Markerless Gait Classification Employing 3D IR-UWB Physiological Motion Sensing

Human gait refers to the propulsion achieved by the effort of human limbs, a reflex progression resulting from the rhythmic reciprocal bursts of flexor and extensor activity. Several quantitative models are followed by health professionals to diagnose gait abnormality. Marker-based gait quantification is considered a gold standard by the research and health communities. It reconstructs motion in 3D and provides parameters to measure gait. But, it is an expensive and intrusive technique, limited to soft tissue artefact, prone to incorrect marker positioning, and skin sensitivity problems. Hence, markerless, swiftly deployable, non-intrusive, camera-less prototypes would be a game changing possibility, and an example is proposed here. This paper illustrates a 3D gait motion analyser employing impulse radio ultra-wide band (IR-UWB) wireless technology. The prototype can measure 3D motion and determine quantitative parameters considering anatomical reference planes. Knee angles have been calculated from the gait by applying vector algebra. Simultaneously, the model has been corroborated with the popular markerless camera based 3D motion capturing system, the Kinect sensor. Bland and Altman (B&A) statistics has been applied to the proposed prototype and Kinect sensor results to verify the measurement agreement. Finally, the proposed prototype has been incorporated with popular supervised machine learning such as, k-nearest neighbour (kNN), support vector machine (SVM) and the deep learning technique deep neural multilayer perceptron (DMLP) network to automatically recognize gait abnormalities, with promising results presented

3D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon

Gait disorder diagnosis and rehabilitation is one area where human perception and observation are highly integrated. Predominantly, gait evaluation, comprises technological devices for gait analysis such as, dedicated force sensors, cameras, and wearable sensor based solutions, however they are limited by insufficient gait parameter recognition, post processing, installation costs, mobility, and skin irritation issues. Thus, the proposed study concentrates on the creation of a widely deployable, noncontact and non-intrusive gait recognition method from impulse radio ultra wideband (IR-UWB) sensing phenomenon, where a standalone IR-UWB system can detect gait problems with less human intervention. A 3D human motion model for gait identification from IR-UWB has been proposed with embracing spherical trigonometry and vector algebra to determine knee angles. Subsequently, normal and abnormal walking subjects were involved in this study. Abnormal gait subjects belong to the spastic gait category only. The prototype has been tested in both the anechoic and multipath environments. The outcomes have been corroborated with a simultaneously deployed Kinect Xbox sensor and supported by statistical graphical approach Bland and Altman (B&A) analysis

Imaging haemodynamic changes related to seizures: comparison of EEG-based general linear model, independent component analysis of fMRI and intracranial EEG

Background: Simultaneous EEG-fMRI can reveal haemodynamic changes associated with epileptic activity which may contribute to understanding seizure onset and propagation. Methods: Nine of 83 patients with focal epilepsy undergoing pre-surgical evaluation had seizures during EEG-fMRI and analysed using three approaches, two based on the general linear model (GLM) and one using independent component analysis (ICA): 1. EEGs were divided into up to three phases: early ictal EEG change, clinical seizure onset and late ictal EEG change and convolved with a canonical haemodynamic response function (HRF) (canonical GLM analysis). 2. Seizures lasting three scans or longer were additionally modelled using a Fourier basis set across the entire event (Fourier GLM analysis). 3. Independent component analysis (ICA) was applied to the fMRI data to identify ictal BOLD patterns without EEG. The results were compared with intracranial EEG. Results: The canonical GLM analysis revealed significant BOLD signal changes associated with seizures on EEG in 7/9 patients, concordant with the seizure onset zone in 4/7. The Fourier GLM analysis revealed changes in BOLD signal corresponding with the results of the canonical analysis in two patients. ICA revealed components spatially concordant with the seizure onset zone in all patients (8/9 confirmed by intracranial EEG). Conclusion: Ictal EEG-fMRI visualises plausible seizure related haemodynamic changes. The GLM approach to analysing EEG-fMRI data reveals localised BOLD changes concordant with the ictal onset zone when scalp EEG reflects seizure onset. ICA provides additional information when scalp EEG does not accurately reflect seizures and may give insight into ictal haemodynamics

Pathways from uncertainty to anxiety: An evaluation of a hierarchical model of trait and disorder-specific intolerance of uncertainty on anxiety disorder symptoms

Uncertainty is central to anxiety-related pathology and intolerance of uncertainty (IU) appears to be a transdiagnostic risk and maintaining factor. The aim of the present study was to evaluate a hierarchical model to identify the unique contributions of trait and disorder-specific IU (i.e., uncertainty specific to generalised anxiety disorder, social anxiety, obsessive compulsive disorder, and panic disorder) to disorder-specific symptoms, beyond other disorder-specific cognitive vulnerabilities (i.e., negative metacognitive beliefs, fear of negative evaluation, inflated responsibility, and agoraphobic cognitions, respectively). Participants (N = 506) completed a battery of online questionnaires. Structural equation modelling was used to evaluate model fit, as well as direct and indirect pathways. Trait and disorder-specific IU were significantly associated with multiple cognitive vulnerability factors and disorder symptoms. Indirect effects between trait IU and symptoms were observed through disorder-specific IU and cognitive vulnerabilities. The relative contribution of trait IU and disorder-specific IU to symptoms varied and theoretical and clinical implications are highlighted. Limitations include the cross-sectional design and reliance on self-report. Avenues for further research include a need for replication and extension of the model in different samples and using experimental and multi-method research methods

The Benefits of Fit-for-Purpose Land Administration for Urban Community Resilience in a Time of Climate Change and COVID-19 Pandemic

The major global pressures of rapid urbanization and urban growth are being compounded by climate impacts, resulting in increased vulnerability for urban dwellers, with these vulnerabilities exacerbated during the COVID-19 pandemic. Much of this is concentrated in urban and peri-urban areas where urban development spreads into hazard-prone areas. Often, this development is dominated by poor-quality homes in informal settlements or slums with poor tenure security. Lessons from a resilience-building project in the Pacific shows that a fit-for-purpose (FFP) approach to land administration can provide solutions by increasing the number of households with security of tenure, and consequently, improving resilience outcomes as informal settlements grow. This paper specifically discusses the influence of FFP land administration on reducing vulnerabilities to external shocks, such as climate change and COVID-19. It proposes ways to be better manage urban growth through the responsible governance of land tenure rights and more effective land-use planning to improve resilience to multiple shocks and stresses, hence, delivering improved access to safe land and shelter. Land administration systems can contribute to enhanced resilience to the shocks of climate extremes and pandemics by improving tenure security and enhancing land-use planning controls. It is argued that climate change adaptation and disaster risk reduction need to be better mainstreamed into two major elements of land governance: (i) securing and safeguarding of land rights, and (ii) planning and control of land use

Simultaneous intracranial EEG and fMRI of interictal epileptic discharges in humans

Simultaneous scalp EEG–fMRI measurements allow the study of epileptic networks and more generally, of the coupling between neuronal activity and haemodynamic changes in the brain. Intracranial EEG (icEEG) has greater sensitivity and spatial specificity than scalp EEG but limited spatial sampling. We performed simultaneous icEEG and functional MRI recordings in epileptic patients to study the haemodynamic correlates of intracranial interictal epileptic discharges (IED). Two patients undergoing icEEG with subdural and depth electrodes as part of the presurgical assessment of their pharmaco-resistant epilepsy participated in the study. They were scanned on a 1.5 T MR scanner following a strict safety protocol. Simultaneous recordings of fMRI and icEEG were obtained at rest. IED were subsequently visually identified on icEEG and their fMRI correlates were mapped using a general linear model (GLM). On scalp EEG–fMRI recordings performed prior to the implantation, no IED were detected. icEEG–fMRI was well tolerated and no adverse health effect was observed. intra-MR icEEG was comparable to that obtained outside the scanner. In both cases, significant haemodynamic changes were revealed in relation to IED, both close to the most active electrode contacts and at distant sites. In one case, results showed an epileptic network including regions that could not be sampled by icEEG, in agreement with findings from magneto-encephalography, offering some explanation for the persistence of seizures after surgery. Hence, icEEG–fMRI allows the study of whole-brain human epileptic networks with unprecedented sensitivity and specificity. This could help improve our understanding of epileptic networks with possible implications for epilepsy surgery

Single- and Multi-Distribution Dimensionality Reduction Approaches for a Better Data Structure Capturing

In recent years, the huge expansion of digital technologies has vastly increased the volume of data to be explored, such that reducing the dimensionality of data is an essential step in data exploration. The integrity of a dimensionality reduction technique relates to the goodness of maintaining the data structure. Dimensionality reduction techniques such as Principal Component Analyses (PCA) and Multidimensional Scaling (MDS) globally preserve the distance ranking at the expense of neglecting small-distance preservation. Conversely, the structure capturing of some other methods such as Isomap, Locally Linear Embedding (LLE), Laplacian Eigenmaps t-Stochastic Neighbour Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and TriMap rely on the number of neighbours considered. This paper presents a dimensionality reduction technique, Same Degree Distribution (SDD) that does not rely on the number of neighbours, thanks to using degree-distributions in both high and low dimensional spaces. Degree-distribution is similar to Student-t distribution and is less expensive than Gaussian distribution. As such, it enables better global data preservation in less computational time. Moreover, to improve the data structure capturing, SDD has been extended to Multi-SDDs (MSDD), which employs various degree distributions on top of SDD. The proposed approach and its extension demonstrated a greater performance compared with eight other benchmark methods, tested in several popular synthetics and real datasets such as Iris, Breast Cancer, Swiss Roll, MNIST, and Make Blob evaluated by the co-ranking matrix and Kendall’s Tau coefficient. For further work, we aim to approximate the number of distributions and their degrees in relation to the given dataset. Reducing the computational complexity is another objective for further work

Neck atonia with a focal stimulation-induced seizure arising from the SMA: pathophysiological considerations.

A 28-year-old patient with pharmacoresistant non-lesional right frontal epilepsy underwent extra-operative intracranial EEG recordings and electrical cortical stimulation (ECS) to map eloquent cortex. Right supplementary motor area (SMA) ECS induced a brief seizure with habitual symptoms involving neck tingling followed by asymmetric tonic posturing. An additional feature was neck atonia. During atonia and sensory aura, discharges were seen in the mesial frontal electrodes and precentral gyrus. Besides motor signs, atonia, although rare and not described in the neck muscles, and sensations have been reported with SMA stimulation. The mechanisms underlying neck atonia in seizures arising from the SMA can be explained by supplementary negative motor area (SNMA) - though this was not mapped in electrodes overlying the ictal onset zone in our patient - or primary sensorimotor cortex activation through rapid propagation. Given the broad spectrum of signs elicited by SMA stimulation and rapid spread of seizures arising from the SMA, caution should be taken to not diagnose these as non-epileptic, as had previously occurred in this patient

Gender classification based on gait analysis using ultrawide band radar augmented with artificial intelligence

The identification of individuals based on their walking patterns, also known as gait recognition, has garnered considerable interest as a biometric trait. The use of gait patterns for gender classification has emerged as a significant research domain with diverse applications across multiple fields. The present investigation centers on the classification of gender based on gait utilizing data from Ultra-wide band radar. A total of 181 participants were included in the study, and data was gathered using Ultra-wide band radar technology. This study investigates various preprocessing techniques, feature extraction methods, and dimensionality reduction approaches to efficiently process Ultra-wide band radar data. The data quality is improved through the utilization of a two-pulse canceller and discrete wavelet transform. The hybrid feature dataset is generated through the creation of gray-level co-occurrence matrices and subsequent extraction of statistical features. Principal Component Analysis is utilized for dimensionality reduction, and prediction probabilities are incorporated as features for classification optimization. The present study employs k-fold cross-validation to train and assess machine learning classifiers, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, Multi-Layer Perceptron, K-Nearest Neighbors, and Extra Tree Classifier. The Multilayer Perceptron exhibits superior performance, achieving an accuracy of 0.936. The Support Vector Machine and k-Nearest Neighbors classifiers closely trail behind, both achieving an accuracy of 0.934. This research is of the utmost importance due to its capacity to offer solutions to crucial problems in multiple domains. The findings indicate that the utilization of UWB radar data for gait-based gender classification holds promise in diverse domains, including biometrics, surveillance, and healthcare. The present study makes a valuable contribution to the progress of gender classification systems that rely on gait patterns