6,451 research outputs found

    Deep Learning Techniques for Electroencephalography Analysis

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    In this thesis we design deep learning techniques for training deep neural networks on electroencephalography (EEG) data and in particular on two problems, namely EEG-based motor imagery decoding and EEG-based affect recognition, addressing challenges associated with them. Regarding the problem of motor imagery (MI) decoding, we first consider the various kinds of domain shifts in the EEG signals, caused by inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and impede robust cross-subject generalization. We build a two-stage model ensemble architecture and propose two objectives to train it, combining the strengths of curriculum learning and collaborative training. Our subject-independent experiments on the large datasets of Physionet and OpenBMI, verify the effectiveness of our approach. Next, we explore the utilization of the spatial covariance of EEG signals through alignment techniques, with the goal of learning domain-invariant representations. We introduce a Riemannian framework that concurrently performs covariance-based signal alignment and data augmentation, while training a convolutional neural network (CNN) on EEG time-series. Experiments on the BCI IV-2a dataset show that our method performs superiorly over traditional alignment, by inducing regularization to the weights of the CNN. We also study the problem of EEG-based affect recognition, inspired by works suggesting that emotions can be expressed in relative terms, i.e. through ordinal comparisons between different affective state levels. We propose treating data samples in a pairwise manner to infer the ordinal relation between their corresponding affective state labels, as an auxiliary training objective. We incorporate our objective in a deep network architecture which we jointly train on the tasks of sample-wise classification and pairwise ordinal ranking. We evaluate our method on the affective datasets of DEAP and SEED and obtain performance improvements over deep networks trained without the additional ranking objective

    Management strategies and contributory factors for resistance exercise-induced muscle damage: an exploration of dietary protein, exercise load, and sex

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    The World Health Organisation recommends that resistance exercise be performed at least twice per week to benefit general health and wellbeing. However, resistance exercise is associated with acute muscle damage that potentially can dampen muscle adaptations promoted by chronic resistance training. The extent to which muscle is damaged by exercise is influenced by various factors, including age, training status, exercise type, and – notable to this thesis – sex. To this end, establishing sex-specific management strategies for exercise-induced muscle damage (EIMD) is important to optimise the benefits of exercise. Two EIMD management strategies were focussed on in this thesis: dietary protein supplementation and exercise load manipulation. It was identified in this thesis that research into the impact both of protein supplementation and exercise load on EIMD heavily underrepresent female populations (chapters 3 and 5), despite well-documented sex differences in EIMD responses. Therefore, future research priority should be placed on bridging the sex data gap by conducting high-quality studies centralising around female-focussed and sex-comparative methodological designs. Both peri-exercise protein supplementation and exercise load manipulation in favour of lighter loads were revealed to be effective management strategies for resistance EIMD in males through systematic and scoping review of the current literature (chapters 3 and 5, respectively). Due to a lack of data from females, it is only appropriate for these strategies to be recommended for males at present. To decipher whether protein supplementation and lower exercise loads are beneficial for managing EIMD in females, a randomised controlled trial (RCT) (chapter 4) and a protocol for an RCT (chapter 6) involving male and female participants are presented in this thesis. The incorporation of ecologically-valid resistance exercise in the RCT in chapter 4 highlighted that even mild muscle damage is attenuated in females, reflected in diminished increases in post-exercise creatine kinase concentration and muscle soreness compared with males; however, the reason for this difference requires further investigation. This study, while supporting sex differences, contrasted previous studies, as neither males nor females experienced an attenuation of EIMD during milk protein supplementation. This difference likely owed to the lower severity of muscle damage induced in the current study relative to previous studies, and accordingly, future research should seek to discover alternative management strategies for mild EIMD. A protocol for an RCT examining the impact of exercise load on EIMD in untrained males and females is described in Chapter 6 of this thesis and may be used as guidance for researchers developing similar, sex-comparative studies. It was hypothesised that females will experience attenuated muscle damage relative to males and low-load exercise will induce less muscle damage than high-load exercise in both sexes. A lack of methodological consistency among EIMD studies was a recurring finding throughout this thesis, which posed an issue when attempting to compare between-study outcomes and reach a consensus. Achieving greater uniformity in study designs by adopting comparable methods relating to EIMD markers and time-points of assessment would help improve understanding of the factors influencing the magnitude of EIMD and effective management strategies. While there are limitations with several EIMD markers – for example the variability of biomarkers and subjectivity of perceptual assessments – once the optimal markers are determined, these should be consistently used moving forward. Overall, this thesis has contributed to the current body of knowledge by demonstrating that milk protein ingestion is not an effective management strategy for muscle damage following ecologically-valid resistance exercise; therefore, alternative strategies to mitigate mild muscle damage should be investigated. Further, this work supported previous reports of sex differences in EIMD and indicated that the attenuation of EIMD in females relative to males was not attributed to sex differences in body composition; thus, the aetiology of such differences necessitates further exploration by means of high-quality sex comparative research. Finally, this thesis reached the consensus recommendation that lower exercise loads can be utilised to reduce muscle damage in males; nonetheless, supporting evidence for the application of this recommendation to females is required

    Design, fabrication and stiffening of soft pneumatic robots

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    Although compliance allows the soft robot to be under-actuated and generalise its control, it also impacts the ability of the robot to exert forces on the environment. There is a trade-off between robots being compliant or precise and strong. Many mechanisms that change robots' stiffness on demand have been proposed, but none are perfect, usually compromising the device's compliance and restricting its motion capabilities. Keeping the above issues in mind, this thesis focuses on creating robust and reliable pneumatic actuators, that are designed to be easily manufactured with simple tools. They are optimised towards linear behaviour, which simplifies modelling and improve control strategies. The principle idea in relation to linearisation is a reinforcement strategy designed to amplify the desired, and limit the unwanted, deformation of the device. Such reinforcement can be achieved using fibres or 3D printed structures. I have shown that the linearity of the actuation is, among others, a function of the reinforcement density and shape, in that the response of dense fibre-reinforced actuators with a circular cross-section is significantly more linear than that of non-reinforced or non-circular actuators. I have explored moulding manufacturing techniques and a mixture of 3D printing and moulding. Many aspects of these techniques have been optimised for reliability, repeatability, and process simplification. I have proposed and implemented a novel moulding technique that uses disposable moulds and can easily be used by an inexperienced operator. I also tried to address the compliance-stiffness trade-off issue. As a result, I have proposed an intelligent structure that behaves differently depending on the conditions. Thanks to its properties, such a structure could be used in applications that require flexibility, but also the ability to resist external disturbances when necessary. Due to its nature, individual cells of the proposed system could be used to implement physical logic elements, resulting in embodied intelligent behaviours. As a proof-of-concept, I have demonstrated use of my actuators in several applications including prosthetic hands, octopus, and fish robots. Each of those devices benefits from a slightly different actuation system but each is based on the same core idea - fibre reinforced actuators. I have shown that the proposed design and manufacturing techniques have several advantages over the methods used so far. The manufacturing methods I developed are more reliable, repeatable, and require less manual work than the various other methods described in the literature. I have also shown that the proposed actuators can be successfully used in real-life applications. Finally, one of the most important outcomes of my research is a contribution to an orthotic device based on soft pneumatic actuators. The device has been successfully deployed, and, at the time of submission of this thesis, has been used for several months, with good results reported, by a patient

    Vortex motions in the solar atmosphere

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    Vortex flows, related to solar convective turbulent dynamics at granular scales and their interplay with magnetic fields within intergranular lanes, occur abundantly on the solar surface and in the atmosphere above. Their presence is revealed in high-resolution and high-cadence solar observations from the ground and from space and with state-of-the-art magnetoconvection simulations. Vortical flows exhibit complex characteristics and dynamics, excite a wide range of different waves, and couple different layers of the solar atmosphere, which facilitates the channeling and transfer of mass, momentum and energy from the solar surface up to the low corona. Here we provide a comprehensive review of documented research and new developments in theory, observations, and modelling of vortices over the past couple of decades after their observational discovery, including recent observations in Hα , innovative detection techniques, diverse hydrostatic modelling of waves and forefront magnetohydrodynamic simulations incorporating effects of a non-ideal plasma. It is the first systematic overview of solar vortex flows at granular scales, a field with a plethora of names for phenomena that exhibit similarities and differences and often interconnect and rely on the same physics. With the advent of the 4-m Daniel K. Inouye Solar Telescope and the forthcoming European Solar Telescope, the ongoing Solar Orbiter mission, and the development of cutting-edge simulations, this review timely addresses the state-of-the-art on vortex flows and outlines both theoretical and observational future research directions

