100 research outputs found

    Design and Evaluation of a Hardware System for Online Signal Processing within Mobile Brain-Computer Interfaces

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    Brain-Computer Interfaces (BCIs) sind innovative Systeme, die eine direkte Kommunikation zwischen dem Gehirn und externen Geräten ermöglichen. Diese Schnittstellen haben sich zu einer transformativen Lösung nicht nur für Menschen mit neurologischen Verletzungen entwickelt, sondern auch für ein breiteres Spektrum von Menschen, das sowohl medizinische als auch nicht-medizinische Anwendungen umfasst. In der Vergangenheit hat die Herausforderung, dass neurologische Verletzungen nach einer anfänglichen Erholungsphase statisch bleiben, die Forscher dazu veranlasst, innovative Wege zu beschreiten. Seit den 1970er Jahren stehen BCIs an vorderster Front dieser Bemühungen. Mit den Fortschritten in der Forschung haben sich die BCI-Anwendungen erweitert und zeigen ein großes Potenzial für eine Vielzahl von Anwendungen, auch für weniger stark eingeschränkte (zum Beispiel im Kontext von Hörelektronik) sowie völlig gesunde Menschen (zum Beispiel in der Unterhaltungsindustrie). Die Zukunft der BCI-Forschung hängt jedoch auch von der Verfügbarkeit zuverlässiger BCI-Hardware ab, die den Einsatz in der realen Welt gewährleistet. Das im Rahmen dieser Arbeit konzipierte und implementierte CereBridge-System stellt einen bedeutenden Fortschritt in der Brain-Computer-Interface-Technologie dar, da es die gesamte Hardware zur Erfassung und Verarbeitung von EEG-Signalen in ein mobiles System integriert. Die Architektur der Verarbeitungshardware basiert auf einem FPGA mit einem ARM Cortex-M3 innerhalb eines heterogenen ICs, was Flexibilität und Effizienz bei der EEG-Signalverarbeitung gewährleistet. Der modulare Aufbau des Systems, bestehend aus drei einzelnen Boards, gewährleistet die Anpassbarkeit an unterschiedliche Anforderungen. Das komplette System wird an der Kopfhaut befestigt, kann autonom arbeiten, benötigt keine externe Interaktion und wiegt einschließlich der 16-Kanal-EEG-Sensoren nur ca. 56 g. Der Fokus liegt auf voller Mobilität. Das vorgeschlagene anpassbare Datenflusskonzept erleichtert die Untersuchung und nahtlose Integration von Algorithmen und erhöht die Flexibilität des Systems. Dies wird auch durch die Möglichkeit unterstrichen, verschiedene Algorithmen auf EEG-Daten anzuwenden, um unterschiedliche Anwendungsziele zu erreichen. High-Level Synthesis (HLS) wurde verwendet, um die Algorithmen auf das FPGA zu portieren, was den Algorithmenentwicklungsprozess beschleunigt und eine schnelle Implementierung von Algorithmusvarianten ermöglicht. Evaluierungen haben gezeigt, dass das CereBridge-System in der Lage ist, die gesamte Signalverarbeitungskette zu integrieren, die für verschiedene BCI-Anwendungen erforderlich ist. Darüber hinaus kann es mit einer Batterie von mehr als 31 Stunden Dauerbetrieb betrieben werden, was es zu einer praktikablen Lösung für mobile Langzeit-EEG-Aufzeichnungen und reale BCI-Studien macht. Im Vergleich zu bestehenden Forschungsplattformen bietet das CereBridge-System eine bisher unerreichte Leistungsfähigkeit und Ausstattung für ein mobiles BCI. Es erfüllt nicht nur die relevanten Anforderungen an ein mobiles BCI-System, sondern ebnet auch den Weg für eine schnelle Übertragung von Algorithmen aus dem Labor in reale Anwendungen. Im Wesentlichen liefert diese Arbeit einen umfassenden Entwurf für die Entwicklung und Implementierung eines hochmodernen mobilen EEG-basierten BCI-Systems und setzt damit einen neuen Standard für BCI-Hardware, die in der Praxis eingesetzt werden kann.Brain-Computer Interfaces (BCIs) are innovative systems that enable direct communication between the brain and external devices. These interfaces have emerged as a transformative solution not only for individuals with neurological injuries, but also for a broader range of individuals, encompassing both medical and non-medical applications. Historically, the challenge of neurological injury being static after an initial recovery phase has driven researchers to explore innovative avenues. Since the 1970s, BCIs have been at one forefront of these efforts. As research has progressed, BCI applications have expanded, showing potential in a wide range of applications, including those for less severely disabled (e.g. in the context of hearing aids) and completely healthy individuals (e.g. entertainment industry). However, the future of BCI research also depends on the availability of reliable BCI hardware to ensure real-world application. The CereBridge system designed and implemented in this work represents a significant leap forward in brain-computer interface technology by integrating all EEG signal acquisition and processing hardware into a mobile system. The processing hardware architecture is centered around an FPGA with an ARM Cortex-M3 within a heterogeneous IC, ensuring flexibility and efficiency in EEG signal processing. The modular design of the system, consisting of three individual boards, ensures adaptability to different requirements. With a focus on full mobility, the complete system is mounted on the scalp, can operate autonomously, requires no external interaction, and weighs approximately 56g, including 16 channel EEG sensors. The proposed customizable dataflow concept facilitates the exploration and seamless integration of algorithms, increasing the flexibility of the system. This is further underscored by the ability to apply different algorithms to recorded EEG data to meet different application goals. High-Level Synthesis (HLS) was used to port algorithms to the FPGA, accelerating the algorithm development process and facilitating rapid implementation of algorithm variants. Evaluations have shown that the CereBridge system is capable of integrating the complete signal processing chain required for various BCI applications. Furthermore, it can operate continuously for more than 31 hours with a 1800mAh battery, making it a viable solution for long-term mobile EEG recording and real-world BCI studies. Compared to existing research platforms, the CereBridge system offers unprecedented performance and features for a mobile BCI. It not only meets the relevant requirements for a mobile BCI system, but also paves the way for the rapid transition of algorithms from the laboratory to real-world applications. In essence, this work provides a comprehensive blueprint for the development and implementation of a state-of-the-art mobile EEG-based BCI system, setting a new benchmark in BCI hardware for real-world applicability

