Journal of Mechanics in Medicine and Biology

Recently, there has been an increasing interest in upper limb prosthetic hand control, but most of these studies focused on the detection of exact motion intentions. Few results have been reported on the perturbation resistance of prostheses, which is extremely important for their uses in daily life. Understanding the human upper limb reflexive mechanism would provide an efficient solution to improve the perturbation resistance of prostheses. In this study, upper limb reflexive responses to force perturbation were measured in the conditions as determined from activities of daily living. Specifically, not only muscle activities, but also joint angle changes of the reflexive responses to multi-direction, multi-site perturbation during a reaching motion were recorded and analyzed. Results showed a direction-dependency of the upper limb reflexive mechanism and the different roles of shoulder, elbow, and wrist during the responses. The results could be applied to the control system of prosthetic devices for improving their perturbation resistance.

In this paper, an electrocardiogram (ECG)-based pattern analysis methodology is presented for the detection of artrial flutter and atrial fibrillation using fractal dimension (FD) of continuous wavelet transform (CWT) coefficients of raw ECG signals, sample entropy of heart beat interval time series, and mean heart beat interval features. Accurate diagnosis of atrial tachyarrhythmias is important, as they have different therapeutic options and clinical decisions. In view of this, we have made an attempt to develop a discrimination mechanism between artrial flutter and atrial fibrillation. The methodology consists of mean heart beat interval detection using Pan Tompkins algorithm, calculation of sample entropy of heart beat interval time series, computation of box counting FD from CWT coefficients of raw ECG, statistical significance test, and subsequent pattern classification using different classifiers. Different wavelet basis functions like Daubechies-4, Daubechies-6, Symlet-2, Symlet-4, Symlet-6, Symlet-8, Coiflet-2, Coiflet-5, Biorthogonal-1.3, Biorthogonal-3.1, and Mayer wavelet have been used to compute CWT coefficients. Features are evaluated using statistical analysis and subsequently two-class pattern classification is done using unsupervised (k-means, fuzzy c-means, and Gaussian mixture model) and supervised (error back propagation neural network and support vector machine) techniques. In order to reduce the bias in choosing the training and testing set, k-fold cross validation is used. The obtained results are compared and discussed. It is found that the supervised classifiers provide higher accuracy in comparison to the set of unsupervised classifiers.

This study demonstrates the development of the heart depolarization vector locus cardiogram (HPVLC, from limb leads and a modified Einthoven's triangle) as a diagnostic measure of the left ventricular depolarization strength. Our work involves the reconstruction of the "equivalent heart electrical-activity vector (HAV)" for the QRS complex from limb leads voltages of a sample ECG recording, and plotting the progression of the cardiac vector during the QRS complex.

A realistic visualization of the progression of the equivalent-dipole HAV during the QRS complex is possible by staging the HPVLC of the QRS complex from the onset of the QRS till the end of the depolarization stage. This can enable the characterization of the HPVLC by means of an analytical function. By studying the HPVLC for various electro-cardiological disorders, it is possible to determine the ranges of the analytical function's parameters for normal and disordered electro-cardiological states, for diagnostic purpose.

We have seen that the monitored ECG is theoretically derived from HAV components on the sides of the Einthoven triangle. Nevertheless, in cardiac practice, the monitored ECG is employed in diagnosis of heart diseases. From the ECG, we can obtain the heart rate, and therefrom the heart rate variability, which too has diagnostic applications. Many nonlinear methods have been proposed to analyze ECG and HRV for detection of cardiac abnormalities, using linear and nonlinear methods. Herein, we have shown how HRV signal can be analyzed in terms of four recurrence quantification analysis (RQA) features, which are then combined into an Integrated Index to enable better separation of normal and diabetic subjects.

