Health Monitoring System Research Articles

Dynamic Wireless Sensor Network-Based Structural Health Monitoring System for Retractable Roof Structure

Retractable roof structure (RRS) works optimally under varying weather, but is prone to damage due to its complexity, requiring monitoring. Given RRS's large scale, wireless sensor networks (WSNs) are preferred, with dynamic WSNs better adapted to its motion than static ones. A tree-type network topology was designed for dynamic WSN, enabling fast self-routing. It is divided into subnets, each with a relay node as the parent and subordinate relay and sensor nodes as children. Network address reassignment method was proposed to sustain WSN communication during motion. This method involves operations of subnet affiliation change and relay node replacement, the former adjusts the hierarchy between relay nodes across different subnets while preserving parent-child relation within each subnet, and the latter swaps the roles of relay and sensor nodes within one subnet to form a new subnet. Basic communication module with processing, communication, and power management units was used to connect nodes. Dynamic WSN-based structural health monitoring (SHM) system with 372 sensors was developed for the Textile City Sports Centre Gymnasium. Mean errors between simulated and measured results were 10.4% for displacement, 13.6% for stress during construction, and 13.3% for tension during service, indicating good agreement. This suggests that the SHM system operated well in both phases. Maximal stress and displacement after unloading were 215.9 MPa and 549 mm, below allowable limits. Monitoring results revealed temperature field non-uniformity with a 9°C maximal difference, and strong temperature-stress correlation, with Pearson coefficients of 0.971, 0.973, and 0.955 for tie rod, chord, and web. RRS movement significantly impacted midspan tie rods in long main trusses, with a mean tension change exceeding 1000 kN. The research methods and conclusions offer valuable references for developing and applying RRS's SHM system.

Homicides by offenders with psychotic illness in Italy and Turkiye: A comparison of offender and crime-scene profiles

ObjectiveThe primary aim of this study was to conduct a comparative analysis of homicide cases and their perpetrators with psychotic illnesses in Italy and Turkiye, identifying the extent to which country-specific factors influence offender profiles and crime-scene characteristics. MethodThis cross-national chart review study recruited individuals with psychotic illnesses from forensic psychiatric centers in Italy and Turkiye who were referred for criminal responsibility assessments. Data were collected on the offenders' background characteristics, psychiatric history, victim profiles, and crime-scene details. ResultsCompared to the Italian sample, a higher prevalence of hospitalizations, contact with mental health services, and past offending history prior to the index homicide in the Turkish sample highlight inadequate community mental health care and monitoring systems, as well as insufficient supervision by the criminal justice system in Turkiye. Turkish offenders were less likely to exhibit organized crime scenes and post-crime behavior, with fewer stressors preceding the offense, suggesting a greater role of positive psychotic symptoms in their homicidal acts. ConclusionDespite certain similarities, the differences between the two samples highlight the impact of sociocultural, healthcare, and legal systems on offender profiles and crime-scene characteristics. These findings emphasize the need for tailored mental health services, forensic psychiatric assessments and legal supervision that take into account country-specific factors.

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.

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.

Integration of Foundation Models and Federated Learning in AIoT-Based Aircraft Health Monitoring Systems

The study presents a comprehensive framework for integrating foundation models (FMs), federated learning (FL), and Artificial Intelligence of Things (AIoT) technologies to enhance aircraft health monitoring systems (AHMSs). The proposed architecture uses the strengths of both centralized and decentralized learning approaches, combining the broad knowledge capture of foundation models with the privacy-preserving and adaptive nature of federated learning. Through extensive simulations on a representative aircraft fleet, the integrated FM + FL approach demonstrated consistently superior performance compared to standalone implementations across multiple key metrics, including prediction accuracy, model size efficiency, and convergence speed. The framework establishes a robust digital twin ecosystem for real-time monitoring, predictive maintenance, and fleet-wide optimization. Comparative analysis reveals significant improvements in anomaly detection capabilities and reduced false alarm rates compared to traditional methods. The study conducts a systematic evaluation of the benefits and limitations of FM, FL, and integrated approaches in AHMS, examining their implications for system robustness, scalability, and security. Statistical analysis confirms that the integrated approach substantially enhances precision and recall in identifying potential failures while optimizing computational resources and training time. This paper outlines a detailed aviation ecosystem architecture integrating these advanced AI technologies across centralized processing, client, and communication domains. Future research directions are identified, focusing on improving model efficiency, ensuring generalization across diverse operational conditions, and addressing regulatory and ethical considerations.