    WiGlove : A Passive Dynamic Orthosis for Home-based Post-stroke Rehabilitation of Hand and Wrist

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    Stroke survivors often experience varying levels of motor function deficits in their hands affecting their ability to perform activities of daily life. Recovering their hand functions through neurorehabilitation is a significant step in their recovery towards independent living. Home-based rehabilitation using robotic devices allows stroke survivors to train at their convenience independent of factors such as the availability of therapists’ appointments and the need for frequent travel to outpatient clinics. While many robotic solutions have been proposed to address the above concerns, most focus on training only the wrist or the fingers, neglecting the synergy between the two. To address this, the WiGlove was co-designed to allow hemiparetic stroke survivors to train both the wrist and fingers in the comfort of their homes. The central hypothesis of this work is to investigate if a device designed using user-centred methods featuring aspects of usability such as easy donning and doffing and wireless operation, can act as a feasible tool for home-based rehabilitation of the hand and wrist following stroke. In order to aid this investigation, we tackled this task in three stages of usability and feasibility evaluations. Firstly, healthy participants tried the current state of the art, the SCRIPT Passive Orthosis, as well as the WiGlove, in a counterbalanced, within-subject experiment and attested to WiGlove’s improvement in several aspects of usability such as ease of don/doffing, suitability for ADL, unblocked natural degrees of freedom, safety and aesthetic appeal. Subsequently, a heuristic evaluation with six stroke therapists validated these improvements and helped identify issues they perceived to potentially affect the device’s acceptance. Integrating this feedback, the updated WiGlove was subjected to a six-week summative feasibility evaluation with two stroke survivors, with varying levels of impairment, in their homes without supervision from the therapists. Results from this study were overwhelmingly positive on the usability and acceptance of the WiGlove. Furthermore, in the case of the first participant who trained with it for a total of 39 hours, notable improvements were observed in the participant’s hand functions. It showed that even without a prescribed training protocol, both participants were willing to train regularly with the WiGlove and its games, sometimes several times a day. These results demonstrate that WiGlove can be a promising tool for home-based rehabilitation for stroke survivors and serve as evidence for a larger user study with more participants with varying levels of motor impairments due to stroke. The findings of this study also offer preliminary evidence supporting the effectiveness of training with the WiGlove, particularly in the case of the first participant, who exhibited a significant reduction of tone in the hand as a result of increased training intensity. Owing to the participant’s satisfaction with the device, it was requested by him to extend his involvement in the study by using the WiGlove for a longer duration which was facilitated

    Design and Modelling of a Magnetic Fluid Based Artificial Muscle for Gait Rehabilitation

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    Robotic gait rehabilitation systems have seen a plateau in functional gait rehabilitation outcomes for hemiparetic stroke survivors over the past 5 years, particularly when using improvements in walking speed as a key metric. Research continues into various types of robotic systems, and many have been seen to increase rates of independent walking. Why then have improvements in users’ walking speed remained sluggish? It is suggested that the issue may lie either in the design of the robotic systems themselves or in approach these systems take to providing training. A such a ground up approach is taken for this research into improving robotic gait rehabilitation techniques. This first required a closer look at how hemiparetic gait patterns vary with walking speed though this in turn necessitated consideration of the targeting effect. Caused by the presence of a distinctly marked shape along their path, this effect was found to have no significant impact on the kinematic parameters of hemiparetic stroke survivors. This allowed gait analysis into the kinematic gait patterns of stroke survivors to be carried out and relationships between said pattern and the participants walking speed to be obtained. It was found that there existed compensatory gait techniques that related to walking speed and it was suggested that these could be encouraged as beneficial traits to improve functional rehabilitation outcomes. This still left the consideration of the robotic system itself though. Soft robotics and smart materials had been suggested as a potential avenue for designing improved robotic systems that would allow for high user engagement and autonomy while removing the tethering common in current designs. A magnetic fluid muscle design and FEA model was proposed and validated. The design was iterated on using the FEA model to improve its functionality and gather details about its potential for use in gait rehabilitation

