Continuous Monitoring Applications Research Articles

Advanced Triboelectric Nanogenerator Sensing Technologies for High‐Efficiency Cardiovascular Monitoring

Triboelectric nanogenerators (TENGs) have emerged as transformative technologies in biosensing, offering unprecedented energy efficiency and precision in monitoring vital physiological signals. This review delves into the cutting‐edge advancements in TENG sensors, highlighting their exceptional potential in bioengineering applications. Key operating mechanisms and advanced materials are explored, with a focus on their impact on sensor sensitivity, durability, and biocompatibility. Cardiovascular monitoring is presented as a pivotal application, where TENG sensors demonstrate exceptional capability in detecting subtle mechanical signals such as pulse waves and heartbeats in real time. Their self‐powered nature eliminates the need for external energy sources, and their inherent scalability and adaptability make them ideal for integration into wearable or implantable devices. Benefits such as miniaturization, energy efficiency, and biocompatibility are discussed, alongside challenges like material fatigue and long‐term stability in biomedical environments. Future directions include optimizing TENG materials for enhanced mechanical robustness and expanding their integration into advanced medical diagnostics. This review provides a comprehensive roadmap for leveraging TENG technologies to revolutionize continuous cardiovascular monitoring and broader medical applications.

Corrosion State Monitoring Based on Multi-Granularity Synergistic Learning of Acoustic Emission and Electrochemical Noise Signals

Corrosion monitoring is crucial for ensuring the structural integrity of equipment. Acoustic emission (AE) and electrochemical noise (EN) have been proven to be highly effective for the detection of corrosion. Due to the complementary nature of these two techniques, previous studies have demonstrated that combining both signals can facilitate research on corrosion monitoring. However, current machine learning models have not yet been able to effectively integrate these two different modal types of signals. Therefore, a new deep learning framework, CorroNet, is designed to synergistically integrate AE and EN signals at the algorithmic level for the first time. The CorroNet leverages multimodal learning, enhances accuracy, and automates the monitoring process. During training, paired AE-EN data and unpaired EN data are used, with AE signals serving as anchors to help the model better align EN signals with the same corrosion stage. A new feature alignment loss function and a probability distribution consistency loss function are designed to facilitate more effective feature learning to improve classification performance. Experimental results demonstrate that CorroNet achieves superior accuracy in corrosion stage classification compared to other state-of-the-art models, with an overall accuracy of 97.01%. Importantly, CorroNet requires only EN signals during the testing phase, making it suitable for stable and continuous monitoring applications. This framework offers a promising solution for real-time corrosion detection and structural health monitoring.

Creation of pH-Sensitive Bioreceptors for Affinity-Type Electrochemical Biosensor Applications