A Support Vector Machine-Based Intelligent System for Real-Time Structural Health Monitoring of Port Tower Cranes

A Support Vector Machine-Based Intelligent System for Real-Time Structural Health Monitoring of Port Tower Cranes

Development of Internet of Things and Artificial Intelligence for intelligent sanitation systems: A literature review

Adequate sanitation is crucial for human health and well-being, yet billions worldwide lack access to basic facilities. This comprehensive review examines the emerging field of intelligent sanitation systems, which leverage Internet of Things (IoT) and advanced Artificial Intelligence (AI) technologies to address global sanitation challenges. The existing intelligent sanitation systems and applications is still in their early stages, marked by inconsistencies and gaps. The paper consolidates fragmented research from both academic and industrial perspectives based on PRISMA protocol, exploring the historical development, current state, and future potential of intelligent sanitation solutions. The assessment of existing intelligent sanitation systems focuses on system detection, health monitoring, and AI enhancement. The paper examines how IoT-enabled data collection and AI-driven analytics can optimize sanitation facility performance, predict system failures, detect health risks, and inform decision-making for sanitation improvements. By synthesizing existing research, identifying knowledge gaps, and discussing opportunities and challenges, this review provides valuable insights for practitioners, academics, engineers, policymakers, and other stakeholders. It offers a foundation for understanding how advanced IoT and AI techniques can enhance the efficiency, sustainability, and safety of the sanitation industry.

Mapping the Public Health and Health Promotion Capacity in Oman

Abstract Background An essential condition to enabling people to increase control over their health is to have sufficient capacity for public health (PH) and health promotion (HP). Despite the recognition of its importance, a comprehensive view of the PH and HP capacity in the sultanate of Oman is not yet available. This study provides a mapping of the country’s PH and HP capacity at national, governorate and district levels. Methods An online quantitative survey was performed amongst 70 health policy maker and experts in PH and HP using a questionnaire based on the PH capacity framework of Aluttis et al., yielding data on the country specific context with relevance for PH and 6 PH capacity domains, each divided in subdomains: leadership and governance; organizational structures, workforce, financial resources, partnerships, and knowledge development. Internal consistencies of the scales measuring the capacity subdomains ranged from .75 to .99. Results The survey revealed relatively low scores, representing partial development of capacities, for most of the public health capacity domains. The highest levels of capacity were observed for knowledge development (health information and monitoring systems, reporting systems and knowledge infrastructure), organizational structures (institutional capacity, program delivery structures, PH within health services, and response emergencies) and partnerships (formal consortia, informal partnerships and multisectoral coordination). In contrast, leadership and governance, workforce development, and financial resources score lower. Conclusions Mapping the strengths and weaknesses of PH and HP capacity in Oman allows to make recommendations for improving population health. Whereas all domains of PH and HP capacity are partially developed, there is a need to further improve capacity in all domains. Key messages • PH and HP capacities are partially developed in Oman. • Mapping the PH and HP capacity allows to identify strengths and weaknesses and make recommendations for improving public health.

The INPHET- a global approach to Environmental Public Health Tracking

Abstract Environment and health monitoring and information systems are fundamental infrastructure for evidence provision for policy making. The International Network for Public Health and Environment Tracking (INPHET) was formed by a community of environmental public health practitioners to address common interests and activities that advance environmental public health surveillance (EPHS) and data systems, and their uses to inform public health actions. Its main goal is to address the vast inequalities across countries and communities in their ability to monitor, assess, inform and mitigate environmental and health issues. INPHET supports the relevant role of environmental epidemiology as the pillar science providing evidence for decision making and interventions aiming at reducing and preventing environmental related diseases and contributes to an overall vision for public health. As a “Community of Practice”, INPHET focuses on sharing experiences and expertise; addressing common challenges for implementing and enhancing the science of EPHS/tracking; improving understanding of the link between the environment and health; and providing the appropriate evidence that informs public policy. The network, initiated in conference meetings in 2012-13 and formally launched in Modena, Italy in 2014, has carried out several activities which aim at sharing expertise and learning from different models of environmental and health data integration and surveillance systems that directly inform policy. These include Twinning cooperation with Euro-Asian and Balkan countries and sharing of expertise with African Public Health Agencies. Some experiences are presented in this workshop and highlight the EPHS/EPHT model adopted by several national public health agencies. It promotes that such integration and uses of environmental and health data is key for EPHS, including their accessibility and usability for communities of public health researchers, practitioners and policy makers.

IoT-Based Smart System for Continuous Patient Health Monitoring

IoT-Based Smart System for Continuous Patient Health Monitoring

Detecting Multi-Scale Defects in Material Extrusion Additive Manufacturing of Fiber-Reinforced Thermoplastic Composites: A Review of Challenges and Advanced Non-Destructive Testing Techniques.

Additive manufacturing (AM) defects present significant challenges in fiber-reinforced thermoplastic composites (FRTPCs), directly impacting both their structural and non-structural performance. In structures produced through material extrusion-based AM, specifically fused filament fabrication (FFF), the layer-by-layer deposition can introduce defects such as porosity (up to 10-15% in some cases), delamination, voids, fiber misalignment, and incomplete fusion between layers. These defects compromise mechanical properties, leading to reduction of up to 30% in tensile strength and, in some cases, up to 20% in fatigue life, severely diminishing the composite's overall performance and structural integrity. Conventional non-destructive testing (NDT) techniques often struggle to detect such multi-scale defects efficiently, especially when resolution, penetration depth, or material heterogeneity pose challenges. This review critically examines manufacturing defects in FRTPCs, classifying FFF-induced defects based on morphology, location, and size. Advanced NDT techniques, such as micro-computed tomography (micro-CT), which is capable of detecting voids smaller than 10 µm, and structural health monitoring (SHM) systems integrated with self-sensing fibers, are discussed. The role of machine-learning (ML) algorithms in enhancing the sensitivity and reliability of NDT methods is also highlighted, showing that ML integration can improve defect detection by up to 25-30% compared to traditional NDT techniques. Finally, the potential of self-reporting FRTPCs, equipped with continuous fibers for real-time defect detection and in situ SHM, is investigated. By integrating ML-enhanced NDT with self-reporting FRTPCs, the accuracy and efficiency of defect detection can be significantly improved, fostering broader adoption of AM in aerospace applications by enabling the production of more reliable, defect-minimized FRTPC components.

Anomaly detection for bridge health monitoring data based on multiple encoded images and convolutional neural network

In bridge health monitoring systems, abnormal monitoring data inevitably appear due to the complex operational conditions, and these anomalous data will seriously affect the accuracy and reliability of the results of bridge health assessment. Hence, it is vital to detect the abnormal data before further processing. However, the pattern diversity and sample scarcity usually make the accuracy of data anomaly detection unsatisfactory. A novel anomaly detection method for bridge health monitoring data based on encoded images and convolutional neural network (CNN) is proposed in this paper. First, the raw time series are encoded into images to highlight the intrinsic features in the anomaly data, and multiple encoding techniques are adopted to represent the data in different perspectives. Then, the CNN is utilized to establish the mapping between the encoded images and the anomaly patterns for achieving the data anomaly detection. At last, the proposed method is verified with the acceleration data measured from an actual large span cable-stayed bridge. It shows that the proposed method can significantly improve the performance of data anomaly detection while the results are hardly affected by the small sample sizes, and a detection accuracy of over 95% is achieved based on the multiple encoded images.

Research on bridge safety early warning method based on strain energy theory and health monitoring data

Bridges are technology-intensive and heavily invested permanent infrastructure. After completion and opening to traffic, bridge structures are easy to be affected by factors such as traffic load and atmospheric environment. Therefore, it is necessary to do safety warning and evaluation of bridges, especially the abnormal behavior in the early stages of bridge operation. In this research paper, a large-span continuous rigid frame bridge installed with the health monitoring system (HMS), of which a large amount of health monitoring data are collected by the HMS, is used as an example, and then a bridge safety early warning method is proposed when the bridge is during early operating period. First of all, the research finding that the internal stress of the prototype bridge obeys normal distribution through statistical analysis is used; next, we deduce that the strain energy inside the prototype bridge is subject to the Non-central Chi-square distribution combined with the strain energy and statistical theories; in the end, the key probability density distribution function of strain energy and its parameters are derived by using the key stress distribution function of the high performance concrete C50 strength grade used in the prototype bridge. The method recommended in this paper is conducive to the formulation of bridge preventive maintenance strategies.

