Health Monitoring Applications Research Articles

An in-sensor humidity computing system for contactless human-computer interaction.

Being capable of processing large amounts of redundant data and decreasing power consumption, in-sensor computing approaches play significant roles in neuromorphic computing and are attracting increasing interest in perceptual information processing. Herein, we proposed a high performance humidity-sensitive memristor based on a Ti/graphene oxide (GO)/HfOx/Pt structure and verified its potential for application in remote health management and contactless human-machine interfaces. Since GO possesses abundant hydrophilic groups (carbonyl, epoxide, and hydroxyl), the memristor shows a high humidity sensitivity, fast response, and wide response range. By utilizing the proton-modulated redox reaction, humidity exposure to the memristor induces a dynamic change in the switching between high and low resistance states, ensuring essential synaptic learning functions, such as paired-pulse facilitation, spike number-dependent plasticity, and spike amplitude-dependent plasticity. More importantly, based on the humidity-induced salient features originating from the abundant hydrophilic functional groups in GO, we have implemented a noncontact human-machine interface utilizing the respiratory mode in humans, demonstrating the potential of promoting health monitoring applications and effectively blocking virus transmission. In addition, the high recognition accuracy of contactless handwriting in a 5 × 5 array artificial neural network was successfully achieved, which is attributed to the excellent emulated synaptic behaviors. This study provides a feasible method to develop an excellent humidity-sensitive memristor for constructing efficient in-sensor computing for application in health management and contactless human-computer interaction.

Towards Hardware Supported Domain Generalization in DNN-based Edge Computing Devices for Health Monitoring.

Deep neural network (DNN) models have shown remarkable success in many real-world scenarios, such as object detection and classification. Unfortunately, these models are not yet widely adopted in health monitoring due to exceptionally high requirements for model robustness and deployment in highly resource-constrained devices. In particular, the acquisition of biosignals, such as electrocardiogram (ECG), is subject to large variations between training and deployment, necessitating domain generalization (DG) for robust classification quality across sensors and patients. The continuous monitoring of ECG also requires the execution of DNN models in convenient wearable devices, which is achieved by specialized ECG accelerators with small form factor and ultra-low power consumption. However, combining DG capabilities with ECG accelerators remains a challenge. This article provides a comprehensive overview of ECG accelerators and DG methods and discusses the implication of the combination of both domains, such that multi-domain ECG monitoring is enabled with emerging algorithm-hardware co-optimized systems. Within this context, an approach based on correction layers is proposed to deploy DG capabilities on the edge. Here, the DNN fine-tuning for unknown domains is limited to a single layer, while the remaining DNN model remains unmodified. Thus, computational complexity (CC) for DG is reduced with minimal memory overhead compared to conventional fine-tuning of the whole DNN model. The DNN model-dependent CC is reduced by more than 2.5 × compared to DNN fine-tuning at an average increase of F1 score by more than 20% on the generalized target domain. In summary, this article provides a novel perspective on robust DNN classification on the edge for health monitoring applications.

Wearable MIMO Antenna for Health Monitoring Applications

Wearable MIMO Antenna for Health Monitoring Applications

GPS-Based Network Synchronization of Wireless Sensors for Extracting Propagation of Disturbance on Structural Systems.

Wireless sensor networks (WSNs) have gained a positive popularity for structural health monitoring (SHM) applications. The underlying reason for using WSNs is the vast number of devices supporting wireless networks available these days. However, some of these devices are expensive. The main objective of this paper is to develop a cost-effective WSN based on low power consumption and long-range radios, which can perform real-time, real-scale acceleration data analyses. Since a detection system for vibration propagation is proposed in this paper, the synchronized monitoring of acceleration data is necessary. To meet this need, a Pulse Per Second (PPS) synchronization method is proposed with the help of GPS (Global Positioning System) receivers, representing an addition to the synchronization method based on real-time clock (RTC). As a result, RTC+PPS is the term used when referring to this method in this paper. In summary, the experiments presented in this research consist in performing specific and synchronized measurements on a full-scale steel I-beam. Finally, it is possible to perform measurements with a synchronization success of 100% in a total of 30 samples, thereby obtaining the propagation of vibrations in the structure under consideration by implementing the RTS+PPS method.

