153 research outputs found

    Real-time EMG based pattern recognition control for hand prostheses : a review on existing methods, challenges and future implementation

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    Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations

    Towards electrodeless EMG linear envelope signal recording for myo-activated prostheses control

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    After amputation, the residual muscles of the limb may function in a normal way, enabling the electromyogram (EMG) signals recorded from them to be used to drive a replacement limb. These replacement limbs are called myoelectric prosthesis. The prostheses that use EMG have always been the first choice for both clinicians and engineers. Unfortunately, due to the many drawbacks of EMG (e.g. skin preparation, electromagnetic interferences, high sample rate, etc.); researchers have aspired to find suitable alternatives. One proposes the dry-contact, low-cost sensor based on a force-sensitive resistor (FSR) as a valid alternative which instead of detecting electrical events, detects mechanical events of muscle. FSR sensor is placed on the skin through a hard, circular base to sense the muscle contraction and to acquire the signal. Similarly, to reduce the output drift (resistance) caused by FSR edges (creep) and to maintain the FSR sensitivity over a wide input force range, signal conditioning (Voltage output proportional to force) is implemented. This FSR signal acquired using FSR sensor can be used directly to replace the EMG linear envelope (an important control signal in prosthetics applications). To find the best FSR position(s) to replace a single EMG lead, the simultaneous recording of EMG and FSR output is performed. Three FSRs are placed directly over the EMG electrodes, in the middle of the targeted muscle and then the individual (FSR1, FSR2 and FSR3) and combination of FSR (e.g. FSR1+FSR2, FSR2-FSR3) is evaluated. The experiment is performed on a small sample of five volunteer subjects. The result shows a high correlation (up to 0.94) between FSR output and EMG linear envelope. Consequently, the usage of the best FSR sensor position shows the ability of electrode less FSR-LE to proportionally control the prosthesis (3-D claw). Furthermore, FSR can be used to develop a universal programmable muscle signal sensor that can be suitable to control the myo-activated prosthesis

    Co-Adaptive Control of Bionic Limbs via Unsupervised Adaptation of Muscle Synergies

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    Objective: In this work, we present a myoelectric interface that extracts natural motor synergies from multi-muscle signals and adapts in real-time with new user inputs. With this unsupervised adaptive myocontrol (UAM) system, optimal synergies for control are continuously co-adapted with changes in user motor control, or as a function of perturbed conditions via online non-negative matrix factorization guided by physiologically informed sparseness constraints in lieu of explicit data labelling. Methods: UAM was tested in a set of virtual target reaching tasks completed by able-bodied and amputee subjects. Tests were conducted under normative and electrode perturbed conditions to gauge control robustness with comparisons to non-adaptive and supervised adaptive myocontrol schemes. Furthermore, UAM was used to interface an amputee with a multi-functional powered hand prosthesis during standardized Clothespin Relocation Tests, also conducted in normative and perturbed conditions. Results: In virtual tests, UAM effectively mitigated performance degradation caused by electrode displacement, affording greater resilience over an existing supervised adaptive system for amputee subjects. Induced electrode shifts also had negligible effect on the real world control performance of UAM with consistent completion times (23.91 +/- 1.33 s) achieved across Clothespin Relocation Tests in the normative and electrode perturbed conditions. Conclusion: UAM affords comparable robustness improvements to existing supervised adaptive myocontrol interfaces whilst providing additional practical advantages for clinical deployment. Significance: The proposed system uniquely incorporates neuromuscular control principles with unsupervised online learning methods and presents a working example of a freely co-adaptive bionic interface.Peer reviewe

    Classification of EMG signals to control a prosthetic hand using time-frequesncy representations and Support Vector Machines

