Markerless Video Analysis for Movement Quantification in Pediatric Epilepsy Monitoring

This paper proposes a markerless video analytic system for quantifying body part movements in pediatric epilepsy monitoring. The system utilizes colored pajamas worn by a patient in bed to extract body part movement trajectories, from which various features can be obtained for seizure detection and analysis. Hence, it is non-intrusive and it requires no sensor/marker to be attached to the patient’s body. It takes raw video sequences as input and a simple user-initialization indicates the body parts to be examined. In background/foreground modeling, Gaussian mixture models are employed in conjunction with HSV-based modeling. Body part detection follows a coarse-to-fine paradigm with graphcut-based segmentation. Finally, body part parameters are estimated with domain knowledge guidance. Experimental studies are reported on sequences captured in an Epilepsy Monitoring Unit at a local hospital. The results demonstrate the feasibility of the proposed system in pediatric epilepsy monitoring and seizure detection

Self-Reporting Technologies for Supporting Epilepsy Treatment

Epilepsy diagnosis and treatment relies heavily on patient self-reporting for informing clinical decision-making. These self-reports are traditionally collected from handwritten patient journals and tend to be either incomplete or inaccurate. Recent mobile and wearable health tracking developments stand to dramatically impact clinical practice through supporting patient and caregiver data collection activities. However, the specific types and characteristics of the data that clinicians need for patient care are not well known. In this study, we conducted interviews, a literature review, an expert panel, and online surveys to assess the availability and quality of patient-reported data that is useful but reported as being unavailable, difficult for patients to collect, or unreliable during epilepsy diagnosis and treatment, respectively. The results highlight important yet underexplored data collection and design opportunities for supporting the diagnosis, treatment, and self-management of epilepsy and expose notable gaps between clinical data needs and current patient practices

The development and validation of a movement evaluation system for children with cerebral palsy

The development of objective assessment tools for evaluation in physiotherapy is vital. Currently, the outcomes resulting from an intervention are generated by clinical assessments that are almost exclusively based on subjective criteria which rely upon the assessor’s expertise and consistency. The aim of this project was to develop an objective clinical tool to measure head and trunk postural control in sitting for children with cerebral palsy (CP). It is preferable for any objective measurement tool to be useable with as wide a range of patients and conditions as possible. Ideally, the tool should also be ‘clinically-friendly’ for both therapist and patient. This project took children with CP as a starting point, as representing one of the most challenging groups to assess and to quantify. The project was specifically focused on head-trunk control in sitting because of the importance of this posture for activities of daily living. The Literature Reviews confirmed that head-trunk control status in sitting could be defined by an aligned sitting posture without any external support for the head, trunk and upper limbs. The Method selected was video-based (Dartfish) to meet the requirement of ‘clinically-friendly’ and developed to quantify alignment (and deviations from alignment) of the head and trunk with small errors when compared to a 3D motion capture system (Vicon). The Dartfish method was also used to classify the positions of the upper limbs in comparison with the standard clinical classification; it showed that a simplified representation of the hands and elbows can reflect the clinical judgement. The combination of both these elements enabled the quantification of head/trunk control in children with CP for the first time. The work presented in this thesis makes a new and major contribution to postural assessment. It also provides the basis for the development of a fully automated system for the objective assessment of control using 2D-video recording. This work confirmed that clinical assessments can be objectively replicated, representing a major advance in the validation of physiotherapy interventions

Automated early prediction of cerebral palsy: interpretable pose-based assessment for the identification of abnormal infant movements