Introduction: Many disease states are characterized by the presence, absence, or change of molecular biological markers, or biomarkers. Common affinity-type recognition elements used in the detection of biomarkers are antibodies, either Immunoglobulins (IgGs) or recombinant fragments. While highly specific, the dissociation kinetics of antibodies highly favors the antibody-antigen complex, resulting in single, endpoint measurements [1]. Endpoint measurements are useful for ex vivo assays, but further antibody engineering is required for in vivo continuous monitoring. A key burden to overcome for using antibodies in continuous monitoring applications is the lack of binding site regeneration [2].Thus, we aim to create pH-modulable single chain fragment variables (scFvs), in which the KD of the scFv can be controlled to favor dissociation. We posit that through mutagenesis of residues within the complementarity-determining regions (CDRs) to histidine, we are able to modulate antibody binding kinetics through a change in pH [3,4]. There is a basis in the literature that supports this antibody design approach [5]. Those working with therapeutic antibodies currently employ similar techniques to effectively “recycle,” or regenerate the binding site of the antibody in vivo [6]. We seek to expand these techniques into the biosensing space to develop continuous, antibody-based biosensors. Methods: ClusPro was utilized to predict the antigen-binding residues in the CDRs of an anti-insulin scFv. From the predicted residues, we selected three amino acid residues to mutate to histidine with the intent of generating pH-modulable mutants. Three single mutants and a triple mutant containing all mutations were designed. The wild-type (WT) scFv, as well as the four mutants were all expressed as a soluble protein using Escherichia coli (E. coli) as a host microorganism. The binding ability of the WT and four mutants was assessed via two assays in differing pH with Bio-Layer Interferometry (BLI). The BLI assay workflow is as follows: biotinylated insulin was immobilized to Octet Streptavidin tips in buffers of either pH 7.4 or 6.0. Following immobilization, either the WT or mutants were introduced to bind to insulin. The resulting association and dissociation sensorgrams were recorded, and nonlinear fitting of the data was performed in GraphPad Prism 10.0. Results and Discussion: Compared to the WT, the triple histidine mutations in the CDR did not eliminate the scFv’s ability to bind to insulin. For the WT, the apparent KD decreased slightly between pH 7.4 and pH 6.0. This could be attributed to the change in pH affecting the streptavidin-biotin solution, or could be due to an artifact of the assay. Further studies would be needed to discern between the two hypotheses. However, there was a pronounced increase in the apparent KD of the triple mutant in a solution of pH 6.0 compared to pH 7.4. These results indicate the combination of mutations led to an observed pH-dependent binding. Further experimentation is needed with the single mutants to elucidate which residue is responsible for pH dependence, or if it is a combination of the three. Conclusions: We employed a rational mutation strategy to generate pH-dependent binding of an anti-insulin scFv. Upon a change in solution pH from 7.4 to 6.0, the triple mutant’s binding equilibrium is shifted, leading to an increase in KD. Further studies are needed to understand the reversibility of this interaction as well as to elucidate what specific residue interactions are contributing to this change.

Research Progress on the Application of CGM in Patients with Diabetes and Hemodialysis.

Diabetes mellitus is the main cause of end-stage renal disease (ESKD), and most patients need hemodialysis (HD) treatment after they progress to uremia. Patients with diabetes and HD have obvious blood glucose fluctuation, hyperglycemia and hypoglycemia may both related to the higher mortality. Therefore, maintaining blood glucose stability is the main treatment strategy to improve the prognosis of patients. It is challenging to evaluate the blood glucose control of patients with diabetes and HD. The traditional blood glucose detection methods have certain limitations, they may be affected by many factors in HD patients. The application of continuous glucose monitoring (CGM) system is gradually recognized, CGM can monitor blood glucose real-time, timely, and predictive capabilities, there are fewer factors that are affected blood glucose in HD patients.

Zn-Modified TiO2 Thin-Films for Real-Time Formaldehyde Sensing at Room Temperature

Abstract This study explores the fabrication and application of zinc-modified titanium dioxide (Zn-TiO2) thin-films for real-time recognition of volatile organic compounds (VOCs), with a particular emphasis on formaldehyde (HCHO) sensing at room temperature. The Zn-TiO2 thin-films were produced using an economical spray-pyrolysis method. Structural, morphological, and optical characterizations confirmed the successful integration of zinc with varied Wt.% (0, 2, 4, and 6) into the TiO2 lattice. The real-time monitoring capabilities of the sensors were assessed against a range of VOCs, highlighting its specificity for formaldehyde detection amidst diverse environmental constituents. The fabricated thin film sensors with zinc dopant were optimized to enhance the sensor's performance. 4 Wt.% Zn-TiO2 demonstrated excellent sensitivity to formaldehyde vapor at ambient conditions, showcasing a rapid and selective response. The underlying sensing mechanism was explored, emphasizing the role of zinc doping in tailoring the material's surface properties and facilitating enhanced adsorption of formaldehyde molecules. The study underscores the potential of Zn-modified TiO2 thin films as a reliable and efficient platform for real-time VOC monitoring, with a specific focus on HCHO sensing at room-temperature. The sensor shows remarkable stability and repeatability, making it a promising candidate for continuous monitoring applications.

Unveiling Artificial Intelligence's Power: Precision, Personalization, and Progress in Rheumatology.

