Journal: Medical & biological engineering & computing
In total hip arthroplasty, determining the impingement free range of motion requirement is a complex task. This is because in the native hip, motion is restricted by both impingement as well as soft tissue restraint. The aim of this study is to determine a range of motion benchmark which can identify motions which are at risk from impingement and those which are constrained due to soft tissue. Two experimental methodologies were used to determine motions which were limited by impingement and those motions which were limited by both impingement and soft tissue restraint. By comparing these two experimental results, motions which were limited by impingement were able to be separated from those motions which were limited by soft tissue restraint. The results show motions in extension as well as flexion combined with adduction are limited by soft tissue restraint. Motions in flexion, flexion combined with abduction and adduction are at risk from osseous impingement. Consequently, these motions represent where the maximum likely damage will occur in femoroacetabular impingement or at most risk of prosthetic impingement in total hip arthroplasty.
Respiratory disease is the leading cause of death in the UK. Methods for assessing pulmonary function and chest wall movement are essential for accurate diagnosis, as well as monitoring response to treatment, operative procedures and rehabilitation. Despite this, there is a lack of low-cost devices for rapid assessment. Spirometry is used to measure air flow expired, but cannot infer or directly measure full chest wall motion. This paper presents the development of a low-cost chest wall motion assessment system. The prototype was developed using four Microsoft Kinect sensors to create a 3D time-varying representation of a patient’s torso. An evaluation of the system in two phases is also presented. Initially, static volume of a resuscitation mannequin with that of a Nikon laser scanner is performed. This showed the system has slight underprediction of 0.441 %. Next, a dynamic analysis through the comparison of results from the prototype and a spirometer in nine cystic fibrosis patients and thirteen healthy subjects was performed. This showed an agreement with correlation coefficients above 0.8656 in all participants. The system shows promise as a method for assessing respiratory disease in a cost-effective and timely manner. Further work must now be performed to develop the prototype and provide further evaluations.
The frequently used digital signature algorithms, such as RSA and the Digital Signature Algorithm (DSA), lack forward-secure function. The result is that, when private keys are renewed, trustworthiness is lost. In other words, electronic medical records (EMRs) signed by revoked private keys are no longer trusted. This significant security threat stands in the way of EMR adoption. This paper proposes an efficient forward-secure group certificate digital signature scheme that is based on Shamir’s (t,n) threshold scheme and Schnorr’s digital signature scheme to ensure trustworthiness is maintained when private keys are renewed and to increase the efficiency of EMRs' authentication processes in terms of number of certificates, number of keys, forward-secure ability and searching time.
Caloric vestibular stimulation (CVS) and galvanic vestibular stimulation (GVS) act primarily on the peripheral vestibular system. Although the electrical current applied during GVS is thought to flow through peripheral vestibular organs, some current may spread into areas within the central nervous system, particularly when the bilateral galvanic vestibular stimulation (bGVS) method is used. According to Alexander’s law, the magnitude of nystagmus increases with eccentric gaze movement, due to the function of the neural integrator (NI); thus, if the information for vestibular stimulation corresponds to Alexander’s law, the peripheral vestibular organ is stimulated. Therefore, it would appear that if CVS results comply with Alexander’s law, and bGVS results do not, the sites stimulated by bGVS are not perfectly located in the peripheral vestibular area. In our experiments on normal human subjects, the magnitude of nystagmus under CVS increased with rising gaze eccentricity in the direction that the magnitude of the nystagmus increases, and this change was found to follow Alexander’s law. However, in the case of nystagmus under bGVS, results did not follow Alexander’s law. In addition, study of the influences of bGVS at different current intensities on nystagmus magnitude showed that bGVS at 5 mA distorted nystagmus magnitude more than at 3 mA, which suggests bGVS acts not only on the peripheral vestibular nerves, but also on some areas of the central nervous system, particularly the NI. According to our experiments, bGVS directly affects neural integrator function.
In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.
Despite an easy control and the direct effects on vestibular neurons, the clinical applications of galvanic vestibular stimulation (GVS) have been restricted because of its unclear activities as input. On the other hand, some critical conclusions have been made in the peripheral and the central processing of neural information by kinetic stimuli with different motion frequencies. Nevertheless, it is still elusive how the neural responses to simultaneous GVS and kinetic stimulus are modified during transmission and integration at the central vestibular area. To understand how the neural information was transmitted and integrated, we examined the neuronal responses to GVS, kinetic stimulus, and their combined stimulus in the vestibular nucleus. The neuronal response to each stimulus was recorded, and its responding features (amplitude and baseline) were extracted by applying the curve fitting based on a sinusoidal function. Twenty-five (96.2%) comparisons of the amplitudes showed that the amplitudes decreased during the combined stimulus (p < 0.001). However, the relations in the amplitudes (slope = 0.712) and the baselines (slope = 0.747) were linear. The neuronal effects by the different stimuli were separately estimated; the changes of the amplitudes were mainly caused by the kinetic stimulus and those of the baselines were largely influenced by GVS. Therefore, the slopes in the comparisons implied the neural sensitivity to the applied stimuli. Using the slopes, we found that the reduced amounts of the neural information were transmitted. Overall, the comparisons of the responding features demonstrated the linearity and the subadditivity in the neural transmission.
