Musculoskeletal Modeling and Control of the Human Upper Limb during Manual Wheelchair Propulsion: Application in Functional Electrical Stimulation Rehabilitation Therapy

Manual wheelchair users rely on their upper limbs for independence and daily activities. The high incidence of upper limb injuries can be attributed to the significant muscular demands imposed by propulsion as a repetitive movement. People with spinal cord injury are at high risk for upper limb injuries, including neuromusculoskeletal pathologies and nociceptive pain, as human upper limbs are poorly designed to facilitate chronic weight-bearing activities, such as manual wheelchair propulsion. Comprehending the underlying biomechanical mechanisms of motor control and developing appropriate rehabilitation tasks are essential to deal with the effects of poor motor control on the performance of manual wheelchair users and prevent long-term upper limb disability, which can interrupt electrical signals between the brain and muscles. Functional electrical stimulation utilizes low-intensity electrical signals to artificially generate body movements by stimulating the damaged peripheral nerves of patients with impaired motor control. Therefore, this study investigates the central nervous system strategy to control human movements, which can be used for task-specific functional electrical stimulation rehabilitation therapy. To this aim, two degrees of freedom musculoskeletal model of the upper limb, including six muscles, is developed, and an optimal controller consisting of two separate optimal parts is proposed to track the desired trajectories in the joint space and estimate the optimal muscle activations regarding physiological constraints. The simulation results are validated with electromyography datasets collected from twelve participants. This study's primary advantages are generating optimal joint torques, accurate trajectory tracking, and good similarities between estimated and measured muscle activations

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

Human intention has long been a primary emphasis in the field of electromyography (EMG) research. This being considered, the movement of the exoskeleton hand can be accurately predicted based on the user's preferences. The EMG is a nonlinear signal formed by muscle contractions as the human hand moves and easily captured noise signal from its surroundings. Due to this fact, this study aims to estimate wrist desired velocity based on EMG signals using ANN and FL mapping methods. The output was derived using EMG signals and wrist position were directly proportional to control wrist desired velocity. Ten male subjects, ranging in age from 21 to 40, supplied EMG signal data set used for estimating the output in single and double muscles experiments. To validate the performance, a physical model of an exoskeleton hand was created using Sim-mechanics program tool. The ANN used Levenberg training method with 1 hidden layer and 10 neurons, while FL used a triangular membership function to represent muscles contraction signals amplitude at different MVC levels for each wrist position. As a result, PID was substituted to compensate fluctuation of mapping outputs, resulting in a smoother signal reading while improving the estimation of wrist desired velocity performance. As a conclusion, ANN compensates for complex nonlinear input to estimate output, but it works best with large data sets. FL allowed designers to design rules based on their knowledge, but the system will struggle due to the large number of inputs. Based on the results achieved, FL was able to show a distinct separation of wrist desired velocity hand movement when compared to ANN for similar testing datasets due to the decision making based on rules setting setup by the designer

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research

Effective control of an exoskeleton robot (ER) using a human-robot interface is crucial for assessing the robot's movements and the force they produce to generate efficient control signals. Interestingly, certain surveys were done to show off cutting-edge exoskeleton robots. The review papers that were previously published have not thoroughly examined the control strategy, which is a crucial component of automating exoskeleton systems. As a result, this review focuses on examining the most recent developments and problems associated with exoskeleton control systems, particularly during the last few years (2017–2022). In addition, the trends and challenges of cooperative control, particularly multi-information fusion, are discussed

Multimodal series elastic actuator for human-machine interaction with applications in robot-aided rehabilitation

Series elastic actuators (SEAs) are becoming an elemental building block in collaborative robotic systems. They introduce an elastic element between the mechanical drive and the end-effector, making otherwise rigid structures compliant when in contact with humans. Topologically, SEAs are more amenable to accurate force control than classical actuation techniques, as the elastic element may be used to provide a direct force estimate. The compliant nature of SEAs provides the potential to be applied in robot-aided rehabilitation. This thesis proposes the design of a novel SEA to be used in robot-aided musculoskeletal rehabilitation. An active disturbance rejection controller is derived and experimentally validated and multiobjective optimization is executed to tune the controller for best performance in human-machine interaction. This thesis also evaluates the constrained workspaces for individuals experiencing upper-limb musculoskeletal disorders. This evaluation can be used as a tool to determine the kinematic structure of devices centred around the novel SEA

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

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

Human Machine Interaction

In this book, the reader will find a set of papers divided into two sections. The first section presents different proposals focused on the human-machine interaction development process. The second section is devoted to different aspects of interaction, with a special emphasis on the physical interaction

Human-Robot Collaborations in Industrial Automation

Technology is changing the manufacturing world. For example, sensors are being used to track inventories from the manufacturing floor up to a retail shelf or a customer’s door. These types of interconnected systems have been called the fourth industrial revolution, also known as Industry 4.0, and are projected to lower manufacturing costs. As industry moves toward these integrated technologies and lower costs, engineers will need to connect these systems via the Internet of Things (IoT). These engineers will also need to design how these connected systems interact with humans. The focus of this Special Issue is the smart sensors used in these human–robot collaborations