monitoring system based on the Internet. The experimental results show that the monitoring deformation displacement curve based on the Internet centralized remote real-time long-span bridge health monitoring system technology presents a stable fluctuation state. The cumulative shape variable fluctuates in the range of -5 mm to 5 mm, indicating that the deformation trend of the bridge has always existed.

Development of a Real-Time Wheel Load Quantification System for the Transit Environment

With recent (prepandemic) growth in both transit ridership and the number of passenger rail systems nationwide, researchers have been increasingly interested in quantifying the rail transit loading environment. Research that stemmed from this renewed interest provided engineers with greater insights into the loading demands placed on the track structure of heavy, light, and commuter rail systems. Although results from this earlier work were useful in a general manner, it was not possible to provide agencies with immediately actionable information on wheel loads, since the relevant data were analyzed and reported at a later date. As a result, agencies were unable to monitor their rolling stock wheel health in real time. In addition, trend analysis was not possible because it was not feasible to track specific wheels over time. To address these limitations, researchers at the University of Illinois have developed an economical system that both provides real-time notifications to transit agencies when it detects problematic loading conditions, and tracks specific wheels over time. This paper provides a framework for installing and launching this real-time wheel health monitoring system that transit agencies can replicate, as well as presents some preliminary data that have been collected. By receiving actionable wheel load data and better understanding the wheel deterioration trends present on their networks, agencies can remove bad actor wheels from service before they damage the track structure, improving the state of good repair. In addition, a more thorough understanding of the loading environment will allow them to plan maintenance and design more effectively.

IoT Based Surveillance and Health Monitoring System for Elderly and Physically Challenged People

With the increase in global life expectancy and the advance of technology, the creation of age-friendly environments is a priority in the design of new products for elderly people healthcare. This paper presents a proposal for a real-time health monitoring system of older adults living in geriatric residences. This system was developed to help caregivers to have a better control in monitoring the health of their patients and have closer communication with their patients’ family members. To validate the feasibility and effectiveness of this proposal, a prototype was built, using a biometric bracelet connected to a mobile application, which allows real-time visualization of all the information generated by the sensors (heart rate, body temperature, and blood oxygenation) in the bracelet. Using these data, caregivers can make decisions about the health status of their patients. The evaluation found that the users perceived the system to be easy to learn and use, providing initial evidence that our proposal could improve the quality of the adult’s healthcare.

Multi-Layered Non-Local Bayes Model for Lung Cancer Early Diagnosis Prediction with the Internet of Medical Things.

The Internet of Things (IoT) has been influential in predicting major diseases in current practice. The deep learning (DL) technique is vital in monitoring and controlling the functioning of the healthcare system and ensuring an effective decision-making process. In this study, we aimed to develop a framework implementing the IoT and DL to identify lung cancer. The accurate and efficient prediction of disease is a challenging task. The proposed model deploys a DL process with a multi-layered non-local Bayes (NL Bayes) model to manage the process of early diagnosis. The Internet of Medical Things (IoMT) could be useful in determining factors that could enable the effective sorting of quality values through the use of sensors and image processing techniques. We studied the proposed model by analyzing its results with regard to specific attributes such as accuracy, quality, and system process efficiency. In this study, we aimed to overcome problems in the existing process through the practical results of a computational comparison process. The proposed model provided a low error rate (2%, 5%) and an increase in the number of instance values. The experimental results led us to conclude that the proposed model can make predictions based on images with high sensitivity and better precision values compared to other specific results. The proposed model achieved the expected accuracy (81%, 95%), the expected specificity (80%, 98%), and the expected sensitivity (80%, 99%). This model is adequate for real-time health monitoring systems in the prediction of lung cancer and can enable effective decision-making with the use of DL techniques.

Fuzzy-logic-based IoMT framework for COVID19 patient monitoring

Smart healthcare is an integral part of a smart city, which provides real time and intelligent remote monitoring and tracking services to patients and elderly persons. In the era of an extraordinary public health crisis due to the spread of the novel coronavirus (2019-nCoV), which caused the deaths of millions and affected a multitude of people worldwide in different ways, the role of smart healthcare has become indispensable. Any modern method that allows for speedy and efficient monitoring of COVID19-affected patients could be highly beneficial to medical staff. Several smart-healthcare systems based on the Internet of Medical Things (IoMT) have attracted worldwide interest in their growing technical assistance in health services, notably in predicting, identifying and preventing, and their remote surveillance of most infectious diseases. In this paper, a real time health monitoring system for COVID19 patients based on edge computing and fuzzy logic technique is proposed. The proposed model makes use of the IoMT architecture to collect real time biological data (or health information) from the patients to monitor and analyze the health conditions of the infected patients and generates alert messages that are transmitted to the concerned parties such as relatives, medical staff and doctors to provide appropriate treatment in a timely fashion. The health data are collected through sensors attached to the patients and transmitted to the edge devices and cloud storage for further processing. The collected data are analyzed through fuzzy logic in edge devices to efficiently identify the risk status (such as low risk, moderate risk and high risk) of the COVID19 patients in real time. The proposed system is also associated with a mobile app that enables the continuous monitoring of the health status of the patients. Moreover, once alerted by the system about the high risk status of a patient, a doctor can fetch all the health records of the patient for a specified period, which can be utilized for a detailed clinical diagnosis.