Development of an End-Effector Type Therapeutic Robot with Sliding Mode Control for Upper-Limb Rehabilitation

Geriatric disorders, strokes, spinal cord injuries, trauma, and workplace injuries are all prominent causes of upper limb disability. A two-degrees-of-freedom (DoFs) end-effector type robot, iTbot (intelligent therapeutic robot) was designed to provide upper limb rehabilitation therapy. The non-linear control of iTbot utilizing modified sliding mode control (SMC) is presented in this paper. The chattering produced by a conventional SMC is undesirable for this type of robotic application because it damages the mechanical structure and causes discomfort to the robot user. In contrast to conventional SMC, our proposed method reduces chattering and provides excellent dynamic tracking performance, allowing rapid convergence of the system trajectory to its equilibrium point. The performance of the developed robot and controller was evaluated by tracking trajectories corresponding to conventional passive arm movement exercises, including several joints. According to the results of experiment, the iTbot demonstrated the ability to follow the desired trajectories effectively

Upper-limb Kinematic Analysis and Artificial Intelligent Techniques for Neurorehabilitation and Assistive Environments

Stroke, one of the leading causes of death and disability around the world, usually affects the motor cortex causing weakness or paralysis in the limbs of one side of the body. Research efforts in neurorehabilitation technology have focused on the development of robotic devices to restore motor and cognitive function in impaired individuals, having the potential to deliver high-intensity and motivating therapy. End-effector-based devices have become an usual tool in the upper- limb neurorehabilitation due to the ease of adapting to patients. However, they are unable to measure the joint movements during the exercise. Thus, the first part of this thesis is focused on the development of a kinematic reconstruction algorithm that can be used in a real rehabilitation environment, without disturbing the normal patient-clinician interaction. On the basis of the algorithm found in the literature that presents some instabilities, a new algorithm is developed. The proposed algorithm is the first one able to online estimate not only the upper-limb joints, but also the trunk compensation using only two non-invasive wearable devices, placed onto the shoulder and upper arm of the patient. This new tool will allow the therapist to perform a comprehensive assessment combining the range of movement with clinical assessment scales. Knowing that the intensity of the therapy improves the outcomes of neurorehabilitation, a ‘self-managed’ rehabilitation system can allow the patients to continue the rehabilitation at home. This thesis proposes a system to online measure a set of upper-limb rehabilitation gestures, and intelligently evaluates the quality of the exercise performed by the patients. The assessment is performed through the study of the performed movement as a whole as well as evaluating each joint independently. The first results are promising and suggest that this system can became a a new tool to complement the clinical therapy at home and improve the rehabilitation outcomes. Finally, severe motor condition can remain after rehabilitation process. Thus, a technology solution for these patients and people with severe motor disabilities is proposed. An intelligent environmental control interface is developed with the ability to adapt its scan control to the residual capabilities of the user. Furthermore, the system estimates the intention of the user from the environmental information and the behavior of the user, helping in the navigation through the interface, improving its independence at home.El accidente cerebrovascular o ictus es una de las causas principales de muerte y discapacidad a nivel mundial. Normalmente afecta a la corteza motora causando debilidad o parálisis en las articulaciones del mismo lado del cuerpo. Los esfuerzos de investigación dentro de la tecnología de neurorehabilitación se han centrado en el desarrollo de dispositivos robóticos para restaurar las funciones motoras y cognitivas en las personas con esta discapacidad, teniendo un gran potencial para ofrecer una terapia de alta intensidad y motivadora. Los dispositivos basados en efector final se han convertido en una herramienta habitual en la neurorehabilitación de miembro superior ya que es muy sencillo adaptarlo a los pacientes. Sin embargo, éstos no son capaces de medir los movimientos articulares durante la realización del ejercicio. Por tanto, la primera parte de esta tesis se centra en el desarrollo de un algoritmo de reconstrucción cinemática que pueda ser usado en un entorno de rehabilitación real, sin perjudicar a la interacción normal entre el paciente y el clínico. Partiendo de la base que propone el algoritmo encontrado en la literatura, el cual presenta algunas inestabilidades, se ha desarrollado un nuevo algoritmo. El algoritmo propuesto es el primero capaz de estimar en tiempo real no sólo las articulaciones del miembro superior, sino también la compensación del tronco usando solamente dos dispositivos no invasivos y portátiles, colocados sobre el hombro y el brazo del paciente. Esta nueva herramienta permite al terapeuta realizar una valoración más exhaustiva combinando el rango de movimiento con las escalas de valoración clínicas. Sabiendo que la intensidad de la terapia mejora los resultados de la recuperación del ictus, un sistema de rehabilitación ‘auto-gestionado’ permite a los pacientes continuar con la rehabilitación en casa. Esta tesis propone un sistema para medir en tiempo real un conjunto de gestos de miembro superior y evaluar de manera inteligente la calidad del ejercicio realizado por el paciente. La valoración se hace a través del estudio del movimiento ejecutado en su conjunto, así como evaluando cada articulación independientemente. Los primeros resultados son prometedores y apuntan a que este sistema puede convertirse en una nueva herramienta para complementar la terapia clínica en casa y mejorar los resultados de la rehabilitación. Finalmente, después del proceso de rehabilitación pueden quedar secuelas motoras graves. Por este motivo, se propone una solución tecnológica para estas personas y para personas con discapacidades motoras severas. Así, se ha desarrollado una interfaz de control de entorno inteligente capaz de adaptar su control a las capacidades residuales del usuario. Además, el sistema estima la intención del usuario a partir de la información del entorno y el comportamiento del usuario, ayudando en la navegación a través de la interfaz, mejorando su independencia en el hogar