    Mixed Multivariable Models to Improve Dental Age Estimation in a Worldwide Sample

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    Despite popular acknowledgment that dental development is the age estimation method least affected by environment, there has been a persistent call for population-specific methods designed to tease out minute differences for increased precision. Many of these methods fail to provide adequate statistical justification for the need to limit the population and do not use a realistic sample, limiting their application. Because both forensic and bioarchaeological contexts necessitate generalizable methods that can be applied to unknown demographic context, this research explores the impacts of compounding variables and incorporating diversity on precision and accuracy. Development and eruption data was collected on a cross-sectional sample of 2,656 individuals with known chronological age between birth and 26 years old. The sample consisted of individuals from 10 countries (Angola, Australia, Brazil, Colombia, France, Netherlands, Saudi Arabia, Spain, South Africa and the United States). Ordinal data was recorded from a combination of dental radiographs, Lodox scans, and CT scans; continuous data was collected using 3D interlandmark distance on CT samples only. A Mixed Cumulative Probit algorithm was used to create univariate models for each tooth, and a subset of multivariate models for comparison. Training samples from six countries (Brazil, Colombia, France, Saudi Arabia, Spain, South Africa and the United States) were used to develop the age estimation models, each tested on a holdout sample and independent samples from the remaining countries. Two main comparisons were made: 1. Pooled, global model performance vs. all population-specific model performance and 2. Model performance of different variables (i.e., univariate or multivariate, ordinal or continuous). Accuracy, precision, bias, and generalizability were used as measures of performance. Pooled models often matched or out-performed the population-specific models and were the only consistently generalizable option. Multivariate and continuous/mixed models showed promise for increasing the accuracy and precision of the method, but more variations and increased samples are needed for an adequate comparison of performance. Method adjustments and future directions to build on the present work are recommended and discussed

    Deep Learning Models for Stable Gait Prediction Applied to Exoskeleton Reference Trajectories for Children With Cerebral Palsy

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    Gait trajectory prediction models have several applications in exoskeleton control; they can be used as feed-forward input to low-level controllers and to generate reference/target trajectories for position-controlled exoskeletons. In our study, we implement four deep learning models (LSTM, FCN, CNN and Transformer) that perform one-step-ahead gait trajectory prediction after training on gait patterns of typically developing children. We propose a methodology that optimises for stability in long-term forecasts, and evaluate the performance of the models on typically developing (TD) and Cerebral Palsy (CP) gait during recursive prediction of 200 time-steps in the future (which may lead to propagation of errors) and in the presence of varying levels of Gaussian noise (1%-5%). Results on TD gait show that the FCN and Transformer, with mean absolute errors (MAEs) for one-step-ahead predictions between 1.17.-1.63., are the most suitable for the intended application. We also proposed an approach for generating adaptive trajectories that can be used as reference trajectories for position-controlled exoskeletons. Gait patterns from children with Cerebral Palsy were fed into gait trajectory prediction models trained on typically developing gait only, to generate corrective patterns. Preliminary results show that the gait patterns of typically developing children were introduced onto the generated trajectories