    Bio-Inspired Soft Artificial Muscles for Robotic and Healthcare Applications

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    Soft robotics and soft artificial muscles have emerged as prolific research areas and have gained substantial traction over the last two decades. There is a large paradigm shift of research interests in soft artificial muscles for robotic and medical applications due to their soft, flexible and compliant characteristics compared to rigid actuators. Soft artificial muscles provide safe human-machine interaction, thus promoting their implementation in medical fields such as wearable assistive devices, haptic devices, soft surgical instruments and cardiac compression devices. Depending on the structure and material composition, soft artificial muscles can be controlled with various excitation sources, including electricity, magnetic fields, temperature and pressure. Pressure-driven artificial muscles are among the most popular soft actuators due to their fast response, high exertion force and energy efficiency. Although significant progress has been made, challenges remain for a new type of artificial muscle that is easy to manufacture, flexible, multifunctional and has a high length-to-diameter ratio. Inspired by human muscles, this thesis proposes a soft, scalable, flexible, multifunctional, responsive, and high aspect ratio hydraulic filament artificial muscle (HFAM) for robotic and medical applications. The HFAM consists of a silicone tube inserted inside a coil spring, which expands longitudinally when receiving positive hydraulic pressure. This simple fabrication method enables low-cost and mass production of a wide range of product sizes and materials. This thesis investigates the characteristics of the proposed HFAM and two implementations, as a wearable soft robotic glove to aid in grasping objects, and as a smart surgical suture for perforation closure. Multiple HFAMs are also combined by twisting and braiding techniques to enhance their performance. In addition, smart textiles are created from HFAMs using traditional knitting and weaving techniques for shape-programmable structures, shape-morphing soft robots and smart compression devices for massage therapy. Finally, a proof-of-concept robotic cardiac compression device is developed by arranging HFAMs in a special configuration to assist in heart failure treatment. Overall this fundamental work contributes to the development of soft artificial muscle technologies and paves the way for future comprehensive studies to develop HFAMs for specific medical and robotic requirements

    Chapter 34 - Biocompatibility of nanocellulose: Emerging biomedical applications

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    Nanocellulose already proved to be a highly relevant material for biomedical applications, ensued by its outstanding mechanical properties and, more importantly, its biocompatibility. Nevertheless, despite their previous intensive research, a notable number of emerging applications are still being developed. Interestingly, this drive is not solely based on the nanocellulose features, but also heavily dependent on sustainability. The three core nanocelluloses encompass cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs), and bacterial nanocellulose (BNC). All these different types of nanocellulose display highly interesting biomedical properties per se, after modification and when used in composite formulations. Novel applications that use nanocellulose includewell-known areas, namely, wound dressings, implants, indwelling medical devices, scaffolds, and novel printed scaffolds. Their cytotoxicity and biocompatibility using recent methodologies are thoroughly analyzed to reinforce their near future applicability. By analyzing the pristine core nanocellulose, none display cytotoxicity. However, CNF has the highest potential to fail long-term biocompatibility since it tends to trigger inflammation. On the other hand, neverdried BNC displays a remarkable biocompatibility. Despite this, all nanocelluloses clearly represent a flag bearer of future superior biomaterials, being elite materials in the urgent replacement of our petrochemical dependence

    Stretchable Surface Electromyography Electrode Array Based on Liquid Metal and Conductive Polymer

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    Electromyography (EMG), the science of detecting and interpreting muscle electrical activity, plays a crucial role in clinical diagnostics and research. It enables assessment of muscle function, detection of abnormalities, and monitoring of rehabilitation progress. However, the current use of EMG devices is primarily limited to clinical settings, preventing its potential to revolutionize personal health management. If surface electromyography (sEMG) electrodes are stretchable, arrayed, reusable and able to continuously record, their applications for personal health management are broadened. Existing electrodes lack these essential features, hampering their widespread adoption. This thesis addresses these limitations by designing an adhesive dry electrode using tannic acid, polyvinyl alcohol, and PEDOT:PSS (TPP). Through meticulous optimization, TPP electrodes offer superior stretchability and adhesiveness compared to conventional Ag/AgCl electrodes. This ensures stable and long-term skin contact for recording. Furthermore, a metal-polymer electrode array patch (MEAP) is introduced, featuring liquid metal (LM) circuits and TPP electrodes. MEAPs exhibit better conformability than current commercial arrays, resulting in higher signal quality and stable recordings, even during significant skin deformations caused by muscle movements. Manufactured using scalable screen-printing, MEAPs combine stretchable materials and array architecture for real-time monitoring of muscle stress, fatigue, and tendon displacement. They hold great promise in reducing muscle and tendon injuries and enhancing performance in both daily exercise and professional sports. In addition, a pilot study compares MEAP performance in clinical electrodiagnostics with needle electrodes, demonstrating the non-invasive advantage of MEAP by successfully recording the signals from the same motor unit as the needle. These advancements position MEAP at the forefront of the EMG field, poised to drive breakthroughs in electrodiagnostics, personalized medicine, sports science, and rehabilitation
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