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    Myoelectric signals (MES) are viable control signals for externally-powered prosthetic devices. They may improve both the functionality and the cosmetic appearance of these devices. Conventional controllers, based on the signal\u27s amplitude features in the control strategy, lack a large number of controllable states because signals from independent muscles are required for each degree of freedom (DoF) of the device. Myoelectric pattern recognition systems can overcome this problem by discriminating different residual muscle movements instead of contraction levels of individual muscles. However, the lack of long-term robustness in these systems and the design of counter-intuitive control/command interfaces have resulted in low clinical acceptance levels. As a result, the development of robust, easy to use myoelectric pattern recognition-based control systems is the main challenge in the field of prosthetic control. This dissertation addresses the need to improve the controller\u27s robustness by designing a pattern recognition-based control system that classifies the user\u27s intention to actuate the prosthesis. This system is part of a cost-effective prosthetic hand prototype developed to achieve an acceptable level of functional dexterity using a simple to use interface. A Support Vector Machine (SVM) classifier implemented as a directed acyclic graph (DAG) was created. It used wavelet features from multiple surface EMG channels strategically placed over five forearm muscles. The classifiers were evaluated across seven subjects. They were able to discriminate five wrist motions with an accuracy of 91.5%. Variations of electrode locations were artificially introduced at each recording session as part of the procedure, to obtain data that accounted for the changes in the user\u27s muscle patterns over time. The generalization ability of the SVM was able to capture most of the variability in the data and to maintain an average classification accuracy of 90%. Two principal component analysis (PCA) frameworks were also evaluated to study the relationship between EMG recording sites and the need for feature space reduction. The dimension of the new feature set was reduced with the goal of improving the classification accuracy and reducing the computation time. The analysis indicated that the projection of the wavelet features into a reduced feature space did not significantly improve the accuracy and the computation time. However, decreasing the number of wavelet decomposition levels did lower the computational load without compromising the average signal classification accuracy. Based on the results of this work, a myoelectric pattern recognition-based control system that uses an SVM classifier applied to time-frequency features may be used to discriminate muscle contraction patterns for prosthetic applications

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

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    abstract: 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 peripheral nervous system (PNS)–Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PMI has been selected to denote human–machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the 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

    Myoelectric Control for Active Prostheses via Deep Neural Networks and Domain Adaptation

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    Recent advances in Biological Signal Processing (BSP) and Machine Learning (ML), in particular, Deep Neural Networks (DNNs), have paved the way for development of advanced Human-Machine Interface (HMI) systems for decoding human intent and controlling artificial limbs. Myoelectric control, as a subcategory of HMI sys- tems, deals with detecting, extracting, processing, and ultimately learning from Electromyogram (EMG) signals to command external devices, such as hand prostheses. In this context, hand gesture recognition/classification via Surface Electromyography (sEMG) signals has attracted a great deal of interest from many researchers. De- spite extensive progress in the field of myoelectric prosthesis, however, there are still limitations that should be addressed to achieve a more intuitive upper limb pros- thesis. Through this Ph.D. thesis, first, we perform a literature review on recent research works on pattern classification approaches for myoelectric control prosthesis to identify challenges and potential opportunities for improvement. Then, we aim to enhance the accuracy of myoelectric systems, which can be used for realizing an accu- rate and efficient HMI for myocontrol of neurorobotic systems. Beside improving the accuracy, decreasing the number of parameters in DNNs plays an important role in a Hand Gesture Recognition (HGR) system. More specifically, a key factor to achieve a more intuitive upper limb prosthesis is the feasibility of embedding DNN-based models into prostheses controllers. On the other hand, transformers are considered to be powerful DNN models that have revolutionized the Natural Language Processing (NLP) field and showed great potentials to dramatically improve different computer vision tasks. Therefore, we propose a Transformer-based neural network architecture to classify and recognize upper-limb hand gestures. Finally, another goal of this thesis is to design a modern DNN-based gesture detection model that relies on minimal training data while providing high accuracy. Although DNNs have shown superior accuracy compared to conventional methods when large amounts of data are available for training, their performance substantially decreases when data are limited. Collecting large datasets for training may be feasible in research laboratories, but it is not a practical approach for real-life applications. We propose to solve this problem, by designing a framework which utilizes a combination of temporal convolutions and attention mechanisms

    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

    Towards identification of finger flexions using single channel surface electromyography - able bodied and amputee subjects