Cerebral Palsy (CP) is currently the most common chronic motor disability occurring in infants, affecting an estimated 1 in every 400 babies born in the UK each year. Techniques which can lead to an early diagnosis of CP have therefore been an active area of research, with some very promising results using tools such as the General Movements Assessment (GMA). By using video recordings of infant motor activity, assessors are able to classify an infant’s neurodevelopmental status based upon specific characteristics of the observed infant movement. However, these assessments are heavily dependent upon the availability of highly skilled assessors. As such, we explore the feasibility of the automated prediction of CP using machine learning techniques to analyse infant motion. We examine the viability of several new pose-based features for the analysis and classification of infant body movement from video footage. We extensively evaluate the effectiveness of the extracted features using several proposed classification frameworks, and also reimplement the leading methods from the literature for direct comparison using shared datasets to establish a new state-of-the-art. We introduce the RVI-38 video dataset, which we use to further inform the design, and establish the robustness of our proposed complementary pose-based motion features. Finally, given the importance of explainable AI for clinical applications, we propose a new classification framework which also incorporates a visualisation module to further aid with interpretability. Our proposed pose-based framework segments extracted features to detect movement abnormalities spatiotemporally, allowing us to identify and highlight body-parts exhibiting abnormal movement characteristics, subsequently providing intuitive feedback to clinicians. We suggest that our novel pose-based methods offer significant benefits over other approaches in both the analysis of infant motion and explainability of the associated data. Our engineered features, which are directly mapped to the assessment criteria in the clinical guidelines, demonstrate state-of-the-art performance across multiple datasets; and our feature extraction methods and associated visualisations significantly improve upon model interpretability

Robotic Platforms for Assistance to People with Disabilities

People with congenital and/or acquired disabilities constitute a great number of dependents today. Robotic platforms to help people with disabilities are being developed with the aim of providing both rehabilitation treatment and assistance to improve their quality of life. A high demand for robotic platforms that provide assistance during rehabilitation is expected because of the health status of the world due to the COVID-19 pandemic. The pandemic has resulted in countries facing major challenges to ensure the health and autonomy of their disabled population. Robotic platforms are necessary to ensure assistance and rehabilitation for disabled people in the current global situation. The capacity of robotic platforms in this area must be continuously improved to benefit the healthcare sector in terms of chronic disease prevention, assistance, and autonomy. For this reason, research about human–robot interaction in these robotic assistance environments must grow and advance because this topic demands sensitive and intelligent robotic platforms that are equipped with complex sensory systems, high handling functionalities, safe control strategies, and intelligent computer vision algorithms. This Special Issue has published eight papers covering recent advances in the field of robotic platforms to assist disabled people in daily or clinical environments. The papers address innovative solutions in this field, including affordable assistive robotics devices, new techniques in computer vision for intelligent and safe human–robot interaction, and advances in mobile manipulators for assistive tasks

Vip Interneuron Cell And Circuit Dysfunction Underlying Dravet Syndrome

GABAergic inhibitory interneurons of the cerebral cortex expressing vasoactive intestinal peptide (VIP-INs) are rapidly emerging as important regulators of network dynamics and normal circuit development. Several recent studies have also identified VIP-IN dysfunction in models of genetically determined neurodevelopmental disorders (NDDs). In this dissertation, we review the known circuit functions of VIP-INs and how they may relate to accumulating evidence implicating VIP-IN dysfunction in the mechanisms of prominent NDDs. We highlight recurring VIP-IN mediated circuit motifs that are shared across cerebral cortical areas, and how VIP-IN activity can shape sensory input, development, and behavior. Ultimately, we extract a set of themes that inform our understanding of how VIP-INs influence pathogenesis of NDDs. We focus on a particularly enticing disease candidate: Dravet Syndrome, a severe NDD characterized by epilepsy, autism spectrum disorder (ASD), and intellectual disability (ID) caused by loss of function variants in SCN1A which codes for the voltage-gated Na+ channel α subunit, Nav1.1. We go on to show that Nav1.1 is expressed in VIP-INs, and loss of a single copy causes VIP-INs to be hypoexcitable in acute brain slices from Scn1a+/- mice. Using this same model, we show that this intrinsic hypoexcitability translates to decreased VIP-IN activity and impaired cortical network dynamics in vivo using two-photon calcium imaging. We find that the above results are replicated when using a conditional deletion of Scn1a in VIP-INs. However, these conditional mutants do not have epilepsy like the global model, but do replicate core features of ASD and ID. This dissociates the roles of VIP-IN dysfunction from potential involvement of other cell types in Dravet pathogenesis. Finally, using publicly available single cell RNA sequencing (scRNA-seq) data from the Allen Institute, we also identify several underexplored disease-associated genes that are highly expressed in VIP-INs. We survey these genes and their shared related disease phenotypes that may broadly implicate VIP-INs in ASD and ID rather than epilepsy. We conclude with a discussion of the relevance of cell type-specific investigations to drive the potential development of therapeutics targeting VIP-INs in the age of genomic diagnosis and precision medicine

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject