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.
Remote patient monitoring should reduce mortality rates, improve care, and reduce costs. We present an overview of the available technologies for the remote monitoring of chronic obstructive pulmonary disease (COPD) patients, together with the most important medical information regarding COPD in a language that is adapted for engineers. Our aim is to bridge the gap between the technical and medical worlds and to facilitate and motivate future research in the field. We also present a justification, motivation, and explanation of how to monitor the most important parameters for COPD patients, together with pointers for the challenges that remain. Additionally, we propose and justify the importance of electrocardiograms (ECGs) and the arterial carbon dioxide partial pressure (PaCO2) as two crucial physiological parameters that have not been used so far to any great extent in the monitoring of COPD patients. We cover four possibilities for the remote monitoring of COPD patients: continuous monitoring during normal daily activities for the prediction and early detection of exacerbations and life-threatening events, monitoring during the home treatment of mild exacerbations, monitoring oxygen therapy applications, and monitoring exercise. We also present and discuss the current approaches to decision support at remote locations and list the normal and pathological values/ranges for all the relevant physiological parameters. The paper concludes with our insights into the future developments and remaining challenges for improvements to continuous remote monitoring systems. Graphical abstract ᅟ.
Perturbation-based gait assessment has been used to quantify gait stability in older adults. However, knowledge on which perturbation type is most suitable to identify poor gait stability is lacking. We evaluated the effects of ipsi- and contra-lateral sway, belt acceleration and deceleration, and visual and auditory perturbations on medio-lateral (ML) and anterior-posterior (AP) margins of stability (MoS) in young and older adults. We aimed to evaluate (1) which perturbation type disturbed the gait pattern substantially, (2) how participants recovered, and (3) whether recovery responses could discriminate between young and older adults. Nine young (25.1 ± 3.4 years) and nine older (70.1 ± 7.6 years) adults walked on the CAREN Extended (Motek BV, The Netherlands). The perturbation effect was quantified by deviation in MoS over six post-perturbation steps compared to baseline walking. Contra-lateral sway and deceleration perturbations resulted in the largest ML (1.9-4 times larger than other types) and AP (1.6-5.6 times larger than other types) perturbation effects, respectively. After both perturbation types, participants increased MoS by taking wider, shorter, and faster steps. No differences between young and older adults were found. We suggest to evaluate the potential of using contra-lateral sway and deceleration perturbations for fall risk identification by including both healthy and frail older adults. Graphical abstract Margins of stability during steady state (left) and perturbed (right) gait to quantify reactive gait stability in response to various perturbation types in young and older adults.
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.