This paper describes the development of a pneumatic robot for functional movement training of the arm and hand after stroke. The device is based on the Wilmington Robotic Exoskeleton (WREX), a passive, mobile arm support developed for children with arm weakness caused by a debilitative condition. Previously, we scaled WREX for use by adults, instrumented it with potentiometers, and incorporated a simple grip strength sensor. The resulting passive device (Training WREX or «T-WREX») allows individuals with severe motor impairment to practice functional movements (reaching, eating, and washing) in a simple virtual reality environment called Java Therapy 2.0. However, the device is limited since it can only apply a fixed pattern of assistive forces to the arm. In addition, its gravity balance function does not restore full range of motion. Therefore, we are also developing a robotic version of WREX named Pneu-WREX, which can apply a wide range of forces to the arm during naturalistic movements. Pneu-WREX uses pneumatic actuators, non-linear force control, and passive counter-balancing to allow application of a wide range of forces during naturalistic upper extremity movements. Besides a detailed description of the mechanical design and kinematics of Pneu-WREX, we present results from a survey of 29 therapists on the use of such a robotic device.

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The potential for using an exoskeleton to support mobility has been considered for some time. The paper describes the procedures associated with the analysis, design and implementation of a model for a lightweight design of such an exoskeletonand shows how the integration of motion analysis with modelling supported the development of the concept. It then proceeds to consider the implementation of the identified control and operational strategies in model form and how the basic concepts developed are being deployed in support of an implementation of system to support the rehabilitation of the lower limbs.

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The development of a prototype robotic system to facilitate upper extremity (UE) rehabilitation in individuals who sustain neurological impairments such as cervical level spinal cord injuries (SCI), acquired brain injuries (ABIs) or stroke (CVA) is described. A control system based on electromyography (EMG) signals has been implemented to provide the appropriate amount of assistance or resistance necessary to progress a patient’s movement recovery. Use of EMG signals has potential advantages over systems based only on torque and position sensors. The prototype system includes programmable mechanical impedance, adjustable thresholds and control gains. This robotic rehabilitation device would be used to provide repeated motor practice in an effort to promote neurological recovery and improve functional use of the UE.

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LOPES aims for an active role of the patient by selective and partial support of gait functions during robotic treadmill training sessions. Virtual model control (VMC) was applied to the robot as an intuitive method for translating current treadmill gait rehabilitation therapy programs into robotic rehabilitation therapy. Virtual models are proposed for the selective control of gait functions during treadmill training. From this collection of models several, representing the extremes of the entire set of virtual models, were implemented. The results show that VMC is a promising method for the control of a gait rehabilitation robot.

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A new experimental platform permits us to study a novel variety of issues of human motor control, particularly full 3-D movements involving the major seven degrees-of-freedom (DOF) of the human arm. We incorporate a seven DOF robot exoskeleton, and can minimize weight and inertia through gravity, Coriolis, and inertia compensation, such that subjects’ arm movements are largely unaffected by the manipulandum. Torque perturbations can be individually applied to any or all seven joints of the human arm, thus creating novel dynamic environments, or force fields, for subjects to respond and adapt to. Our first study investigates a joint space force field where the shoulder velocity drives a disturbing force in the elbow joint. Results demonstrate that subjects learn to compensate for the force field within about 100 trials, and from the strong presence of aftereffects when removing the field in some randomized catch trials, that an inverse dynamics, or internal model, of the force field is formed by the nervous system. Interestingly, while post-learning hand trajectories return to baseline, joint space trajectories remained changed in response to the field, indicating that besides learning a model of the force field, the nervous system also chose to exploit the space to minimize the effects of the force field on the realization of the endpoint trajectory plan. Further applications for our apparatus include studies in motor system redundancy resolution and inverse kinematics, as well as rehabilitation.

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