Modelling of extended de-weight fuzzy control for an upper-limb exoskeleton

Performing heavy physical tasks, overhead work and long working hours are some examples of activities that can lead to musculoskeletal problems in humans. To overcome this issue, automated robots such as the upper-limb exoskeleton is used to assist humans while performing tasks. However, several concerns in developing the exoskeleton have been raised such as the control strategies used. In this study, a control strategy known as the extended de-weight fuzz was proposed to ensure that the exoskeleton could be maneuvered to the desired position with the least number of errors and minimum torque requirement. The extended de-weight fuzzy is a combination of the fuzzy-based PD and fuzzy-based de-weight controller systems. The extended de-weight fuzzy was then compared with the fuzzy-based PD and PID controllers, and the performances of these controllers were compared in terms of their deviations and required torques to perform tasks. The findings show that the proposed control strategy performs better than the fuzzy-based PD and PID controller systems

Design and evaluation of a soft and wearable robotic glove for hand rehabilitation

In the modern world, due to an increased aging population, hand disability is becoming increasingly common. The prevalence of conditions such as stroke is placing an ever-growing burden on the limited fiscal resources of health care providers and the capacity of their physical therapy staff. As a solution, this paper presents a novel design for a wearable and adaptive glove for patients so that they can practice rehabilitative activities at home, reducing the workload for therapists and increasing the patient’s independence. As an initial evaluation of the design’s feasibility the prototype was subjected to motion analysis to compare its performance with the hand in an assessment of grasping patterns of a selection of blocks and spheres. The outcomes of this paper suggest that the theory of design has validity and may lead to a system that could be successful in the treatment of stroke patients to guide them through finger flexion and extension, which could enable them to gain more control and confidence in interacting with the world around them

The-state-of-the-art of soft robotics to assist mobility: a review of physiotherapist and patient identified limitations of current lower-limb exoskeletons and the potential soft-robotic solutions

Background: Soft, wearable, powered exoskeletons are novel devices that may assist rehabilitation, allowing users to walk further or carry out activities of daily living. However, soft robotic exoskeletons, and the more commonly used rigid exoskeletons, are not widely adopted clinically. The available evidence highlights a disconnect between the needs of exoskeleton users and the engineers designing devices. This review aimed to explore the literature on physiotherapist and patient perspectives of the longer-standing, and therefore greater evidenced, rigid exoskeleton limitations. It then offered potential solutions to these limitations, including soft robotics, from an engineering standpoint. Methods: A state-of-the-art review was carried out which included both qualitative and quantitative research papers regarding patient and/or physiotherapist perspectives of rigid exoskeletons. Papers were themed and themes formed the review’s framework. Results: Six main themes regarding the limitations of soft exoskeletons were important to physiotherapists and patients: safety; a one-size-fits approach; ease of device use; weight and placement of device; cost of device; and, specific to patients only, appearance of the device. Potential soft-robotics solutions to address these limitations were offered, including compliant actuators, sensors, suit attachments fitting to user’s body, and the use of control algorithms. Conclusions: It is evident that current exoskeletons are not meeting the needs of their users. Solutions to the limitations offered may inform device development. However, the solutions are not infallible and thus further research and development is required

Achieving Practical Functional Electrical Stimulation-driven Reaching Motions In An Individual With Tetraplegia

Functional electrical stimulation (FES) is a promising technique for restoring the ability to complete reaching motions to individuals with tetraplegia due to a spinal cord injury (SCI). FES has proven to be a successful technique for controlling many functional tasks such as grasping, standing, and even limited walking. However, translating these successes to reaching motions has proven difficult due to the complexity of the arm and the goaldirected nature of reaching motions. The state-of-the-art systems either use robots to assist the FES-driven reaching motions or control the arm of healthy subjects to complete planar motions. These controllers do not directly translate to controlling the full-arm of an individual with tetraplegia because the muscle capabilities of individuals with spinal cord injuries are unique and often limited due to muscle atrophy and the loss of function caused by lower motor neuron damage. This dissertation aims to develop a full-arm FES-driven reaching controller that is capable of achieving 3D reaching motions in an individual with a spinal cord injury. Aim 1 was to develop a complete-arm FES-driven reaching controller that can hold static hand positions for an individual with high tetraplegia due to SCI. We developed a combined feedforward-feedback controller which used the subject-specific model to automatically determine the muscle stimulation commands necessary to hold a desired static hand position. Aim 2 was to develop a subject-specific model-based control strategy to use FES to drive the arm of an individual with high tetraplegia due to SCI along a desired path in the subject’s workspace. We used trajectory optimization to find feasible trajectories which explicitly account for the unique muscle characteristics and the simulated arm dynamics of our subject with tetraplegia. We then developed a model predictive control controller to iii control the arm along the desired trajectory. The controller developed in this dissertation is a significant step towards restoring full arm reaching function to individuals with spinal cord injuries

Development of an exoskeleton robot for upper-limb rehabilitation

To assist or rehabilitate individuals with impaired upper-limb function, we have developed an upper-limb exoskeleton robot, the ETS-MARSE (motion assistive robotic-exoskeleton for superior extremity). The MARSE is comprised of a shoulder motion support part, an elbow and forearm motion support part, and a wrist motion support part. It is designed to be worn on the lateral side of the upper limb in order to provide naturalistic movements of the shoulder (i.e., vertical and horizontal flexion/extension, and internal/external rotation), elbow (i.e., flexion/extension), forearm (i.e., pronation/supination), and wrist joint (i.e., radial/ulnar deviation, and flexion/extension). This thesis focuses on the modeling, design (mechanical and electrical components), development, and control of the developed MARSE. The proposed MARSE was modeled based on the upper-limb biomechanics; it has a relatively low weight, an excellent power/weight ratio, can be easily fitted or removed, and is able to effectively compensate for gravity. Moreover, to avoid complex cable routing that could be found in many exoskeleton systems, a novel power transmission mechanism was introduced for assisting shoulder joint internal/external rotation and for forearm pronation/supination. The exoskeleton was designed for use by typical adults. However, provisions are included for link length adjustments to accommodate a wide range of users. The entire exoskeleton arm was fabricated primarily in aluminum except the high stress joint sections which were fabricated in mild steel to give the exoskeleton structure a relatively light weight. Brushless DC motors (incorporated with Harmonic Drives) were used to actuate the developed MARSE. The kinematic model of the MARSE was developed based on modified Denavit-Hartenberg notations. In dynamic modeling and control, robot parameters such as robot arm link lengths, upper-limb masses, and inertia, are estimated according to the upper limb properties of a typical adult. Though the exoskeleton was developed with the goal of providing different forms of rehab therapy (namely passive arm movements, active-assisted therapy, and resistive therapy), this research concentrated only on passive form of rehabilitation. Passive arm movements and exercises are usually performed slowly compared to the natural speed of arm movement. Therefore, to control the developed MARSE, a computationally inexpensive a PID controller and a PID-based compliance controller were primarily employed. Further, realizing the dynamic modeling of human arm movement which is nonlinear in nature, a nonlinear computed torque control (CTC) and a modified sliding mode exponential reaching law (mSMERL) techniques were employed to control the MARSE. Note that to improve transient tracking performance and to reduce chattering, this thesis proposed the mSMERL, a novel nonlinear control strategy that combined the concept of boundary layer technique and the exponential reaching law. The control architecture was implemented on a field-programmable gate array (FPGA) in conjunction with a RT-PC. In experiments, typical rehabilitation exercises for single and multi joint movements (e.g., reaching) were performed. Experiments were carried out with healthy human subjects where trajectories (i.e., pre-programmed trajectories recommended by therapist/clinician) tracking the form of passive rehabilitation exercises were carried out. This thesis also focused on the development of a 7DoFs upper-limb prototype (lower scaled) ‘master exoskeleton arm’ (mExoArm). Furthermore, experiments were carried out with the mExoArm where subjects (robot users) operate the mExoArm (like a joystick) to maneuver the MARSE to provide passive rehabilitation. Experimental results show that the developed MARSE can effectively perform passive rehabilitation exercises for shoulder, elbow and wrist joint movements. Using mExoArm offers users some flexibility over pre-programmed trajectories selection approach, especially in choosing range of movement and speed of motion. Moreover, the mExoArm could potentially be used to tele-operate the MARSE in providing rehabilitation exercises

Analysis of derived features for the motion classification of a passive lower limb exoskeleton

Analysis of Derived Features for the Motion Classification of a PassiveLowerLimbExoskeleton The recognition of human motion intentions is a fundamental requirement to control efficiently an exoskeleton system. The exoskeleton control can be enhanced or subsequent motions can be predicted, if the current intended motion is known. At H2T research has been carried out with a classification system based on Hidden Markov Models (HMMs) to classify the multi-modal sensor data acquired from a unilateral passive lower-limb exoskeleton. The training data is formed of force vectors, linear accelerations and Euler angles provided by 7 3D-force sensors and 3 IMUs. The recordings consist of data of 10 subjects performing 14 different types of daily activities, each one carried out 10 times. This master thesis attempts to improve the motion classification by using physical meaningful derived features from the raw data aforementioned. The knee vector moment and the knee and ankle joint angles, which respectively give a kinematic and dynamic description of a motion, were the derived features considered. Firstly, these new features are analysed to study their patterns and the resemblance of the data among different subjects is quantified in order to check their consistency. Afterwards, the derived features are evaluated in the motion classification system to check their performance. Various configurations of the classifier were tested including different preprocessors of the data employed and the structure of the HMMs used to represent each motion. Some setups combining derived features and raw data led to good results (e.g. norm of the moment vector and IMUs got 89.39% of accuracy), but did not improve the best results of previous works (e.g. 2 IMUs and 1 Force Sensor got 90.73% of accuracy). Although the classification results are not improved, it is proved that these derived features are a good representation of their primary features and a suitable option if a dimensional reduction of the data is pursued. At the end, possible directions of improvement are suggested to improve the motion classification concerning the results obtained along the thesis.Outgoin

Control avanzado para robótica asistencial y sanitaria

El contenido de los capítulos 3 y 4 están sujetos a confidencialidad. 148 p.En las últimas décadas, observando la necesidad mejorar la calidad de vida de enfermos con movilidad reducida, y los progresos obtenidos gracias a la utilización de los robots en la industria, los dispositivos robóticos han sido propuestos para aplicaciones asistenciales y sanitarias, como la rehabilitación. Los robots de rehabilitación permiten emular los ejercicios de un fisioterapeuta obteniendo tratamientos de mayor precisión y frecuencia. Asimismo, funcionan como una herramienta de medición que permite cuantificar fuerzas y/o movimientos. Y con ayuda de un interfaz gráfico, construyen un entorno de realidad virtual facilitando e incentivando el proceso de rehabilitación.Sin embargo, debido a su reciente introducción al ámbito clínico, muchas de las áreas de la robótica de rehabilitación no han sido estudiadas en profundidad, existiendo varios aspectos a mejorar. Ante esta situación, en esta tesis doctoral se ha planteado indagar en el control de dispositivos robóticos para terapias de rehabilitación.Para lograr este objetivo, la tesis se ha estructurado en tres grandes bloques. En el primero, se ha planteado una metodología de modelado cinemático y dinámico para los dispositivo robóticos de rehabilitación. Esta metodología se ha implementado y validado en el robot de rehabilitación de los miembros superiores UHP (Universal Haptic Pantograph), consiguiendo el modelo del robot necesario para el diseño de controladores avanzadosEn el segundo bloque se ha analizado la problemática de control asociada a los robots de rehabilitación, deduciendo la necesidad de implementar controles avanzados adaptados a los requerimientos particularidades de la robótica de rehabilitación. Con el fin de dar respuesta a estas necesidades, se ha propuesto un algoritmo de control dividido en dos niveles: los de nivel de tarea, que generan una referencia de fuerza o de posición dependiendo del estado de recuperación del paciente y del ejercicio seleccionado; y los de nivel de dispositivo, que siguen a la referencia generada por los de nivel de tarea generando movimientos suaves y seguros. Asimismo, con el fin de simplificar las dificultades introducidas por los sensores, el algoritmo de control diseñado se ha dotado con estimadores que permiten calcular la posición y la fuerza de contacto entre el dispositivo robótico y el usuario.Por último, en el tercer bloque de la tesis se ha diseñado e implementado una plataforma de control y ejecución, que además de permitir la ejecución en tiempo real del algoritmo de control, sirve de puente de comunicación entre el robot de rehabilitación, el usuario y el controlador. Esta plataforma de control y ejecución ha permitido realizar diferentes pruebas experimentales del algoritmo de control propuesto, lo que ha posibilitado validar su funcionamiento en diferentes escenarios