The aim of this study is the objective assessment of gait abnormalities in diabetic patients and the quantification of the benefits of physical activity in improving gait quality. Patients were equipped with foot-switches and knee goniometers and were asked to walk at their natural pace for 2.5 min. A statistical gait analysis was performed extracting from hundreds of strides the "atypical" cycles, i.e., the cycles which do not show the usual sequence of gait phases (heel contact, flat foot contact, push off, swing), the duration of the heel contact phase, and the knee kinematics in the sagittal plane. A sample population of 27 non-neuropathic type 2 diabetic patients was examined before and after attending a light-intensity physical activity program that lasted for four months. A fuzzy classifier was used to assign a score to the gait abnormalities of each patient in baseline conditions and after the program completion. More than 50% of the subjects showed a significant reduction in their gait abnormalities and on average, the most frequent improvements were the reduction of atypical cycles and heel contact duration. Furthermore, we found that in basal conditions, the left side is more affected by gait abnormalities than the right (P < 0.003).

A laboratory centrifuge is a piece of laboratory equipment, driven by a motor, which spins liquid samples at high speed. There are various types of centrifuges, depending on the size and the sample capacity. Like all other centrifuges, laboratory centrifuges work by the sedimentation principle, where the centripetal acceleration is used to separate substances of greater and lesser density. The information for synthesizing the molecules that allow organisms to survive and replicate is encoded in genomic DNA. Extracted and purified genomic DNA is very important for the analysis of single nucleotide polymorphisms (SNPs), disease states, and for many other multiplex and real-time PCR applications. The aim of this work is to design a genomic DNA extraction system that satisfies downstream application needs necessary for the successful completion of experiments and able to isolate a purified genomic DNA from many sources from bacteria to humans and also is able to encompass tissues from blood to muscle and from leaf to seed. Results revealed that the proposed system works with high efficiency and spins at up to 12,000 RPM to facilitate separation of the different phases of the extraction.

The mechanics of beam vibration is of fundamental importance in understanding the shift of resonant frequency of microcantilever and nanocantilever sensors. Unlike the simpler Euler–Bernoulli beam theory, the Timoshenko beam theory takes into consideration rotational inertia and shear deformation. For the case of microcantilevers and nanocantilevers, the minute size, and hence low mass, means that the topmost deviation from the Euler–Bernoulli beam theory to be expected is shear deformation. This paper considers the extent of shear deformation for varying Poisson's ratio of the beam material, with special emphasis on solids with negative Poisson's ratio, which are also known as auxetic materials. Here, it is shown that the Timoshenko beam theory approaches the Euler–Bernoulli beam theory if the beams are of solid cross-sections and the beam material possess high auxeticity. However, the Timoshenko beam theory is significantly different from the Euler–Bernoulli beam theory for beams in the form of thin-walled tubes regardless of the beam material's Poisson's ratio. It is herein proposed that calculations on beam vibration can be greatly simplified for highly auxetic beams with solid cross-sections due to the small shear correction term in the Timoshenko beam deflection equation.

Epilepsy is a common neurological disorder characterized by recurrence seizures. Alcoholism causes organic changes in the brain, resulting in seizure attacks similar to epileptic fits. Hence, it is challenging to differentiate the cause of fits as epileptic or alcoholism, which is important for deciding on the treatment in the neurology ward. The focus of this paper is to automatically differentiate epileptic, normal, and alcoholic electroencephalogram (EEG) signals. As the EEG signals are non-linear and dynamic in nature, it is difficult to tell the subtle changes in these signals with the help of linear techniques or by the naked eye. Therefore, to analyze the normal (control), epileptic, and alcoholic EEG signals, two non-linear methods, such as recurrence plots (RPs) and then recurrence quantification analysis (RQA) are adopted. Approximately 10 RQA parameters have been used to classify the EEG signals into three distinct classes, i.e., normal, epileptic, and alcoholic. Six classifiers, such as support vector machine (SVM), radial basis probabilistic neural network (RBPNN), decision tree (DT), Gaussian mixture model (GMM), k-nearest neighbor (kNN), and fuzzy Sugeno classifiers have been developed to accomplish this task. Results show that the GMM classifier outperformed the other classifiers with a classification sensitivity of 99.6%, specificity of 98.3%, and accuracy of 98.6%.

