517 research outputs found
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
Consumer Neuroscience e Brand Relationship: misurare l’associazione implicita tra il Sé del consumatore e il brand.
Il presente elaborato si focalizza sulla connessione tra Consumer Neuroscience e Brand Relationship con un focus specifico sul Sé del consumatore, analizzato attraverso uno strumento di misurazione indiretta del comportamento. L’obiettivo è stato quello di contribuire alla validazione e all’utilizzo nel contesto italiano di un SC-IAT per lo studio dell’associazione tra Sé e brand, interpretandone i risultati tramite un’analisi di matrice neuroscientifica su stimoli brand-related. Il vantaggio di questo strumento, rispetto allo IAT tradizionale, è quello di poter ‘fotografare’ un’istantanea sulla relazione senza la necessità di utilizzare una dimensione comparativa. Misurando direttamente la forza dell’associazione tra il concetto del brand e quello del Sé. Per farlo, l’autore è passato attraverso fasi distinte che hanno prima indagato gli aspetti puramente psicometrici dello strumento, per dedicarsi successivamente a un test neuroscientifico. I risultati hanno evidenziato delle buone performance del SC-IAT, così pensato, suggerendo approfondimenti futuri e applicazioni a brand dalla differente architettura. Inoltre, l’analisi neurofisiologica ha evidenziato come lo strumento possa risultare efficace nel fornire un’interpretazione aggiuntiva agli indicatori neurofisiologici testati durante la visualizzazione di uno stimolo relativo al brand
Caractérisation mécanique in vivo des tissus mous : application à la peau humaine et la chéloïde
The development of keloids, benign tumors on human skin, is not exclusively due to biological or genetic factors. The presence of anatomical sites favorable to the appearance of these tumors, while others are lacking them, attests to the importance of the mechanical environment of the tissue. The thesis aims to address the problem of keloid growth by developing a patient-specific pipeline, SofTI, based on in vivo experimental measurements and numerical modeling. The objective is to prevent further propagation of keloidic scars via a medical containment solution by identifying optimal material parameters to quantify mechanical stress and map its privileged direction locally. Additionally, the research work introduces MARSAC methodology to characterize the anisotropy in an undamaged skin by estimating Langer's line and stiffness along and across it with an in vivo multi-axial annular suction experiment. The method was used to analyze intra-subject and subject-to-subject variability over a clinical trial.Le développement des chéloïdes, tumeurs bénignes sur la peau humaine, n'est pas exclusivement dû à des facteurs biologiques ou génétiques. La présence de sites anatomiques favorables à l'apparition de ces tumeurs, tandis que d'autres en manquent, atteste de l'importance de l'environnement mécanique du tissu. La thèse vise à résoudre le problème de la croissance des chéloïdes en développant une méthode patient-spécifique, SofTI, basée sur des mesures expérimentales in vivo et une modélisation numérique. L'objectif est de prévenir la propagation des cicatrices chéloïdiennes à l'aide d'une solution médicale de contention en identifiant les paramètres matériau optimaux pour quantifier les contraintes mécaniques et cartographier ses directions privilégiées localement. De plus, le travail de recherche présente la méthodologie MARSAC pour charactériser l'anisotropie dans la peau saine en identifiant la ligne de Langer et la raideur le long et à travers celle-ci partant d'une expérience d'aspiration annulaire multi-axiale in vivo. La méthode a été employée pour analyser la variabilité intra- et inter-sujets sur un essai clinique
The Use of Skeletal Muscle to Amplify Action Potentials in Transected Peripheral Nerves
Upper limb amputees suffer with problems associated with control and attachment of prostheses. Skin-surface electrodes placed over the stump, which detect myoelectric signals, are traditionally used to control hand movements. However, this method is unintuitive, the electrodes lift-off, and signal selectivity can be an issue.
One solution to these limitations is to implant electrodes directly on muscles. Another approach is to implant electrodes directly into the nerves that innervate the muscles. A significant challenge with both solutions is the reliable transmission of biosignals across the skin barrier.
In this thesis, I investigated the use of implantable muscle electrodes in an ovine model using myoelectrodes in combination with a bone-anchor, acting as a conduit for signal transmission. High-quality readings were obtained which were significantly better than skin-surface electrode readings. I further investigated the effect of electrode configurations to achieve the best signal quality.
For direct recording from nerves, I tested the effect of adsorbed endoneural basement membrane proteins on nerve regeneration in vivo using microchannel neural interfaces implanted in rat sciatic nerves. Muscle and nerve signal recordings were obtained and improvements in sciatic nerve function were observed.
Direct skeletal fixation of a prosthesis to the amputation stump using a bone-anchor has been proposed as a solution to skin problems associated with traditional socket-type prostheses. However, there remains a concern about the risk of infection between the implant and skin. Achieving a durable seal at this interface is therefore crucial, which formed the final part of the thesis. Bone-anchors were optimised for surface pore size and coatings to facilitate binding of human dermal fibroblasts to optimise skin-implant seal in an ovine model. Implants silanised with Arginine-Glycine-Aspartic Acid experienced significantly increased dermal tissue infiltration. This approach may therefore improve the soft tissue seal, and thus success of bone-anchored implants.
By addressing both the way prostheses are attached to the amputation stump, by way of direct skeletal fixation, as well as providing high fidelity biosignals for high-level intuitive prosthetic control, I aim to further the field of limb loss rehabilitation
COVID-19 Booster Vaccine Acceptance in Ethnic Minority Individuals in the United Kingdom: a mixed-methods study using Protection Motivation Theory
Background: Uptake of the COVID-19 booster vaccine among ethnic minority individuals has been lower than in the general population. However, there is little research examining the psychosocial factors that contribute to COVID-19 booster vaccine hesitancy in this population.Aim: Our study aimed to determine which factors predicted COVID-19 vaccination intention in minority ethnic individuals in Middlesbrough, using Protection Motivation Theory (PMT) and COVID-19 conspiracy beliefs, in addition to demographic variables.Method: We used a mixed-methods approach. Quantitative data were collected using an online survey. Qualitative data were collected using semi-structured interviews. 64 minority ethnic individuals (33 females, 31 males; mage = 31.06, SD = 8.36) completed the survey assessing PMT constructs, COVID-19conspiracy beliefs and demographic factors. 42.2% had received the booster vaccine, 57.6% had not. 16 survey respondents were interviewed online to gain further insight into factors affecting booster vaccineacceptance.Results: Multiple regression analysis showed that perceived susceptibility to COVID-19 was a significant predictor of booster vaccination intention, with higher perceived susceptibility being associated with higher intention to get the booster. Additionally, COVID-19 conspiracy beliefs significantly predictedintention to get the booster vaccine, with higher conspiracy beliefs being associated with lower intention to get the booster dose. Thematic analysis of the interview data showed that barriers to COVID-19 booster vaccination included time constraints and a perceived lack of practical support in the event ofexperiencing side effects. Furthermore, there was a lack of confidence in the vaccine, with individuals seeing it as lacking sufficient research. Participants also spoke of medical mistrust due to historical events involving medical experimentation on minority ethnic individuals.Conclusion: PMT and conspiracy beliefs predict COVID-19 booster vaccination in minority ethnic individuals. To help increase vaccine uptake, community leaders need to be involved in addressing people’s concerns, misassumptions, and lack of confidence in COVID-19 vaccination
Gurus and Media: Sound, image, machine, text and the digital
Gurus and Media is the first book dedicated to media and mediation in domains of public guruship and devotion. Illuminating the mediatisation of guruship and the guru-isation of media, it bridges the gap between scholarship on gurus and the disciplines of media and visual culture studies. It investigates guru iconographies in and across various time periods and also the distinctive ways in which diverse gurus engage with and inhabit different forms of media: statuary, games, print publications, photographs, portraiture, films, machines, social media, bodies, words, graffiti, dolls, sound, verse, tombs and more.