Recent Trends of Functional Composites and Structures for Electromechanical Sensors: A Review

With the rapid advancement of modern technology, flexible and wearable electronics, particularly electromechanical sensors, have gained considerable attention for motion‐based applications in medical health monitoring and artificial intelligence. Correspondingly, extensive efforts have been dedicated to enhancing their performance and practicality. Electromechanical sensors based on functional composites and structures accurately detect and monitor the body, generating the corresponding output signals. However, there are several limitations in manufacturing composite sensors, such as the selection and combination of functional materials, geometrical structures, constructed conductive pathways, stability of output signals, and operational lifetime. Thus, this review summarizes notable trends in the electromechanical sensors using functional composites and structures. This also provides an overview of different types of electromechanical pressure and strain sensors, exploring their operational mechanisms regarding triboelectricity, piezoelectricity, piezocapacitance, and piezoresistivity. The unique characteristics of functional materials, including conductive polymers, nanostructured metals, and carbon nanomaterials and composites, are analyzed alongside various design concepts for highly flexible and stretchable sensors. Furthermore, potential applications concerning human motion and human–machine interfaces are also recommended. Additionally, several future outlooks are reviewed for insights into future prospects and strategies. Thus, this review can assist readers in understanding current electromechanical devices more accurately.

A Multifunctional Flexible Tactile Sensor Based on Resistive Effect for Simultaneous Sensing of Pressure and Temperature.

Flexible tactile sensors with multifunctional sensing functions have attracted much attention due to their wide applications in artificial limbs, intelligent robots, human-machine interfaces, and health monitoring devices. Here, a multifunctional flexible tactile sensor based on resistive effect for simultaneous sensing of pressure and temperature is reported. The sensor features a simple design with patterned metal film on a soft substrate with cavities and protrusions. The decoupling of pressure and temperature sensing is achieved by the reasonable arrangement of metal layers in the patterned metal film. Systematically experimental and numerical studies are carried out to reveal the multifunctional sensing mechanism and show that the proposed sensor exhibits good linearity, fast response, high stability, good mechanical flexibility, and good microfabrication compatibility. Demonstrations of the multifunctional flexible tactile sensor to monitor touch, breathing, pulse and objects grabbing/releasing in various application scenarios involving coupled temperature/pressure stimuli illustrate its excellent capability of measuring pressure and temperature simultaneously. These results offer an effective tool for multifunctional sensing of pressure and temperature and create engineering opportunities for applications of wearable health monitoring and human-machine interfaces.

IDENTIFICATION OF DAMAGE IN STEEL BEAM BY NATURAL FREQUENCY USING XGB MODEL

Beams have played a significant role in engineering applications and they have been commonly used for modelling civil problems. Different models and methods have been developed to identify the damage to the beams. In this article, the extreme gradient boosting (XGB) model was developed to predict the location, width and depth of the saw-cut of steel beams by the change of natural frequencies. The natural frequencies of a steel beam in different scenarios were identified by the finite element method (FEM). The criterions to evaluate the accuracy of the models were the R squared (RSQ) and the mean square error (MSE). The result indicated that combining the FEM method with XGB would hold significant potential for applications in structural health monitoring.

PGGait: Gait Recognition Based on Millimeter-Wave Radar Spatio-Temporal Sensing of Multidimensional Point Clouds.

Gait recognition, crucial in biometrics and behavioral analytics, has applications in human-computer interaction, identity verification, and health monitoring. Traditional sensors face limitations in complex or poorly lit settings. RF-based approaches, particularly millimeter-wave technology, are gaining traction for their privacy, insensitivity to light conditions, and high resolution in wireless sensing applications. In this paper, we propose a gait recognition system called Multidimensional Point Cloud Gait Recognition (PGGait). The system uses commercial millimeter-wave radar to extract high-quality point clouds through a specially designed preprocessing pipeline. This is followed by spatial clustering algorithms to separate users and perform target tracking. Simultaneously, we enhance the original point cloud data by increasing velocity and signal-to-noise ratio, forming the input of multidimensional point clouds. Finally, the system inputs the point cloud data into a neural network to extract spatial and temporal features for user identification. We implemented the PGGait system using a commercially available 77 GHz millimeter-wave radar and conducted comprehensive testing to validate its performance. Experimental results demonstrate that PGGait achieves up to 96.75% accuracy in recognizing single-user radial paths and exceeds 94.30% recognition accuracy in the two-person case. This research provides an efficient and feasible solution for user gait recognition with various applications.