This review examines the increasing use of artificial intelligence (AI) in rheumatology, focusing on its potential impact in key areas. AI, including machine learning (ML) and deep learning (DL), is revolutionizing diagnosis, treatment personalization, and prognosis prediction in rheumatologic diseases. Specifically, AI models based on convolutional neural networks (CNNs) demonstrate significant efficacy in analyzing medical images for disease classification and severity assessment. Predictive AI models also have the ability to forecast disease trajectories and treatment responses, enabling more informed clinical decisions. The role of wearable devices and mobile applications in continuous disease monitoring is discussed, although their effectiveness varies across studies. Despite existing challenges, such as data privacy concerns and issues of model generalizability, the compelling results highlight the transformative potential of AI in rheumatologic disease management. As AI technologies continue to evolve, further research will be essential to address these challenges and fully harness the potential of AI to improve patient outcomes in rheumatology.

A review of the application of continuous real-time monitoring in noise risk management

Background: In the year 2020, the Mineral's Council Chief Executive Officer (CEO) Zero Harm Forum (CEOZHF) reviewed the slow progress towards achieving the 2024 Occupational Health Milestones and agreed that there was a need to improve the current measures in place to reduce occupational diseases. Following discussions with Group Environmental Engineers, it was proposed to adopt key occupational health indicators for tracking progress through Continuous Real-Time Monitoring (CRTM) to reduce workplace exposures. Methods: A systematic review methodology was employed to gather data from reputable academic databases. Results: CRTM in Noise Risk Management enables an encompassing practical and intuitive analysis of building noise pollution with prompt alerts for poor acoustic comfort. Real-time graphical display of noise measurements, utilizing a warning signal framework, facilitated area-specific alerts and responses. Additionally, CRTM enabled the visualization of noise levels and sources through frequency analysis, contributing to noise pollution analysis for zoning and strategic planning in smart cities. Conclusions: While CRTM has been extensively applied in noise monitoring globally, its utilization in the mining industry, especially in South Africa, remains limited. This study underscores the feasibility of implementing CRTM in Noise Risk Management, emphasizing the need for further research within the mining industry.

A Study on Cu Decorated Anodic TiO2 Nanotubes for Non-Enzymatic Glucose Sensing and Photoelectrochemical Water Splitting Application

Free-standing, surface-modified TiO2 nanotubes(TNTs) decorated with copper nanostructures have been extensively studied as promising materials for their application in biosensing and photo-electrochemical splitting of water. Here, the TNTs are prepared by electrochemical anodization followed by modification with copper nanostructures via UV-assisted photo-reduction technique. Field-emission scanning electron microscopy and X-ray diffraction studies confirmed the structural and morphological properties of the TNTs, along with their tubular architecture and mixed-phase composition of Anatase-Rutile. Energy-dispersive spectroscopic analysis verified the successful deposition of copper. Diffuse reflectance spectroscopy revealed an electronic band gap of 3.2 eV. The copper-modified TNTs showed an enhanced sensitivity in the sensing of glucose to the tune of 0.52 mA mM−1 cm−2 with a high linear range of 0.5 to 7 mM and showed superior selectivity against interferents. It was found that the modified TNTs exhibited a higher photocurrent response of 1.09 mA cm−2 compared with pristine TNTs (0.69 mA cm−2). These findings indicate the promising potential of copper-modified TNTs for continuous glucose monitoring and photo-electrochemical applications.

Enhancing structural damage evaluation of PC girder bridges through digital twinning Bayesian model updating

Developing a unified analytical framework to assess the structural performance of deteriorated prestressed concrete (PC) girder bridges is important for their maintenance. This study aims to construct a physics-based digital twin framework for PC girder bridges incorporating Bayesian model selection and updating, a high-fidelity three-dimensional (3D) finite element (FE) model, and a method for simulating prestressed tendon damage. In this study, three PC girder specimens were designed and tested to evaluate the effects of tendon rupture near the anchorage region on structural performance. Bayesian model selection was used to determine the most plausible model class based on the estimated model evidence. The most probable values (MPVs) of the model parameters, derived from the estimated posterior probability density functions (PDFs), were then used to calibrate the FE model, which reflects a healthy state of the PC girder bridge. This updated FE model serves as the baseline for conducting what-if scenario simulations. Subsequently, nonlinear segments of the constitutive model, obtained from coupon tests, and a simulation method for tendon rupture were incorporated into the updated FE model. Nonlinear static simulation results obtained through the updated model were found to be consistent with both the observed crack pattern and the load–deflection curve. The reliability of the digital twinning FE model for assessing tendon damage and for predicting the residual load-bearing capacity was verified through comparison with measured data. Therefore, the digital twinning Bayesian model updating approach shows potential applications in continuous structural health monitoring (SHM) of PC bridges.