Heart failure (HF), one of the most common diseases in the world, causes left ventricular dysfunction (LV) and high mortality. HF patients are stratified into two groups based on their LV ejection fraction (EF) — HF with normal EF (HFNEF) and with reduced EF (HFREF). EF is a commonly used measure of LV contractile performance. Despite preserved EF, a complex mixture of systolic and diastolic dysfunction and variable degrees of LV remodelling underlying HFNEF poses challenges to diagnose and provide pharmacological treatment for HFNEF. In recent years, the velocity flow mapping (VFM) technique has been developed to generate flow velocity vector fields by post-processing color Doppler echocardiographic (echo) images. We aim to obtain the intra-LV blood flow patterns for patients with HFNEF, HFREF, and normal subjects, in order to characterize the LV performance outcomes of normal subjects and HF patients.

Two subjects from each group of HFNEF, HFREF, and normal underwent echo scans. Velocity vector distributions throughout the cardiac cycle were then analysed using the VFM technique. In each subject, the outflow rate during systole, inflow rate during diastole, as well as wall stress-based pressure-normalized contractility index, dσ*/dt_max, were computed and compared among the groups.

This study demonstrated the use of VFM to visualize LV blood flow patterns in HF patients and normal subjects. Different patterns of flow distributions were observed in these subjects. In HFREF patients, dσ*/dt_max, the peak outflow rate and peak inflow rate during early filling were markedly reduced. In HFNEF patients, peak outflow rates were increased compared to those of normal subjects.

This paper presents a fully integrated system for implanted micro-system devices with efficient power and data transfer based on amplitude shift keying (ASK) modulation techniques. A proposed efficient class-E power amplifier is presented. The design presents a full transcutaneous inductive powering system to transfer power and data from an outside human body to implanted devices such as implanted microsystems to stimulate and monitor the nerves and muscles with low band frequency of 13.56 MHz according to the industrial–scientific–medical (ISM) band to avoid the tissue damage. A novel ASK demodulator powered with 1.9 V is proposed with a power recovery system. The modulation index is 13% and the modulation rate 7.3% with data rate 1 Mbit/s, and with power efficiency 66%. The system has been designed using 0.35-μm fabricated CMOS technology. The mathematical model is given and the design is simulated using OrCAD PSpice 16.2 software tool and for real-time simulation, the electronic workbench MULISIM 11 has been used to simulate the class-E power amplifier.

In recent years, the application of discrete wavelet transform (DWT) on biosignal processing has made a significant impact on developing several applications. However, the existing user-friendly software based on graphical user interfaces (GUI) does not allow the freedom of saving the wavelet coefficients in .txt or .xls format and to analyze the frequency spectrum of wavelet coefficients at any desired wavelet decomposition level. This work describes the development of mathematical models for the implementation of DWT in a GUI environment. This proposed software based on GUI is developed under the visual basic (VB) platform. As a preliminary tool, the end user can perform "j" level of decomposition on a given input signal using the three most popular wavelet functions — Daubechies, Symlet, and Coiflet over "n" order. The end user can save the output of wavelet coefficients either in .txt or .xls file format for any further investigations. In addition, the users can gain insight into the most dominating frequency component of any given wavelet decomposition level through fast Fourier transform (FFT). This feature is highly essential in signal processing applications for the in-depth analysis on input signal components. Hence, this GUI has the hybrid features of FFT with DWT to derive the frequency spectrum of any level of wavelet coefficient. The novel feature of this software becomes more evident for any signal processing application. The proposed software is tested with three physiological signal — electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG) — samples. Two statistical features such as mean and energy of wavelet coefficient are used as a performance measure for validating the proposed software over conventional software. The results of proposed software is compared and analyzed with MATLAB wavelet toolbox for performance verification. As a result, the proposed software gives the same results as the conventional toolbox and allows more freedom to the end user to investigate the input signal.