Guided waves in long range nondestructive testing and structural health monitoring: Principles, history of applications and prospects

The early work on long range guided wave nondestructive testing (NDT) was largely done in industry, and multiple applications were developed before a surge in academic interest starting in the early 1990s. This paper reviews the history of the field, covering the topics that must be addressed in the design of a test procedure, as well as applications in both NDT and Structural Health Monitoring (SHM). The use of guided waves in long range NDT is relatively mature, and largely limited to simple structures in applications that are difficult or impossible to address by conventional point inspection methods. Point methods are impractical in most large area SHM applications where it is necessary to detect isolated defects since it is not possible to cover the whole area of interest with transducers. Guided waves therefore have great potential in SHM as they enable a large area of structure to be monitored from a limited number of transduction locations, so reducing the added weight, cost and complexity of the attachment to the structure. It is also possible to improve the sensitivity of the test relative to that obtained in one-off inspection by monitoring the change in signals with time using baseline subtraction. Current SHM deployments are largely in the same types of structure as those where guided wave NDT is used, but there is substantial potential for increasing the range of applications. The paper concludes with a review of the research and development needs for this potential to be realised.

A dual-mode fiber-shaped flexible capacitive strain sensor fabricated by direct ink writing technology for wearable and implantable health monitoring applications

Flexible fiber-shaped strain sensors show tremendous potential in wearable health monitoring and human‒machine interactions due to their compatibility with everyday clothing. However, the conductive and sensitive materials generated by traditional manufacturing methods to fabricate fiber-shaped strain sensors, including sequential coating and solution extrusion, exhibit limited stretchability, resulting in a limited stretch range and potential interface delamination. To address this issue, we fabricate a fiber-shaped flexible capacitive strain sensor (FSFCSS) by direct ink writing technology. Through this technology, we print parallel helical Ag electrodes on the surface of TPU tube fibers and encapsulate them with a high dielectric material BTO@Ecoflex, endowing FSFCSS with excellent dual-mode sensing performance. The FSFCSS can sense dual-model strain, namely, axial tensile strain and radial expansion strain. For axial tensile strain sensing, FSFCSS exhibits a wide detection range of 178%, a significant sensitivity of 0.924, a low detection limit of 0.6%, a low hysteresis coefficient of 1.44%, and outstanding mechanical stability. For radial expansion strain sensing, FSFCSS demonstrates a sensitivity of 0.00086 mmHg−1 and exhibits excellent responsiveness to static and dynamic expansion strain. Furthermore, FSFCSS was combined with a portable data acquisition circuit board for the acquisition of physiological signals and human‒machine interaction in a wearable wireless sensing system. To measure blood pressure and heart rate, FSFCSS was combined with a printed RF coil in series to fabricate a wireless hemodynamic sensor. This work enables simultaneous application in wearable and implantable health monitoring, thereby advancing the development of smart textiles.

Dual-Stage Surficial Microstructure to Enhance the Sensitivity of MXene Pressure Sensors for Human Physiological Signal Acquisition.

Accompanying the rapid growth of wearable electronics, flexible pressure sensors have received great interest due to their promising application in health monitoring, human-machine interfaces, and intelligent robotics. The high sensitivity over a wide responsive range, integrated with excellent repeatability, is a crucial requirement for the fabrication of reliable pressure sensors for various wearable scenes. In this work, we developed a highly sensitive and long-life flexible pressure sensor by constructing surficial microarrayed architecture polydimethylsiloxane (PDMS) film as a substrate and Ti3C2TX MXene/bacterial cellulose (BC) hybrid as an active sensing layer. The specific surficial morphology of PDMS couples with nanointercalated structure of Ti3C2Tx MXene/BC can effectively improve the sensitivity through controlling the stress distribution and layer spacing under different levels of pressure loading. In addition, abundant spontaneous hydrogen bonds between BC and Ti3C2Tx MXene nanosheets endow the MXene coating with highly adhesive strength on the PDMS surface; hence, the cyclic stability of the pressure sensor is greatly boosted. As a result, the obtained MXene/BC/PDMS (MBP) pressure sensor delivers high sensitivity (528.87 kPa-1), fast response/recovery time (45 ms/29 ms), low detection limit (0.6 Pa), and outstanding repeatability of up to 8000 cycles. Those excellent sensing properties of the MBP sensor allow it to serve as a reliable wearable device to monitor full-range human physiological motions, and it is expected to be applied in next-generation portable electronics, such as E-skins, smart healthcare, and the Internet of Things technology.

Flexible Accelerated-Wound-Healing Antibacterial Hydrogel-Nanofiber Scaffold for Intelligent Wearable Health Monitoring.

Flexible epidermal sensors hold significant potential in personalized healthcare and multifunctional electronic skins. Nonetheless, achieving both robust sensing performance and efficient antibacterial protection, especially in medical paradigms involving electrophysiological signals for wound healing and intelligent health monitoring, remains a substantial challenge. Herein, we introduce a novel flexible accelerated-wound-healing biomaterial based on a hydrogel-nanofiber scaffold (HNFS) via electrostatic spinning and gel cross-linking. We effectively engineer a multifunctional tissue nanoengineered skin scaffold for wound treatment and health monitoring. Key features of HNFS include high tensile strength (24.06 MPa) and elasticity (214.67%), flexibility, biodegradability, and antibacterial properties, enabling assembly into versatile sensors for monitoring human motion and electrophysiological signals. Moreover, in vitro and in vivo experiments demonstrate that HNFS significantly enhances cell proliferation and skin wound healing, provide a comprehensive therapeutic strategy for smart sensing and tissue repair, and guide the development of high-performance "wound healing-health monitoring" bioelectronic skin scaffolds. Therefore, this study provides insights into crafting flexible and repairable skin sensors, holding potential for multifunctional health diagnostics and intelligent medical applications in intelligent wearable health monitoring and next-generation artificial skin fields.

Electrospun PVDF/aromatic HBP of 4th gen based flexible and self-powered TENG for wearable energy harvesting and health monitoring

In recent times, high-performance wearable electronic devices that can transform mechanical force into electrical energy for biomedical monitoring applications are receiving an increasing amount of attention. In the present study, we focused on a flexible, self-powered and wearable triboelectric nanogenerator (TENG) based on electrospun polyvinylidene fluoride (PVDF)/aromatic hyperbranched polyester of 4th generation (Ar.HBP-G4, 0–40 wt.-% w.r.t. PVDF content) blend nanoweb as tribo-negative layer and melt-blown thermoplastic polyurethane (TPU) as tribo-positive layer for energy harvesting and human health monitoring applications. Among the varying Ar.HBP-G4 content used, incorporation of Ar.HBP-G4 (10 wt.-%) in PVDF (P-Ar.HBP-G4-10) showed higher increase in the triboelectric output voltage when compared to pristine PVDF and other Ar.HBP-G4 weight ratios. The optimized P-Ar.HBP-G4-10/TPU based TENG exhibited a peak-to-peak voltage (Vp-p) of 124.4 V under an applied load of 9.8 N and frequency 1 Hz which is superior to many other TENGs reported elsewhere. Higher triboelectric performance of P-Ar.HBP-G4 blend based TENG compared to that of neat PVDF is attributed to the effect of Ar.HBP-G4-10 in enhancing the degree of crystallinity and polar β-crystalline phase content (98.3%) in PVDF. The ability of the TENG to power up portable electronic devices is demonstrated when it is powered for 750 s while connected through a capacitor and a rectifier, and the TENG was able to operate 45 light-emitting diodes directly. Evaluation of the triboelectric output of the TENG device attached to different parts of the human body reveal significantly better output voltage and sensitivity for human health monitoring. The results of this work pave a new way to develop TENG based on P-Ar.HBP-G4 nanowebs for sustainable energy generation and wearable healthcare monitoring systems.

An adaptive cepstrum feature representation method with variable frame length and variable filter banks for acoustic emission signals

Acoustic emission technology is widely employed in defect detection, structural health monitoring, fault diagnosis, and other applications due to its benefits of non-contact, real-time, sensitivity, and flexibility. To implement efficient and timely intelligent monitoring, it is crucial to promptly and precisely gather the time-varying properties in the non-stationary acoustic emission signals. The current work proposes an adaptive cepstrum feature representation method that combines variable frame length and variable filter bank. The variable frame length is implemented based on the spectral distribution correlation, and the optimal frame length and frame rate are selected by iterative operations. The variable filter bank, on the other hand, characterizes the local measure features of variable frame length signals by setting dynamic weight distributions. The proposed adaptive cepstrum algorithm can be used as an auxiliary tool to initially analyze complex non-stationary signals, and thus effectively identify the time-varying and frequency components in acoustic emission signals. The superiority of the proposed method has been confirmed on simulation data. Furthermore, the practical performance of the proposed method is evaluated and illustrated based on the burst signal from laser shock peening processing and the mixed signal from pipeline leakage. The proposed method can be extended to other broadband signals and can be used for feature pre-processing before deep learning network modeling to improve recognition accuracy due to its high time–frequency resolution.