Application of Continuous Glucose Monitoring for Diabetes Detection

Application of Continuous Glucose Monitoring for Diabetes Detection

Direct Memory Access-Based Data Storage for Long-Term Acquisition Using Wearables in an Energy-Efficient Manner.

This study introduces a lightweight storage system for wearable devices, aiming to optimize energy efficiency in long-term and continuous monitoring applications. Utilizing Direct Memory Access and the Serial Peripheral Interface protocol, the system ensures efficient data transfer, significantly reduces energy consumption, and enhances the device autonomy. Data organization into Time Block Data (TBD) units, rather than files, significantly diminishes control overhead, facilitating the streamlined management of periodic data recordings in wearable devices. A comparative analysis revealed marked improvements in energy efficiency and write speed over existing file systems, validating the proposed system as an effective solution for boosting wearable device performance in health monitoring and various long-term data acquisition scenarios.

All-Fiber Flexible Electrochemical Sensor for Wearable Glucose Monitoring.

Wearable sensors, specifically microneedle sensors based on electrochemical methods, have expanded extensively with recent technological advances. Today's wearable electrochemical sensors present specific challenges: they show significant modulus disparities with skin tissue, implying possible discomfort in vivo, especially over extended wear periods or on sensitive skin areas. The sensors, primarily based on polyethylene terephthalate (PET) or polyimide (PI) substrates, might also cause pressure or unease during insertion due to the skin's irregular deformation. To address these constraints, we developed an innovative, wearable, all-fiber-structured electrochemical sensor. Our composite sensor incorporates polyurethane (PU) fibers prepared via electrospinning as electrode substrates to achieve excellent adaptability. Electrospun PU nanofiber films with gold layers shaped via thermal evaporation are used as base electrodes with exemplary conductivity and electrochemical catalytic attributes. To achieve glucose monitoring, gold nanofibers functionalized by gold nanoflakes (AuNFs) and glucose oxidase (GOx) serve as the working electrode, while Pt nanofibers and Ag/AgCl nanofibers serve as the counter and reference electrode. The acrylamide-sodium alginate double-network hydrogel synthesized on electrospun PU fibers serves as the adhesive and substance-transferring layer between the electrodes. The all-fiber electrochemical sensor is assembled layer-by-layer to form a robust structure. Given the stretchability of PU nanofibers coupled with a high specific surface area, the manufactured porous microneedle glucose sensor exhibits enhanced stretchability, superior sensitivity at 31.94 μA (lg(mM))-1 cm-2, a broad detection range (1-30 mM), and a significantly low detection limit (1 mM, S/N = 3), as well as satisfactory biocompatibility. Therefore, the novel electrochemical microneedle design is well-suited for wearable or even implantable continuous monitoring applications, thereby showing promising significant potential within the global arena of wearable medical technology.

COVID-19 Vaccine Effectiveness and Digital Pandemic Surveillance in Germany (eCOV Study): Web Application-Based Prospective Observational Cohort Study.

