Health Monitoring System Research Articles

Development of a Semi-Autonomous Pulse and Receive Concrete Inspection System

Concrete structures are being subjected to increasing loads and used beyond their intended lifespan. When combined with operators shrinking maintenance budgets it is imperative structures are monitored for damage. Acoustic Emission (AE) and AE Tomography offer a method for damage detection and characterisation. However, they are encumbered by their equipment and setup requirements, and best used on areas where damage is already known to have occurred. An autonomous pulse catch system could be used to identify these areas. To explore the potential of such a system, seven concrete beams differentiated by their aggregate size were tested using an automated pulse and receive system. The effect of aggregate size, and pulse frequency on attenuation and wavespeed are investigated. The pulse regime consisted of narrowband pulses ranging from 30 kHz to 500 kHz. The results show that high frequency pulses experienced greater attenuation than low frequency pulses. Whilst a link appears between the size of the aggregate and the rate of attenuation of high frequency signals. A strong negative correlation between amplitude loss and pulse frequency was observed. For the specimen containing coarse aggregate, pulse velocity was constant between 100 kHz and 500 kHz, whereas specimens containing coarse aggregate experienced frequency dependant attenuation. This testing shows the significant potential for an automated pulse and receive system, which could lead to the development of an autonomous health monitoring system. In addition, the approach can be instrumental in developing large data sets, that can be used in Machine Learning processes, to develop a deeper understanding of AE signal propagation in concrete materials.

UNDERSTANDING THE ROLE OF ENHANCED PUBLIC HEALTH MONITORING SYSTEMS: A SURVEY ON TECHNOLOGICAL INTEGRATION AND PUBLIC HEALTH BENEFITS

Introduction: Various changes in technologies have impacted the public health monitoring systems especially in the United States. AI and IoT, Big Data, block chains have enhanced the ways that diseases are monitored, vaccinations are administered and emergency responses are coordinated. Issues like the infrastructure constraints, privacy issues and variability in technological implementation graduality’s limit the extent to which these technologies can be scaled up in U.S. public health systems. Objective: The aim of this study is to evaluate the impact of technological improvement on the improvement of the public health monitoring systems in United States. It assesses the utilization of superior technologies and considers the observed enhancement of general health status together with the problems identified in the application of technologies. Methods: A quantitative cross-sectional survey was filled by 400 participants from the PH and technology workers working throughout the USA in the public health sector. Community and purposive sampling techniques were used and data were self-administered from the participants through an online structured questionnaire. The survey included questions about participants’ degrees of awareness of AI, IoTs, Big Data and blockchain, concerning their applicability to public health surveillance and the potential challenges to their application. Descriptive statistics, chi-square, independent sample t test, one way analysis of variance, Pearson correlation analysis and multiple regression analysis were conducted in SPSS V 26. Results: The current study revealed that the use of AI, IoT, Big Data and blockchain technologies has accelerated the enhancement of public health surveillance, including disease detection and vaccination management. Awareness of these technologies was directly related to perceived impact on public health to the extent of r = 0.75, p < 0.001. Other issues like lack of funding, lack of technical knowledge and issues surrounding privacy were cited as major reasons for inadequate scaling up. Conclusion: The results point out that new technology has a potential for advancing the system for monitor public health in the United States but there are some barriers. Challenges that prevent expansion and growth of these technologies include inadequate infrastructure, funding and data protection must be overcome in order to enhance the efficiency of the technologies to deliver on their intended health benefits. More work is necessary to expand understanding of longevity of these phenomena and ways of preventing these challenges.