An electrocardiogram (ECG) signal represents the sum total of millions of cardiac cells' depolarization potentials. It helps to identify the cardiac health of the subject by inspecting its P-QRS-T wave. The heart rate variability (HRV) data, extracted from the ECG signal, reflects the balance between sympathetic and parasympathetic components of the autonomic nervous system. Hence, HRV signal contains information on the imbalance between these two nervous system components that results in cardiac arrhythmias. Thus in this paper, we have analyzed HRV signal abnormalities to determine and classify arrhythmias. The HRV signals are non-stationary and non-linear in nature. In this work, we have used continuous wavelet transform (CWT) coupled with principal component analysis (PCA) to extract the important features from the heart rate signals. These features are fed to the probabilistic neural network (PNN) classifier, for automated classification. Our proposed system demonstrates an average accuracy of 80% and sensitivity and specificity of 82% and 85.6%, respectively, for arrhythmia detection and classification. Our system can be operated on larger data sets. Our CWT–PCA analysis resulted in eigenvalues which constituted the HRV signal analysis parameters. We have shown and plotted the distribution of the parameters' mean values and the standard deviation for arrhythmia classification. We found some overlap in the distribution of these eigenvalue parameters for the different arrhythmia classes, which mitigates the effective use of these parameters to separate out the various arrhythmia classes. Therefore, we have formulated a HRV Integrated Index (HRVID) of these eigenvalues, and determined and plotted the mean values and standard deviation of HRVID for the various arrhythmia classifications. From this information, it can be seen that the HRVID is able to distinguish among the various arrhythmia classes. Hence, we have made a case for the employment of this HRVID as an index to effectively diagnose arrhythmia disorders.

A large section of the world's population is affected by diabetes mellitus (DM), commonly referred to as "diabetes." Every year, the number of cases of DM is increasing. Diabetes has a strong genetic basis, hence it is very difficult to cure, but can be controlled with medications to prevent subsequent organ damage. Therefore, early diagnosis of diabetes is very important. In this paper, we examine how diabetes affects cardiac health, which is reflected through heart rate variability (HRV), as observed in electrocardiography (ECG) signals. Such signals provide clues for both the presence and severity of diabetes as well as diabetes-induced cardiac impairments. Heart rate (HR) is a non-linear and non-stationary signal. Thus, extracting useful information from HRV signals is a difficult task. We review several sophisticated signal processing and information extraction methods in order to establish measurable relationships between the presence and the extent of diabetes as well as the changes in the HRV signals. Furthermore, we discuss a typical range of values for several statistical, geometric, time domain, frequency domain, time–frequency, and non-linear features for HR signals from 15 normal and 15 diabetic subjects. We found that non-linear analysis is the most suitable approach to capture and analyze the subtle changes in HRV signals caused by diabetes.

Computer-aided magnetic resonance (MR) fluid motion tracking and cardiac vorticity quantification of the right atrial flow is implemented in this study to suggest a new method for the diagnosis of an atrial septal defect (ASD). MR signals of blood moving in a cardiac chamber can be represented as an image and vary in intensity at every consecutive cardiac phase. A method was devised to perform flow analysis using MR imaging without modification of scan mode or protocol that allows velocity encoding. A single vortex or multiple vortices may appear in the cardiac chamber. However, velocity fields in any flow scenario are normally unable to reveal information for a concise analysis; therefore, in addition to velocity maps, vorticity flow maps on which the velocity field is superimposed are presented. Through a case study, the difference in vortex strengths pre- and post-atrial septal occlusion can be examined, and the results can be verified using computational fluid dynamics. Based on this framework, the degree of vortical flow was assessed for the right atrium of a subject with atrial septal defect. A relationship can be established between right atrial vorticity and the ASD. As such, there is clear utility of the developed system in its potential as a prognostic and investigative tool for the quantitative assessment of cardiac abnormalities parallel to examining magnetic resonance images.

This paper presents a new random noise cancellation technique for cancelling muscle artifact effects from ECG using ALE in the transformed domain. For this a transform domain variable step size griffith least mean square (TVGLMS) algorithm is proposed. The technique is based on the adaptation of the gradient of the error surface. The method frees both the step size and the gradient from observation noise and reduces the gradient mis-adjustment error. The sluggishness introduced due to the averaging of the gradient in the time domain is overcome by the transformed domain approach. The proposed algorithm uses a discrete cosine transform (DCT)-based signal decomposition due to its improved frequency resolution compared to a discrete Fourier transform (DFT). Furthermore, as the data used symmetrical, DCT usage results in low leakage (bias and variance). The performance of the proposed method has been tested on ECG signals combined with WGN, extracted from MIT database, and compared with several existing techniques like LMS, NLMS, and VGLMS.

