616 research outputs found

    Neuromorphic hardware for somatosensory neuroprostheses

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    In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies

    Desarrollo de nuevos dispositivos y algoritmos para la monitorización ambulatoria de personas con epilepsia

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    La epilepsia es una enfermedad crónica con un enorme impacto sociosanitario. Aunque en la actualidad se dispone de una gran cantidad de fármacos antiepilépticos y de otros tratamientos más selectivos como la cirugía o la estimulación cerebral, un porcentaje considerable de pacientes no están controlados y continúan teniendo crisis epilépticas. Estas personas suelen vivir condicionadas por la posibilidad de un ataque epiléptico y sus posibles consecuencias, como accidentes, lesiones o incluso la muerte súbita inexplicable. En este contexto, un dispositivo capaz de monitorizar el estado de salud y avisar de un posible ataque epiléptico contribuiría a mejorar la calidad de vida de estas personas. La presente Tesis Doctoral se centra en el desarrollo de un novedoso sistema de monitorización ambulatoria que permita identificar y predecir los ataques epilépticos. Dicho sistema está compuesto por diferentes sensores capaces de registrar de forma sincronizada diferentes señales biomédicas. Mediante técnicas de aprendizaje automático supervisado, se han desarrollado diferentes modelos predictivos capaces de clasificar el estado de la persona epiléptica en normal, preictal (antes de la crisis) e ictal (crisis)

    Exploration of peripheral electrical stimulation adapted as a modulation tool for reciprocal inhibition through the activation of afferent fibers during gait

