Real-time Health Monitoring System Research Articles

Integrating Deep Learning and CCTV for Real-Time Health Monitoring in Schools

Abstract—This project proposes a real-time health monitoring system for schools, integrating deep learning techniques with CCTV technology to enhance student well-being. By utilizing advanced computer vision algorithms, the system analyzes video feeds from cameras to continuously assess students' health, focusing on facial expressions and behaviors. The DeepFace model is employed to detect and recognize individual students and analyze their facial expressions for signs of distress or other health-related issues. When the system identifies concerning health indicators, it generates immediate alerts, which are sent to school administrators through a user-friendly web interface. This interface allows administrators to monitor health analytics, view real-time alerts, and access historical data for effective decision-making and intervention. The proposed framework offers a proactive approach to student health monitoring, ensuring a safer and more supportive school environment. Keywords—DeepFace, CCTV, Pattern recognition, colour intensity

Decision-Making Model for Life Cycle Management of Aircraft Components

This paper presents a novel decision-making framework for the life cycle management of aircraft components, integrating advanced data analytics, artificial intelligence, and predictive maintenance strategies. The proposed model addresses the challenges of balancing safety, reliability, and cost-effectiveness in aircraft maintenance. By using real-time health monitoring systems, failure probability models, and economic analysis, the framework enables more informed and dynamic maintenance strategies. The model incorporates a comprehensive approach that combines reliability assessment, economic analysis, and continuous re-evaluation to optimize maintenance, replacement, and life extension decisions. The optimization method on the base of genetic algorithm (GA) is employed to minimize total life cycle costs while maintaining component reliability within acceptable thresholds. The framework’s effectiveness is demonstrated through case studies on three distinct aircraft components: mechanical, avionics, and engine. These studies showcase the model’s versatility in handling different failure patterns and maintenance requirements. This study introduces a data-driven decision-making framework for optimizing the life cycle management of aircraft components, focusing on reliability, cost-effectiveness, and safety. To achieve optimal maintenance scheduling and resource allocation, a GA is employed, allowing for an effective exploration of complex solution spaces and enabling dynamic decision-making based on real-time data inputs. The GA-based optimization approach minimizes total life cycle costs while maintaining component reliability, with the framework’s effectiveness demonstrated through case studies on key aircraft components. Key findings from the case study demonstrate significant cost reductions through optimization, with mechanical components showing a 10% more reduction in total life cycle costs, avionics components achieving a 14% more cost reduction, and engine components demonstrating a 7% more decrease in total costs. The research also presents an optimized dynamic maintenance schedule that adapts to real-time component health data, extending component lifespans and reducing unexpected failures. The framework effectively addresses key industry challenges such as no fault found events while minimizing unexpected failures and enhancing the overall reliability and safety of aircraft maintenance practices. Sensitivity analysis further demonstrates the model’s robustness, showing stable performance under varying failure rates, maintenance costs, and degradation rates. The study contributes a scalable approach to predictive maintenance, balancing safety, cost, and resource allocation in dynamic operational environments.

Comprehensive assessment of deep-water vessel implosion mechanisms: OceanGate's Titan submersible failure sequence explained

This study outlines the physical mechanisms involved in a submersible implosion and analyzes the loss of the Titan submersible (‘sub’) that occurred on June 18, 2023 during a mission to visit the wreck of the Titanic. Titan's collapse mechanisms at the moment of implosion are described in detail and outer hull fracturing rate, subsequent implosion rate, accompanying heat release and other key processes are quantified. Plausible causes of the hull's leak leading up to critical loss of the sub's hermetic closure are reviewed using test results made publicly available by the U.S. Marine Board of Investigation. Their data indicated that the bond interfaces between the individual layers of the carbon fiber hull (Hull V2) were critically compromised due to manufacturing defects, voids, porosity, and inadequate adhesive integrity, resulting in significant delamination. Analysis of data from the real-time hull health monitoring system, revealed acoustic anomalies and strain shifts, pointing toward increasing structural fatigue, which went unaddressed prior to the fatal dive. The implosion process can be characterized by an instantaneous collapse of the air volume within the hull under extreme external pressure: even the tiniest leak would lead to destruction of the vessel's structural integrity. The destruction was the more devastating, because it was accompanied by a secondary explosion due to the heat exchange between the collapsing air volume and the ambient sea water. While the collapsing air was phase-changed into a supercritical state, the generated heat caused the adjacent seawater to evaporate and expand. Hull pieces fragmented by the initial implosion were strewn around during the secondary explosion phase, which ceased rapidly as the steam condensed back into seawater once again. The Titan incident underscores the urgent need for improved design standards, rigorous quality control in manufacturing, and enhanced real-time monitoring to prevent similar failures of future deep-sea exploration vehicles.

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

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

An examination of high-speed aircraft – Part 2: Safety and reliability

This paper presents a detailed accident investigation into incidents involving high-speed vehicles, particularly supersonic and hypersonic platforms. Examining challenges related to engine performance, structural integrity, and economic aspects in military aerospace, the study emphasizes the importance of real-time health monitoring systems. A key highlight is the exploration of how these systems support the autonomy of hypersonic vehicle. While recognizing challenges related to sensitive technologies and data, the paper outlines research directions encompassing human factors, simulation and training programs, and policy advocacy for integrating high-speed aircraft into existing airspaces. In summary, this research contributes valuable insights to the understanding of high-speed aviation accidents, with implications for improving safety and efficiency in the future and ultimately shows that high-speed aviation safety cannot be treated in the same manner as its subsonic counterpart.

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.

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

RETRACTED ARTICLE: 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.