Effects of a robot-assisted training of grasp and pronation/supination in chronic stroke: a pilot study

<p>Abstract</p> <p>Background</p> <p>Rehabilitation of hand function is challenging, and only few studies have investigated robot-assisted rehabilitation focusing on distal joints of the upper limb. This paper investigates the feasibility of using the <it>HapticKnob</it>, a table-top end-effector device, for robot-assisted rehabilitation of grasping and forearm pronation/supination, two important functions for activities of daily living involving the hand, and which are often impaired in chronic stroke patients. It evaluates the effectiveness of this device for improving hand function and the transfer of improvement to arm function.</p> <p>Methods</p> <p>A single group of fifteen chronic stroke patients with impaired arm and hand functions (Fugl-Meyer motor assessment scale (FM) 10-45/66) participated in a 6-week 3-hours/week rehabilitation program with the <it>HapticKnob</it>. Outcome measures consisted primarily of the FM and Motricity Index (MI) and their respective subsections related to distal and proximal arm function, and were assessed at the beginning, end of treatment and in a 6-weeks follow-up.</p> <p>Results</p> <p>Thirteen subjects successfully completed robot-assisted therapy, with significantly improved hand and arm motor functions, demonstrated by an average 3.00 points increase on the FM and 4.55 on the MI at the completion of the therapy (4.85 FM and 6.84 MI six weeks post-therapy). Improvements were observed both in distal and proximal components of the clinical scales at the completion of the study (2.00 FM wrist/hand, 2.55 FM shoulder/elbow, 2.23 MI hand and 4.23 MI shoulder/elbow). In addition, improvements in hand function were observed, as measured by the Motor Assessment Scale, grip force, and a decrease in arm muscle spasticity. These results were confirmed by motion data collected by the robot.</p> <p>Conclusions</p> <p>The results of this study show the feasibility of this robot-assisted therapy with patients presenting a large range of impairment levels. A significant homogeneous improvement in both hand and arm function was observed, which was maintained 6 weeks after end of the therapy.</p

Development and Control of a 3-DoF Exoskeleton Robot for Forearm and Wrist Rehabilitation

The research conducted under this project directly contributes to the development of a forearm and wrist rehabilitation robot (UWM-FWRR). Upper extremity impairment following stroke, trauma, sports injuries, occupational injuries, spinal cord injuries, and orthopaedic injuries results in significant deficits in hand manipulation and the performance of everyday tasks. Strokes affect nearly 800,000 people in the United States each year. Rehabilitation programs are the main method of promoting functional recovery in individuals with finger impairment. The conventional therapeutic approach requiring a long commitment by both the clinician and the patient. Robotic devices (RDs) are novel and rapidly expanding technologies in hand rehabilitation. However, existing RDs have not been able to fully restore hand functionality as they cannot provide the independent joint control and levels of velocity and torque required. Our customer discovery [1] reveals that therapists often prescribe therapeutic devices for passive arm/leg movement assistance but no therapeutic devices exist for combined hand, wrist, and forearm movements that can be used at home/clinic. Regaining hand strength and mobility plays an important role in supporting essential activities of daily living, such as eating, and thus has the potential to improve the physical and mental status of both stroke patients and their family caregivers. Therefore, through this research author has develop UWM-FWRR that can provide rehabilitative exercises for forearm and, wrist movements. In contrast to existing RDs, developed UWM-FWRR is a portable, light weight, low cost, and novel powered rehabilitation device that will be developed to provide therapeutic exercises to a wide group of patients with different degrees of impairments. This innovation provides an opportunity for the patients to perform exercises not only with the guidance of a therapist at clinic but also be used at home as a telerehabilitation device through smartphone application (Future works)