The purpose of this paper is to develop a mathematical description of cargo transport in the entrance regions of axons and dendrites. The model accounts for the difference in microtubule (MT) orientation between axons and dendrites: axons have a uniform MT polarity orientation while dendrites have a mixed MT polarity orientation. Because of that, cargos pulled by dynein motors can enter dendrites but cannot enter axons. It is thus assumed that cargos are targeted to axons by associating them with kinesin motors and cargos are targeted to dendrites by associating them with dynein motors. Analytical solutions of the developed equations describing cargo concentrations in the entrance regions of axons and dendrites are obtained.

To understand the physiology of tissue burns for successful clinical treatment, it is important to investigate the thermal behavior of human skin tissue subjected to heat injury. In this paper, a fundamental solution-based hybrid finite element formulation is proposed for numerically simulating steady-state temperature distribution inside a multilayer human skin tissue during burning. In the present approach, since only element boundary integrals are involved, the computational dimension is reduced by one as the fundamental solutions used analytically satisfies the bioheat governing equation. Further, in multi-layer skin modeling, the burn is applied via a heating disk at constant temperature on a part of the epidermal surface of the skin tissue. Numerical results from the proposed approach are firstly verified by comparing them with exact solutions of a simple single-layered model or the results from conventional finite element method. Thereafter, a sensitivity analysis is carried out to reveal the effect of biological and environmental parameters on temperature distribution inside the skin tissue subjected to heat injury.

Pulse wave velocity (PWV), based on two-site measurement, is a well-known predictor of arterial stiffness. Interest focused increasingly on simplifying the PWV measurement results in attempts at determining it at a single site. We aimed to validate a new tonometric method (IIM-2010A) for assessment of PWV at radial artery in healthy subjects <65 years of age. PWV measurements were performed in 46 healthy adults (25 men and 21 women) aged 21–65 years (39.6 ± 15.5 years) using Complior device and IIM-2010A respectively. In a subgroup of 21 humans, the measurements were repeated after 1 week using IIM-2010A with the same protocol. There was a strong correlation between PWV obtained by IIM-2010A and PWV obtained by Complior, as well as between pulse transit time (PTT) measurements (r = 0.79 and r = 0.85, respectively, P < 0.01 for both). Although PTT was significantly lower measured by IIM-2010A, no significant difference was found in PWV. The mean difference of PWV with SD was -0.1 ± 1.2 m/s between two repeated measurements at intervals of 1 week. Bland–Altman's plot indicated no trend for the reproducibility of measurements to vary with their underlying mean value. Intraclass correlation coefficient (= 0.87) confirmed this excellent week-to-week reproducibility of PWV. The method provides a simple, easily-obtainable, and reproducible measurement of PWV in young and middle-aged subjects, and has potential to detect premature arterial aging for the management of primary prevention.

Footwear is an extremely important clothing item worn by all individuals. Currently, there is insufficient research regarding the influence of dress shoes on standing stability and energy consumption while walking. Therefore, the aim of this study was to evaluate the influence of dress shoes on the performance of normal subjects based on stability and energy consumption analysis. Fifteen normal subjects were recruited in this research study to stand and walk with and without shoes. The stability of the subjects in quiet standing was measured by the use of a force plate based on center of pressure (COP) sway. The energy consumption was evaluated by a heart rate monitoring system (Polar Electro) based on the physiological cost index (PCI). The mean values of PCI while walking with and without shoes were 0.29 ± 0.117 and 0.265 ± 0.112 beats/m, respectively (p-value > 0.05). The amplitudes of COP sways in the mediolateral and anteroposterior directions were 10.4 ± 3.5 and 25 ± 6.92 mm while standing with shoes and 9.3 ± 2.84 and 22.5 ± 5.25 mm in barefoot standing, respectively (p-value > 0.05). It can be concluded that wearing dress shoes does not influence the performance of subjects while standing or walking.

