A robotic arm design for stroke patients

Author name used in this publication: K. W. E. ChengAuthor name used in this publication: K. DingAuthor name used in this publication: C. D. XuVersion of RecordPublishe

A compact robotic device for upper-limb reaching rehabilitation

This paper presents a compact linear-motion robotic device for upper-extremity reaching rehabilitation. Starting from conceptual design, the paper describes electronic circuit design and program development. The work develops a prototype that provides active and passive rehabilitation training. In active training, subjects actively move their arm with assistive or resistive force from the device to finish predefined displacement and force profiles. In passive training, subjects remain passive while the device moves the limb following the pre-defined displacement profile. Engineering specifications with adequate safety factor are determined and standard electronic and readily available mechanical components are exploited to keep the total cost low

MEASURING REGULARITY OF FINE UPPER LIMB MOVEMENTS WITH A HAPTIC PLATFORM FOR MOTOR LEARNING AND REHABILITATION

Robot-assisted systems for arm training are being increasingly used to target moderate-to-severe upper limb impairments in rehabilitation facilities, while hand fine motor skills are seldom being targeted by these machines. This manuscript describes and tests the feasibility of a system based on a haptic interface aimed to complement the efficacy of robotic training in the rehabilitation and motor learning associated with upper extremities movements. End-effector kinematics associated with different trajectory tasks performed by 11 healthy adults were used to extract measures of smoothness, under different testing conditions that included the presence or absence of visual and haptic feedback, the use of dominant vs. non dominant hand, different shapes (crosses and circles), and the verse with which movements were done. The normalized mean square jerk, extracted from the system together with specific speed parameters, was able to capture differences in regularity between the different shapes (MSJratio significantly higher when drawing crosses, p < 1.0 E-4), and that haptic feedback significantly influences this smoothness measure (MSJratio significantly higher when haptic feedback is present, p < 5.0 E-4). The proposed system may be used as a means to monitor the progress of movement regularity in robot-mediated therapy, and the results obtained experimentally highlight the influence of haptic feedback on the smoothness of finalized upper extremity fine movements

MEASURING REGULARITY OF FINE UPPER LIMB MOVEMENTS WITH A HAPTIC PLATFORM FOR MOTOR LEARNING AND REHABILITATION

Robot-assisted systems for arm training are being increasingly used to target moderate-to-severe upper limb impairments in rehabilitation facilities, while hand fine motor skills are seldom being targeted by these machines. This manuscript describes and tests the feasibility of a system based on a haptic interface aimed to complement the efficacy of robotic training in the rehabilitation and motor learning associated with upper extremities movements. End-effector kinematics associated with different trajectory tasks performed by 11 healthy adults were used to extract measures of smoothness, under different testing conditions that included the presence or absence of visual and haptic feedback, the use of dominant vs. non dominant hand, different shapes (crosses and circles), and the verse with which movements were done. The normalized mean square jerk, extracted from the system together with specific speed parameters, was able to capture differences in regularity between the different shapes (MSJratio significantly higher when drawing crosses, p < 1.0 E-4), and that haptic feedback significantly influences this smoothness measure (MSJratio significantly higher when haptic feedback is present, p < 5.0 E-4). The proposed system may be used as a means to monitor the progress of movement regularity in robot-mediated therapy, and the results obtained experimentally highlight the influence of haptic feedback on the smoothness of finalized upper extremity fine movements

Case Study of Patients Participating in a Randomised Controlled Trial of Upper-Limb Robotic Rehabilitation in Acute Stroke Services

This paper presents some findings from a randomised controlled trial in patients with upper-limb weakness in acute stroke services within the UK's National Health Service. Three patients were selected from the robot arm of the trial; one who exhibited a large increase in Fugl-Meyer score (change > 30); one who exhibited a moderate change (10 <; change <; 20) and a subject who demonstrated no change between baseline and follow-up. The results from robot assistance level and target achievement over the course of the treatment are presented for the three patients, demonstrating the system's ability to automatically alter the assistance level as patients progress

A neural tracking and motor control approach to improve rehabilitation of upper limb movements

<p>Abstract</p> <p>Background</p> <p>Restoration of upper limb movements in subjects recovering from stroke is an essential keystone in rehabilitative practices. Rehabilitation of arm movements, in fact, is usually a far more difficult one as compared to that of lower extremities. For these reasons, researchers are developing new methods and technologies so that the rehabilitative process could be more accurate, rapid and easily accepted by the patient. This paper introduces the proof of concept for a new non-invasive FES-assisted rehabilitation system for the upper limb, called smartFES (sFES), where the electrical stimulation is controlled by a biologically inspired neural inverse dynamics model, fed by the kinematic information associated with the execution of a planar goal-oriented movement. More specifically, this work details two steps of the proposed system: an <it>ad hoc </it>markerless motion analysis algorithm for the estimation of kinematics, and a neural controller that drives a synthetic arm. The vision of the entire system is to acquire kinematics from the analysis of video sequences during planar arm movements and to use it together with a neural inverse dynamics model able to provide the patient with the electrical stimulation patterns needed to perform the movement with the assisted limb.</p> <p>Methods</p> <p>The markerless motion tracking system aims at localizing and monitoring the arm movement by tracking its silhouette. It uses a specifically designed motion estimation method, that we named Neural Snakes, which predicts the arm contour deformation as a first step for a silhouette extraction algorithm. The starting and ending points of the arm movement feed an Artificial Neural Controller, enclosing the muscular Hill's model, which solves the inverse dynamics to obtain the FES patterns needed to move a simulated arm from the starting point to the desired point. Both position error with respect to the requested arm trajectory and comparison between curvature factors have been calculated in order to determine the accuracy of the system.</p> <p>Results</p> <p>The proposed method has been tested on real data acquired during the execution of planar goal-oriented arm movements. Main results concern the capability of the system to accurately recreate the movement task by providing a synthetic arm model with the stimulation patterns estimated by the inverse dynamics model. In the simulation of movements with a length of ± 20 cm, the model has shown an unbiased angular error, and a mean (absolute) position error of about 1.5 cm, thus confirming the ability of the system to reliably drive the model to the desired targets. Moreover, the curvature factors of the factual human movements and of the reconstructed ones are similar, thus encouraging future developments of the system in terms of reproducibility of the desired movements.</p> <p>Conclusion</p> <p>A novel FES-assisted rehabilitation system for the upper limb is presented and two parts of it have been designed and tested. The system includes a markerless motion estimation algorithm, and a biologically inspired neural controller that drives a biomechanical arm model and provides the stimulation patterns that, in a future development, could be used to drive a smart Functional Electrical Stimulation system (sFES). The system is envisioned to help in the rehabilitation of post stroke hemiparetic patients, by assisting the movement of the paretic upper limb, once trained with a set of movements performed by the therapist or in virtual reality. Future work will include the application and testing of the stimulation patterns in real conditions.</p

Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: a randomized controlled pilot study

BACKGROUND AND PURPOSE: Providing active assistance to complete desired arm movements is a common technique in upper extremity rehabilitation after stroke. Such active assistance may improve recovery by affecting somatosensory input, motor planning, spasticity or soft tissue properties, but it is labor intensive and has not been validated in controlled trials. The purpose of this study was to investigate the effects of robotically administered active-assistive exercise and compare those with free reaching voluntary exercise in improving arm movement ability after chronic stroke. METHODS: Nineteen individuals at least one year post-stroke were randomized into one of two groups. One group performed 24 sessions of active-assistive reaching exercise with a simple robotic device, while a second group performed a task-matched amount of unassisted reaching. The main outcome measures were range and speed of supported arm movement, range, straightness and smoothness of unsupported reaching, and the Rancho Los Amigos Functional Test of Upper Extremity Function. RESULTS AND DISCUSSION: There were significant improvements with training for range of motion and velocity of supported reaching, straightness of unsupported reaching, and functional movement ability. These improvements were not significantly different between the two training groups. The group that performed unassisted reaching exercise improved the smoothness of their reaching movements more than the robot-assisted group. CONCLUSION: Improvements with both forms of exercise confirmed that repeated, task-related voluntary activation of the damaged motor system is a key stimulus to motor recovery following chronic stroke. Robotically assisting in reaching successfully improved arm movement ability, although it did not provide any detectable, additional value beyond the movement practice that occurred concurrently with it. The inability to detect any additional value of robot-assisted reaching may have been due to this pilot study's limited sample size, the specific diagnoses of the participants, or the inclusion of only individuals with chronic stroke