PENGEMBANGAN SISTEM IOT UNTUK PEMANTAUAN KESEHATAN DOMBA DENGAN ALGORITMA C4.5 BERBASIS THINGSPEAK

Effective livestock health monitoring is one of the primary challenges in modern farming, especially in detecting early health disorders in sheep. This research aims to develop a sheep health monitoring system based on the Internet of Things (IoT) using the C4.5 algorithm and the ThingSpeak platform. The system collects vital sheep data such as body temperature, heart rate, sound, and physical activity in real time through sensors and microphones connected to IoT devices. The data is then transmitted to the ThingSpeak platform for analysis and storage. The C4.5 algorithm is used to build a decision model capable of classifying the health conditions of sheep based on collected parameters such as temperature, heart rate, and respiration. The processed data results are displayed in the form of graphs and warning notifications on the ThingSpeak platform, allowing farmers to monitor livestock health easily and responsively. The accuracy test yielded a 90% accuracy rate using a confusion matrix with a data sampling split of 80% for training data and 20% for testing data. This indicates that the system has a high level of accuracy in detecting sheep health conditions. Consequently, the system has the potential to assist farmers in improving the efficiency of livestock health monitoring automatically and in real time. Moreover, the application of IoT technology and the C4.5 algorithm in the livestock sector is expected to provide innovative solutions to support productivity and animal welfare.

Deep‐Learning‐Based Prediction of Long‐Term Piezoresistive Sensing Performance of MXene/Aramid Nanofiber Sensors

Flexible compressible sensors are widely used in the human health monitoring field for their numerous advantages. However, the dynamic loads and possible injuries associated with long‐term living and exercise pose a challenge to the long‐term piezoresistive performance stability of these sensors. In this study, the application of deep learning for predicting the long‐term performance of these sensors is explored, aiming to enhance the assessment of sensor stability and ensure accurate and reliable long‐term monitoring. Samples with different Ti3C2Tx MXene/aramid nanofiber mass ratios (1:1, 1:2, 1:3) are prepared and piezoresistive characterization is conducted under long‐term loading cycles to obtain training data. Three distinct deep‐learning prediction models, convolutional neural network (CNN), long short‐term memory, and recurrent neural network (RNN), are utilized to assess their influence on prediction accuracy. To assess the effectiveness of the proposed method, its prediction of long‐term piezoresistive sensing performance with experimental data not used for training purposes is compared. The CNN model demonstrates optimal results with a mean absolute error of 0.0251 for the 1:3 mass ratio sample. Based on the experimental results, the model is expected to be integrated into human health monitoring systems, thus improving the assessment of sensor stability throughout its lifetime.

Privacy-Preserving Naïve Bayesian Classification for Health Monitoring Systems

Privacy-Preserving Naïve Bayesian Classification for Health Monitoring Systems

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 18 June 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.

A High-Precision Inverse Finite Element Method for Shape Sensing and Structural Health Monitoring

In the contemporary era, the further exploitation of deep-sea resources has led to a significant expansion of the role of ships in numerous domains, such as in oil and gas extraction. However, the harsh marine environments to which ships are frequently subjected can result in structural failures. In order to ensure the safety of the crew and the ship, and to reduce the costs associated with such failures, it is imperative to utilise a structural health monitoring (SHM) system to monitor the ship in real time. Displacement reconstruction is one of the main objectives of SHM, and the inverse finite element method (iFEM) is a powerful SHM method for the full-field displacement reconstruction of plate and shell structures. However, existing inverse shell elements applied to curved shell structures with irregular geometry or large curvature may result in element distortion. This paper proposes a high-precision iFEM for curved shell structures that does not alter the displacement mode of the element or increase the mesh and node quantities. In reality, it just modifies the methods of calculation. This method is based on the establishment of a local coordinate system on the Gaussian integration point and the subsequent alteration of the stiffness integration. The results of numerical examples demonstrate that the high-precision iFEM is capable of effectively reducing the displacement difference resulting from inverse finite element method reconstruction. Furthermore, it performs well in practical engineering applications.

IoT-Based Medical Health Monitoring System with a Web Interface

Health technology is increasingly developing along with the development of information and communication technology, where these advances are used to improve the quality of health services, especially in remote patient monitoring through telemedicine, M-Health and Telehealth. The development of the times has also brought telemedicine to a wider realm through M-Health and Telehealth which uses digital technology and wireless communication to enable monitoring of patient conditions, besides that websites are also used for wireless monitoring and data collection of patients because they have wide accessibility. This study aims to design a website that can integrate the data of the size of health sensor devices into an Internet of Things-based platform to display body temperature, blood oxygen, and ECG data in real-time. The test results show that all website features are running well, where the website runs well depending on the network used.

