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Hill-type muscle models play a crucial role in biomechanics, aiding in the prediction and understanding of muscle behavior, particularly when direct force measurement is challenging. They are instrumental in evaluating anatomical changes resulting from external loads affecting internal forces on anatomical structures. This study aims to develop a modeling framework based on the Hill model to predict and preempt changes in internal forces caused by various loads. Factors such as increased desk work during the pandemic, musculoskeletal deformations in earthquake victims, injuries from wars, athlete rehabilitation, and cumulative musculoskeletal injuries in daily work are considered. A mathematical model based on the Hill muscle model was designed, verified experimentally and numerically, focusing on the Biceps Brachii muscle and its associated structures. EMG signals were collected using the Biopac Device and simulated in Matlab. This approach offers a platform for assessing current and future states of internal forces, predicting musculoskeletal disorders, and investigating factors affecting muscle performance. Furthermore, the study suggests potential applications in improving rehabilitation processes through prosthetic and orthosis design, as well as enhancing the quality of life for Parkinson's patients.

This article presents an innovative approach to analyzing and extracting electrocardiogram (ECG) signals from the abdomen and thorax of pregnant women, with the primary goal of isolating fetal ECG (fECG) and maternal ECG (mECG) signals. To resolve the difficulties related to the low amplitude of the fECG, various noise sources during signal acquisition, and the overlapping of R waves, we developed a new method for extracting ECG signals using blind source separation techniques. This method is based on independent component analysis algorithms to detect and accurately extract fECG and mECG signals from abdomen and thorax data. To validate our approach, we carried out experiments using a real and reliable database for the evaluation of fECG extraction algorithms. Moreover, to demonstrate real-time applicability, we implemented our method in an embedded card linked to electronic modules that measure blood oxygen saturation (SpO2) and body temperature, as well as the transmission of data to a web server. This enables us to present all information related to the fetus and its mother in a mobile application to assist doctors in diagnosing the fetus's condition. Our results demonstrate the effectiveness of our approach in isolating fECG and mECG signals under difficult conditions and also calculating different heart rates (fBPM and mBPM), which offers promising prospects for improving fetal monitoring and maternal healthcare during pregnancy. [ABSTRACT FROM AUTHOR]

Internet of Medical Things (IoMT) is an emerging subset of Internet of Things (IoT), often called as IoT in healthcare, refers to medical devices and applications with internet connectivity, is exponentially gaining researchers’ attention due to its wide-ranging applicability in biomedical systems for Smart Healthcare systems. IoMT facilitates remote health biomedical system and plays a crucial role within the healthcare industry to enhance precision, reliability, consistency and productivity of electronic devices used for various healthcare purposes. It comprises a conceptualized architecture for providing information retrieval strategies to extract the data from patient records using sensors for biomedical analysis and diagnostics against manifold diseases to provide cost-effective medical solutions, quick hospital treatments, and personalized healthcare. This article provides a comprehensive overview of IoMT with special emphasis on its current and future trends used in biomedical systems, such as deep learning, machine learning, blockchains, artificial intelligence, radio frequency identification, and industry 5.0.

Internet of Medical Things (IoMT) is an emerging subset of Internet of Things (IoT), often called as IoT in healthcare, refers to medical devices and applications with internet connectivity, is exponentially gaining researchers' attention due to its wide-ranging applicability in biomedical systems for Smart Healthcare systems. IoMT facilitates remote health biomedical system and plays a crucial role within the healthcare industry to enhance precision, reliability, consistency and productivity of electronic devices used for various healthcare purposes. It comprises a conceptualized architecture for providing information retrieval strategies to extract the data from patient records using sensors for biomedical analysis and diagnostics against manifold diseases to provide cost-effective medical solutions, quick hospital treatments, and personalized healthcare. This article provides a comprehensive overview of IoMT with special emphasis on its current and future trends used in biomedical systems, such as deep learning, machine learning, blockchains, artificial intelligence, radio frequency identification, and industry 5.0. [ABSTRACT FROM AUTHOR]

Highlights This article summarizes the recent advances in wireless, batteryless, implantable electronics for continuous physiological monitoring. The critical factors that affect the design of implantable electronics for biosensing are discussed. The recent progress of material research for developing various implantable devices is summarized. This article reviews various biomedical applications of implantable devices for human healthcare.

