Person-specific gesture set selection for optimised movement classification from EMG signals

© 2016 IEEE.Movement classification from electromyography (EMG) signals is a promising vector for improvement of human computer interaction and prosthetic control. Conventional work in this area typically makes use of expert knowledge to select a set of movements a priori and then design classifiers based around these movements. The disadvantage of this approach is that different individuals might have different sets of movements that would lead to high classification accuracy. The novel approach we take here is to instead use a data-driven diagnostic test to select a set of person-specific movements. This new approach leads to an optimised set of movements for a specific person with regards to classification performance

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

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

Co-adaptive myoelectric control for upper limb prostheses

[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

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

A Systematic Review and Meta-Analysis of the Incidence of Injury in Professional Female Soccer

The epidemiology of injury in male professional football is well documented and has been used as a basis to monitor injury trends and implement injury prevention strategies. There are no systematic reviews that have investigated injury incidence in women’s professional football. Therefore, the extent of injury burden in women’s professional football remains unknown. PURPOSE: The primary aim of this study was to calculate an overall incidence rate of injury in senior female professional soccer. The secondary aims were to provide an incidence rate for training and match play. METHODS: PubMed, Discover, EBSCO, Embase and ScienceDirect electronic databases were searched from inception to September 2018. Two reviewers independently assessed study quality using the Strengthening the Reporting of Observational Studies in Epidemiology statement using a 22-item STROBE checklist. Seven prospective studies (n=1137 professional players) were combined in a pooled analysis of injury incidence using a mixed effects model. Heterogeneity was evaluated using the Cochrane Q statistic and I2. RESULTS: The epidemiological incidence proportion over one season was 0.62 (95% CI 0.59 - 0.64). Mean total incidence of injury was 3.15 (95% CI 1.54 - 4.75) injuries per 1000 hours. The mean incidence of injury during match play was 10.72 (95% CI 9.11 - 12.33) and during training was 2.21 (95% CI 0.96 - 3.45). Data analysis found a significant level of heterogeneity (total Incidence, X2 = 16.57 P < 0.05; I2 = 63.8%) and during subsequent sub group analyses in those studies reviewed (match incidence, X2 = 76.4 (d.f. = 7), P <0.05; I2 = 90.8%, training incidence, X2 = 16.97 (d.f. = 7), P < 0.05; I2 = 58.8%). Appraisal of the study methodologies revealed inconsistency in the use of injury terminology, data collection procedures and calculation of exposure by researchers. Such inconsistencies likely contribute to the large variance in the incidence and prevalence of injury reported. CONCLUSIONS: The estimated risk of sustaining at least one injury over one football season is 62%. Continued reporting of heterogeneous results in population samples limits meaningful comparison of studies. Standardising the criteria used to attribute injury and activity coupled with more accurate methods of calculating exposure will overcome such limitations