The COVID-19 pandemic posed significant challenges to global health systems. Efficient public health responses required a rapid and secure collection of health data to improve the understanding of SARS-CoV-2 and examine the vaccine effectiveness (VE) and drug safety of the novel COVID-19 vaccines. This study (COVID-19 study on vaccinated and unvaccinated subjects over 16 years; eCOV study) aims to (1) evaluate the real-world effectiveness of COVID-19 vaccines through a digital participatory surveillance tool and (2) assess the potential of self-reported data for monitoring key parameters of the COVID-19 pandemic in Germany. Using a digital study web application, we collected self-reported data between May 1, 2021, and August 1, 2022, to assess VE, test positivity rates, COVID-19 incidence rates, and adverse events after COVID-19 vaccination. Our primary outcome measure was the VE of SARS-CoV-2 vaccines against laboratory-confirmed SARS-CoV-2 infection. The secondary outcome measures included VE against hospitalization and across different SARS-CoV-2 variants, adverse events after vaccination, and symptoms during infection. Logistic regression models adjusted for confounders were used to estimate VE 4 to 48 weeks after the primary vaccination series and after third-dose vaccination. Unvaccinated participants were compared with age- and gender-matched participants who had received 2 doses of BNT162b2 (Pfizer-BioNTech) and those who had received 3 doses of BNT162b2 and were not infected before the last vaccination. To assess the potential of self-reported digital data, the data were compared with official data from public health authorities. We enrolled 10,077 participants (aged ≥16 y) who contributed 44,786 tests and 5530 symptoms. In this young, primarily female, and digital-literate cohort, VE against infections of any severity waned from 91.2% (95% CI 70.4%-97.4%) at week 4 to 37.2% (95% CI 23.5%-48.5%) at week 48 after the second dose of BNT162b2. A third dose of BNT162b2 increased VE to 67.6% (95% CI 50.3%-78.8%) after 4 weeks. The low number of reported hospitalizations limited our ability to calculate VE against hospitalization. Adverse events after vaccination were consistent with previously published research. Seven-day incidences and test positivity rates reflected the course of the pandemic in Germany when compared with official numbers from the national infectious disease surveillance system. Our data indicate that COVID-19 vaccinations are safe and effective, and third-dose vaccinations partially restore protection against SARS-CoV-2 infection. The study showcased the successful use of a digital study web application for COVID-19 surveillance and continuous monitoring of VE in Germany, highlighting its potential to accelerate public health decision-making. Addressing biases in digital data collection is vital to ensure the accuracy and reliability of digital solutions as public health tools.

A 36-nW Electrocardiogram Anomaly Detector Based on a 1.5-bit Non-Feedback Delta Quantizer for Always-on Cardiac Monitoring.

An always-on electrocardiogram (ECG) anomaly detector (EAD) with ultra-low power (ULP) consumption is proposed for continuous cardiac monitoring applications. The detector is featured with a 1.5-bit non-feedback delta quantizer (DQ) based feature extractor, followed by a multiplier-less convolutional neural network (CNN) engine, which eliminates the traditional high-resolution analog-to-digital converter (ADC) in conventional signal processing systems. The DQ uses a computing-in-capacitor (CIC) subtractor to quantize the sample-to-sample difference of ECG signal into 1.5-bit ternary codes, which is insensitive to low-frequency baseline wandering. The subsequent event-driven classifier is composed of a low-complexity coarse detector and a systolic-array-based CNN engine for ECG anomaly detection. The DQ and the digital CNN are fabricated in 65-nm and 180-nm CMOS technology, respectively, and the two chips are integrated on board through wire bonding. The measured detection accuracy is 90.6% ∼ 91.3% when tested on the MIT-BIH arrhythmia database, identifying three different ECG anomalies. Operating at 1 V and 1.4 V power supplies for the DQ and the digital CNN, respectively, the measured long-term average power consumption of the core circuits is 36 nW, which makes the detector among those state-of-the-art always-on cardiac anomaly detection devices with the lowest power consumption.

Experimental Approaches to Iron Determination in Potentiometric Sensors Using (2-2-(4-Dinitrophenyl) Hydrazono) Methylphenol

The study presents a PVC membrane-based potentiometric sensor for iron (III) detection, demonstrating impressive selectivity in Fe(III) ion-selective electrodes (ISE). The sensor achieves a detection limit of 4.0×10−7 M and a Nernstian response slope of 19.4 mV per decade change within a concentration range of 7.4×10−7 to 1×10−1 M. Operating efficiently within the pH range of 3.6–10, the sensor maintains its potential divergence-free utility for at least eight weeks and has a rapid response time of approximately twenty seconds. The sensor’s selectivity for Fe3+ ions makes it a robust tool for specific Fe(III) ion detection. The sensor’s reliability and longevity make it a valuable tool for continuous monitoring applications, providing accurate and timely information on the presence of iron ions in diverse settings.