Biomaterials for reliable wearable health monitoring: Applications in skin and eye integration

Recent advancements in biomaterials have significantly impacted wearable health monitoring, creating opportunities for personalized and non-invasive health assessments. These developments address the growing demand for customized healthcare solutions. Durability is a critical factor for biomaterials in wearable applications, as they must withstand diverse wearing conditions effectively. Therefore, there is a heightened focus on developing biomaterials that maintain robust and stable functionalities, essential for advancing wearable sensing technologies. This review examines the biomaterials used in wearable sensors, specifically those interfaced with human skin and eyes, highlighting essential strategies for achieving long-lasting and stable performance. We specifically discuss three main categories of biomaterials—hydrogels, fibers, and hybrid materials—each offering distinct properties ideal for use in durable wearable health monitoring systems. Moreover, we delve into the latest advancements in biomaterial-based sensors, which hold the potential to facilitate early disease detection, preventative interventions, and tailored healthcare approaches. We also address ongoing challenges and suggest future directions for research on material-based wearable sensors to encourage continuous innovation in this dynamic field.

Multi-sensor fusion for biomechanical analysis: evaluation of dynamic interactions between self-contained breathing apparatus and firefighter using computational methods

Understanding the complex three-dimensional (3D) dynamic interactions between self-contained breathing apparatus (SCBA) and the human torso is critical to assessing potential impacts on firefighter health and informing equipment design. This study employed a multi-inertial sensor fusion technology to quantify these interactions. Six volunteer firefighters performed walking and running experiments on a treadmill while wearing the SCBA. Calculations of interaction forces and moments from the multi-inertial sensor technology were validated against a 3D motion capture system. The predicted interaction forces and moments showed good agreement with the measured data, especially for the forces (normal and lateral) and moments (x- and z-direction components) with relative root mean square errors (RMSEs) below 9.4%, 7.7%, 7.7%, and 7.8%, respectively. Peak pack force reached up to 150 N, significantly exceeding the SCBA’s intrinsic weight during SCBA carriage. The proposed multi-inertial sensor fusion technique can effectively evaluate the 3D dynamic interactions and provide a scientific basis for health monitoring and ergonomic optimization of SCBA systems for firefighters.

Confounder-adjusted covariances of system outputs and applications to structural health monitoring

Automated damage detection is an integral component of each structural health monitoring (SHM) system. Typically, measurements from various sensors are collected and reduced to damage-sensitive features, and diagnostic values are generated by statistically evaluating the features. Since changes in data do not only result from damage, it is necessary to determine the confounding factors (environmental or operational variables) and to remove their effects from the measurements or features. Many existing methods for correcting confounding effects are based on different types of mean regression. This neglects potential changes in higher-order statistical moments, but in particular, the output covariances are essential for generating reliable diagnostics for damage detection. This article presents an approach to explicitly quantify the changes in the covariance, using conditional covariance matrices based on a non-parametric, kernel-based estimator. The method is applied to the Munich Test Bridge and the KW51 Railway Bridge in Leuven, covering both raw sensor measurements (acceleration, strain, inclination) and extracted damage-sensitive features (natural frequencies). The results show that covariances between different vibration or inclination sensors can significantly change due to temperature changes, and the same is true for natural frequencies. To highlight the advantages, it is explained how conditional covariances can be combined with standard approaches for damage detection, such as the Mahalanobis distance and principal component analysis. As a result, more reliable diagnostic values can be generated with fewer false alarms.

EARTHQUAKE-RESISTANT BUILDING DESIGN: INNOVATIONS AND CHALLENGES

This study provides a comprehensive systematic review of innovations in earthquake-resistant building design, focusing on advancements in materials, technologies, and methodologies aimed at enhancing structural resilience. A total of 32 peer-reviewed articles were analyzed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The findings highlight the critical role of advanced materials such as fiber-reinforced polymers (FRPs) and shape memory alloys (SMAs) in improving seismic performance, particularly through enhanced energy dissipation and structural flexibility. Technological integrations like Building Information Modeling (BIM), artificial intelligence (AI), and structural health monitoring (SHM) systems were identified as transformative tools that optimize design processes, predict structural vulnerabilities, and enable real-time risk management. Advanced simulation techniques, including finite element analysis (FEA) and computational fluid dynamics (CFD), were shown to significantly improve the accuracy and efficiency of seismic design. Despite these innovations, challenges related to cost, regulatory inconsistencies, and limited access to cutting-edge technologies persist, particularly in developing regions. The study concludes that while these advancements have revolutionized earthquake-resistant design, further efforts are needed to address these barriers and promote global resilience to seismic hazards.

Fusion-driven fault diagnosis based on adaptive tuning feature mode decomposition and synergy graph enhanced transformer for bearings under noisy conditions

Bearing fault diagnosis is critical for maintaining the reliability of health monitoring in electromechanical systems. However, traditional feature extraction methods often struggle to accurately capture fault information in complex environments. This paper proposes an adaptive tuning feature mode decomposition (ATFMD) method, based on the intrinsic signal characteristics, to effectively extract robust features. ATFMD dynamically adjusts to complex fault signals, providing more representative feature inputs and pruning redundant features induced by noise. Additionally, given the limitations of single-dimensional feature domains in fully revealing fault information, this study constructs feature topology graphs by mapping spatial phase and time–frequency characteristics, offering a comprehensive representation of fault information. To achieve complementary fusion of fault information within the feature topological graph, this paper proposes the synergy graph enhanced transformer (SGET). SGET optimizes the fusion process by reinforcing feature interactions through its synergy graph representation module. Additionally, a hierarchical cross-attention mechanism is employed to modulate attention distribution across feature dimensions, enhancing the sensitivity to critical features during fusion. Experimental validation was conducted on two distinct rotating machinery transmission systems. The results demonstrate that the proposed method maintains exceptional robustness and generalization, even in the presence of severe noise and complex fault conditions. Compared to the leading methods, including MS-DGCNs, CapsFormer, TFT, and ConvFormer, the proposed method achieves notable accuracy improvements of 8.13 %, 8.71 %, 11.94 %, and 6.25 %, respectively, under challenging conditions.

A Comprehensive Review of Machine Learning Approaches in Livestock Health Monitoring

Livestock health monitoring has emerged as a crucial area in agricultural technology, where Machine Learning (ML) approaches offer promising solutions for disease detection, heat stress management, and behavioral anomaly identification. This review explores the latest advancements in applying machine learning models to livestock health monitoring, focusing on methods such as deep learning, IoT integration, and multimodal frameworks. Techniques like Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and hybrid models have significantly improved early detection of communicable and non-communicable diseases. Additionally, AI-based systems monitoring heat stress and behavioral changes have enhanced overall livestock management, reducing economic losses and improving animal welfare. This review synthesizes current trends and challenges in deploying ML models in real-world farming environments, particularly integrating IoT devices to collect and analyze health data. Moreover, it highlights future directions in improving the accuracy and scalability of ML models, including optimizing data collection and leveraging real-time analytics. This review aims to guide future developments in AI-driven livestock health monitoring systems by providing a comprehensive overview of current research.

A Highly Sensitive, Low Creep Hydrogel Sensor for Plant Growth Monitoring

Ion−conducting hydrogels show significant potential in plant growth monitoring. Nevertheless, traditional ionic hydrogel sensors experience substantial internal creep and inadequate sensitivity, hindering precise plant growth monitoring. In this study, we developed a flexible hydrogel sensor composed of polyvinyl alcohol and acrylamide. The hydrogel sensor exhibits low creep and high sensitivity. Polyvinyl alcohol, acrylamide, and glycerol are crosslinked to create a robust interpenetrating double network structure. The strong interactions, such as van der Waals forces, between the networks minimize hydrogel creep under external stress, reducing the drift ratio by 50% and the drift rate by more than 60%. Additionally, sodium chloride and AgNWs enrich the hydrogel with conductive ions and pathways, enhancing the sensor’s conductivity and demonstrating excellent response time (0.4 s) and recovery time (0.3 s). When used as a sensor for plant growth monitoring, the sensor exhibits sensitivity to small strains and stability for long−term monitoring. This sensor establishes a foundation for developing plant health monitoring systems utilizing renewable biomass materials.

EarMonitor: Non-clinical Assessment of Ear Health Conditions Using a Low-cost Endoscope Camera on Smartphones

Hearing loss affects 20% of the global population, a rate that is increasing dramatically as the world's population ages. Early prevention and identification of ear diseases can significantly reduce the risk of becoming disabled with hearing impairment. We propose EarMonitor, an interactive, vision-based ear health monitoring system that enables users to examine their ear conditions with a low-cost hand-held endoscope. EarMonitor can detect six ear health conditions suitable for self-assessment. It can particularly recognize complications from ear diseases, helping users better understand the results. In the wild, our computer vision algorithm achieves a detection sensitivity of 0.949 for earwax buildup and blockage in 100 external auditory canal photos; our deep learning model achieves an average detection sensitivity of 0.861 for the other five conditions considering complications in 350 tympanic membrane photos. We validated EarMonitor 's effectiveness through a user study involving 17 participants and two experts, leading to valuable insights regarding the design and interpretation of non-clinical assessment devices.

Integrating radiological technology in environmental health surveillance to enhance public safety

The integration of radiological technology into environmental health surveillance represents a significant advancement in enhancing public safety. Radiological technologies, including advanced imaging and monitoring systems, offer powerful tools for detecting and assessing environmental hazards that impact public health. This review explores the potential benefits, methodologies, and challenges associated with integrating radiological technology into environmental health surveillance. Recent advancements in radiological technology, such as high-resolution imaging systems and portable radiation detectors, have greatly improved the ability to monitor and analyze environmental radiation levels with precision. These technologies enable real-time detection of radiation exposure, providing critical data for assessing risks associated with radiation sources, such as industrial activities, nuclear power plants, and natural radioactive materials. By integrating these tools into environmental health surveillance frameworks, public health agencies can enhance their capacity to identify and address radiation-related hazards more effectively. The application of radiological technology in environmental health surveillance facilitates comprehensive monitoring of radiation levels across various environments, including urban, rural, and industrial areas. This data is crucial for identifying contamination hotspots, tracking changes in radiation levels over time, and evaluating the effectiveness of radiation control measures. Additionally, the integration of radiological technology supports the development of predictive models for assessing potential health impacts and guiding emergency response strategies in the event of radiation incidents. Despite its advantages, integrating radiological technology into environmental health surveillance presents several challenges. These include the high costs of advanced equipment, the need for specialized training for personnel, and the management of large volumes of data generated by monitoring systems. Addressing these challenges requires a coordinated approach involving government agencies, research institutions, and industry stakeholders to ensure effective implementation and sustainability. In conclusion, integrating radiological technology into environmental health surveillance has the potential to significantly enhance public safety by providing accurate and timely data on radiation exposure. Continued innovation and collaboration in this field will be essential for optimizing the benefits of radiological technology and addressing the challenges associated with its integration into environmental health monitoring systems. Keywords: Integrating; Radiological Technology; Environmental Health Surveillance; Enhance; Public Safety

CardioGuard: AI-driven ECG authentication hybrid neural network for predictive health monitoring in telehealth systems