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    The most accessible manner to perform physical activity and allow locomotion in human beings is walking. This activity is allowed thanks to reciprocal Ia inhibition mechanism, controlled by the spinal and supraspinal inhibitory circuits. The idea of this mechanism is to deactivate the antagonist muscle while the agonist is being contracted, allowing the proper muscle coordination necessary to walk. The interruption of spinal fibers produced after Spinal Cord Injury, disrupt this control on reciprocal Ia inhibition. The result of this lack of control is a co-activation of antagonist muscles generating spasticity of lower limbs which induce walking impairments. The importance of walking recovery for the independence and society re-integration of patient, raise the quantity of emerging walking rehabilitation therapies. One of these therapies, the application of peripheral nerve stimulation, has demonstrated promising results although more studies are necessary. This theory is the base of this Master Thesis which aim is to develop and validate a gait neuromodu- lation platform that induce neuroplasticity of spinal circuits, improving reciprocal Ia inhibition. The idea of the platform is to deliver afferent stimulation into the Common Peroneal Nerve innervating Tibialis Anterior muscle, to induce reciprocal Ia inhibition onto the antagonist Soleus muscle. This platform has been validated in 20 healthy volunteers in order to assess its effectiveness. The first part of the experimental protocol is an off-line analysis of Gait Cycle to evaluate the activation of mus- cles during the different phases of this cycle. Then, there is an assessment of the activity of antagonist muscle previous to the stimulation intervention by using the analysis of soleus H-reflex. Posteri- orly, the afferent stimulation is applied during a 10 minutes treadmill training using three different strategies depending on patient: In-phase stimulation during swing phase, Out-of-phase stimulation during stance phase, and Control strategy to check if stimulation has a real effect. The final processes of experimental protocol are two different assessments of the soleus activity, one immediately after the intervention and other 30 minutes after to evaluate the duration of effects. The results obtained demonstrate that afferent electrical stimulation has a real effect on modulation of reciprocal Ia inhibition. On the one hand, when electrical stimulation is applied during the swing phase, there is an improvement of reciprocal Ia inhibition. On the other hand, when stimulation is delivered during the stance phase, there is a worsening of reciprocal Ia inhibition. These results conclude that afferent electrical stimulation, applied at the swing phase of gait cycle, is a promising strategy to induce reciprocal Ia inhibition in Spinal Cord Injury patients. The induc- tion of this inhibitory circuit will lead to the proper activation of muscles during walking, recovering impaired walkingLa forma más accesible de locomoción y actividad física en los seres humanos es caminar. Esta activi- dad se realiza gracias al mecanismo de inhibición recíproca, controlado por los circuitos inhibitorios espinales y supraespinales. La idea de este mecanismo es desactivar el músculo antagonista mientras se contrae el agonista, permitiendo la adecuada coordinación muscular durante la marcha. La interrupción de las fibras espinales tras una Lesión de la Médula Espinal desajusta el control de la inhibition reciprocal. El resultado de esta falta de control es una co-activación de los músculos antago- nistas generando espasticidad en las extremidades inferiores, lo que genera alteraciones en la marcha. La importancia de la recuperación de la marcha para lograr la independencia y la reintegración del paciente en la sociedad, ha incrementado el número de terapias emergentes en rehabilitación de la marcha. Una de estas terapias, la estimulación del nervio periférico, ha demostrado resultados prom- etedores. Esta teoría es la base de esta Tesis de Máster cuyo objetivo es desarrollar y validar una plataforma de neuromodulación de la marcha que induzca la neuroplasticidad de los circuitos espinales, mejorando los valores de inhibición recíproca. La idea es aplicar estimulación aferente en el Nervio Peroneo Común que inerva el músculo Tibial Anterior para inducir la inhibición recíproca en su músculo antagonista Soleo. Esta plataforma ha sido validada en 20 voluntarios sanos con el fin de evaluar su eficacia. La primera parte del protocolo experimental es un análisis del ciclo de la marcha para evaluar la activación de cada músculo durante las diferentes fases de este ciclo. Luego, previo a la intervención de estimu- lación, hay una evaluación de la actividad del músculo antagonista analizando el reflejo H del soleo. La intervención de estimulación aferente se aplica durante un entrenamiento de marcha con una du- ración de 10 minutos, utilizando tres estrategias diferentes dependiendo del paciente: estimulación ’In-phase’ durante la fase de oscilación, estimulación ’Out-of-phase’ durante la fase de postura, y ’Control’ para comprobar si la estimulación tiene un efecto real. Los procesos finales del protocolo son dos evaluaciones de la actividad del soleo, una inmediatamente después de la intervención y otra 30 minutos después para evaluar la duración de los efectos. Los resultados obtenidos demuestran que la estimulación eléctrica aferente tiene un efecto real en la modulación de la inhibición recíproca. Por un lado, cuando la estimulación eléctrica se aplica durante la fase de oscilación, hay una mejora de la inhibición recíproca. Por otro lado, cuando la estimulación se administra durante la fase de postura, hay un empeoramiento de la inhibición recíproca. Estos resultados concluyen que la estimulación eléctrica aferente, administrada en la fase de oscilación del ciclo de la marcha, es una estrategia prometedora para inducir la inhibición recíproca en pacientes con Lesión de la Médula Espinal. La inducción de este circuito inhibidor generará la adecuada acti- vación de los músculos durante la marcha, recuperando el ciclo de marcha normalLa manera més accessible de locomoció i activitat física en els éssers humans és caminar. Aquesta ac- tivitat es realitza gràcies al mecanisme d’inhibició recíproca, controlat pels circuits inhibitoris espinals i supraespinals. La idea d’aquest mecanisme és desactivar el múscul antagonista mentre es contrau l’agonista, permetent la coordinació muscular adequada durant la marxa. La interrupció de les fibres espinals després d’una lesió medul·lar desajusta el control de la inhibició reciprocal. El resultat d’aquesta manca de control és una coactivació dels músculs antagonistes gen- erant espasticitat a les extremitats inferiors, cosa que genera alteracions a la marxa. La importància de la recuperació de la marxa per a la independència i la reintegració del pacient a la societat, ha incrementat el nombre de teràpies emergents de rehabilitació de la marxa. Una daquestes teràpies, lestimulació del nervi perifèric, ha demostrat resultats prometedors. Aquesta teoria és la base dáquesta Tesi de Màster que té com a objectiu desenvolupar una plataforma de neuromodulació de la marxa que indueixi la neuroplasticitat dels circuits espinals, millorant els valors de inhibició recíproca. La idea és aplicar una estimulació aferent al Nervi Peroneal Comú que inerva el múscul Tibial Anterior per induir la inhibició recíproca al múscul antagonista Soli. Aquesta plataforma ha estat validada en 20 voluntaris sans per avaluar-ne l’eficàcia. La primera part del protocol experimental és una anàlisi del cicle de marxa per avaluar l’activació de cada múscul durant les diferents fases del cicle de la marxa. Després, amb la intervenció d’estimulació prèvia, hi ha una avaluació de l’activitat del múscul antagonista analitzant el reflex H del soli. La inter- venció d’estimulació aferent s’aplica durant un entrenament de marxa amb una durada de 10 min- uts, utilitzant tres estratègies diferents depenent del pacient: estimulació ’In-phase’ durant la fase d’oscil·lació, estimulació ’Out-of-phase’ durant la fase de postura, i ’Control’ per comprovar si la es- timulació té un efecte real. Els processos finals del protocol són dues avaluacions de l’activitat de soli, una immediatament després de la intervenció i una altra 30 minuts després per avaluar la durada dels efectes. Els resultats obtinguts demostren que l’estimulació elèctrica aferent té un efecte real en la modulació de la inhibició recíproca. D’una banda, quan s’aplica l’estimulació elèctrica durant la fase d’oscil·lació, hi ha una millora de la inhibició recíproca. D’altra banda, quan s’administra l’estimulació durant la fase de postura, hi ha un empitjorament de la inhibició recíproca. Aquests resultats conclouen que l’estimulació elèctrica aferent, a la fase d’oscil·lació del cicle de la marxa, és una estratègia prometedora per induir la inhibició recíproca en pacients amb lesió medul·lar. La inducció d’aquest circuit inhibidor generarà a l’activació adequada dels músculs durant la marxa, recuperant el cicle de marxa norma

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    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