Deep‐Learning‐Assisted Neck Motion Monitoring System Self‐Powered Through Biodegradable Triboelectric Sensors

AbstractIn the new era of artificial intelligence (AI) and the Internet of Things (IoT), big data collection and analysis for intelligent sports are of great importance in monitoring human health. Herein, naturally, biodegradable triboelectric nanogenerators (NB‐TENGs) are developed based on low‐cost, recyclable, and environmentally friendly corn bracts, which are further applied in neck motion recognition. Three NB‐TENGs are integrated into an elastic collar to create a neck‐condition monitoring triboelectric sensor (NCM‐TS). An intelligent behavioral monitoring system is achieved by combining NCM‐TS with a deep learning model, which allows the recognition of four types of neck motion with an average accuracy of 94%. The developed neck motion monitoring sensor has broad potential applications in sports health monitoring, rehabilitation training, and healthcare.

Experimental and Numerical Study on Vibration-Based Damage Detection and Localization in Laminated Composite Plates

Damage detection in composite materials is crucial for ensuring the safety and reliability of engineering structures. Conventional methods often face challenges in accurately identifying damage in plate-like structures, particularly in scenarios involving multiple damages or small-scale delamination. This study focuses on investigating the detection and localization of delamination in composite plates by employing both experimental and numerical modal analysis. An eight-ply woven Glass-Epoxy composite laminate with and without damage was prepared with the aid of hand lamination technique. Laminate was fixed to a Clamped-Free-Free-Free (CFFF) boundary condition for experimental modal analysis by introducing controlled damage to examine its impact on modal properties. To validate the natural frequencies (NFs) of damaged and undamaged composite laminates, a numerical analysis was conducted using ANSYS Parametric Design Language (APDL). Further, to advance the understanding of using modal shapes and their spatial derivatives for damage localization in composite plates under various damage situations, post-processing of simulation results was conducted using MATLAB. Finite Difference Method has been employed to calculate the derivatives, and a novel damage index (DI) is proposed to enhance damage localization capabilities. The results affirm that the proposed DI is effective and precise in identifying damage in plate-like structures, both for individual and multiple damage scenarios. This research study presents a novel approach for identifying and pinpointing damage in composite plates, thereby making a valuable contribution to the field of structural health monitoring (SHM) applications

Additively Manufactured Wireless Sensors for Rugged IoT, Structural Health Monitoring, Smart Agriculture and Smart Cities Applications

In this talk, inkjet-/3D-printed antennas, interconnects, ‘smart’ encapsulation and packages, RF electronics, microfluidics and sensors fabricated on glass, PET, paper and other flexible substrates are introduced as a system-level solution for ultra-low-cost mass production of Millimeter-Wave Modules for Communication, Energy Harvesting and Sensing applications. Prof. Tentzeris will touch up the state-of-the-art area of fully-integrated printable broadband wireless modules covering characterization of 3D printed materials up to E-band, novel printable ‘ramp’ interconnects and cavities for IC embedding as well as printable structures for self-diagnostic and anti-counterfeiting packages. The presented approach could potentially set the foundation for the truly convergent wireless sensor ad-hoc networks of the future with enhanced cognitive intelligence and ‘rugged’ packaging. Prof. Tentzeris will discuss issues concerning the power sources of ‘near-perpetual’ RF modules, including flexible miniaturized batteries as well as power-scavenging approaches involving thermal, EM, vibration and solar energy forms. The final step of the presentation will involve examples from shape-changing 4D-printed (origami) packages, reflectarrays and mmW wearable (e.g. biomonitoring) antennas and RF modules. Special attention will be paid on the integration of ultrabroadband (Gb/sec) inkjet-printed nanotechnology-based backscattering communication modules as well as miniaturized printable wireless (e.g.CNT) sensors for Internet of Things (IoT), 5G and smart agriculture/biomonitoring applications. It has to be noted that the talk will review and present challenges for inkjet-printed organic active and nonlinear devices as well as future directions in the area of environmentally-friendly (‘green’) RF electronics and ‘smart-skin’ conformal sensors.

