For describing the state of the wrist, either the force or movement of wrist can be measured as the training target in the simultaneous electromyography control. However, the relationship between the force and movement is so complex that only the force or movement is not precise enough to describe its actual situations. In this paper, we propose a novel platform that can acquire three degrees of freedom (DOF) wrist motion/force synchronously with multi-channel electromyography signals in a hemi-constraint way. The self-made wrist force-movement mapping device establishes a stable relationship between the wrist movement and force. Meanwhile, the elicited wrist movement can be directly fed back to the subjects via laser cursor. The information of the cursor can directly reflect the 3-DOF movement of the wrist without any decoupling algorithms. Through this platform, the support vector regression model learned from the training data can well predict the arbitrary combinations of 3-DOF wrist movements. The cross-validation result indicates that the regression accuracy of free 3-DOF movements can reach a similar performance to that of 2-DOF regular movements (in terms of R2, regular movement vs. free movement, p?>?0.1).
The aim of this study was to fully characterize the mechanical behavior of an external hexagonal implant connection (ø3.5 mm, 10-mm length) with an in vitro study, a three-dimensional finite element analysis, and a probabilistic fatigue study. Ten implant-abutment assemblies were randomly divided into two groups, five were subjected to a fracture test to obtain the maximum fracture load, and the remaining were exposed to a fatigue test with 360,000 cycles of 150?±?10 N. After mechanical cycling, all samples were attached to the torque-testing machine and the removal torque was measured in Newton centimeters. A finite element analysis (FEA) was then executed in ANSYS® to verify all results obtained in the mechanical tests. Finally, due to the randomness of the fatigue phenomenon, a probabilistic fatigue model was computed to obtain the probability of failure associated with each cycle load. FEA demonstrated that the fracture corresponded with a maximum stress of 2454 MPa obtained in the in vitro fracture test. Mean life was verified by the three methods. Results obtained by the FEA, the in vitro test, and the probabilistic approaches were in accordance. Under these conditions, no mechanical etiology failure is expected to occur up to 100,000 cycles.
The biomechanical changes in the spinal column are considered to be the main responsible for rachialgia. Although radiological techniques use ionizing radiation, they are the most applied tools to assess the biomechanics of the spine. To face this problem, non-invasive techniques must be developed. Vertebral Metrics is an ionizing radiation-free instrument designed to detect the 3D position of each vertebrae in a standing position. Using a stereo vision system combined with low intensity UV light, recognition is achieved with software capable of distinguishing fluorescent marks. The fluorescent marks are the skin projection of the vertex of the spinal processes. This paper presents a major development of Vertebral Metrics and its evaluation. It performs a scan in less than 45 s with a resolution on the order of 1 mm, in each spatial direction, therefore, allowing an accurate analysis of the spine. The instrument was applied to patients without associated pathology. Statistically significant differences between consecutive scans were not found. A positive correlation between the 3D positions of each vertebra and the homologous position of the other vertebrae was observed. Using Vertebral Metrics, innovative results can be obtained. It can be used in areas such as orthopedics, neurosurgery, and rehabilitation.
In order to solve the problem of the short lifespan of the neural electrode caused by micro motion, this study designed a novel neural electrode based on lumped compliance compliant mechanism to control different modes of micro-motion in a more effective way. According to the mathematical modeling of the novel neural electrode, the equivalent bending stiffness and equivalent tensile (compression) stiffness were calculated. The results of the finite element analysis based on the mathematical modeling revealed that the novel neural electrode showed excellent micro-motion-attenuation capability. The static analysis results showed that the novel design dramatically reduced the maximum displacement of the brain in 51% and the maximum stress in 41% under longitudinal micro-motion environment. It also effectively reduced the 5.1% maximum stress while maintaining the maximum displacement under lateral micro-motion environment. The experimental results based on the tissue injury evaluation system also confirmed that the novel electrode is more effective in micro-motion attenuation than the reference one. In detail, the strain of the brain tissue caused by the implantation of the neural electrode was decreased by 1.26 to 27.84% at the insertion depth of 3 mm, and 0.522 to 17.24% at the insertion depth of 4.5 mm, which has convinced the effectiveness of the design.