Development and pilot testing of HEXORR: Hand EXOskeleton Rehabilitation Robot

<p>Abstract</p> <p>Background</p> <p>Following acute therapeutic interventions, the majority of stroke survivors are left with a poorly functioning hemiparetic hand. Rehabilitation robotics has shown promise in providing patients with intensive therapy leading to functional gains. Because of the hand's crucial role in performing activities of daily living, attention to hand therapy has recently increased.</p> <p>Methods</p> <p>This paper introduces a newly developed Hand Exoskeleton Rehabilitation Robot (HEXORR). This device has been designed to provide full range of motion (ROM) for all of the hand's digits. The thumb actuator allows for variable thumb plane of motion to incorporate different degrees of extension/flexion and abduction/adduction. Compensation algorithms have been developed to improve the exoskeleton's backdrivability by counteracting gravity, stiction and kinetic friction. We have also designed a force assistance mode that provides extension assistance based on each individual's needs. A pilot study was conducted on 9 unimpaired and 5 chronic stroke subjects to investigate the device's ability to allow physiologically accurate hand movements throughout the full ROM. The study also tested the efficacy of the force assistance mode with the goal of increasing stroke subjects' active ROM while still requiring active extension torque on the part of the subject.</p> <p>Results</p> <p>For 12 of the hand digits'15 joints in neurologically normal subjects, there were no significant ROM differences (P > 0.05) between active movements performed inside and outside of HEXORR. Interjoint coordination was examined in the 1^stand 3^rddigits, and no differences were found between inside and outside of the device (P > 0.05). Stroke subjects were capable of performing free hand movements inside of the exoskeleton and the force assistance mode was successful in increasing active ROM by 43 ± 5% (P < 0.001) and 24 ± 6% (P = 0.041) for the fingers and thumb, respectively.</p> <p>Conclusions</p> <p>Our pilot study shows that this device is capable of moving the hand's digits through nearly the entire ROM with physiologically accurate trajectories. Stroke subjects received the device intervention well and device impedance was minimized so that subjects could freely extend and flex their digits inside of HEXORR. Our active force-assisted condition was successful in increasing the subjects' ROM while promoting active participation.</p

Adaptive Control of a Wearable Exoskeleton for Upper-Extremity Neurorehabilitation

The paper describes the implementation and testing of two adaptive controllers developed for a wearable, underactuated upper extremity therapy robot – RUPERT (Robotic Upper Extremity Repetitive Trainer). The controllers developed in this study were used to implement two adaptive robotic therapy modes – the adaptive co-operative mode and the adaptive active-assist mode – that are based on two different approaches for providing robotic assistance for task practice. The adaptive active-assist mode completes therapy tasks when a subject is unable to do so voluntarily. This robotic therapy mode is a novel implementation of the idea of an active-assist therapy mode; it utilizes the measure of a subject’s motor ability, along with their real-time movement kinematics to initiate robotic assistance at the appropriate time during a movement trial. The adaptive co-operative mode, on the other hand, is based on the idea of enabling task completion instead of completing the task for the subject. Both these therapy modes were designed to adapt to a stroke subject's motor ability, and thus encourage voluntary participation from the stroke subject. The two controllers were tested on three stroke subjects practicing robot-assisted reaching movements. The results from this testing demonstrate that an underactuated wearable exoskeleton, such as RUPERT, can be used for administering robot-assisted therapy, in a manner that encourages voluntary participation from the subject undergoing therapy