The increasing integration of telehealth systems underscores the importance of robust and secure methods for patient data management. Traditional authentication methods, such as passwords and PINs, are prone to breaches, underscoring the need for more secure alternatives. Therefore, there is a need for alternative approaches that provide enhanced security and user convenience. Biometric-based authentication systems uses individuals unique physical or behavioral characteristics for identification, have emerged as a promising solution. Specifically, Electrocardiogram (ECG) signals have gained attention among various biometric modalities due to their uniqueness, stability, and non-invasiveness. This paper presents CardioGaurd, a deep learning-based authentication system that leverages ECG signals—unique, stable, and non-invasive biometric markers. The proposed system uses a hybrid Convolution and Long short-term memory based model to obtain rich characteristics from the ECG signal and classify it as authentic or fake. CardioGaurd not only ensures secure access but also serves as a predictive tool by analyzing ECG patterns that could indicate early signs of cardiovascular abnormalities. This dual functionality enhances patient security and contributes to AI-driven disease prevention and early detection. Our results demonstrate that CardioGaurd offers superior performance in both security and potential predictive health insights compared to traditional models, thus supporting a shift towards more proactive and personalized telehealth solutions.

Time-convolutional network with joint time-frequency domain loss based on arithmetic optimization algorithm for dynamic response reconstruction

Structural Health Monitoring (SHM) systems provide extensive data on in-service bridges, which is crucial for evaluating structural performance. However, data loss frequently occurs due to environmental factors and technical failures. Although missing data reconstruction problem has been largely studied, the accuracy of response reconstruction in the frequency domain is often overlooked. To address this issue, this study proposes to integrate a time-frequency joint loss function within an Arithmetic Optimization Algorithm-Temporal Convolutional Network (AOA-TCN), which leverages temporal and spatial dependencies among measurement points to reconstruct missing dynamic deflection data. The time-frequency joint loss function is enhanced with linear decay weights to increase sensitivity to low-order modal contributions, with an adaptive weighting mechanism balancing loss contributions from time and frequency domains. These adaptive weight parameters are optimized through AOA to enhance the TCN's generalizability. The feasibility and effectiveness of the proposed method are first demonstrated using simulated dynamic deflection data of a finite element model of a continuous beam bridge, and then experimentally verified on a real-world simply-supported beam bridge. The results indicate that the proposed method achieves higher data reconstruction accuracy and more precise modal analysis results compared to existing methods. The Modal Assurance Criterion (MAC) between mode shapes identified using measured data and reconstructed response reached 96.9 %, proving that the proposed method is capable of reconstructing the missing structural response while retaining its frequency-domain information, which is valuable for structural integrity assessment based on modal parameter identification of operational bridges.

Acoustic Emission (AE) based health monitoring of RC beams strengthened with mechanically anchored hybrid Fiber Reinforced Cementitious Matrix (FRCM) system

The debonding between fabric and matrix/substrate hampers the strength and durability of fiber-reinforced cementitious matrix (FRCM) strengthened elements. One of the ways to improve the performance is to use mechanical anchorages. However, damage mechanisms associated with such systems are complex, and structural health monitoring of such systems is quite challenging. While earlier studies have concentrated on monitoring the health of FRCM coupons using acoustic emission techniques, they did not account for the effect of impregnation or use of mechanical anchorages. Also, very few studies are available on the health monitoring of full-scale FRCM-strengthened specimens. The current investigation employs AE-based health monitoring to classify the damage characteristics of reinforced concrete (RC) beams strengthened with FRCM. Two strengthening strategies, with and without mechanical anchorages, are employed. The non-anchored system involves conventional strengthening using two layers of pre-impregnated fabric, while the mechanically anchored method utilizes pre-impregnated fabric and a distributed anchorage system. The AE data is then analyzed using several parameters correlated with the digital image correlation (DIC) technique. Trends of sentry function, historical index, and cumulative signal strength (CSS) provide insights into major event occurrences and changes in the underlying damage modes. Three different b-values indicate a transition from micro to macro cracks during different loading stages. On the other hand, frequency content and RA-AF analysis are employed as qualitative tools to identify the source and type of damage. The strengthened RC beams with mechanical anchorages exhibited a 20.9 % increase in flexural capacity compared to those without, as evidenced by the AE parameters and DIC techniques. Overall, the study revealed that acoustic emission health monitoring could effectively evaluate the overall condition of the strengthened system, even with the complexity of a multi-part composite system.