Impedance cardiography is a low-cost noninvasive technique, based on monitoring of the thoracic impedance, for estimation of stroke volume (SV). Impedance cardiogram (ICG) is the negative of the first derivative of the impedance signal. A technique for beat-to-beat SV estimation using impedance cardiography and artificial neural network (ANN) is proposed. A three-layer feed-forward ANN with error back-propagation algorithm is optimized by examining the effects of number of neurons in the hidden layer, activation function, training algorithm, and set of input parameters. The input parameters are obtained by automatic detection of the ICG characteristic points, and the target values are obtained by beat-to-beat SV measurements from time-aligned Doppler echocardiogram. The technique is evaluated using an ICG-echocardiography database with recordings from subjects with normal health in the under-rest and post-exercise conditions and from subjects with cardiovascular disorders in the under-rest condition. The proposed technique performed much better than the earlier established equation-based estimations, and it resulted in correlation coefficient of 0.93 for recordings from subjects with cardiovascular disorders. It may be helpful in improving the acceptability of impedance cardiography in clinical practice.
Vascular morphology characterization is useful for disease diagnosis, risk stratification, treatment planning, and prediction of treatment durability. To quantify the dynamic surface geometry of tubular-shaped anatomic structures, we propose a simple, rigorous Lagrangian cylindrical coordinate system to monitor well-defined surface points. Specifically, the proposed system enables quantification of surface curvature and cross-sectional eccentricity. Using idealized software phantom examples, we validate the method’s ability to accurately quantify longitudinal and circumferential surface curvature, as well as eccentricity and orientation of eccentricity. We then apply the method to several medical imaging data sets of human vascular structures to exemplify the utility of this coordinate system for analyzing morphology and dynamic geometric changes in blood vessels throughout the body.
A 3D convolution neural network (CNN) of deep learning architecture is supplied with essential visual features to accurately classify and segment granulation, necrotic eschar, and slough tissues in pressure ulcer color images. After finding a region of interest (ROI), the features are extracted from both the original and convolved with a pre-selected Gaussian kernel 3D HSI images, combined with first-order models of current and prior visual appearance. The models approximate empirical marginal probability distributions of voxel-wise signals with linear combinations of discrete Gaussians (LCDG). The framework was trained and tested on 193 color pressure ulcer images. The classification accuracy and robustness were evaluated using the Dice similarity coefficient (DSC), the percentage area distance (PAD), and the area under the ROC curve (AUC). The obtained preliminary DSC of 92%, PAD of 13%, and AUC of 95% are promising.
This paper presents the design and simulation of a handheld device for people with hand tremor, such as Parkinson’s and essential tremor patients. This device can be used as a pen for smartphones or as a spoon. The designed system includes two links, which are connected to two servomotors, which are mounted in orthogonal directions. To attenuate the effect of hand tremor on the tip of device, PID and computed torque methods are used to actively control the system. These controllers are used to control the rotation of the motors for moving the links in opposite directions of the hand tremor. Performance of the device with mentioned controllers is studied for different applications and finally, the results of both controllers are discussed and compared. Based on the presented results in this study, the designed device is able to suppress the hand tremor up to 75% during eating and 65% during following a spiral pattern.
The conductivity of brain tissues is not only essential for electromagnetic source estimation (ESI), but also a key reflector of the brain functional changes. Different from the other brain tissues, the conductivity of whiter matter (WM) is highly anisotropic and a tensor is needed to describe it. The traditional electrical property imaging methods, such as electrical impedance tomography (EIT) and magnetic resonance electrical impedance tomography (MREIT), usually fail to image the anisotropic conductivity tensor of WM with high spatial resolution. The diffusion tensor imaging (DTI) is a newly developed technique that can fulfill this purpose. This paper reviews the existing anisotropic conductivity models of WM based on the DTI and discusses their advantages and disadvantages, as well as identifies opportunities for future research on this subject. It is crucial to obtain the linear conversion coefficient between the eigenvalues of anisotropic conductivity tensor and diffusion tensor, since they share the same eigenvectors. We conclude that the electrochemical model is suitable for ESI analysis because the conversion coefficient can be directly obtained from the concentration of ions in extracellular liquid and that the volume fraction model is appropriate to study the influence of WM structural changes on electrical conductivity.