    Human Gait Analysis using Spatiotemporal Data Obtained from Gait Videos

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    Mit der Entwicklung von Deep-Learning-Techniken sind Deep-acNN-basierte Methoden zum Standard für Bildverarbeitungsaufgaben geworden, wie z. B. die Verfolgung menschlicher Bewegungen und Posenschätzung, die Erkennung menschlicher Aktivitäten und die Erkennung von Gesichtern. Deep-Learning-Techniken haben den Entwurf, die Implementierung und den Einsatz komplexer und vielfältiger Anwendungen verbessert, die nun in einer Vielzahl von Bereichen, einschließlich der Biomedizintechnik, eingesetzt werden. Die Anwendung von Computer-Vision-Techniken auf die medizinische Bild- und Videoanalyse hat zu bemerkenswerten Ergebnissen bei der Erkennung von Ereignissen geführt. Die eingebaute Fähigkeit von convolutional neural network (CNN), Merkmale aus komplexen medizinischen Bildern zu extrahieren, hat in Verbindung mit der Fähigkeit von long short term memory network (LSTM), die zeitlichen Informationen zwischen Ereignissen zu erhalten, viele neue Horizonte für die medizinische Forschung geschaffen. Der Gang ist einer der kritischen physiologischen Bereiche, der viele Störungen im Zusammenhang mit Alterung und Neurodegeneration widerspiegeln kann. Eine umfassende und genaue Ganganalyse kann Einblicke in die physiologischen Bedingungen des Menschen geben. Bestehende Ganganalyseverfahren erfordern eine spezielle Umgebung, komplexe medizinische Geräte und geschultes Personal für die Erfassung der Gangdaten. Im Falle von tragbaren Systemen kann ein solches System die kognitiven Fähigkeiten beeinträchtigen und für die Patienten unangenehm sein. Außerdem wurde berichtet, dass die Patienten in der Regel versuchen, während des Labortests bessere Leistungen zu erbringen, was möglicherweise nicht ihrem tatsächlichen Gang entspricht. Trotz technologischer Fortschritte stoßen wir bei der Messung des menschlichen Gehens in klinischen und Laborumgebungen nach wie vor an Grenzen. Der Einsatz aktueller Ganganalyseverfahren ist nach wie vor teuer und zeitaufwändig und erschwert den Zugang zu Spezialgeräten und Fachwissen. Daher ist es zwingend erforderlich, über Methoden zu verfügen, die langfristige Daten über den Gesundheitszustand des Patienten liefern, ohne doppelte kognitive Aufgaben oder Unannehmlichkeiten bei der Verwendung tragbarer Sensoren. In dieser Arbeit wird daher eine einfache, leicht zu implementierende und kostengünstige Methode zur Erfassung von Gangdaten vorgeschlagen. Diese Methode basiert auf der Aufnahme von Gehvideos mit einer Smartphone-Kamera in einer häuslichen Umgebung unter freien Bedingungen. Deep neural network (NN) verarbeitet dann diese Videos, um die Gangereignisse zu extrahieren. Die erkannten Ereignisse werden dann weiter verwendet, um verschiedene räumlich-zeitliche Parameter des Gangs zu quantifizieren, die für jedes Ganganalysesystem wichtig sind. In dieser Arbeit wurden Gangvideos verwendet, die mit einer Smartphone-Kamera mit geringer Auflösung außerhalb der Laborumgebung aufgenommen wurden. Viele Deep- Learning-basierte NNs wurden implementiert, um die grundlegenden Gangereignisse wie die Fußposition in Bezug auf den Boden aus diesen Videos zu erkennen. In der ersten Studie wurde die Architektur von AlexNet verwendet, um das Modell anhand von Gehvideos und öffentlich verfügbaren Datensätzen von Grund auf zu trainieren. Mit diesem Modell wurde eine Gesamtgenauigkeit von 74% erreicht. Im nächsten Schritt wurde jedoch die LSTM-Schicht in dieselbe Architektur integriert. Die eingebaute Fähigkeit von LSTM in Bezug auf die zeitliche Information führte zu einer verbesserten Vorhersage der Etiketten für die Fußposition, und es wurde eine Genauigkeit von 91% erreicht. Allerdings gibt es Schwierigkeiten bei der Vorhersage der richtigen Bezeichnungen in der letzten Phase des Schwungs und der Standphase jedes Fußes. Im nächsten Schritt wird das Transfer-Lernen eingesetzt, um die Vorteile von bereits trainierten tiefen NNs zu nutzen, indem vortrainierte Gewichte verwendet werden. Zwei bekannte Modelle, inceptionresnetv2 (IRNV-2) und densenet201 (DN-201), wurden mit ihren gelernten Gewichten für das erneute Training des NN auf neuen Daten verwendet. Das auf Transfer-Lernen basierende vortrainierte NN verbesserte die Vorhersage von Kennzeichnungen für verschiedene Fußpositionen. Es reduzierte insbesondere die Schwankungen in den Vorhersagen in der letzten Phase des Gangschwungs und der Standphase. Bei der Vorhersage der Klassenbezeichnungen der Testdaten wurde eine Genauigkeit von 94% erreicht. Da die Abweichung bei der Vorhersage des wahren Labels hauptsächlich ein Bild betrug, konnte sie bei einer Bildrate von 30 Bildern pro Sekunde ignoriert werden. Die vorhergesagten Markierungen wurden verwendet, um verschiedene räumlich-zeitliche Parameter des Gangs zu extrahieren, die für jedes Ganganalysesystem entscheidend sind. Insgesamt wurden 12 Gangparameter quantifiziert und mit der durch Beobachtungsmethoden gewonnenen Grundwahrheit verglichen. Die NN-basierten räumlich-zeitlichen Parameter zeigten eine hohe Korrelation mit der Grundwahrheit, und in einigen Fällen wurde eine sehr hohe Korrelation erzielt. Die Ergebnisse belegen die Nützlichkeit der vorgeschlagenen Methode. DerWert des Parameters über die Zeit ergab eine Zeitreihe, eine langfristige Darstellung des Ganges. Diese Zeitreihe konnte mit verschiedenen mathematischen Methoden weiter analysiert werden. Als dritter Beitrag in dieser Dissertation wurden Verbesserungen an den bestehenden mathematischen Methoden der Zeitreihenanalyse von zeitlichen Gangdaten vorgeschlagen. Zu diesem Zweck werden zwei Verfeinerungen bestehender entropiebasierter Methoden zur Analyse von Schrittintervall-Zeitreihen vorgeschlagen. Diese Verfeinerungen wurden an Schrittintervall-Zeitseriendaten von normalen und neurodegenerativen Erkrankungen validiert, die aus der öffentlich zugänglichen Datenbank PhysioNet heruntergeladen wurden. Die Ergebnisse zeigten, dass die von uns vorgeschlagene Methode eine klare Trennung zwischen gesunden und kranken Gruppen ermöglicht. In Zukunft könnten fortschrittliche medizinische Unterstützungssysteme, die künstliche Intelligenz nutzen und von den hier vorgestellten Methoden abgeleitet sind, Ärzte bei der Diagnose und langfristigen Überwachung des Gangs von Patienten unterstützen und so die klinische Arbeitsbelastung verringern und die Patientensicherheit verbessern

