392 research outputs found

    Co-adaptive myoelectric control for upper limb prostheses

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    [ES] Mucha gente en el mundo se ve afectada por la pérdida de una extremidad (las predicciones estiman que en 2050 habrá más de 3 millones de personas afectadas únicamente en los Estados Unidos de América). A pesar de la continua mejora en las técnicas de amputación y la prostética, vivir sin una extremidad sigue limitando las actividades de los afectados en su vida diaria, provocando una disminución en su calidad de vida. En este trabajo nos centramos en los casos de amputaciones de extremidades superiores, entendiendo por ello la pérdida de cualquier parte del brazo o antebrazo. Esta tesis trata sobre el control mioeléctrico (potenciales eléctricos superficiales generados por la contracción de los músculos) de prótesis de extremidades superiores. Los estudios en este campo han crecido exponencialmente en las últimas décadas intentando reducir el hueco entre la parte investigadora más dinámica y propensa a los cambios e innovación (por ejemplo, usando técnicas como la inteligencia artificial) y la industria prostética, con una gran inercia y poco propensa a introducir cambios en sus controladores y dispositivos. El principal objetivo de esta tesis es desarrollar un nuevo controlador implementable basado en filtros adaptativos que supere los principales problemas del estado del arte. Desde el punto de vista teórico, podríamos considerar dos contribuciones principales. Primero, proponemos un nuevo sistema para modelar la relación entre los patrones de la señales mioélectricas y los movimientos deseados; este nuevo modelo tiene en cuenta a la hora de estimar la posición actual el valor de los estados pasados generando una nueva sinergia entre máquina y ser humano. En segundo lugar, introducimos un nuevo paradigma de entrenamiento más eficiente y personalizado autónomamente, el cual puede aplicarse no sólo a nuestro nuevo controlador, sino a otros regresores disponibles en la literatura. Como consecuencia de este nuevo protocolo, la estructura humano-máquina difiere con respecto del actual estado del arte en dos características: el proceso de aprendizaje del controlador y la estrategia para la generación de las señales de entrada. Como consecuencia directa de todo esto, el diseño de la fase experimental resulta mucho más complejo que con los controladores tradicionales. La dependencia de la posición actual de la prótesis con respecto a estados pasados fuerza a la realización de todos los experimentos de validación del nuevo controlador en tiempo real, algo costoso en recursos tanto humanos como de tiempo. Por lo tanto, una gran parte de esta tesis está dedicada al trabajo de campo necesario para validar el nuevo modelo y estrategia de entrenamiento. Como el objetivo final es proveer un nuevo controlador implementable, la última parte de la tesis está destinada a testear los métodos propuestos en casos reales, tanto en entornos simulados para validar su robustez ante rutinas diarias, como su uso en dispositivos prostéticos comerciales. Como conclusión, este trabajo propone un nuevo paradigma de control mioélectrico para prótesis que puede ser implementado en una prótesis real. Una vez se ha demostrado la viabilidad del sistema, la tesis propone futuras líneas de investigación, mostrando algunos resultados iniciales.[CA] Molta gent en el món es veu afectada per la pèrdua d'una extremitat (les prediccions estimen que en 2050 hi haurà més de 3 milions de persones afectades únicament als Estats Units d'Amèrica). Malgrat la contínua millora en les tècniques d'amputació i la prostètica, viure sense una extremitat continua limitant les activitats dels afectats en la seua vida diària, provocant una disminució en la seua qualitat de vida. En aquest treball ens centrem en els casos d'amputacions d'extremitats superiors, entenent per això la pèrdua de qualsevol part del braç o avantbraç. Aquesta tesi tracta sobre el control mioelèctric (potencials elèctrics superficials generats per la contracció dels músculs) de pròtesis d'extremitats superiors. Els estudis en aquest camp han crescut exponencialment en les últimes dècades intentant reduir el buit entre la part investigadora més dinàmica i propensa als canvis i innovació (per exemple, usant tècniques com la intel·ligència artificial) i la indústria prostètica, amb una gran inèrcia i poc propensa a introduir canvis en els seus controladors i dispositius. Aquesta tesi contribueix a la investigació des de diversos punts de vista. El principal objectiu és desenvolupar un nou controlador basat en filtres adaptatius que supere els principals problemes de l'estat de l'art. Des del punt de vista teòric, podríem considerar dues contribucions principals. Primer, proposem un nou sistema per a modelar la relació entre els patrons de la senyals mioelèctrics i els moviments desitjats; aquest nou model té en compte a l'hora d'estimar la posició actual el valor dels estats passats generant una nova sinergia entre màquina i ésser humà. En segon lloc, introduïm un nou paradigma d'entrenament més eficient i personalitzat autònomament, el qual pot aplicar-se no sols al nostre nou controlador, sinó a uns altres regresors disponibles en la literatura. Com a conseqüència d'aquest nou protocol, l'estructura humà-màquina difereix respecte a l'actual estat de l'art en dues característiques: el procés d'aprenentatge del controlador i l'estratègia per a la generació dels senyals d'entrada. Com a conseqüència directa de tot això, el disseny de la fase experimental resulta molt més complex que amb els controladors tradicionals. La dependència de la posició actual de la pròtesi respecte a estats passats força a la realització de tots els experiments de validació del nou controlador en temps real, una cosa costosa en recursos tant humans com de temps. Per tant, una gran part d'aquesta tesi està dedicada al treball de camp necessari per a validar el nou model i estratègia d'entrenament. Com l'objectiu final és proveir un nou controlador implementable, l'última part de la tesi està destinada a testar els mètodes proposats en casos reals, tant en entorns simulats per a validar la seua robustesa davant rutines diàries, com el seu ús en dispositius prostètics comercials. Com a conclusió, aquest treball proposa un nou paradigma de control mioelèctric per a pròtesi que pot ser implementat en una pròtesi real. Una vegada s'ha demostrat la viabilitat del sistema, la tesi proposa futures línies d'investigació, mostrant alguns resultats inicials.[EN] Many people in the world suffer from the loss of a limb (predictions estimate more than 3 million people by 2050 only in the USA). In spite of the continuous improvement in the amputation rehabilitation and prosthetic restoration, living without a limb keeps limiting the daily life activities leading to a lower quality of life. In this work, we focus in the upper limb amputation case, i.e., the removal of any part of the arm or forearm. This thesis is about upper limb prosthesis control using electromyographic signals (the superficial electric potentials generated during muscle contractions). Studies in this field have grown exponentially in the past decades trying to reduce the gap between a fast growing prosthetic research field, with the introduction of machine learning, and a slower prosthetic industry and limited manufacturing innovation. This thesis contributes to the field from different perspectives. The main goal is to provide and implementable new controller based on adaptive filtering that overcomes the most common state of the art concerns. From the theoretical point of view, there are two main contributions. First, we propose a new system to model the relationship between electromyographic signals and the desired prosthesis movements; this new model takes into account previous states for the estimation of the current position generating a new human-machine synergy. Second, we introduce a new and more efficient autonomously personalized training paradigm, which can benefit not only to our new proposed controller but also other state of the art regressors. As a consequence of this new protocol, the human-machine structure differs with respect to current state of the art in two features: the controller learning process and the input signal generation strategy. As a direct aftereffect of all of this, the experimental phase design results more complex than with traditional controllers. The current state dependency on past states forces the experimentation to be in real time, a very high demanding task in human and time resources. Therefore, a major part of this thesis is the associated fieldwork needed to validate the new model and training strategy. Since the final goal is to provide an implementable new controller, the last part of the thesis is devoted to test the proposed methods in real cases, not only analyzing the robustness and reliability of the controller in real life situations but in real prosthetic devices. As a conclusion, this work provides a new paradigm for the myoelectric prosthetic control that can be implemented in a real device. Once the thesis has proven the system's viability, future work should continue with the development of a physical device where all these ideas are deployed and used by final patients in a daily basis.The work of Carles Igual Bañó to carry out this research and elaborate this dissertation has been supported by the Ministerio de Educación, Cultura y Deporte under the FPU Grant FPU15/02870. One visiting research fellowships (EST18/00544) was also funded by the Ministerio de Educación, Cultura y Deporte of Spain.Igual Bañó, C. (2021). Co-adaptive myoelectric control for upper limb prostheses [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/168192TESI

    Studies of the relationship between the surface electromyogram, joint torque and impedance

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    This compendium-format dissertation (i.e., comprised mostly of published and in-process articles) primarily reports on system identification methods that relate the surface electromyogram (EMG)—the electrical activity of skeletal muscles—to mechanical kinetics. The methods focus on activities of the elbow and hand-wrist. The relationship between the surface EMG and joint impedance was initially studied. My work provided a complete second-order EMG-based impedance characterization of stiffness, viscosity and inertia over a complete range of nominal torques, from a single perturbation trial with slowly varied torque. A single perturbation trial provides a more convenient method for impedance evaluation. The RMS errors of the EMG-based method were 20.01% for stiffness and 7.05% for viscosity, compared with the traditional mechanical measurement. Three projects studied the relationship between EMG and force/torque, a topic that has been studied for a number of years. Optimal models use whitened EMG amplitude, combining multiple EMG channels and a polynomial equation to describe this relationship. First, we used three techniques to improve current models at the elbow joint. Three more features were extracted from the EMG (waveform length, slope sign change rate and zero crossing rate), in addition to EMG amplitude. Each EMG channel was used separately, compared to previous studies which combined multiple channels from biceps and, separately, from triceps muscles. Finally, an exponential power law model was used. Each of these improvement techniques showed better performance (P\u3c0.05 and ~0.7 percent maximum voluntary contraction (%MVC) error reduction from a nominal error of 5.5%MVC) than the current “optimal” model. However, the combination of pairs of these techniques did not further improve results. Second, traditional prostheses only control 1 degree of freedom (DoF) at a time. My work provided evidence for the feasibility of controlling 2-DoF wrist movements simultaneously, with a minimum number of electrodes. Results suggested that as few as four conventional electrodes, optimally located about the forearm, could provide 2-DoF simultaneous, independent and proportional control with error ranging from 9.0–10.4 %MVC, which is similar to the 1-DoF approach (error from 8.8–9.8 %MVC) currently used for commercial prosthesis control. The third project was similar to the second, except that this project studied controlling a 1-DoF wrist with one hand DoF simultaneously. It also demonstrated good performance with the error ranging from 7.8-8.7 %MVC, compared with 1-DoF control. Additionally, I participated in two team projects—EMG decomposition and static wrist EMG to torque—which are described herein

    Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling

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    Objectives: Robotic prosthetic limbs promise to replace mechanical function of lost biological extremities and restore amputees' capacity of moving and interacting with the environment. Despite recent advances in biocompatible electrodes, surgical procedures, and mechatronics, the impact of current solutions is hampered by the lack of intuitive and robust man-machine interfaces. Approach: Based on authors' developments, this work presents a biomimetic interface that synthetizes the musculoskeletal function of an individual's phantom limb as controlled by neural surrogates, i.e. electromyography-derived neural activations. With respect to current approaches based on machine learning, our method employs explicit representations of the musculoskeletal system to reduce the space of feasible solutions in the translation of electromyograms into prosthesis control commands. Electromyograms are mapped onto mechanical forces that belong to a subspace contained within the broader operational space of an individual's musculoskeletal system. Results: Our results show that this constraint makes the approach applicable to real-world scenarios and robust to movement artefacts. This stems from the fact that any control command must always exist within the musculoskeletal model operational space and be therefore physiologically plausible. The approach was effective both on intact-limbed individuals and a transradial amputee displaying robust online control of multi-functional prostheses across a large repertoire of challenging tasks. Significance: The development and translation of man-machine interfaces that account for an individual's neuromusculoskeletal system creates unprecedented opportunities to understand how disrupted neuro-mechanical processes can be restored or replaced via biomimetic wearable assistive technologies

    Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing

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    There are some practical factors, such as arm position change and donning/doffing, which prevent robust myoelectric control. The objective of this study is to precisely characterize the impacts of the two representative factors on myoelectric controllability in practical control situations, thereby providing useful references that can be potentially used to find better solutions for clinically reliable myoelectric control. To this end, a real-time target acquisition task was performed by fourteen subjects including one individual with congenital upper-limb deficiency, where the impacts of arm position change, donning/doffing and a combination of both factors on control performance was systematically evaluated. The changes in online performance were examined with seven different performance metrics to comprehensively evaluate various aspects of myoelectric controllability. As a result, arm position change significantly affects offline prediction accuracy, but not online control performance due to real-time feedback, thereby showing no significant correlation between offline and online performance. Donning/doffing was still problematic in online control conditions. It was further observed that no benefit was attained when using a control model trained with multiple position data in terms of arm position change, and the degree of electrode shift caused by donning/doffing was not severely associated with the degree of performance loss under practical conditions (around 1 cm electrode shift). Since this study is the first to concurrently investigate the impacts of arm position change and donning/doffing in practical myoelectric control situations, all findings of this study provide new insights into robust myoelectric control with respect to arm position change and donning/doffing.DFG, 325093850, Open Access Publizieren 2017 - 2018 / Technische Universität Berli

    Translating Research on Myoelectric Control into Clinics-Are the Performance Assessment Methods Adequate?

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    Missing an upper limb dramatically impairs daily-life activities. Significant efforts in overcoming the issues arising from this disability have been made in both academia and industry, although their clinical outcome is still limited. Translation of prosthetic research into clinics has been challenging because of the difficulties in meeting the necessary requirements of the market. In this perspective, we focus on myocontrol algorithms for upper limb prostheses and we emphasize that one relevant factor determining the relatively small clinical impact of these methods is the limit of commonly used laboratory performance metrics. The laboratory conditions, in which the majority of the solutions are being evaluated, fail to sufficiently replicate real-life challenges. We qualitatively substantiate this argument with data from seven transradial amputees. Their ability to control a myoelectric prosthesis was tested by measuring the accuracy of offline EMG signal classification, as a typical laboratory performance metrics, as well as by clinical scores when performing standard tests of daily living. Despite all subjects reached relatively high classification accuracy offline, their clinical scores were largely different and were not strongly predicted by classification accuracy. As argued in previous reports, we reinforce the suggestion to test myocontrol systems using clinical tests on amputees, fully fitted with sockets and prostheses highly resembling the systems they would use in daily living, as evaluation benchmark. Agreement on this level of testing for systems developed in research laboratories would facilitate clinically relevant progresses in this field.<br

    Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography

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    One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive PNS-Machine Interfaces was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PNS-Machine Interface (PMI) has been selected to denote human-machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the peripheral nervous system (PNS) in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it

    Doctor of Philosophy

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    dissertationUpper limb amputees desire an artificial arm that allows for multiple degrees of freedom of control over the movements of the prosthesis, coupled with direct sensory feedback. The goal of this work was to assess if it is feasible to interface artificial limbs to severed nerves of human upper limb amputees. Longitudinal intrafascicular electrodes were interfaced to severed nerve stumps of long-term human amputees. Initial studies conducted for two days following electrode implantation showed that it is possible to provide discrete, unitary, painless, graded sensations of touch, joint movement and position referred to the missing limb. Amputees were able to generate and control motor nerve activity uniquely associated with the missing limb movements. Longer term studies conducted for a period of up to 4 weeks showed recorded motor nerve activity and elicited sensations remained stable and there was no significant change in the stimulation parameters. Finally, amputees were able to control a modified Utah Artificial Arm. Results of our studies show that it is possible to interface an artificial limb to the severed nerves of upper limb amputees. Further work is required to refine the hardware which can be eventually incorporated into the artificial arm, allowing the amputees to wear the prosthesis and more precisely execute movements related to real life activities of daily living

    Grasp force estimation from the transient EMG using high-density surface recordings.

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    Objective: Understanding the neurophysiological signals underlying voluntary motor control and decoding them for prosthesis control are among the major challenges in applied neuroscience and bioengineering. Usually, information from the electrical activity of residual forearm muscles (i.e. the electromyogram, EMG) is used to control different functions of a prosthesis. Noteworthy, forearm EMG patterns at the onset of a contraction (transient phase) have shown to contain predictive information about upcoming grasps. However, decoding this information for the estimation of grasp force was so far overlooked. Approach: High Density-EMG signals (192 channels) were recorded from twelve participants performing a pick-and-lift task. The final grasp force was estimated offline using linear regressors, with four subsets of channels and ten features obtained using three channels-features selection methods. Two different evaluation metrics (absolute error and R2), complemented with statistical analysis, were used to select the optimal configuration of the parameters. Different windows of data starting at the grasp force (GF) onset were compared to determine the time at which the grasp force can be ascertained from the EMG signals. Main results: The prediction accuracy improved by increasing the window length from the moment of the onset and kept improving until the steady state at which a plateau of performances was reached. With our methodology, estimations of the grasp force through 16 EMG channels reached an absolute error of 2.52% the maximum voluntary force using only transient information and 1.99% with the first 500ms of data following the onset. Significance: The final GF estimation from transient EMG was comparable to the one obtained using steady state data, confirming our hypothesis that the transient phase contains information about the final grasp force. This result paves the way to fast online myoelectric controllers capable of decoding grasp strength from the very early portion of the EMG signal

    Myoelectric Control Architectures to Drive Upper Limb Exoskeletons

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    Myoelectric interfaces are sensing devices based on electromyography (EMG) able to read the electrical activity of motoneurons and muscles. These interfaces can be used to infer movement volition and to control assistive devices. Currently, these interfaces are widely used to control robotic prostheses for amputees, but their use could be beneficial even for people suffering from motor disabilities where the peripheral nervous system is intact and the impairment is only due to the muscles, e.g. muscular dystrophy, myopathies, or ageing. In combination with recent robotic orthoses and exoskeletons, myoelectric interfaces could dramatically improve these patients’ quality of life. Unfortunately, despite a wide plethora of methodologies has been proposed so far, a natural, intuitive, and reliable interface able to follow impaired subjects’ volition is still missing. The first contribution of this work is to provide a review of existing approaches. In this work we found that existing EMG-based control interfaces can be viewed as specific cases of a generic myoelectric control architecture composed by three distinct functional modules: a decoder to extract the movement intention from EMG signals, a controller to accomplish the desired motion through an actual command given to the actuators, and an adapter to connect them. The latter is responsible for translating the signal from decoder’s output to controller’s input domain and for modulating the level of provided assistance. We used this concept to analyse the case of study of linear regression decoders and an elbow exoskeleton. This thesis has the scientific objective to determine how these modules affect performance of EMG-driven exoskeletons and wearer’s fatigue. To experimentally test and compare myoelectric interfaces this work proposes: (1) a procedure to automatically tune the decoder module in order to equally compare or to normalize the decoder output among different sessions and subjects; (2) a procedure to automatically tune gravity compensation even for subjects suffering from severe disabilities, allowing them to perform the experimental tests; (3) a methodology to guide the impaired patients through the experimental session; (4) an evaluation procedure and metrics allowing statistically significant and unbiased comparison of different myoelectric interfaces. A further contribution of this work is the design of an experimental test bed composed by an elbow exoskeleton and by a software framework able to collect EMG signals and make them available to the exoskeleton’s actuators with minimal latency. Using this test bed, we were able to test different myoelectric interfaces based on our architecture, with different modules choices and tunings. We used linear regression decoders calibrated to predict the muscular torque, low-level controllers having torque or velocity as reference, and adapters consisting of a properly dimensioned gain or simple dynamic systems, such as an integrator or a mass-damping system. The results we obtained allow to conclude that EMG-based control is a viable technology to assist muscular weakness patients. Moreover, all the components of the myoelectric control architecture – decoder, adapter, controller, and their tuning – significantly affect the task-based performance measures we collect. Further investigations should be devoted to a methodology to automatically tune all the components, not the decoders only, and to the quantitative study of the effect the adapter has on the regulation of the assistance level and of the tradeoff between speed and accuracy
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