Chronic scapholunate ligament (SL) injuries are difficult to treat and can lead to wrist dysfunction. Whilst several tendon reconstruction techniques have been employed in the management of SL instability, SL gap reappearance after surgery has been reported. Using a finite element model and cadaveric study data, we investigated the performance of the Corella, scapholunate axis (SLAM) and modified Brunelli tenodesis (MBT) techniques. Scapholunate dorsal and volar gap and angle were obtained following virtual surgery undertaken using each of the three reconstruction methods with the wrist positioned in flexion, extension, ulnar deviation and radial deviation, in addition to the ulnar-deviated clenched fist and neutral positions. From the study, it was found that, following simulated scapholunate interosseous ligament rupture, the Corella technique was better able to restore the SL gap and angle close to the intact ligament for all wrist positions investigated, followed by SLAM and MBT. The results suggest that for the tendon reconstruction techniques, the use of multiple junction points between scaphoid and lunate may be of benefit.
With the rapid development of DNA microarray technology, large amount of genomic data has been generated. Classification of these microarray data is a challenge task since gene expression data are often with thousands of genes but a small number of samples. In this paper, an effective gene selection method is proposed to select the best subset of genes for microarray data with the irrelevant and redundant genes removed. Compared with original data, the selected gene subset can benefit the classification task. We formulate the gene selection task as a manifold regularized subspace learning problem. In detail, a projection matrix is used to project the original high dimensional microarray data into a lower dimensional subspace, with the constraint that the original genes can be well represented by the selected genes. Meanwhile, the local manifold structure of original data is preserved by a Laplacian graph regularization term on the low-dimensional data space. The projection matrix can serve as an importance indicator of different genes. An iterative update algorithm is developed for solving the problem. Experimental results on six publicly available microarray datasets and one clinical dataset demonstrate that the proposed method performs better when compared with other state-of-the-art methods in terms of microarray data classification.
Pilon fractures can be caused by high-energy vertical forces which may result in long-term patient immobilization. Many experts in orthopedic surgery recommend the use of a Delta external fixator for type III Pilon fracture treatment. This device can promote immediate healing of fractured bone, minimizing the rate of complications as well as allowing early mobilization. The characteristics of different types of the Delta frame have not been demonstrated yet. By using the finite element method, this study was conducted to determine the biomechanical characteristics of six different configurations (Model 1 until Model 6). CT images from the lower limb of a healthy human were used to reconstruct three-dimensional models of foot and ankle bones. All bones were assigned with isotropic material properties and the cartilages were assigned to exhibit hyperelasticity. A linear link was used to simulate 37 ligaments at the ankle joint. Axial loads of 70 and 350 N were applied at the proximal tibia to simulate the stance and swing phase. The metatarsals and calcaneus were fixed distally in order to prevent rigid body motion. A synthetic ankle bone was used to validate the finite element model. The simulated results showed that Delta3 produced the highest relative micromovement (0.09 mm, 7 μm) during the stance and swing phase, respectively. The highest equivalent von Mises stress was found at the calcaneus pin of the Delta4 (423.2 MPa) as compared to others. In conclusion, Delta1 external fixator was the most favorable option for type III Pilon fracture treatment.
A stable grasp is attained through appropriate hand preshaping and precise fingertip forces. Here, we have proposed a method to decode grasp patterns from motor imagery and subsequent fingertip force estimation model with a slippage avoidance strategy. We have developed a feature-based classification of electroencephalography (EEG) associated with imagination of the grasping postures. Chaotic behaviour of EEG for different grasping patterns has been utilised to capture the dynamics of associated motor activities. We have computed correlation dimension (CD) as the feature and classified with “one against one” multiclass support vector machine (SVM) to discriminate between different grasping patterns. The result of the analysis showed varying classification accuracies at different subband levels. Broad categories of grasping patterns, namely, power grasp and precision grasp, were classified at a 96.0% accuracy rate in the alpha subband. Furthermore, power grasp subtypes were classified with an accuracy of 97.2% in the upper beta subband, whereas precision grasp subtypes showed relatively lower 75.0% accuracy in the alpha subband. Following assessment of fingertip force distributions while grasping, a nonlinear autoregressive (NAR) model with proper prediction of fingertip forces was proposed for each grasp pattern. A slippage detection strategy has been incorporated with automatic recalibration of the regripping force. Intention of each grasp pattern associated with corresponding fingertip force model was virtualised in this work. This integrated system can be utilised as the control strategy for prosthetic hand in the future.
