Neuro-fuzzy modeling of multi-field surface neuroprostheses for hand grasp

154 p.Las neuroprótesis aplican pulsos eléctricos a los nervios periféricos con el objetivo de sustituir funciones motrices/sensoriales perdidas, dando asistencia e influyendo positivamente en la rehabilitación motriz de personas con disfunciones motrices causadas por trastornos neurológicos. La complejidad de la neuroanatomía del antebrazo y la mano, su dimensionalidad, las diversas tareas no-cíclicas, la variabilidad de movimientos entre sujetos y la reducida selectividad de las neuroprótesis superficiales, ha dado lugar al diseño de un número reducido de neuroprótesis orientadas a agarres básicos. La posibilidad de hacer más selectiva la estimulación mediante los electrodos multi-campo, junto con el conocimiento sobre la incomodidad y los movimientos que genera la aplicación de la estimulación eléctrica funcional (FES por sus siglas en inglés) en miembro superior, podrían ser base fundamental para el desarrollo de neuroprótesis de agarre más avanzadas. La presente tesis describe un análisis de incomodidad como resultado de FES en el miembro superior, y propone modelos neuro-difusos para neuroprótesis de agarre tanto para personas sanas como para personas con trastornos neurológicos. El conocimiento generado respecto a la incomodidad puede ser utilizado como guía para desarrollar aplicaciones de FES de miembro superior más cómodas. Del mismo modo, los modelos propuestos en esta tesis pueden ser utilizados para apoyar el diseño y la validación de sistemas de control avanzados en neuroprótesis dirigidas a la función de agarre.Tecnalia; Intelligent Control Research Grou

Human inspired humanoid robots control architecture

This PhD Thesis tries to present a different point of view when talking about the development of control architectures for humanoid robots. Specifically, this Thesis is focused on studying the human postural control system as well as on the use of this knowledge to develop a novel architecture for postural control in humanoid robots. The research carried on in this thesis shows that there are two types of components for postural control: a reactive one, and other predictive or anticipatory. This work has focused on the development of the second component through the implementation of a predictive system complementing the reactive one. The anticipative control system has been analysed in the human case and it has been extrapolated to the architecture for controlling the humanoid robot TEO. In this way, its different components have been developed based on how humans work without forgetting the tasks it has been designed for. This control system is based on the composition of sensorial perceptions, the evaluation of stimulus through the use of the psychophysics theory of the surprise, and the creation of events that can be used for activating some reaction strategies (synergies) The control system developed in this Thesis, as well as the human being does, processes information coming from different sensorial sources. It also composes the named perceptions, which depend on the type of task the postural control acts over. The value of those perceptions is obtained using bio-inspired evaluation techniques of sensorial inference. Once the sensorial input has been obtained, it is necessary to process it in order to foresee possible disturbances that may provoke an incorrect performance of a task. The system developed in this Thesis evaluates the sensorial information, previously transformed into perceptions, through the use of the “Surprise Theory”, and it generates some events called “surprises” used for predicting the evolution of a task. Finally, the anticipative system for postural control can compose, if necessary, the proper reactions through the use of predefined movement patterns called synergies. Those reactions can complement or substitute completely the normal performance of a task. The performance of the anticipative system for postural control as well as the performance of each one of its components have been tested through simulations and the application of the results in the humanoid robot TEO from the RoboticsLab research group in the Systems Engineering and Automation Department from the Carlos III University of Madrid. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Esta Tesis Doctoral pretende aportar un punto de vista diferente en el desarrollo de arquitecturas de control para robots humanoides. En concreto, esta Tesis se centra en el estudio del sistema de control postural humano y en la aplicación de este conocimiento en el desarrollo de una nueva arquitectura de control postural para robots humanoides. El estudio realizado en esta Tesis pone de manifiesto la existencia de una componente de control postural reactiva y otra predictiva o anticipativa. Este trabajo se ha centrado en el desarrollo de la segunda componente mediante la implementación de un sistema predictivo que complemente al sistema reactivo. El sistema de control anticipativo ha sido estudiado en el caso humano y extrapolado para la arquitectura de control del robot humanoide TEO. De este modo, sus diferentes componentes han sido desarrollados inspirándose en el funcionamiento humano y considerando las tareas para las que dicho robot ha sido concebido. Dicho sistema está basado en la composición de percepciones sensoriales, la evaluación de los estímulos mediante el uso de la teoría psicofísica de la sorpresa y la generación de eventos que sirvan para activar estrategias de reacción (sinergias). El sistema de control desarrollado en esta Tesis, al igual que el ser humano, procesa información de múltiples fuentes sensoriales y compone las denominadas percepciones, que dependen del tipo de tarea sobre la que actúa el control postural. El valor de estas percepciones es obtenido utilizando técnicas de evaluación bioinspiradas de inferencia sensorial. Una vez la entrada sensorial ha sido obtenida, es necesario procesarla para prever posibles perturbaciones que puedan ocasionar una incorrecta realización de una tarea. El sistema desarrollado en esta Tesis evalúa la información sensorial, previamente transformada en percepciones, mediante la ‘Teoría de la Sorpresa’ y genera eventos llamados ‘sorpresas’ que sirven para predecir la evolución de una tarea. Por último, el sistema anticipativo de control postural puede componer, si fuese necesario, las reacciones adecuadas mediante el uso de patrones de movimientos predefinidos llamados sinergias. Dichas reacciones pueden complementar o sustituir por completo la ejecución normal de una tarea. El funcionamiento del sistema anticipativo de control postural y de cada uno de sus componentes ha sido probado tanto por medio de simulaciones como por su aplicación en el robot humanoide TEO del grupo de investigación RoboticsLab en el Departamento de Ingeniería de Sistemas y Automática de la Universidad Carlos III de Madrid