A proposed tacho-less order tracking technique suitable for railway vehicle condition monitoring applications

To perform a condition monitoring for rotating machines with variable running speed, order tracking is considered as an effective technique suitable for their non-stationary vibration signals. A tachometer signal is necessary to perform a computed order tracking (COT) technique; however, in some applications it's not possible to add tachometer hardware due to its complexity or shortage of power. To improve the applicability of order tracking techniques, some tacho-less order tracking techniques have been introduced in the last few years. These techniques are working off-line by inspecting the vibration signals in time-frequency domain to extract the machine rotating speed. In this paper, a new tacho-less technique suitable for continuous structural health monitoring applications of railway vehicles, is proposed. In the presented approach, a coarse angular speed of the railway axle is estimated firstly using estimation train speed. Then this coarse angular speed is used to improve the applicability of the traditional tacho-less techniques to be able to work online. The train speed is estimated from two acceleration signals obtained from vertical vibration of axle-boxes from same side of train. The sequential properties of the two signals are used to estimate the delay time of one signal with respect to the other one by means of cross-correlation function. By knowing the horizontal distance between the two sensors collected the vibration signals, the train speed can be estimated. This modification of tacho-less technique improves the effectiveness and robustness of the traditional maxima tracking strategy to be performed automatically. Thereafter, signal processing technique based on the computed order tracking as well as averaging technique can be applied to extract the useful information from noisy and non-stationary signals. This technique has been proposed in a framework of a project investigating crack detection using vibration measurements, therefore, vibration signals of cracked axle have been processed using tacho-based method and using the proposed tacho-less technique and a good agreement has been found between the two techniques.

Enhancing Parkinson’s Disease Prediction Using Deep Learning-Based Convolutional Neural Networks

Recently significant advances in the field of medical diagnostics can be seen. Nonetheless, diagnosing Parkinson's disease (PD) remains difficult, especially in terms of timeliness and precision. The inability of present technological methods to identify this complicated neurodegenerative disease emphasizes the urgent need for more research. PD is a complicated neurological condition that is becoming more commonplace worldwide. A timely and precise PD diagnosis is critical because it directly determines the quality of patient care and the success of treatment. However, our current diagnostic approaches, which primarily rely on clinical assessments, have substantial limits in terms of sensitivity and specificity. Thus, the need for a more reliable and objective diagnosing procedure is essential. Convolutional neural networks (CNNs) are employed in this study to investigate complex spiral and wave patterns and to overcome this significant challenge. Drawings from both PD patients and their healthy counterparts were included in the large dataset that was used to train the robust CNN model, which is a key component of the research technique. The training and intensive testing of the model on this dataset confirms its capacity to distinguish between PD instances and non-PD cases. The results of this investigation demonstrate how well the CNN model can predict Parkinson's disease (PD) based on spiral and wave patterns, with a 93% classification accuracy. The high sensitivity and specificity of the model are especially impressive because they significantly lower the chances of false positives and false negatives. The discussion that follows offers a considered evaluation of these findings, emphasising the model's potential for early diagnosis, its capacity to support professional judgments, and its crucial role in enhancing patient outcomes. This study highlights the great potential of deep learning methods for disease diagnosis, especially for PD. The effective integration of the CNN model into a React application for real-time prediction and continuous monitoring of patient performance has important implications for telemedicine and remote healthcare management. This breakthrough makes Parkinson's disease treatment more approachable and proactive.

Hierarchical Piezoelectric Composites for Noninvasive Continuous Cardiovascular Monitoring.

