New directions in treatment of the ischemic stroke

Stroke remains a leading cause of death and disability worldwide. The cause is decrease or total blocking of blood perfusion in brain and fast recognition is essential to conduct proper treatment. Currently commonly used methods are thrombolysis and mechanical thrombectomy, which high efficacy is observed only if they are performed within a few hours of the first symptoms. Hence loads of people, who are diagnosed later, become unable to undergo effective treatment.In the study are presented pathogenesis, epidemiology, classification, diagnosis and current treatment of the disease, but the main aim of the study is to present the results of the reports on new treatments for ischemic stroke. The research method is a review of the available literature published in PubMed, UpToDate and Google scholar databases.In the study were reviewed new methods, which can be alternative for currently used ones. They are small molecules including fluoxetine, aripiprazole, L-DOPA, cholinergic and noradrenergic drugs, growth factors (HCG, EPO), monoclonal antibodies, allogenic stem cells, neurostimulators,  robotic therapy and telerehabilitation.They give hope for curing a larger number of patients and improving effects of rehabilitation after ischemic stroke. However most of the methods, which are presented in this study require the use of tests that will allow checking their safety and application

Using brain-computer interaction and multimodal virtual-reality for augmenting stroke neurorehabilitation

Every year millions of people suffer from stroke resulting to initial paralysis, slow motor recovery and chronic conditions that require continuous reha bilitation and therapy. The increasing socio-economical and psychological impact of stroke makes it necessary to find new approaches to minimize its sequels, as well as novel tools for effective, low cost and personalized reha bilitation. The integration of current ICT approaches and Virtual Reality (VR) training (based on exercise therapies) has shown significant improve ments. Moreover, recent studies have shown that through mental practice and neurofeedback the task performance is improved. To date, detailed in formation on which neurofeedback strategies lead to successful functional recovery is not available while very little is known about how to optimally utilize neurofeedback paradigms in stroke rehabilitation. Based on the cur rent limitations, the target of this project is to investigate and develop a novel upper-limb rehabilitation system with the use of novel ICT technolo gies including Brain-Computer Interfaces (BCI’s), and VR systems. Here, through a set of studies, we illustrate the design of the RehabNet frame work and its focus on integrative motor and cognitive therapy based on VR scenarios. Moreover, we broadened the inclusion criteria for low mobility pa tients, through the development of neurofeedback tools with the utilization of Brain-Computer Interfaces while investigating the effects of a brain-to-VR interaction.Todos os anos, milho˜es de pessoas sofrem de AVC, resultando em paral isia inicial, recupera¸ca˜o motora lenta e condic¸˜oes cr´onicas que requerem re abilita¸ca˜o e terapia cont´ınuas. O impacto socioecon´omico e psicol´ogico do AVC torna premente encontrar novas abordagens para minimizar as seque las decorrentes, bem como desenvolver ferramentas de reabilita¸ca˜o, efetivas, de baixo custo e personalizadas. A integra¸c˜ao das atuais abordagens das Tecnologias da Informa¸ca˜o e da Comunica¸ca˜o (TIC) e treino com Realidade Virtual (RV), com base em terapias por exerc´ıcios, tem mostrado melhorias significativas. Estudos recentes mostram, ainda, que a performance nas tare fas ´e melhorada atrav´es da pra´tica mental e do neurofeedback. At´e a` data, na˜o existem informac¸˜oes detalhadas sobre quais as estrat´egias de neurofeed back que levam a uma recupera¸ca˜o funcional bem-sucedida. De igual modo, pouco se sabe acerca de como utilizar, de forma otimizada, o paradigma de neurofeedback na recupera¸c˜ao de AVC. Face a tal, o objetivo deste projeto ´e investigar e desenvolver um novo sistema de reabilita¸ca˜o de membros supe riores, recorrendo ao uso de novas TIC, incluindo sistemas como a Interface C´erebro-Computador (ICC) e RV. Atrav´es de um conjunto de estudos, ilus tramos o design do framework RehabNet e o seu foco numa terapia motora e cognitiva, integrativa, baseada em cen´arios de RV. Adicionalmente, ampli amos os crit´erios de inclus˜ao para pacientes com baixa mobilidade, atrav´es do desenvolvimento de ferramentas de neurofeedback com a utilizac¸˜ao de ICC, ao mesmo que investigando os efeitos de uma interac¸˜ao c´erebro-para-RV