Intelligent flight control systems

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms

Mechatronic Systems

Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools

A survey on bio-signal analysis for human-robot interaction

The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of each domain's benefits and drawbacks, and finally, a recommendation for a new strategy for robotic systems

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

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

Refining the relationship between the mechanical demands on the spine and injury mechanisms through improved estimates of load exposure and tissue tolerance

The low back loading to which an individual is exposed has been linked to injury and the reporting of low back pain. Despite extensive research on the spine and workplace loading exposures, statistics indicate that efforts to date have not led to large reductions in the reporting of these injuries. One possible cause for the apparent ineffectiveness of interventions may be a poorly defined understanding of the mechanical exposures of the spine during work related activities. There are sophisticated models that can predict spine loads and are responsive to how an individual moves and uses their muscles, however the models are complex and require extensive data collection to be implemented. This fact has prevented these models from being employed in industrial settings and the simplified surrogate methods that are being employed may not be predicting load exposures well. Therefore, this work focused on examining surrogate methods that can produce estimates of spine loading equal to our most complex laboratory based models. In addition, our understanding of spine tolerance to combined motion and load has been based upon in-vitro work that has not accurately represented coupled physiologic compression and flexion or has not investigated potentially beneficial loading scenarios. The result has been a lack of clear data indicating when motion should be treated as the primary influence in injury development or when load is the likely injury causing exposure. As a result, research was conducted to determine the interplay between load and motion in cumulative injury development, as well as investigating the potential of static rest periods in mitigating the effects of cumulative compression. Study one examined the potential utility of artificial neural networks as a data reduction approach in obtaining estimates of time-varying loads and moments equal in magnitude to those of EMG-assisted and rigid link models. It was found that the neural network approach under predicted peak force and moment exposures, but produced strong predictions of average and cumulative exposures. Therefore this method may be a viable approach to document cumulative loads in industrial settings. Study two compared the load and moment estimates from a currently employed, posture match based ergonomic assessment tool (3DMatch) to those obtained with an EMG-assisted model and those predicted with a rigid link modeling approach. The results indicated that 3DMatch over predicted peak moments and cumulative compression. However, simple correction approaches were developed which can adjust the predictions to obtain more physiologic estimates. Study three employed flexion/extension motion with repetitive compression loading profiles in an in-vitro study, with both load and motion profiles being obtained from measures in study 1. It was found that at loads above 30% of a spine’s compressive tolerance, repetitive flexion/extension would not lead to intervertebral disc injury prior to an endplate or vertebral fracture occurring. However, as loads fall below 30% the likelihood of experiencing a herniation increases, while the overall likelihood of an injury occurring decreases. Comparison to relevant studies indicated that while repetitive flexion did not alter the site of injury it appeared to degrade the ability of the spine to tolerate compression. Finally, study four employed dynamic compression while the spine was maintained in a neutral posture to investigate the effects of ‘rest’, or periods of static low level loading, on altering the amount of load tolerated prior to injury. It was found that there was a non-linear relationship between load magnitude and compressive tolerance, with increasing load magnitude exposures leading to decreasing cumulative load tolerances. Periods of low level static loading did not alter the resistance of the spinal unit to cumulative compression or impact the number of cycles tolerated to failure. In summary, this work has examined methods that may allow for better predictions of spine loading in the workplace without the large data demands of sophisticated laboratory approaches. Where possible, suggestions for optimal implementation of these surrogates have been developed. Additionally, in-vitro work has indicated a load threshold of 30%, above which herniation is not likely to occur during dynamic repetitive loading. Furthermore, the insertion of static rest periods into dynamic loading scenarios did not improve the spine’s failure tolerance to loading, indicating that care should be exercised when determining optimal loading paradigms. In combination, the applied methods that have been developed and the information regarding injury development that has been obtained will help to refine our understanding of the exposures and tolerances that define mechanical injury in the spine