Electrical stimulators are often prescribed to correct foot drop walking. However, commercial foot drop stimulators trigger inappropriately under certain non-gait scenarios. Past researches addressed this limitation by defining stimulation control based on automaton of a gait cycle executed by foot drop of affected limb/foot only. Since gait is a collaborative activity of both feet, this research highlights the role of normal foot for robust gait detection and stimulation triggering. A novel bipedal gait model is proposed where gait cycle is realized as an automaton based on concurrent gait sub-phases (states) from each foot. The input for state transition is fused information from feet-worn pressure and inertial sensors. Thereafter, a bipedal gait model-based stimulation control algorithm is developed. As a feasibility study, bipedal gait model and stimulation control are evaluated in real-time simulation manner on normal and simulated foot drop gait measurements from 16 able-bodied participants with three speed variations, under inappropriate triggering scenarios and with foot drop rehabilitation exercises. Also, the stimulation control employed in commercial foot drop stimulators and single foot gait-based foot drop stimulators are compared alongside. Gait detection accuracy (98.9%) and precise triggering under all investigations prove bipedal gait model reliability. This infers that gait detection leveraging bipedal periodicity is a promising strategy to rectify prevalent stimulation triggering deficiencies in commercial foot drop stimulators.
Needles are advanced tools commonly used in minimally invasive medical procedures. The accurate manoeuvrability of flexible needles through soft tissues is strongly determined by variations in tissue stiffness, which affects the needle-tissue interaction and thus causes needle deflection. This work presents a variable stiffness mechanism for percutaneous needles capable of compensating for variations in tissue stiffness and undesirable trajectory changes. It is composed of compliant segments and rigid plates alternately connected in series and longitudinally crossed by four cables. The tensioning of the cables allows the omnidirectional steering of the tip and the stiffness tuning of the needle. The mechanism was tested separately under different working conditions, demonstrating a capability to exert up to 3.6 N. Afterwards, the mechanism was integrated into a needle, and the overall device was tested in gelatine phantoms simulating the stiffness of biological tissues. The needle demonstrated the capability to vary deflection (from 11.6 to 4.4 mm) and adapt to the inhomogeneity of the phantoms (from 21 to 80 kPa) depending on the activation of the variable stiffness mechanism.
People with transtibial amputation often experience skin breakdown due to the pressures and shear stresses that occur at the limb-socket interface. The purpose of this research was to create a transtibial finite element model (FEM) of a contemporary prosthesis that included complete socket geometry, two frictional interactions (limb-liner and liner-socket), and an elastomeric liner. Magnetic resonance imaging scans from three people with characteristic transtibial limb shapes (i.e., short-conical, long-conical, and cylindrical) were acquired and used to develop the models. Each model was evaluated with two loading profiles to identify locations of focused stresses during stance phase. The models identified five locations on the participants’ residual limbs where peak stresses matched locations of mechanically induced skin issues they experienced in the 9 months prior to being scanned. The peak contact pressure across all simulations was 98 kPa and the maximum resultant shear stress was 50 kPa, showing reasonable agreement with interface stress measurements reported in the literature. Future research could take advantage of the developed FEM to assess the influence of changes in limb volume or liner material properties on interface stress distributions.