Mobile eye-trackers are currently used during real-world tasks (e.g. gait) to monitor visual and cognitive processes, particularly in ageing and Parkinson’s disease (PD). However, contextual analysis involving fixation locations during such tasks is rarely performed due to its complexity. This study adapted a validated algorithm and developed a classification method to semi-automate contextual analysis of mobile eye-tracking data. We further assessed inter-rater reliability of the proposed classification method. A mobile eye-tracker recorded eye-movements during walking in five healthy older adult controls (HC) and five people with PD. Fixations were identified using a previously validated algorithm, which was adapted to provide still images of fixation locations (n = 116). The fixation location was manually identified by two raters (DH, JN), who classified the locations. Cohen’s kappa correlation coefficients determined the inter-rater reliability. The algorithm successfully provided still images for each fixation, allowing manual contextual analysis to be performed. The inter-rater reliability for classifying the fixation location was high for both PD (kappa = 0.80, 95% agreement) and HC groups (kappa = 0.80, 91% agreement), which indicated a reliable classification method. This study developed a reliable semi-automated contextual analysis method for gait studies in HC and PD. Future studies could adapt this methodology for various gait-related eye-tracking studies.
An understanding of athlete ground reaction forces and moments (GRF/Ms) facilitates the biomechanist’s downstream calculation of net joint forces and moments, and associated injury risk. Historically, force platforms used to collect kinetic data are housed within laboratory settings and are not suitable for field-based installation. Given that Newton’s Second Law clearly describes the relationship between a body’s mass, acceleration, and resultant force, is it possible that marker-based motion capture can represent these parameters sufficiently enough to estimate GRF/Ms, and thereby minimize our reliance on surface embedded force platforms? Specifically, can we successfully use partial least squares (PLS) regression to learn the relationship between motion capture and GRF/Ms data? In total, we analyzed 11 PLS methods and achieved average correlation coefficients of 0.9804 for GRFs and 0.9143 for GRMs. Our results demonstrate the feasibility of predicting accurate GRF/Ms from raw motion capture trajectories in real-time, overcoming what has been a significant barrier to non-invasive collection of such data. In applied biomechanics research, this outcome has the potential to revolutionize athlete performance enhancement and injury prevention. Graphical Abstract Using data science to model high-fidelity motion and force plate data frees biomechanists from the laboratory.
Multiscale entropy (MSE) has been a prevalent algorithm to quantify the complexity of biomedical time series. Recent developments in the field have tried to alleviate the problem of undefined MSE values for short signals. Moreover, there has been a recent interest in using other statistical moments than the mean, i.e., variance, in the coarse-graining step of the MSE. Building on these trends, here we introduce the so-called refined composite multiscale fuzzy entropy based on the standard deviation (RCMFEσ) and mean (RCMFEμ) to quantify the dynamical properties of spread and mean, respectively, over multiple time scales. We demonstrate the dependency of the RCMFEσ and RCMFEμ, in comparison with other multiscale approaches, on several straightforward signal processing concepts using a set of synthetic signals. The results evidenced that the RCMFEσ and RCMFEμ values are more stable and reliable than the classical multiscale entropy ones. We also inspect the ability of using the standard deviation as well as the mean in the coarse-graining process using magnetoencephalograms in Alzheimer’s disease and publicly available electroencephalograms recorded from focal and non-focal areas in epilepsy. Our results indicated that when the RCMFEμ cannot distinguish different types of dynamics of a particular time series at some scale factors, the RCMFEσ may do so, and vice versa. The results showed that RCMFEσ-based features lead to higher classification accuracies in comparison with the RCMFEμ-based ones. We also made freely available all the Matlab codes used in this study at http://dx.doi.org/10.7488/ds/1477 .
Mechanical analysis of movement plays an important role in clinical management of neurological and orthopedic conditions. There has been increasing interest in performing movement analysis in real-time, to provide immediate feedback to both therapist and patient. However, such work to date has been limited to single-joint kinematics and kinetics. Here we present a software system, named human body model (HBM), to compute joint kinematics and kinetics for a full body model with 44 degrees of freedom, in real-time, and to estimate length changes and forces in 300 muscle elements. HBM was used to analyze lower extremity function during gait in 12 able-bodied subjects. Processing speed exceeded 120 samples per second on standard PC hardware. Joint angles and moments were consistent within the group, and consistent with other studies in the literature. Estimated muscle force patterns were consistent among subjects and agreed qualitatively with electromyography, to the extent that can be expected from a biomechanical model. The real-time analysis was integrated into the D-Flow system for development of custom real-time feedback applications and into the gait real-time analysis interactive lab system for gait analysis and gait retraining.