Deep learning-based automated identification on vortex-induced vibration of long suspenders for the suspension bridge

Large amplitude vortex-induced vibration (VIV) of long suspenders, caused by vortex-shedding and wake flow, will decrease the service life of high-strength wires and its anchorage systems, hence listed as an essential monitoring indicator of the structural health monitoring system in the suspension bridges. Traditionally, a static vibration amplitude limitation is usually predefined and used to identify VIV from continuously monitored acceleration data of bridge suspenders. Due to the complexity of the starting vibration of the suspenders, it may not be able to flexibly adapt to the suspenders VIV under different conditions. In addition, transient but significant vibration events may not be captured since it relies only on static vibration amplitude limits without considering the temporal information of vibration. To address the above-mentioned issues, a novel framework to autonomously identify the suspenders VIV is proposed using the multimodal fusion techniques with deep neural networks. The main contributions of the methodology are: i) by calculating the wind field and vibration characteristic, two vibration response parameters are identified as indexes for the identification of suspenders VIV based on the significant difference method of big data analysis; ii) Two different modal information of time history images and power spectral density (PSD) sequences are used as inputs to the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) to extract the suspenders vibration response features from the time and frequency domain perspectives, respectively; and iii) The multimodal information is concentrated and enhanced by the multi-head attention (MHA) mechanism to achieve automated identification of the suspenders VIV. The proposed framework is validated with data collected from a full-scale long-span suspension bridge in comparison with the base model. The results show that the proposed framework can efficiently identify the full-cycle development process of the suspenders VIV. This study provides a convenient strategy for suspenders VIV identification, which can be used for bridge management and vibration mitigation device design.

Deep learning-based approach for identifying vortex-induced vibrations in stay cables

Due to variations in wind speed profiles along the length of bridge stay cables, vortex-induced vibrations (VIV) exhibit multimodal characteristics, presenting challenges for VIV identification. Currently, the VIV identification is concentrated on the stable stage of VIV, lacking an available early warning system for detecting the initial developing stage of VIV. In this study, a deep learning-based approach that integrates energy distribution ratio features derived from frequency band wavelet packet decomposition to recognize VIV of stay cable was proposed. Firstly, vibration characteristics induced by vortices in cable-stayed bridges were analyzed based on field monitoring data from the bridge health monitoring system, aiming to propose suitable feature indicators for VIV identification. Secondly, using root mean square as label classification, a deep learning model was constructed, incorporating convolutional neural networks, long short-term memory networks, and attention mechanisms. Finally, four different stages in the evolution of stay cables VIV were identified utilizing field monitoring datasets to analyze the optimal parameter. Meanwhile, effective early warning recognition was achieved through the classification and recognition of confusion matrix. This study provides technical support for early warning systems and structural condition assessment concerning bridge stay cable VIV.

A flexural-beam-type wide-frequency piezoelectric energy harvester for vertical shaft lifting system vibration monitoring

Abstract The coal mine lifting system may experience serious safety accidents due to severe problems with the bucket guides and rolling guide shoes. A piezoelectric energy harvesting (PEH) device for vibration sensor monitoring of shaft lifting system is proposed for the first time to monitor health of shaft lifting system. However, there are differences in the vibration frequencies, the working conditions are complex, leading to issues such as low energy recovery efficiency of the PEH and difficulty in achieving self-powered. To enhance PEH adaptability and reliability, a specifically designed flexural-beam-type wide-frequency piezoelectric energy harvester(FBT-WF-PEH) and a method of achieving real-time vibration monitoring through auxiliary power supply have been proposed. The results indicate when the excited frequency is 17Hz, the highest external output voltage is 11.2V, and under an external load of 17.5kΩ, the maximum output power is 7.168mW, demonstrating a good performance in terms of output power, and energy harvest bandwidth. The captive power supply test verified the PEH can utilize the vibration environment to achieve auxiliary power supply for monitoring systems under working conditions, which is of great significance for conducting research on health monitoring systems for lifting equipment. On the other hand, the new structure proposed in this study matches the operating frequency in the shaft lifting system, and the energy harvest efficiency is higher.

Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey

Ubiquitous in-home health monitoring systems have become popular in recent years due to the rise of digital health technologies and the growing demand for remote health monitoring. These systems enable individuals to increase their independence by allowing them to monitor their health from the home and by allowing more control over their well-being. In this study, we perform a comprehensive survey on this topic by reviewing a large number of literature in the area. We investigate these systems from various aspects, namely sensing technologies, communication technologies, intelligent and computing systems, and application areas. Specifically, we provide an overview of in-home health monitoring systems and identify their main components. We then present each component and discuss its role within in-home health monitoring systems. In addition, we provide an overview of the practical use of ubiquitous technologies in the home for health monitoring. Finally, we identify the main challenges and limitations based on the existing literature and provide eight recommendations for potential future research directions toward the development of in-home health monitoring systems. We conclude that despite extensive research on various components needed for the development of effective in-home health monitoring systems, the development of effective in-home health monitoring systems still requires further investigation.