Continuous monitoring of blood pressure (BP) and multiparametric analysis of cardiac functions are crucial for the early diagnosis and therapy of cardiovascular diseases. However, existing monitoring approaches often suffer from bulky and intrusive apparatus, cumbersome testing procedures, and challenging data processing, hampering their applications in continuous monitoring. Here, a heterogeneously hierarchical piezoelectric composite is introduced for wearable continuous BP and cardiac function monitoring, overcoming the rigidity of ceramic and the insensitivity of polymer. By optimizing the hierarchical structure and components of the composite, the developed piezoelectric sensor delivers impressive performances, ensuring continuous and accurate monitoring of BP at Grade A level. Furthermore, the hemodynamic parameters are extracted from the detected signals, such as local pulse wave velocity, cardiac output, and stroke volume, all of which are in alignment with clinical results. Finally, the all-day tracking of cardiac function parameters validates the reliability and stability of the developed sensor, highlighting its potential for personalized healthcare systems, particularly in early diagnosis and timely intervention of cardiovascular disease.

A Wearable Open-Source electrical impedance tomography device

Electrical impedance tomography (EIT) is medical imaging technique in which small electrical signals are used to map the electrical impedance distribution within the body. It is safe and non-invasive, which make it attractive for use in continuous monitoring or outpatient applications, but the high cost of commercial devices is an impediment to its adoption. Over the last 10 years, many research groups have developed their own EIT devices, but few designs for open-source EIT hardware are available. In this work, we present a complete open-source EIT system that is designed to be suitable for monitoring the lungs of free breathing subjects. The device is low-cost, wearable, and is designed to comply with the industry accepted safety standard for EIT. The device has been tested in two regimes: Firstly in terms of measurement uncertainty as a voltage measurement system, and secondly against a set of measures that have been proposed specifically for EIT hardware. The voltage measurement uncertainty of the device was measured to be − 0.7 % ± 0.36 mV. The EIT specific performance was measured in a phantom test designed to be as physiologically representative as practicable, and the device performed similarly to other published devices. This work will contribute to increased accessibility of EIT for study and will contribute to consensus on testing methodology for EIT devices.

Advanced Hybrid Mask Convolutional Neural Network with Backpropagation Optimization for Precise Sensor Node Classification in Wireless Body Area Networks

Wireless Body Area Networks (WBANs) are crucial in continuous health monitoring, fitness tracking, and other applications where a real-time collection of physiological data is needed by sensors worn on the body. This is important in WBAN to achieve reliable data transmission, energy efficiency, and overall system performance. Still WBANs present several challenges: for example, the data that is being collected is heterogeneous since it originates from diverse IMDs measuring different bio-physiological signals; these can be also quite noisy because of motion artifacts when moving; as well high limitations when it comes to energy, bandwidth or storage push for low-complexity methods rather than standard deep learning techniques. Common Convolutional Neural Networks) CNNs( are successfully utilized for spatial information extraction but they cannot catch temporal dependencies well and also, WBAN sensor data has a noisy and multi-modal structure which acts as an additional challenge for traditional CNNs. These limitations emphasize the need of a flexible, fast and precise classification model based on the specific needs of WBAN applications. To overcome these challenges, this paper presents a novel hybrid neural network architecture consisting of combined 2D and 3D convolutions for spatial-temporal feature extraction along with masked convolution layers to provide an ability to adaptively ignore uninterested parts of the data. The model aims at achieving a high classification performance while also balancing with the system computational efficiency, perfect for its ipsi deployment on resource constrained WBAN devices. Then, we apply further backpropagation optimization measures such as adaptive learning rate scheduling and gradient clipping, to improve the stability of training speed and reduce latency which in return finds its way into supporting real-time processing capabilities of the model. By using each of these components, the model is able to deal with the multi-dimension aspects and high noise level nature of WBANs without excessive computation resources [18]. The Hybrid Masked CNN model is shown to out-perform existing approaches without such masking, yielding substantially higher performance in terms of accuracy, precision, recall and F1-score across all metrics defined for the application as compared to traditional 2DCNNs, 3DCNNs and other hybrid models. Consequently, the latency of the model is significantly decreased as well which confirms its applicability to real-time WBAN applications. The obtained results confirm the efficacy of features from hybrid architectures with masked convolutions along with optimization in training techniques for WBAN sensor node classification. The results of this paper improve WBAN technology by providing a solid and scalable solution which can be implemented when more reliability and flexibility are required from such systems in applications like healthcare, fitness or any other field.