A brain-computer interface integrated with virtual reality and robotic exoskeletons for enhanced visual and kinaesthetic stimuli

Brain-computer interfaces (BCI) allow the direct control of robotic devices for neurorehabilitation and measure brain activity patterns following the user’s intent. In the past two decades, the use of non-invasive techniques such as electroencephalography and motor imagery in BCI has gained traction. However, many of the mechanisms that drive the proficiency of humans in eliciting discernible signals for BCI remains unestablished. The main objective of this thesis is to explore and assess what improvements can be made for an integrated BCI-robotic system for hand rehabilitation. Chapter 2 presents a systematic review of BCI-hand robot systems developed from 2010 to late 2019 in terms of their technical and clinical reports. Around 30 studies were identified as eligible for review and among these, 19 were still in their prototype or pre-clinical stages of development. A degree of inferiority was observed from these systems in providing the necessary visual and kinaesthetic stimuli during motor imagery BCI training. Chapter 3 discusses the theoretical background to arrive at a hypothesis that an enhanced visual and kinaesthetic stimulus, through a virtual reality (VR) game environment and a robotic hand exoskeleton, will improve motor imagery BCI performance in terms of online classification accuracy, class prediction probabilities, and electroencephalography signals. Chapters 4 and 5 focus on designing, developing, integrating, and testing a BCI-VR-robot prototype to address the research aims. Chapter 6 tests the hypothesis by performing a motor imagery BCI paradigm self-experiment with an enhanced visual and kinaesthetic stimulus against a control. A significant increase (p = 0.0422) in classification accuracies is reported among groups with enhanced visual stimulus through VR versus those without. Six out of eight sessions among the VR groups have a median of class probability values exceeding a pre-set threshold value of 0.6. Finally, the thesis concludes in Chapter 7 with a general discussion on how these findings could suggest the role of new and emerging technologies such as VR and robotics in advancing BCI-robotic systems and how the contributions of this work may help improve the usability and accessibility of such systems, not only in rehabilitation but also in skills learning and education

Augmented Reality

Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

Eyewear Computing \u2013 Augmenting the Human with Head-Mounted Wearable Assistants

The seminar was composed of workshops and tutorials on head-mounted eye tracking, egocentric vision, optics, and head-mounted displays. The seminar welcomed 30 academic and industry researchers from Europe, the US, and Asia with a diverse background, including wearable and ubiquitous computing, computer vision, developmental psychology, optics, and human-computer interaction. In contrast to several previous Dagstuhl seminars, we used an ignite talk format to reduce the time of talks to one half-day and to leave the rest of the week for hands-on sessions, group work, general discussions, and socialising. The key results of this seminar are 1) the identification of key research challenges and summaries of breakout groups on multimodal eyewear computing, egocentric vision, security and privacy issues, skill augmentation and task guidance, eyewear computing for gaming, as well as prototyping of VR applications, 2) a list of datasets and research tools for eyewear computing, 3) three small-scale datasets recorded during the seminar, 4) an article in ACM Interactions entitled \u201cEyewear Computers for Human-Computer Interaction\u201d, as well as 5) two follow-up workshops on \u201cEgocentric Perception, Interaction, and Computing\u201d at the European Conference on Computer Vision (ECCV) as well as \u201cEyewear Computing\u201d at the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)

Proceedings of the 6th international conference on disability, virtual reality and associated technologies (ICDVRAT 2006)