The results of the application of the methods of the synergetic control theory to a high-dimensional immunology object with uncertainty in its descriptions are reported. The control here is the therapy treated as a problem for constructing an optimal cure program. The control object is presented in continuous and discrete forms, i.e., mathematical models given by a system of ordinary differential equations with a bounded disturbance and a system of stochastic difference equations, respectively. Two algorithms for deriving robust regulators applicable to a 10-dimensional nonlinear multi-loop system with unstable limit states, which models an immune response to the hepatitis B infection, are obtained. Analytical control design for a continuous model relies on the method of nonlinear adaptation on the target manifold. The second algorithm represents a stochastic extension of the method of analytical design of aggregated discrete regulators minimizing the variance of the target macro variable. The numerical simulation of the developed control systems indicates the performance of the designed control algorithms. The results of this study can be used as a component part of the mathematical tools of expert systems and decision support systems. [ABSTRACT FROM AUTHOR]

To safely select the proper therapy for ventricular fibrillation ( V F ), it is essential to distinguish it correctly from ventricular tachycardia ( V T ) and other rhythms. Provided that the required therapy is not the same, an erroneous detection might lead to serious injuries to the patient or even cause ventricular fibrillation ( V F ). The primary innovation of this study lies in employing a CNN to create new features. These features exhibit the capacity and precision to detect and classify cardiac arrhythmias, including V F and V T . The electrocardiographic (ECG) signals utilized for this assessment were sourced from the established MIT-BIH and AHA databases. The input data to be classified are time–frequency (tf) representation images, specifically, Pseudo Wigner–Ville ( P W V ). Previous to Pseudo Wigner–Ville ( P W V ) calculation, preprocessing for denoising, signal alignment, and segmentation is necessary. In order to check the validity of the method independently of the classifier, four different CNNs are used: InceptionV3, MobilNet, VGGNet and AlexNet. The classification results reveal the following values: for VF detection, there is a sensitivity (Sens) of 98.16%, a specificity (Spe) of 99.07%, and an accuracy (Acc) of 98.91%; for ventricular tachycardia ( V T ), the sensitivity is 90.45%, the specificity is 99.73%, and the accuracy is 99.09%; for normal sinus rhythms, sensitivity stands at 99.34%, specificity is 98.35%, and accuracy is 98.89%; finally, for other rhythms, the sensitivity is 96.98%, the specificity is 99.68%, and the accuracy is 99.11%. Furthermore, distinguishing between shockable ( V F / V T ) and non-shockable rhythms yielded a sensitivity of 99.23%, a specificity of 99.74%, and an accuracy of 99.61%. The results show that using tf representations as a form of image, combined in this case with a CNN classifier, raises the classification performance above the results in previous works. Considering that these results were achieved without the preselection of ECG episodes, it can be concluded that these features may be successfully introduced in Automated External Defibrillation (AED) and Implantable Cardioverter Defibrillation (ICD) therapies, also opening the door to their use in other ECG rhythm detection applications. [ABSTRACT FROM AUTHOR]

Healthcare is one of the most important applications of the Internet of Things (IoT), which can be used to investigate the health status of a remote patient. In this paper, an IoT-based health monitoring and ambulance tracking system is proposed. In the IoT-based health monitoring system, various parameters related to a patient's health such as heartbeat, electrocardiography, temperature are measured by certain medical devices (sensors) and transmitted through IoT devices; while in an ambulance they are sent to an internet cloud where they are stored and analyzed for pre-treatment purposes. The main premise of the research is that when an emergency occurs, the ambulance should reach the hospital as quickly as possible and send the information about the patient's condition to the hospital in question to prepare for treatment. Meanwhile, pre-treatment can also be initiated in the ambulance if the doctor recommends it after checking the patient's condition via the IoT platform. The results of this study show that the proposed strategy is very effective in saving the precious lives of patients due to delays in treatment. In addition, it is recommended that the Internet of Things (IoT) provides reliable solutions for effective patient health monitoring and treatment. [ABSTRACT FROM AUTHOR]

The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world. Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease. No doubt, X-ray is considered as a quick screening method, but due to variations in features of images which are of X-rays category with Corona confirmed cases, the domain expert is needed. To address this issue, we proposed to utilize deep learning approaches. In this study, the dataset of COVID-19, lung opacity, viral pneumonia, and lastly healthy patients’ images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients efficiently. The performance of the Swin transformer is compared with the other seven deep learning models, including ResNet50, DenseNet121, InceptionV3, EfficientNetB2, VGG19, ViT, CaIT, Swim transformer provides 98% recall and 96% accuracy on corona affected images of the X-ray category. The proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis, and proposed technique is found better in terms of accuracy. Our system could support clinicians in screening patients for COVID-19, thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients. Also, this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients. [ABSTRACT FROM AUTHOR]

Healthy lifestyle is one of the most important factors in the prevention of premature deaths, chronic diseases, productivity loss, obesity, and other economic and social aspects. The workplace plays an important role in promoting the physical activity and wellbeing of employees. Previous studies are mostly focused on individual interviews, various questionnaires that are a conceptual information about individual health state and might change according to question formulation, specialist competence, and other aspects. In this paper the work ability was mostly related to the employee's physiological state, which consists of three separate systems: cardiovascular, muscular, and neural. Each state consists of several exercises or tests that need to be performed one after another. The proposed data transformation uses fuzzy logic and different membership functions with three or five thresholds, according to the analyzed physiological feature. The transformed datasets are then classified into three stages that correspond to good, moderate, and poor health condition using machine learning techniques. A three-part Random Forest method was applied, where each part corresponds to a separate system. The obtained testing accuracies were 93%, 87%, and 73% for cardiovascular, muscular, and neural human body systems, respectively. The results indicate that the proposed work ability evaluation process may become a good tool for the prevention of possible accidents at work, chronic fatigue, or other health problems. [ABSTRACT FROM AUTHOR]

The results of the application of the methods of the synergetic control theory to a high-dimensional immunology object with uncertainty in its descriptions are reported. The control here is the therapy treated as a problem for constructing an optimal cure program. The control object is presented in continuous and discrete forms, i.e., mathematical models given by a system of ordinary differential equations with a bounded disturbance and a system of stochastic difference equations, respectively. Two algorithms for deriving robust regulators applicable to a 10-dimensional nonlinear multi-loop system with unstable limit states, which models an immune response to the hepatitis B infection, are obtained. Analytical control design for a continuous model relies on the method of nonlinear adaptation on the target manifold. The second algorithm represents a stochastic extension of the method of analytical design of aggregated discrete regulators minimizing the variance of the target macro variable. The numerical simulation of the developed control systems indicates the performance of the designed control algorithms. The results of this study can be used as a component part of the mathematical tools of expert systems and decision support systems.

To safely select the proper therapy for ventricular fibrillation (VF), it is essential to distinguish it correctly from ventricular tachycardia (VT) and other rhythms. Provided that the required therapy is not the same, an erroneous detection might lead to serious injuries to the patient or even cause ventricular fibrillation (VF). The primary innovation of this study lies in employing a CNN to create new features. These features exhibit the capacity and precision to detect and classify cardiac arrhythmias, including VF and VT. The electrocardiographic (ECG) signals utilized for this assessment were sourced from the established MIT-BIH and AHA databases. The input data to be classified are time–frequency (tf) representation images, specifically, Pseudo Wigner–Ville (PWV). Previous to Pseudo Wigner–Ville (PWV) calculation, preprocessing for denoising, signal alignment, and segmentation is necessary. In order to check the validity of the method independently of the classifier, four different CNNs are used: InceptionV3, MobilNet, VGGNet and AlexNet. The classification results reveal the following values: for VF detection, there is a sensitivity (Sens) of 98.16%, a specificity (Spe) of 99.07%, and an accuracy (Acc) of 98.91%; for ventricular tachycardia (VT), the sensitivity is 90.45%, the specificity is 99.73%, and the accuracy is 99.09%; for normal sinus rhythms, sensitivity stands at 99.34%, specificity is 98.35%, and accuracy is 98.89%; finally, for other rhythms, the sensitivity is 96.98%, the specificity is 99.68%, and the accuracy is 99.11%. Furthermore, distinguishing between shockable (VF/VT) and non-shockable rhythms yielded a sensitivity of 99.23%, a specificity of 99.74%, and an accuracy of 99.61%. The results show that using tf representations as a form of image, combined in this case with a CNN classifier, raises the classification performance above the results in previous works. Considering that these results were achieved without the preselection of ECG episodes, it can be concluded that these features may be successfully introduced in Automated External Defibrillation (AED) and Implantable Cardioverter Defibrillation (ICD) therapies, also opening the door to their use in other ECG rhythm detection applications.

This paper presents a novel method for non-parametric identification of parameter-varying (PV) Hammerstein systems where the parameters of both the static nonlinearity and the linear dynamics change with a scheduling variable. The proposed method estimates the individual elements of the PV Hammerstein system by describing the static nonlinear element using a PV Chebychev basis expansion and the linear dynamic element as a non-parametric PV impulse response function with Laguerre basis expansion. The method was validated using Monte-Carlo simulations of a PV Hammerstein model of ankle reflex stiffness during large movements. Results demonstrated that the method is simple, effective, and robust; it accurately identified the PV Hammerstein system in the presence of relatively large colored, time-varying measurement noise (average SNR of 15dB). These results demonstrate the two main contributions of the method: (1) It accurately and precisely estimates both the linear and nonlinear elements of the PV Hammerstein cascade as they vary with a scheduling variable; and (2) Models identified with the method accurately predict the response of the PV Hammerstein system to novel scheduling variable trajectories. To our knowledge, no other Hammerstein method can achieve these.