Down-Conditioning of Soleus Reflex Activity using Mechanical Stimuli and EMG Biofeedback

Spasticity is a common syndrome caused by various brain and neural injuries, which can severely impair walking ability and functional independence. To improve functional independence, conditioning protocols are available aimed at reducing spasticity by facilitating spinal neuroplasticity. This down-conditioning can be performed using different types of stimuli, electrical or mechanical, and reflex activity measures, EMG or impedance, used as biofeedback variable. Still, current results on effectiveness of these conditioning protocols are incomplete, making comparisons difficult. We aimed to show the within-session task- dependent and across-session long-term adaptation of a conditioning protocol based on mechanical stimuli and EMG biofeedback. However, in contrast to literature, preliminary results show that subjects were unable to successfully obtain task-dependent modulation of their soleus short-latency stretch reflex magnitude

下腹部を対象とした極細針によるCTガイド下高正確度穿刺プランニング

早大学位記番号:新8149早稲田大

Aerospace Medicine and Biology. A continuing bibliography (Supplement 226)

This bibliography lists 129 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1981

Robot Assisted Shoulder Rehabilitation: Biomechanical Modelling, Design and Performance Evaluation

The upper limb rehabilitation robots have made it possible to improve the motor recovery in stroke survivors while reducing the burden on physical therapists. Compared to manual arm training, robot-supported training can be more intensive, of longer duration, repetitive and task-oriented. To be aligned with the most biomechanically complex joint of human body, the shoulder, specific considerations have to be made in the design of robotic shoulder exoskeletons. It is important to assist all shoulder degrees-of-freedom (DOFs) when implementing robotic exoskeletons for rehabilitation purposes to increase the range of motion (ROM) and avoid any joint axes misalignments between the robot and human’s shoulder that cause undesirable interaction forces and discomfort to the user. The main objective of this work is to design a safe and a robotic exoskeleton for shoulder rehabilitation with physiologically correct movements, lightweight modules, self-alignment characteristics and large workspace. To achieve this goal a comprehensive review of the existing shoulder rehabilitation exoskeletons is conducted first to outline their main advantages and disadvantages, drawbacks and limitations. The research has then focused on biomechanics of the human shoulder which is studied in detail using robotic analysis techniques, i.e. the human shoulder is modelled as a mechanism. The coupled constrained structure of the robotic exoskeleton connected to a human shoulder is considered as a hybrid human-robot mechanism to solve the problem of joint axes misalignments. Finally, a real-scale prototype of the robotic shoulder rehabilitation exoskeleton was built to test its operation and its ability for shoulder rehabilitation

Development of the control system of a low-cost robot for upper limb home rehabilitation of stroke patents

Stroke is the leading cause of disability in the UK and is expected to increase in prevalence due to an aging population. Stroke patient outcome is improved by early and intense physiotherapy after Stroke, but NHS services are increasingly under strain, particularly in the wake of the COVID 19 pandemic. Over the last thirty years there has been a great deal of development in the use of robotic devices to provide rehabilitation to Stroke patients. The system architecture of a rehabilitation robot is well defined, with a High-Level Controller generating rehabilitation tasks, a trajectory generation procedure to encode the tasks and a Low-Level Controller to implement the tasks. All commercially available rehabilitation robots are high cost, however, and a recent study has found that the cost benefit ratio is too poor to be viable [1]. Further to this, in the wake of the pandemic there has been increased interest in home-based devices. There has been research into low cost devices, but this area of research has not been sufficiently explored. MyPAM is low-cost upper-limb rehabilitation robot designed for home use. There have been two previous iterations, and the first version of MyPAM was proven to improve the outcome of Stroke patients in trials. The current system has been rearchitected, with much focus on the Low Level controller which has been implemented on lower cost hardware than previous iterations. Responsibility for trajectory generation has been moved from the High-Level controller to the Low level controller, and a novel method for affecting the trajectory with Attractors and Repulsors has been designed and validated, which has important implications on patient motivation. A multidomain dynamic model is presented, which is necessary for creating a baseline against which to compare patient performance. A novel integrated end-effector/2-axis force sensor and a novel end-effector/Tristate grip sensor are presented, both based on the MagOne sensing methodology. An Admittance controller with instability protection is presented, and the system integration is discussed