Disabilities that follow cerebrovascular accidents and spinal cord injuries severely impair motor functions and thereby prevent the affected individuals from full and autonomous participation in activities of daily living. Rehabilitation therapy is needed in order to recover from those severe physical traumas. (далее…)

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This paper describes a powered lower-limb orthosis that is intended to provide gait assistance to spinal cord injured (SCI) individuals by providing assistive torques at both hip and knee joints. The orthosis has a mass of 12 kg and is capable of providing maximum joint torques of 40 Nm with hip and knee joint ranges of motion from 105$^{circ}$ flexion to 30 $^{circ}$ extension and 105 $^{circ}$ flexion to 10 $^{circ}$ hyperextension, respectively. A custom distributed embedded system controls the orthosis with power being provided by a lithium polymer battery which provides power for one hour of continuous walking. In order to demonstrate the ability of the orthosis to assist walking, the orthosis was experimentally implemented on a paraplegic subject with a T10 complete injury. Data collected during walking indicates a high degree of step-to-step repeatability of hip and knee trajectories (as enforced by the orthosis) and an average walking speed of 0.8 km/hr. The electrical power required at each hip and knee joint during gait was approximately 25 and 27 W, respectively, contributing to the 117 W overall electrical power required by the device during walking. A video of walking corresponding to the aforementioned data is included in the supplemental material.

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In this paper, we propose iterative learning control (ILC) scheme for exoskeleton arm driven by pneumatic artificial muscles (PAM), with special and unknown parameters, performing repetitive tasks. This desired control input of ILC was estimated by radial basis function (RBF) neural network incorporated experience database. An ILC controller, which uses the position of the joint where an angular sensor is used as the input of the ILC controller, is developed and tested on exoskeleton arm under well controlled conditions. RBF neural network was proposed to obtain the initial value of ILC. The experiment result on the experimental platform show that the algorithm is successful also in the application of exoskeleton arm.

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In this paper, we present a novel approach for teaching motor skills through the use of vibrotactile stimulation. We propose a kinematic-vibrotactile mapping that targets fundamental movements (basic building blocks of human motion) using saltatory vibration patterns where vibrations are delivered and interpreted as movement through a conceptual mapping. Two conceptual mappings are explored: the «follow me» concept and the push/pull metaphor. A user study, approved by a local ethics committee, was conducted to explore how these conceptual mappings affect learnability, recognition accuracy, response time and naturalness. Results show the approach to work effectively with a combination of vibration patterns under each conceptual mapping providing the most useful design.

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This paper presents the development of an arm exoskeleton for displaying a force feedback generated from virtual reality. The developed arm exoskeleton allows the user to obtain the sensation of touch along the joints: wrist, elbow and shoulder. To represent the most realistic touching of virtual object, weight of the device and viscosity of each joint should be compensated. Based on the model derived by Lagrange equation, the model can perform quite well however it is sensitive to model uncertainties of mass and joint friction. A torque disturbance observer for robot manipulator is applied to estimate not only the disturbance at each joint, but also the reaction torque from the user.

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