    The genomic basis of bacterial symbiosis in Osedax and Vestimentifera (Siboglinidae, Annelida)

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    Bacterial symbioses allow annelids to colonise extreme ecological niches, such as hydrothermal vents and whale falls. Yet, the genetic principles sustaining these symbioses remain unclear. Here, I show that different genomic adaptations underpin the symbioses of phylogenetically related annelids with distinct nutritional strategies. Genome compaction and extensive gene losses distinguish the heterotrophic symbiosis of the bone-eating worm Osedax frankpressi from the chemoautotrophic symbiosis of deep-sea Vestimentifera. Osedax’s endosymbionts complement many of the host's metabolic deficiencies, including the loss of pathways to recycle nitrogen and synthesise some amino acids. Osedax’s endosymbionts possess the glyoxylate cycle, which could allow more efficient catabolism of bone-derived nutrients and the production of carbohydrates from fatty acids. Unlike in most Vestimentifera, innate immunity genes are reduced in O. frankpressi, which, however, has an expansion of matrix Metalloproteinases to digest collagen. This study supports that distinct nutritional interactions influence host genome evolution differently in highly specialised symbioses

    Assistive telehealth systems for neurorehabilitation

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    Telehealth is an evolving field within the broader domain of Biomedical Engineering, specifically situated within the context of the Internet of Medical Things (IoMT). In today's society, the importance of Telehealth systems is increasingly recognized, as they enable remote patient treatment by physicians. One significant application in neurorehabilitation is Transcranial Direct Current Stimulation (tDCS), which has demonstrated its effectiveness in modulating mental function and learning over several years. Furthermore, tDCS is widely accepted as a safe approach in the field. This presentation focuses on the development of a non-invasive wearable tDCS device with integrated Internet connectivity. This IoMT device enables remote configuration of treatment parameters, such as session duration, current level, and placebo status. Clinicians can remotely access the device and define these parameters within the approved safety ranges for tDCS treatments. In addition to the wearable tDCS device, a prototype web portal is being developed to collect performance data during neurorehabilitation exercises conducted by individuals at home. This portal also facilitates remote interaction between patients and clinicians. To provide a platform-independent solution for accessing up-to-date healthcare information, a Progressive Web Application (PWA) is being developed. The PWA enables real-time communication between patients and doctors through text chat and video conferencing. The primary objective is to create a cross-platform web application with PWA features that can function effectively as a native application in various operating systems

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics
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