The proceedings of the conferenc

Fused mechanomyography and inertial measurement for human-robot interface

Human-Machine Interfaces (HMI) are the technology through which we interact with the ever-increasing quantity of smart devices surrounding us. The fundamental goal of an HMI is to facilitate robot control through uniting a human operator as the supervisor with a machine as the task executor. Sensors, actuators, and onboard intelligence have not reached the point where robotic manipulators may function with complete autonomy and therefore some form of HMI is still necessary in unstructured environments. These may include environments where direct human action is undesirable or infeasible, and situations where a robot must assist and/or interface with people. Contemporary literature has introduced concepts such as body-worn mechanical devices, instrumented gloves, inertial or electromagnetic motion tracking sensors on the arms, head, or legs, electroencephalographic (EEG) brain activity sensors, electromyographic (EMG) muscular activity sensors and camera-based (vision) interfaces to recognize hand gestures and/or track arm motions for assessment of operator intent and generation of robotic control signals. While these developments offer a wealth of future potential their utility has been largely restricted to laboratory demonstrations in controlled environments due to issues such as lack of portability and robustness and an inability to extract operator intent for both arm and hand motion. Wearable physiological sensors hold particular promise for capture of human intent/command. EMG-based gesture recognition systems in particular have received significant attention in recent literature. As wearable pervasive devices, they offer benefits over camera or physical input systems in that they neither inhibit the user physically nor constrain the user to a location where the sensors are deployed. Despite these benefits, EMG alone has yet to demonstrate the capacity to recognize both gross movement (e.g. arm motion) and finer grasping (e.g. hand movement). As such, many researchers have proposed fusing muscle activity (EMG) and motion tracking e.g. (inertial measurement) to combine arm motion and grasp intent as HMI input for manipulator control. However, such work has arguably reached a plateau since EMG suffers from interference from environmental factors which cause signal degradation over time, demands an electrical connection with the skin, and has not demonstrated the capacity to function out of controlled environments for long periods of time. This thesis proposes a new form of gesture-based interface utilising a novel combination of inertial measurement units (IMUs) and mechanomyography sensors (MMGs). The modular system permits numerous configurations of IMU to derive body kinematics in real-time and uses this to convert arm movements into control signals. Additionally, bands containing six mechanomyography sensors were used to observe muscular contractions in the forearm which are generated using specific hand motions. This combination of continuous and discrete control signals allows a large variety of smart devices to be controlled. Several methods of pattern recognition were implemented to provide accurate decoding of the mechanomyographic information, including Linear Discriminant Analysis and Support Vector Machines. Based on these techniques, accuracies of 94.5% and 94.6% respectively were achieved for 12 gesture classification. In real-time tests, accuracies of 95.6% were achieved in 5 gesture classification. It has previously been noted that MMG sensors are susceptible to motion induced interference. The thesis also established that arm pose also changes the measured signal. This thesis introduces a new method of fusing of IMU and MMG to provide a classification that is robust to both of these sources of interference. Additionally, an improvement in orientation estimation, and a new orientation estimation algorithm are proposed. These improvements to the robustness of the system provide the first solution that is able to reliably track both motion and muscle activity for extended periods of time for HMI outside a clinical environment. Application in robot teleoperation in both real-world and virtual environments were explored. With multiple degrees of freedom, robot teleoperation provides an ideal test platform for HMI devices, since it requires a combination of continuous and discrete control signals. The field of prosthetics also represents a unique challenge for HMI applications. In an ideal situation, the sensor suite should be capable of detecting the muscular activity in the residual limb which is naturally indicative of intent to perform a specific hand pose and trigger this post in the prosthetic device. Dynamic environmental conditions within a socket such as skin impedance have delayed the translation of gesture control systems into prosthetic devices, however mechanomyography sensors are unaffected by such issues. There is huge potential for a system like this to be utilised as a controller as ubiquitous computing systems become more prevalent, and as the desire for a simple, universal interface increases. Such systems have the potential to impact significantly on the quality of life of prosthetic users and others.Open Acces

Haptics: Science, Technology, Applications

This open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility

Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design

Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data

Haptics: Science, Technology, Applications

This open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility