The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided

A distributive approach to tactile sensing for application to human movement

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis thesis investigates on clinical applicability of a novel sensing technology in the areas of postural steadiness and stroke assessment. The mechanically simple Distributive Tactile Sensing approach is applied to extract motion information from flexible surfaces to identify parameters and disorders of human movement in real time. The thesis reports on the design, implementation and testing of smart platform devices which are developed for discrimination applications through the use of linear and non-linear data interpretation techniques and neural networks for pattern recognition. In the thesis mathematical models of elastic plates, based on finite element and finite difference methods, are developed and described. The models are used to identify constructive parameters of sensing devices by investigating sensitivity and accuracy of Distributive Tactile Sensing surfaces. Two experimental devices have been constructed for the investigation. These are a sensing floor platform for standing applications and a sensing chair for sitting applications. Using a linear approach, the sensing floor platform is developed to detect centre of pressure, an important parameter widely used in the assessment of postural steadiness. It is demonstrated that the locus of centre of pressure can be determined with an average deviation of 1.05mm from that of a commercialised force platform in a balance application test conducted with five healthy volunteers. This amounts to 0.4% of the sensor range. The sensing chair used neural networks for pattern recognition, to identify the level of motor impairment in people with stroke through performing functional reaching task while sitting. The clinical studies with six real stroke survivors have shown the robustness of the sensing technique to deal with a range of possible motion in the reaching task investigated. The work of this thesis demonstrates that the novel Distributive Tactile Sensing approach is suited to clinical and home applications as screening and rehabilitation systems. Mechanical simplicity is a merit of the approach and has potential to lead to versatile low-cost units

A virtual coaching environment for improving golf swing technique

As a proficient golf swing is a key element of success in golf, many golfers make significant effort improving their stroke mechanics. In order to help enhance golfing performance, it is important to identify the performance determining factors within the full golf swing. In addition, explicit instructions on specific features in stroke technique requiring alterations must be imparted to the player in an unambiguous and intuitive manner. However, these two objectives are difficult to achieve due to the subjective nature of traditional coaching techniques and the predominantly implicit knowledge players have of their movements. In this work, we have developed a set of visualisation and analysis tools for use in a virtual golf coaching environment. In this virtual coaching studio, the analysis tools allow for specific areas require improvement in a player's 3D stroke dynamics to be isolated. An interactive 3D virtual coaching environment then allows detailed and unambiguous coaching information to be visually imparted back to the player via the use of two virtual human avatars; the first mimics the movements performed by the player; the second takes the role of a virtual coach, performing ideal stroke movement dynamics. The potential of the coaching tool is highlighted in its use by sports science researchers in the evaluation of competing approaches for calculating the X-Factor, a significant performance determining factor for hitting distance in a golf swing

A Robotic Test of Proprioception within the Hemiparetic Arm Post-stroke

Background: Proprioception plays important roles in planning and control of limb posture and movement. The impact of proprioceptive deficits on motor function post-stroke has been difficult to elucidate due to limitations in current tests of arm proprioception. Common clinical tests only provide ordinal assessment of proprioceptive integrity (eg. intact, impaired or absent). We introduce a standardized, quantitative method for evaluating proprioception within the arm on a continuous, ratio scale. We demonstrate the approach, which is based on signal detection theory of sensory psychophysics, in two tasks used to characterize motor function after stroke. Methods: Hemiparetic stroke survivors and neurologically intact participants attempted to detect displacement- or force-perturbations robotically applied to their arm in a two-interval, two-alternative forced-choice test. A logistic psychometric function parameterized detection of limb perturbations. The shape of this function is determined by two parameters: one corresponds to a signal detection threshold and the other to variability of responses about that threshold. These two parameters define a space in which proprioceptive sensation post-stroke can be compared to that of neurologically-intact people. We used an auditory tone discrimination task to control for potential comprehension, attention and memory deficits. Results: All but one stroke survivor demonstrated competence in performing two-alternative discrimination in the auditory training test. For the remaining stroke survivors, those with clinically identified proprioceptive deficits in the hemiparetic arm or hand had higher detection thresholds and exhibited greater response variability than individuals without proprioceptive deficits. We then identified a normative parameter space determined by the threshold and response variability data collected from neurologically intact participants. By plotting displacement detection performance within this normative space, stroke survivors with and without intact proprioception could be discriminated on a continuous scale that was sensitive to small performance variations, e.g. practice effects across days. Conclusions: The proposed method uses robotic perturbations similar to those used in ongoing studies of motor function post-stroke. The approach is sensitive to small changes in the proprioceptive detection of hand motions. We expect this new robotic assessment will empower future studies to characterize how proprioceptive deficits compromise limb posture and movement control in stroke survivors

A review of computer vision-based approaches for physical rehabilitation and assessment

The computer vision community has extensively researched the area of human motion analysis, which primarily focuses on pose estimation, activity recognition, pose or gesture recognition and so on. However for many applications, like monitoring of functional rehabilitation of patients with musculo skeletal or physical impairments, the requirement is to comparatively evaluate human motion. In this survey, we capture important literature on vision-based monitoring and physical rehabilitation that focuses on comparative evaluation of human motion during the past two decades and discuss the state of current research in this area. Unlike other reviews in this area, which are written from a clinical objective, this article presents research in this area from a computer vision application perspective. We propose our own taxonomy of computer vision-based rehabilitation and assessment research which are further divided into sub-categories to capture novelties of each research. The review discusses the challenges of this domain due to the wide ranging human motion abnormalities and difficulty in automatically assessing those abnormalities. Finally, suggestions on the future direction of research are offered

Face and Body gesture recognition for a vision-based multimodal analyser

users, computers should be able to recognize emotions, by analyzing the human's affective state, physiology and behavior. In this paper, we present a survey of research conducted on face and body gesture and recognition. In order to make human-computer interfaces truly natural, we need to develop technology that tracks human movement, body behavior and facial expression, and interprets these movements in an affective way. Accordingly in this paper, we present a framework for a vision-based multimodal analyzer that combines face and body gesture and further discuss relevant issues

What does touch tell us about emotions in touchscreen-based gameplay?

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ACM. It is posted here by permission of ACM for your personal use. Not for redistribution.Nowadays, more and more people play games on touch-screen mobile phones. This phenomenon raises a very interesting question: does touch behaviour reflect the player’s emotional state? If possible, this would not only be a valuable evaluation indicator for game designers, but also for real-time personalization of the game experience. Psychology studies on acted touch behaviour show the existence of discriminative affective profiles. In this paper, finger-stroke features during gameplay on an iPod were extracted and their discriminative power analysed. Based on touch-behaviour, machine learning algorithms were used to build systems for automatically discriminating between four emotional states (Excited, Relaxed, Frustrated, Bored), two levels of arousal and two levels of valence. The results were very interesting reaching between 69% and 77% of correct discrimination between the four emotional states. Higher results (~89%) were obtained for discriminating between two levels of arousal and two levels of valence

Unconstrained video monitoring of breathing behavior and application to diagnosis of sleep apnea

This paper presents a new real-time automated infrared video monitoring technique for detection of breathing anomalies, and its application in the diagnosis of obstructive sleep apnea. We introduce a novel motion model to detect subtle, cyclical breathing signals from video, a new 3-D unsupervised self-adaptive breathing template to learn individuals' normal breathing patterns online, and a robust action classification method to recognize abnormal breathing activities and limb movements. This technique avoids imposing positional constraints on the patient, allowing patients to sleep on their back or side, with or without facing the camera, fully or partially occluded by the bed clothes. Moreover, shallow and abdominal breathing patterns do not adversely affect the performance of the method, and it is insensitive to environmental settings such as infrared lighting levels and camera view angles. The experimental results show that the technique achieves high accuracy (94% for the clinical data) in recognizing apnea episodes and body movements and is robust to various occlusion levels, body poses, body movements (i.e., minor head movement, limb movement, body rotation, and slight torso movement), and breathing behavior (e.g., shallow versus heavy breathing, mouth breathing, chest breathing, and abdominal breathing). © 2013 IEEE