Precise surgical excision of brain tumors depends on the surgeon’s ability to accurately differentiate tumors from healthy brain tissues. We have developed an automated system integrated with biochips, an actuation unit, and electronics to measure the electrical resistivity of ex vivo human brain tissues for differentiating normal and tumor. The electrical resistivity of fresh (n = 48), formalin-fixed for one week (n = 48), and long-term (six months) formalin-fixed (n = 27) healthy human brain samples from different anatomical regions and tumor samples (glioma n = 6; fresh, formalin-fixed for one week, and formalin-fixed for six months) were measured using the automated system. The resistivity of glioma (22.4 ± $1.6~\Omega$ .cm) was significantly lesser than the normal region (98.6 ± $1.4~\Omega$ .cm) for fresh tissue samples (p = 5e-8). The trend of lower resistivity of glioma compared to normal was preserved after one week and six months of formalin fixation. We also report the effects of heterogeneity of normal brain tissue and formalin-fixation on the electrical properties of tissues. White matter regions were found to have higher resistivity compared to grey matter regions. The heterogeneity associated with grey matter regions was lower than the white matter regions. Formalin-fixation was observed to increase the magnitude of resistivity measured while retaining the observed trend across the different regions of the brain and tumors. The study shows that the electrical resistivity could potentially be used as an additional biomarker for delineating normal from the tumor.

Biomechanical energy harvesting shows great potential in the fields of smart electronics and biomedical Internet of Things. However, it is a significant challenge to develop a biomechanically driven energy harvester with high output power and fast charging as a sustainable power source for extended practical applications. Herein, an ultrahigh‐output triboelectric and electromagnetic hybrid generator (UHO‐TEHG) is developed to efficiently harvest biomechanical energy and provide self‐powered systems for numerous applications. The Halbach magnet array is used to concentrate additional magnetic flux in the coil and significantly enhance electromagnetic performance, while the novel poly(ethylene oxide) nanofibers enhance the triboelectric performance. Through the implementation of a mechanical spring‐mass model, and rational integration of electromagnetic and triboelectric generators, the UHO‐TEHG can provide an excellent output power of 1.02 W. Compared with existing mechanical energy harvesting devices, the fabricated device exhibits a much faster battery charging performance. Experimental results reveal remarkable performance related to biomechanical energy harvesting during walking, running, hiking, mountaineering, and self‐powered wireless human motion sensor. Real‐time charging of smartphones, smartwatches, Buds Live, and iTag via customized power management circuits is demonstrated. In addition, the fabricated UHO‐TEHG demonstrates the capability to power healthcare monitoring systems using a laser‐induced hierarchical carbon nanofiber‐based e‐tattoo sensor. [ABSTRACT FROM AUTHOR]

We study a partially observable nonlinear stochastic system with unknown parameters, where the given time scales of the states and measurements may be distinct. The proposed setting is inspired by disease management, particularly leukemia. [ABSTRACT FROM AUTHOR]

There is an immense necessity of several kilo bytes(kb) of embedded memory for Biomedical systems which typically operate in the sub-threshold domain with perfect efficiency. SRAM (Static Random Access Memory) dominates the total power consumption and the overall silicon area, as 70% of the die has been occupied by them. This paper proposes the design of a transmission gate-based SRAM cell for biomedical application eliminating the use of peripheral circuitry during the read operation. It commences the read operation directly with the help of transmission gates with which the data stored in the storage nodes can be read, instead of using the precharge and sense amplifier circuits which suits better for the implantable devices. This topology offers smaller area, reduced delay, low power consumption, and improved data stabilization in the read operation. The cell is implemented in 45nm CMOS technology operated at 0.45v. [ABSTRACT FROM AUTHOR]