The book’s interdisciplinary chapters advance, both conceptually and ethnographically, our understanding of the function of media in the dramatic production of guruship, and reflect on the corporate branding of gurus and on mediated guruship as a series of aesthetic traps for the captivation of devotees and others. They show how different media can further enliven the complex plurality of guruship, for instance in instantiating notions of ‘absent-present’ guruship and demonstrating the mutual mediation of gurus, caste and Hindutva.
Throughout, the book foregrounds contested visions of the guru in the development of devotional publics and pluriform guruship across time and space. Thinking through the guru’s many media entanglements in a single place, the book contributes new insights to the study of South Asian religions and to the study of mediation more broadly
Exploring Virtual Reality and Doppelganger Avatars for the Treatment of Chronic Back Pain
Cognitive-behavioral models of chronic pain assume that fear of pain and subsequent avoidance behavior contribute to pain chronicity and the maintenance of chronic pain. In chronic back pain (CBP), avoidance of movements often plays a major role in pain perseverance and interference with daily life activities. In treatment, avoidance is often addressed by teaching patients to reduce pain behaviors and increase healthy behaviors. The current project explored the use of personalized virtual characters (doppelganger avatars) in virtual reality (VR), to influence motor imitation and avoidance, fear of pain and experienced pain in CBP. We developed a method to create virtual doppelgangers, to animate them with movements captured from real-world models, and to present them to participants in an immersive cave virtual environment (CAVE) as autonomous movement models for imitation.
Study 1 investigated interactions between model and observer characteristics in imitation behavior of healthy participants. We tested the hypothesis that perceived affiliative characteristics of a virtual model, such as similarity to the observer and likeability, would facilitate observers’ engagement in voluntary motor imitation. In a within-subject design (N=33), participants were exposed to four virtual characters of different degrees of realism and observer similarity, ranging from an abstract stickperson to a personalized doppelganger avatar designed from 3d scans of the observer. The characters performed different trunk movements and participants were asked to imitate these. We defined functional ranges of motion (ROM) for spinal extension (bending backward, BB), lateral flexion (bending sideward, BS) and rotation in the horizontal plane (RH) based on shoulder marker trajectories as behavioral indicators of imitation. Participants’ ratings on perceived avatar appearance were recorded in an Autonomous Avatar Questionnaire (AAQ), based on an explorative factor analysis. Linear mixed effects models revealed that for lateral flexion (BS), a facilitating influence of avatar type on ROM was mediated by perceived identification with the avatar including avatar likeability, avatar-observer-similarity and other affiliative characteristics. These findings suggest that maximizing model-observer similarity may indeed be useful to stimulate observational modeling.
Study 2 employed the techniques developed in study 1 with participants who suffered from CBP and extended the setup with real-world elements, creating an immersive mixed reality. The research question was whether virtual doppelgangers could modify motor behaviors, pain expectancy and pain. In a randomized controlled between-subject design, participants observed and imitated an avatar (AVA, N=17) or a videotaped model (VID, N=16) over three sessions, during which the movements BS and RH as well as a new movement (moving a beverage crate) were shown. Again, self-reports and ROMs were used as measures. The AVA group reported reduced avoidance with no significant group differences in ROM. Pain expectancy increased in AVA but not VID over the sessions. Pain and limitations did not significantly differ. We observed a moderation effect of group, with prior pain expectancy predicting pain and avoidance in the VID but not in the AVA group. This can be interpreted as an effect of personalized movement models decoupling pain behavior from movement-related fear and pain expectancy by increasing pain tolerance and task persistence. Our findings suggest that personalized virtual movement models can stimulate observational modeling in general, and that they can increase pain tolerance and persistence in chronic pain conditions. Thus, they may provide a tool for exposure and exercise treatments in cognitive behavioral treatment approaches to CBP
Brain Computations and Connectivity [2nd edition]
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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