Hip implants subject to gait loading due to occupational activities are potentially prone to failures such as osteolysis and aseptic loosening, causing painful revision surgeries. Highly risky gait activities such as carrying a load, stairs up or down and ladder up or down may cause excessive loading at the hip joint, resulting in generation of wear and related debris. Estimation of wear under the above gait activities is thus crucial to design and develop a new and improved implant component. With this motivation, this paper presents an assessment of wear generation of PCD-on-PCD (poly crystalline diamond) hip implants using finite element (FE) analysis. Three-dimensional (3D) FE model of hip implant along with peak gait and peak flexion angle for each activity was used to estimate wear of PCD for 10 million cycles. The maximum and minimum initial contact pressures of 206.19 MPa and 151.89 MPa were obtained for carrying load of 40 kg and sitting down or getting up activity. The simulation results obtained from finite element model also revealed that the maximum linear wear of 0.585 μm occurred for the patients frequently involved in sitting down or getting up gait activity and maximum volumetric wear of 0.025 mm3 for ladder up gait activity. The stair down activity showed the least linear and volumetric wear of 0.158 μm and 0.008 mm3, respectively, at the end of 10 million cycles.
Neuronal excitability is determined in a complex way by several interacting factors, such as membrane dynamics, fibre geometry, electrode configuration, myelin impedance, neuronal terminations\(\dots \) This study aims to increase understanding in excitability, by investigating the impact of these factors on different models of myelinated and unmyelinated fibres (five well-known membrane models are combined with three electrostimulation models, that take into account the spatial structure of the neuron). Several excitability indices (rheobase, polarity ratio, bi/monophasic ratio, time constants\(\dots \)) are calculated during extensive parameter sweeps, allowing us to obtain novel findings on how these factors interact, e.g. how the dependency of excitability indices on the fibre diameter and myelin impedance is influenced by the electrode location and membrane dynamics. It was found that excitability is profoundly impacted by the used membrane model and the location of the neuronal terminations. The approximation of infinite myelin impedance was investigated by two implementations of the spatially extended non-linear node model. The impact of this approximation on the time constant of strength-duration plots is significant, most importantly in the Frankenhaeuser-Huxley membrane model for large electrode-neuron separations. Finally, a multi-compartmental model for C-fibres is used to determine the impact of the absence of internodes on excitability.
Graphical Abstract Electrostimulation models, obtained by combining five membrane models with three representations of the neuronal cable equation, are fed with electrode and stimulus input parameters. The dependency of neuronal excitability on the interaction of these input parameters is determined by deriving excitability indices from the spatiotemporal model response. The impact of the myelin impedance and the fibre diameter on neural excitability is also considered.
For capacitive biopotential measurement, a novel high input impedance front-end is proposed. The input impedance of the front-end can achieve more than 100 GΩ by matching the peripheral parameters. The front-end’s noise model is provided, and noise optimization is given further. The analysis shows the proposed front-end can achieve at least two orders of input impedance more than the non-inverting amplifier circuit with the same peripheral parameters at the cost of only increasing twice input-referred noise. The final experimental results verify the analysis and show the front-end’s feasibility of capacitive sensing electrocardiogram signal.
Graphical abstract A novel high input impedance front-end is proposed, which impedance can achieve more than 100 GΩ by matching the peripheral parameters. The analysis and noise optimization results show the proposed front-end can achieve at least two orders of input impedance more than the non-inverting amplifier circuit with the same peripheral parameters at the cost of only increasing twice input-referred noise. The final experimental results verify the analysis and show its feasibility of capacitive sensing electrocardiogram signal.
A semi-automated processing approach was developed to assess the effects of early postnatal environmental tobacco smoke (ETS) on the cardiorespiratory control of newborn lambs. The system consists of several steps beginning with artifact rejection, followed by the selection of stationary segments, and ending with feature extraction. This approach was used in six lambs exposed to 20 cigarettes/day for the first 15 days of life, while another six control lambs were exposed to room air. On postnatal day 16, electrocardiograph and respiratory signals were obtained from a 6-h polysomnographic recording. The effects of postnatal ETS exposure on heart rate variability, respiratory rate variability, and cardiorespiratory interrelations were explored. The unique results suggest that early postnatal ETS exposure increases respiratory rate variability and decreases the coupling between cardiac and respiratory systems. Potentially harmful consequences in early life include unstable breathing and decreased adaptability of cardiorespiratory function, particularly during early life challenges, such as prematurity or viral infection.