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    This research has established a method for using single channel surface electromyogram (sEMG) recorded from the forearm to identify individual finger flexion. The technique uses the volume conduction properties of the tissues and uses the magnitude and density of the singularities in the signal as a measure of strength of the muscle activity. Methods: SEMG was recorded from the flexor digitorum superficialis muscle during four different finger flexions. Based on the volume conduction properties of the tissues, sEMG was decomposed into wavelet maxima and grouped into four groups based on their magnitude. The mean magnitude and the density of each group were the inputs to the twin support vector machines (TSVM). The algorithm was tested on 11 able-bodied and one trans-radial amputated volunteer to determine the accuracy, sensitivity and specificity. The system was also tested to determine inter-experimental variations and variations due to difference in the electrode location. Results: Accuracy and sensitivity of identification of finger actions from single channel sEMG signal was 93% and 94% for able-bodied and 81% and 84% for trans-radial amputated respectively, and there was only a small inter-experimental variation. Conclusions: Volume conduction properties based sEMG analysis provides a suitable basis for identifying finger flexions from single channel sEMG. The reported system requires supervised training and automatic classification

    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

    Biceps brachii synergy and its contribution to target reaching tasks within a virtual cube

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    Ces dernières années, des travaux importants ont été observés dans le développement du contrôle prothétique afin d'aider les personnes amputées du membre supérieur à améliorer leur qualité de vie au quotidien. Certaines prothèses myoélectriques modernes des membres supérieurs disponibles dans le commerce ont de nombreux degrés de liberté et nécessitent de nombreux signaux de contrôle pour réaliser plusieurs tâches fréquemment utilisées dans la vie quotidienne. Pour obtenir plusieurs signaux de contrôle, de nombreux muscles sont requis mais pour les personnes ayant subi une amputation du membre supérieur, le nombre de muscles disponibles est plus ou moins réduit selon le niveau de l’amputation. Pour accroître le nombre de signaux de contrôle, nous nous sommes intéressés au biceps brachial, vu qu’anatomiquement il est formé de 2 chefs et que de la présence de compartiments a été observée sur sa face interne. Physiologiquement, il a été trouvé que les unités motrices du biceps sont activées à différents endroits du muscle lors de la production de diverses tâches fonctionnelles. De plus, il semblerait que le système nerveux central puisse se servir de la synergie musculaire pour arriver à facilement produire plusieurs mouvements. Dans un premier temps on a donc identifié que la synergie musculaire était présente chez le biceps de sujets normaux et on a montré que les caractéristiques de cette synergie permettaient d’identifier la posture statique de la main lorsque les signaux du biceps avaient été enregistrés. Dans un deuxième temps, on a réussi à démontrer qu’il était possible, dans un cube présenté sur écran, à contrôler la position d’une sphère en vue d’atteindre diverses cibles en utilisant la synergie musculaire du biceps. Les techniques de classification utilisées pourraient servir à faciliter le contrôle des prothèses myoélectriques.In recent years, important work has been done in the development of prosthetic control to help upper limb amputees improve their quality of life on a daily basis. Some modern commercially available upper limb myoelectric prostheses have many degrees of freedom and require many control signals to perform several tasks commonly used in everyday life. To obtain several control signals, many muscles are required, but for people with upper limb amputation, the number of muscles available is more or less reduced, depending on the level of amputation. To increase the number of control signals, we were interested in the biceps brachii, since it is anatomically composed of 2 heads and the presence of compartments was observed on its internal face. Physiologically, it has been found that the motor units of the biceps are activated at different places of the muscle during production of various functional tasks. In addition, it appears that the central nervous system can use muscle synergy to easily produce multiple movements. In this research, muscle synergy was first identified to be present in the biceps of normal subjects, and it was shown that the characteristics of this synergy allowed the identification of static posture of the hand when the biceps signals had been recorded. In a second investigation, we demonstrated that it was possible in a virtual cube presented on a screen to control online the position of a sphere to reach various targets by using muscle synergy of the biceps. Classification techniques have been used to improve the classification of muscular synergy features, and these classification techniques can be integrated with control algorithm that produces dynamic movement of myoelectric prostheses to facilitate the training of prosthetic control
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