The present study investigated the adequacy of the interaction between the center of mass (COM) and the center of pressure (COP) for maintaining dynamic stability during Tai Chi Chuan (TCC) Push Hands movements in a fixed stance. The COM of the whole body and COP were calculated. Four TCC experts, with 10.3 ± 1.7 years' experience in the Push Hands technique, and 4 TCC beginners, with 2.5 ± 1.3 years' Push Hands experience, were recruited. An Expert Vision Eagle motion analysis system collected kinematic data and 4 Kistler force plates collected the ground reaction force data. The expert group of TCC practitioners showed a significantly more vertical (P = 0.001) direction in the neutralizing circle, and significantly larger values for anterior–posterior (A–P) (P = 0.006) and vertical (P = 0.0004) displacement in the enticing circle, than the beginner group. Compared with the beginner group, the expert group demonstrated significantly greater velocity A–P (P = 0.001) and vertical (P = 0.001) COM displacements in the enticing circle. A significant extent main effect (P = 0.0028) was observed for the COP_A–P excursion between the expert and beginner groups during Push Hands movements. The greater A–P force generated by both groups during the initiation of the Push Hands cycle probably reflects the more rapid and forward-oriented nature of this movement. The TCC beginners might have difficulties with movement transfers because of disruptions in the temporal sequencing of the forces. Overall, results indicated that the initial experience-related differences in COM transfers are reflected in the Push Hands movement cycle.

The purpose of this study was to compare kinematics and kinetics during walking for healthy subjects using unstable shoes with different designs. Ten subjects participated in this study, and foot biomechanical data during walking were quantified using motion analysis system and a force plate. Data were collected for unstable shoes condition after accommodation period of one week. With soft material added in the heel region, the peak impact force was effectively reduced when compared among similar shapes. In addition, the soft material added in the rocker bottom showed more to be in dorsiflexed position during the initial stance. The shoe with three rocker curves design reduced the contact area in the heel strike, which may result in increasing human body forward speed. Further studies shall be carried out after adapting to long periods of wearing unstable shoes.

A cost-effective method that integrates high-resolution morphology using an atomic force microscope and immunofluorescence imaging for measuring the local mechanical properties of a cell was developed. By considering the normal indentation conditions and the distribution of the underlying cytoskeleton, a criterion for selecting indentation sites was proposed. PC-12 cells cultivated under normal and high D-glucose medium are employed to demonstrate the applicability of the proposed method. The apparent Young's modulus for each indentation site was estimated by fitting the data with a pyramidal punch contact mechanics model. The results showed that the cell bodies cultivated in the high D-glucose medium were higher but their growth cones were shorter than those cultivated in a normal medium. The Young's moduli of the growth cones were positively correlated with the density of the actin filament in the cytoskeleton. The Young's moduli at the growth cone and the nucleus region of cells cultivated in the high D-glucose medium were higher and lower, respectively, than those of the control group. The results demonstrated the integrated method could correlate local mechanical properties and distribution of actin filament of the growth cone of PC-12 cell.

This paper presents a novel method for recognizing human daily activity by fusion multiple sensor nodes in the wearable sensor systems. The procedure of this method is as follows: firstly, features are extracted from each sensor node and subsequently reduced in dimension by generalized discriminant analysis (GDA), to ensure the real-time performance of activity recognition; then, the reduced features are classified with the multiclass relevance vector machines (RVM); finally, the individual classification results are fused at the decision level, in consideration that the different sensor nodes can provide heterogeneous and complementary information about human activity. Extensive experiments have been carried out on Wearable Action Recognition Database (WARD). Experimental results show that if all the five sensor nodes are fused with the adaptive weighted logarithmic opinion pools (WLOGP) fusion rule, we can even achieve a recognition rate as high as 98.78%, which is far more better than the situations where only single sensor node is available or the activity data is processed by state-of-the-art methods. Moreover, this proposed method is flexible to extension, and can provide a guideline for the construction of the minimum desirable system.

In this study, we investigated to which extent Hill-type muscle models can explain the electro-mechanical delay (EMD). The EMD is a phenomenon that has been well examined in muscle experiments. The EMD is the time lag between a change in muscle stimulation and the subsequent measurable change in muscle force. A variety of processes as, e.g., signal conduction and interaction of contractile and elastic muscle structures contribute to the EMD. The relative contributions of the particular processes have not been fully unveiled so far. Thereto, we simulated isometric muscle contractions using two Hill-type muscle models. Their parameters were extracted from experiments on the cat soleus muscle. In agreement with literature data, predicted EMD values depend on muscle-tendon complex (MTC) length and increase when reducing MTC lengths. The highest EMD values (28 and 27 ms) occur at the lowest MTC length examined (78% of optimal length). Above optimal MTC length, we find EMD saturation (2 ms) in one model. In the other model, the EMD slightly re-increases up to 9 ms at the highest length examined (113% of optimal length). The EMD values predicted by the two models were then compared to EMD values found in the same experiments from which the muscle parameters were extracted. At optimal MTC length, the EMD values, mapping ion release and visco-elastic interactions, predicted by both models (3.5 and 5.5 ms) just partly account for the measured value (15.8 ms). The biggest share (about 9 ms) of the remaining 11 ms can be attributed to signal conduction along the nerve and on the muscle surface. Further potential sources of delayed force generation are discussed.

Patellofemoral arthroplasties are desirable when treating isolated patellofemoral osteoarthritis, due to preservation of the tibiofemoral joint. Since few studies report on new commercial patellofemoral prosthesis biomechanics, a musculoskeletal model enabling analysis of subject-specific knee biomechanics was used to compare four patellofemoral replacement systems (A, B, C, and D) to one another. The prostheses were implanted according to manufacturer guidelines, after which the knee flexed and extended under active muscle loading. An increased patellotrochlear index enabled early patella-trochlear groove engagement. The resurfaced patellae were stable in mediolateral shift and anteroposterior displacement, but only Prosthesis A and D provided a smooth transition between the distal prosthesis border and femoral cartilage. A reduction in the anteroposterior condylar distance displaced the patella posteriorly, resulting in reduced peri-patellar soft tissue tension but an increased patella tendon–quadriceps tendon ratio. The tibial tubercle–trochlear groove distance became pathologic in all replacements. The patella will be stable irrespective of the prosthesis used, but Prosthesis A and D seem to provide a better fit to the trochlear groove anatomy. The increased tibial tubercle–trochlear groove distance emphasizes the importance of extensor alignment in combination with the placement of the prosthesis: an increased Q-angle might lead to excessive lateral wear on the patella button. The extensor mechanism load will increase post-surgery based on the rise in the patella tendon–quadriceps tendon ratio which points to a reduced moment arm. This work provides insight into the dynamic biomechanical function and the design of current commercial patellofemoral replacement systems.

The primary clinical assessments show that single implant–retained dentures (SIDs) are not worse than two implant–retained dentures (TIDs), although the determination of SID biomechanics is still insufficient. The aim of this work was to determine the loading of commonly used denture attachments that occurs while bearing occlusal forces on SIDs. Finite element method analyses, which took into account the possibility of dentures detaching and sliding on the mucous membrane surface, were used. In the contact calculations conducted, an augmented multiplier Lagrangian method with a classical linear friction model was used. We assumed denture-loading conditions that included oblique mastication forces. The distribution of the occlusal loads between the mucous membrane–bearing area and a denture attachment was examined for economical denture solutions with solitary attachments. Variations in denture movement restrictions among the most typical ball, stud, and axially resilient attachments only insignificantly influenced lateral loads borne by single implantological supports. Axial mobility does not reduce the load on the attachments because mastication loads induce denture settlement that is oblique to the implant axis. The assumption of denture loading with vertical forces leads to a serious underestimation of the loads on implantological supports.

This article studies the hydromagnetic peristaltic flow of couple stress fluids through the gap between two concentric channels containing a Darcian porous medium, with the inner channel being rigid. A sinusoidal wave propagates along the outer channel. Long wavelength and low Reynolds number assumptions are used. The effects of couple stress parameter, magnetic field, permeability, and the channel ratio width on pressure and frictional forces on the inner and outer channels are depicted graphically. Mechanical efficiency and trapping are also studied. Pressure diminishes with increasing coupling and permeability parameters whereas it increases with Hartmann number and channel width ratio. Applications of the model include transport of complex bio-waste fluids and magnetic field control of gastro-intestinal disorders.

In this work we study the effect of asymmetry and axial prestraining on the maximum effective mechanical stress in relatively small human abdominal aortic aneurysm (AAA) during a cardiac cycle. Our model is based on the fluid-structure interaction (FSI) methodology. The arterial wall is modeled using large strain and large deformation formulation, with hyperelastic material behavior, and isotropic nonlinear strain energy function (SEF) fitted to the averaged data set of biaxial tests of AAA tissue specimens. The results confirm that the magnitude of the maximum von Mises stress increases significantly with asymmetry and decreases with aneurysmal length. It is found that the amplitude in variation of the maximum effective stress changes in the same manner as the maximum stress. These results indicate that a short asymmetric AAA is under significant dynamic stress, which might be one of the important factors in a progressive AAA growth over time. It is also found that, in most considered cases, magnitude and amplitude of the maximum effective stress of an AAA is smaller when the artery is pre-stretched in axial direction, while a pre-stretched artery with aneurysm has slightly larger relative volume change of lumen during cardiac cycle than the unstretched AAA.

During fetal development the morphology and function of the organs and tissues is determined. An example occurs with the formation of the cerebral cortex. On the external surface of the brain there are numerous folds (gyri, sulci, and fissures) that determine brain function. The exact cause for the formation of patterns of these folds is unknown. This article proposes a reaction-diffusion model in conjunction with a process of surface mechanical strain to explain the morphogenesis of the superficial structure of the brain.The model is solved using finite elements. There have been tests done on the formation of brain patterns through the reaction-diffusion equations with parameters in the space of Turing and by random mechanical strain. Several numerical examples have been developed that show an acceptable correlation between the results and clinical reality. With the model we can represent, qualitatively, the formation of the cerebral cortex by the proposed model. The model can approximate, and explain, lissencephaly and polymicrogyria, diseases that develop in the cerebral cortex and lead to medical complications to sufferers.

Eye blink artifact, the main contamination in electroencephalography (EEG), brings serious problems for the analysis of EEG data. In this paper, an online method for eye blink artifact removal is presented. Canonical correlation analysis (CCA) is used to decompose the recorded signals containing several-channel EEG and one-channel vertical electrooculography (EOG). The identification of the artifactual component is fully automatically implemented based on evaluating the similarity between the reference EOG and decomposed CCA components. This method was compared with an independent component analysis based technique on a synthetic data set and achieved comparable performance for removing eye blink artifact. Moreover, the CCA based method is less time-consuming. The proposed method was finally implemented with Labview for removing eye blink artifact in online test. The online experiment results show that the proposed method could fulfill the identification and suppression of eye blink artifact from contaminated EEG in real-time.

In this study, the effects of threshold variation in image segmentation of micro CT images of cancellous bone in the determination of the architectural parameters and stiffness were investigated. A total of 42 samples of 6 × 6 × 6 mm³ cubes with threshold values set between 500–1100 greyscale in increment of 100 of CT images of six human C5 vertebral bodies were analyzed. Threshold value of 800, based on Otsu's method, was set for the control group. From various threshold values, the respective architectural parameters, and the corresponding stiffness in three orthotropic directions (Exx, Eyy, Ezz) of each cube were computed from the voxel-based micro-finite element models under compressive simulation. The results showed that 1% variation of threshold value resulted in a 3.4% variation in BV/TV, 2% in Tb.N, 3.1% in Tb.Th, 2.9% in BS/BV, 1.8% in Tb.Sp, 29.2% in Exx, 28.7% Eyy and 27.7% in Ezz. Statistical analysis showed that 2.9% threshold variation caused significant change in BV/TV, Tb.Th, Exx, Eyy and Ezz values. The study shows that with threshold variation of more than 2.9%, significant differences in the architectural parameters and stiffness compared to those based on Otsu's method.

Analyzes has been carried out for the two-dimensional whooping cough model. The solution of the arising nonlinear initial/boundary value problem is computed by a homotopy analysis method (HAM). Convergence of the derived solution is highlighted. The plotted graphs show great confidence into the used methodology of solution.