Strain Measurement with Optic Fibers for Structural Health Monitoring of Woven Composites: Comparison with Strain Gauges and Digital Image Correlation Measurements.

In this work, the strains measured with optic fibers and recorded during tensile tests performed on carbon/epoxy composite specimens were compared to those recorded by strain gauges and by Digital Image Correlation (DIC). The work aims at investigating the sensitivity of embedded and glued optic sensors for structural health monitoring applications in comparison with strain gauges and the full field strain map of the DIC. Acrylate, polyimide optic fibers, and three strain gauge sizes are considered to compare the three techniques. Results show hard polyimide-coated sensors are more sensitive to the material pattern than soft acrylate-coated fibers, which also require extensive adhesion length. The work shows a comparable size of strain gauges and material meso-structure is also critical for properly assessing material properties. The Young's modulus computed with the three different techniques is used to define a strategy that supports the selection and the proper size of the adopted strain measuring system for structural health monitoring of composite materials.

Real-time monitoring of self-sensing cementitious composite incorporating hybrid silicon carbide and graphite for enhanced structural health monitoring

This manuscript presents a comprehensive exploration of Silicon Carbide-Graphite Cement Composites (SCGCCs) by investigating their mechanical, electrical, and self-sensing properties. The study systematically varies SiC and graphite content, leading to remarkable insights. In terms of mechanical strength, specimens S50G1 and S50 stand out, exhibiting impressive enhancements of 15.6 % and 9.8 % in compressive strength, respectively, showcasing the pivotal role of particle content in bolstering structural integrity. Electrical resistivity analysis uncovers a percolation threshold at 25 % SiC and 2 % graphite, resulting in a substantial 97 % reduction in resistivity. This critical finding underscores the delicate balance required for optimal conductive networks within the composite material. The self-sensing capabilities of SCGCCs under flexural stress reveal a synergistic effect between SiC and graphite, with specimen S25G2 displaying the lowest voltage drop. The compressive stress analysis unveils a distinctive conductive path reconstruction behavior, offering valuable insights into the material's response to varying stress levels. Advanced CT scan porosity analysis further refines our understanding, indicating a significant reduction in porosity from 4.2 % to 3.1 % in specimen S25. Furthermore, specimens with lower graphite and SiC proportions exhibit efficient void compaction, resulting in a well-integrated microstructure with porosity as low as 2.5 %. These findings not only contribute to the fundamental understanding of SCGCCs but also offer practical applications in smart materials and structural health monitoring. Moreover, the inherent advantage of real-time monitoring within the context of smart mortar extends to the prognostication of an advanced warning signal for impeding the complete failure of the smart concrete matrix.

In-Depth Steel Crack Analysis Using Photoacoustic Imaging (PAI) with Machine Learning-Based Image Processing Techniques and Evaluating PAI-Based Internal Steel Crack Feasibility

Steel plays an indispensable role in our daily lives, permeating various products ranging from essential commodities and recreational gears to information technology devices and general household items. The meticulous evaluation of steel defects holds paramount importance to ensure the secure and dependable operation of the end products. Photoacoustic imaging (PAI) emerges as a promising modality for structural inspection in the realm of health monitoring applications. This study incorporates PAI experimentation to generate an image dataset and employs machine learning techniques to estimate the length and width of surface cracks. Furthermore, the research delves into the feasibility assessment of employing PAI to investigate internal cracks within a steel sample through a numerical simulation-based study. The study’s findings underscore the efficacy of the PAI in achieving precise surface crack detection, with an acceptable root mean square error (RMSE) of 0.63 ± 0.03. The simulation results undergo statistical analysis techniques, including the analysis of variance (ANOVA) test, to discern disparities between pristine samples and those featuring internal cracks at different locations. The results discern statistically significant distinctions in the simulated acoustic responses for samples with internal cracks of varying sizes at identical/different locations (p < 0.001). These results validate the capability of the proposed technique to differentiate between internal crack sizes and positions, establishing it as a viable method for internal crack detection in steel.