An IoT Healthcare System With Deep Learning Functionality for Patient Monitoring

ABSTRACTCurrently, healthcare systems operate under conventional management practices and entail storing and processing substantial medical data. Integrating the Internet of Things (IoT) and wireless sensor networks (WSNs) technologies has facilitated the development of IoT‐enabled healthcare, which possesses advanced data processing capabilities and extensive data storage. This paper proposes a WSN and IoT framework for patient monitoring in high‐speed 5G communications. Based on an artificial neural network (ANN), an intelligent health monitoring system was developed using IoT technology to monitor a person's blood pressure, heart rate, oxygen level, and temperature. Furthermore, the system helps the elderly being in critical cases in their homes to communicate and update their medical condition with the hospital, especially in critical cases, to be treated as soon as possible, especially in remote areas. The experimental results showed the superiority and effectiveness of the proposed system. Moreover, relying on ANNs to extract the basic features, the accuracy reached 96%. The proposed system was implemented practically, and the results were displayed in real time and compared with commercial medical devices. Maximum relative errors are heart rate (2.19), body temperature (2.94), systolic blood pressure (3.4), diastolic blood pressure (2.89), and SpO2 (1.05). On the other hand, the proposed system is much faster than other wireless communication methods, regardless of the detection quality.

Unraveling Magnet Structural Defects in Permanent Magnet Synchronous Machines—Harmonic Diagnosis and Performance Signatures

Rare-earth-based permanent magnets (PMs) have a vital role in numerous sustainable energy systems, such as electrical machines (EMs). However, their production can greatly harm the environment and their supply chain monopoly presents economic threats. Alternative materials are emerging, but the use of rare-earth PMs remains dominant due to their exceptional performance. Damage to magnet structure can cause loss of performance and efficiency, and propagation of cracks in PMs can result in breaking. In this context, prolonging the service life of PMs and ensuring that they remain damage-free and suitable for re-use is important both for sustainability reasons and cost management. This paper presents a new harmonic content diagnosis and motor performance analysis caused by various magnet structure defects or faults, such as cracked or broken magnets. The proposed method is used for modeling the successive physical failure of the magnet structure in the form of crack formation, crack growth, and magnet breakage. A surface-mounted permanent magnet synchronous motor (PMSM) is studied using simulation in Ansys Maxwell software (Version 2023), and different cracks and PM faults are modeled using the two-dimensional finite element method (FEM). The frequency domain simulation results demonstrate the influence of magnet cracks and their propagation on EM performance measures, such as stator current, distribution of magnetic flux density, back EMF, flux linkage, losses, and efficiency. The results show strong potential for application in health monitoring systems, which could be used to reduce the occurrence of in-service failures, thus reducing the usage of rare-earth magnet materials as well as cost.

Smart structural health monitoring (SHM) system for on-board localization of defects in pipes using torsional ultrasonic guided waves

Most reported research for monitoring health of pipelines using ultrasonic guided waves (GW) typically utilize bulky piezoelectric transducer rings and laboratory-grade ultrasonic non-destructive testing (NDT) equipment. Consequently, the translation of these approaches from laboratory settings to field-deployable systems for real-time structural health monitoring (SHM) becomes challenging. In this work, we present an innovative algorithm for damage identification and localization in pipes, implemented on a compact FPGA-based smart GW-SHM system. The custom-designed board, featuring a Xilinx Artix-7 FPGA and front-end electronics, is capable of actuating the PZT thickness shear mode transducers, data acquisition and recording from PZT sensors and generating a damage index (DI) map for localizing the damage on the structure. The algorithm is a variation of the common source method adapted for cylindrical geometry. The utility of the algorithm is demonstrated for detection and localization of defects such as notch and mass loading on a steel pipe, through extensive finite element (FE) method simulations. Experimental results obtained using a C-clamp for applying mass loading on the pipe show good agreement with the FE simulations. The localization error values for experimental data analysed using C code on a processor implemented on the FPGA are consistent with algorithm results generated on a computer running Python code. The system presented in this study is suitable for a wide range of GW-SHM applications, especially in cost-sensitive scenarios that benefit from on-node signal processing over cloud-based solutions.