In recent years, there has been an enormous demand for the security of image multimedia in healthcare organizations. Many schemes have been developed for the security preservation of data in e-health systems however the schemes are not adaptive and cannot resist chosen and known-plaintext attacks. In this contribution, we present an adaptive framework aimed at preserving the security and confidentiality of images transmitted through an e-healthcare system. Our scheme utilizes the 3D-chaotic system to generate a keystream which is used to perform 8-bit and 2-bit permutations of the image. We perform pixel diffusion by a key-image generated using the Piecewise Linear Chaotic Map (PWLCM). We calculate an image parameter using the pixels of the image and perform criss-cross diffusion to enhance security. We evaluate the scheme's performance in terms of histogram analysis, information entropy analysis, statistical analysis, and differential analysis. Using the scheme, we obtain the average Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI) values for an image of size 256 × 256 equal to 99.5996 and 33.499 respectively. Furthermore, the average entropy is 7.9971 and the average Peak Signal to Noise Ratio (PSNR) is 7.4756. We further test the scheme on 50 chest X-Ray images of patients having COVID-19 and viral pneumonia and found the average values of variance, PSNR, entropy, and Structural Similarity Index (SSIM) to be 257.6268, 7.7389, 7.9971, and 0.0089 respectively. Furthermore, the scheme generates completely uniform histograms for medical images which reveals that the scheme can resist statistical attacks and can be applied as a security framework in AI-based healthcare. [ABSTRACT FROM AUTHOR]

Healthy lifestyle is one of the most important factors in the prevention of premature deaths, chronic diseases, productivity loss, obesity, and other economic and social aspects. The workplace plays an important role in promoting the physical activity and wellbeing of employees. Previous studies are mostly focused on individual interviews, various questionnaires that are a conceptual information about individual health state and might change according to question formulation, specialist competence, and other aspects. In this paper the work ability was mostly related to the employee’s physiological state, which consists of three separate systems: cardiovascular, muscular, and neural. Each state consists of several exercises or tests that need to be performed one after another. The proposed data transformation uses fuzzy logic and different membership functions with three or five thresholds, according to the analyzed physiological feature. The transformed datasets are then classified into three stages that correspond to good, moderate, and poor health condition using machine learning techniques. A three-part Random Forest method was applied, where each part corresponds to a separate system. The obtained testing accuracies were 93%, 87%, and 73% for cardiovascular, muscular, and neural human body systems, respectively. The results indicate that the proposed work ability evaluation process may become a good tool for the prevention of possible accidents at work, chronic fatigue, or other health problems.

This review summarizes recent progress in developing wireless, batteryless, fully implantable biomedical devices for real-time continuous physiological signal monitoring, focusing on advancing human health care. Design considerations, such as biological constraints, energy sourcing, and wireless communication, are discussed in achieving the desired performance of the devices and enhanced interface with human tissues. In addition, we review the recent achievements in materials used for developing implantable systems, emphasizing their importance in achieving multi-functionalities, biocompatibility, and hemocompatibility. The wireless, batteryless devices offer minimally invasive device insertion to the body, enabling portable health monitoring and advanced disease diagnosis. Lastly, we summarize the most recent practical applications of advanced implantable devices for human health care, highlighting their potential for immediate commercialization and clinical uses., (© 2023. The Author(s).)

This paper is motivated by a class of positive systems with an input that is subject to an unknown state-dependent power law delay as well as saturation. For example, biological networks have non-negative protein concentration states. Mass action kinetics in these systems result in power law behavior, while complex interactions cause signal propagation delays. Incomplete network characterization makes delay state-dependence unknown. Manipulating network activity via modulated protein concentrations to attain desired performance is restricted by upper-bounds on concentration actuator authority. Here, an innovative control law exploits system dynamics to compensate for control domain restrictions. A Lyapunov stability analysis establishes that the reference tracking error of the closed-loop system is uniformly ultimately bounded. Numerical simulations on a human coagulation model show controller efficacy and better performance compared to the relevant literature. This example application steps toward personalized, closed-loop treatments for trauma coagulopathy, which currently has 30% mortality with open-loop clinical approaches. [ABSTRACT FROM AUTHOR]

Structural identifiability analysis of fractional-order equivalent circuit models (FO-ECMs) obtained through electrochemical impedance spectroscopy (EIS) remains as a challenging problem. Aside from practical challenges such as measurement noise and the selection of excitation signals, no widely accepted analytical or numerical proof addressing the structural identifiability of impedance spectroscopy FO-ECMs exists. Through the use of coefficient mapping technique, this paper proposes novel computationally-efficient algebraic equations for numerical structural identifiability analysis of a widely used FO-ECM with Grünwald–Letnikov fractional derivative approximation and two constant phase elements (CPEs) including the Warburg term. The effects of the length of the data and sampling time on the proposed method for the structural identifiability analysis of the two-CPE FO-ECMs are discussed along with two examples and a discussion of the results. [ABSTRACT FROM AUTHOR]