    An exploratory study evaluating the effectiveness of a data driven approach to identifying coordinative features that are associated with sprint velocity

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    Sprint performance is multifactorial in nature and is dependent on a variety of coordination and motor control features. During the sequential phases of a sprint, the athlete completes a series of spatiotemporal coordination strategies to achieve the fastest possible velocity. The overall aim of the study was to leverage wearable sensor technology and data- driven tools to objectively assess the kinematic and neuromuscular determinants of optimal sprint velocity from a large dataset of university-aged sprinters. To achieve this, we recruited participants to run three 60 m sprints as fast as possible, while being outfitted with wireless electromyography (EMG) and a full-body inertial measurement unit (IMU) suit to obtain full- body 3D kinematics. Five strides about peak sprint velocity were selected and used for inputs into a principal components analysis (PCA). Significant stepwise multivariable regression models were generated for both kinematic and EMG features identified using PCA, with the kinematic model outperforming the EMG model as the kinematic model displayed a higher R2 value. This suggests that the kinematic dataset used in this study is a better predictor of sprint performance when compared to the EMG dataset, and that both may be viable options in the development of data-driven objective sprint coaching tools

    Decoding of Multiple Wrist and Hand Movements Using a Transient EMG Classifier

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    The design of prosthetic controllers by means of neurophysiological signals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the \textit {transient} EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of 96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of 89%. Importantly, for each amputee, it produced at least one \textit {acceptable} combination of wrist-hand movements (i.e., with accuracy >85%). Regarding the cross-subject classifier, while our algorithm obtained promising results with non-amputees (accuracy up to 80%), they were not as good with amputees (accuracy up to 35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments

    EMG- BASED HAND GESTURE RECOGNITION USING DEEP LEARNING AND SIGNAL-TO-IMAGE CONVERSION TOOLS

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    In this paper, deep learning-based hand gesture recognition using surface EMG signals is presented. We use Principal component analysis (PCA) to reduce the data set. Here a threshold-based approach is also proposed to select the principal components (PCs). Then the Continuous wavelet transform (CWT) is carried out to prepare the time-frequency representation of images which is used as the input of the classifier. A very deep convolutional neural network (CNN) is proposed as the gesture classifier. The classifier is trained on 10-fold cross-validation framework and we achieve average recognition accuracy of 99.44%, sensitivity of 97.78% and specificity of 99.68% respectively

    Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation

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    Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices. One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG–EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG–EMG fusion and to develop a novel control system based on the incorporation of EEG–EMG fusion classifiers. A dataset of EEG and EMG signals were collected during dynamic elbow flexion–extension motions and used to develop EEG–EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG–EMG fusion can classify more indirect tasks. A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG–EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG–EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation

    A myoelectric digital twin for fast and realistic modelling in deep learning

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    Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces

    Imagining & Sensing: Understanding and Extending the Vocalist-Voice Relationship Through Biosignal Feedback

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    The voice is body and instrument. Third-person interpretation of the voice by listeners, vocal teachers, and digital agents is centred largely around audio feedback. For a vocalist, physical feedback from within the body provides an additional interaction. The vocalist’s understanding of their multi-sensory experiences is through tacit knowledge of the body. This knowledge is difficult to articulate, yet awareness and control of the body are innate. In the ever-increasing emergence of technology which quantifies or interprets physiological processes, we must remain conscious also of embodiment and human perception of these processes. Focusing on the vocalist-voice relationship, this thesis expands knowledge of human interaction and how technology influences our perception of our bodies. To unite these different perspectives in the vocal context, I draw on mixed methods from cog- nitive science, psychology, music information retrieval, and interactive system design. Objective methods such as vocal audio analysis provide a third-person observation. Subjective practices such as micro-phenomenology capture the experiential, first-person perspectives of the vocalists them- selves. Quantitative-qualitative blend provides details not only on novel interaction, but also an understanding of how technology influences existing understanding of the body. I worked with vocalists to understand how they use their voice through abstract representations, use mental imagery to adapt to altered auditory feedback, and teach fundamental practice to others. Vocalists use multi-modal imagery, for instance understanding physical sensations through auditory sensations. The understanding of the voice exists in a pre-linguistic representation which draws on embodied knowledge and lived experience from outside contexts. I developed a novel vocal interaction method which uses measurement of laryngeal muscular activations through surface electromyography. Biofeedback was presented to vocalists through soni- fication. Acting as an indicator of vocal activity for both conscious and unconscious gestures, this feedback allowed vocalists to explore their movement through sound. This formed new perceptions but also questioned existing understanding of the body. The thesis also uncovers ways in which vocalists are in control and controlled by, work with and against their bodies, and feel as a single entity at times and totally separate entities at others. I conclude this thesis by demonstrating a nuanced account of human interaction and perception of the body through vocal practice, as an example of how technological intervention enables exploration and influence over embodied understanding. This further highlights the need for understanding of the human experience in embodied interaction, rather than solely on digital interpretation, when introducing technology into these relationships
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