Our goal is to enhance the quality of life of patients with hemiplegia by means of an active motion support system that assists the impaired motion such as to make it as close as possible to the motion of an able bodied person. We have developed the Robot Suit HAL (Hybrid Assistive Limb) to actively support and enhance the human motor functions. The purpose of the research presented in this paper is to propose the required control method to support voluntarily motion using a trigger based on patient’s bioelectrical signal. Clinical trials were conducted in order to investigate the effectiveness of the proposed control method. The first stage of the trials, described in this paper, involved the participation of one hemiplegic patient who is not able to bend his right knee. As a result, the motion support provided by the HAL moved the paralyzed knee joint according to his intention and improved the range of the subject’s knee flexion. The first evaluation of the control method with one subject showed promising results for future trials to explore the effectiveness for a wide range of types of hemiplegia.

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This paper presents a new gravity compensation method for an upper extremity exoskeleton mounted on a wheel chair. This new device is dedicated to regular and efficient rehabilitation training for post-stroke and injured people without the continuous presence of a therapist. The exoskeleton is a wearable robotic device attached to the human arm. The user provides information signals to the controller by means of the force sensors around the wrist and the arm, and the robot controller generates the appropriate control signals for different training strategies and paradigms. This upper extremity exoskeleton covers four basic degrees of freedom of the shoulder and the elbow joints with three additional adaptability degrees of freedom in order to match the arm anatomy of different users. For comfortable and efficient rehabilitation, a new heuristic method have been studied and applied on our prototype in order to calculate the gravity compensation model without the need to identify the mass parameters. It is based on the geometric model of the robot and accurate torque measurements of the prototype’s actuators in a set of specifically chosen joint positions. The weight effect has been successfully compensated so that the user can move his arm freely while wearing the exoskeleton without feeling its mass.

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Control and overall system performance of an upper limb exoskeleton, as a wearable robot, is dictated in part by the human machine interface and the implemented control algorithm. The ultimate goal is to develop algorithms so the operator feels as if theexoskeleton is a natural extension the body. The aim of the current research is to compare the system performance of a 7 degree of freedom wearable upper limb exoskeleton (EXO-UL7) using two multi-sensor admittance controllers (1) task space control and (2) joint space control. Multiple force sensors are needed due to the redundancy in the system (7 DOF). This redundancy is explored and a method is developed to calculate a closed form inverse kinematics (IK) solution. The IK solution is used to develop the task space controller. The joint space controller uses the transpose of the jacobian to resolve sensor forces into joint torques. Six subjects performed a peg in hole task. Six targets covered the main part of the device workspace. Velocities and Interaction forces at the upper arm, lower arm, handle and tip were recorded during the experiments. Power exchange between the subject and device was calculated. Task space based control was about 11% lower in mean interaction energy for the peg in hole task compared to joint space control. Task completion time increased with both controllers compared to back-driving the device.

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A wearable action-assist device which assists or executes an action of a wearer by substituting for the wearer is provided with an action-assist tool 2 having an actuator 201 which gives power to the wearer 1, a biosignal sensor 221 which detects a wearer’s biosignal, a biosignal processing unit 3 which acquires from a biosignal «a» detected by the biosignal sensor a nerve transfer signal «b» for operating a wearer’s muscular line skeletal system, and a myoelectricity signal «c» accompanied with a wearer’s muscular line activity, an optional control unit 4 which generates a command signal «d» for causing the actuator 201 to generate power according to the wearer’s intention using the nerve transfer signal «b» and the myoelectricity signal «c» acquired by the biosignal processing unit 3, and a driving current generating unit 5 which generates a current according to the nerve transfer signal b and a current according to the myoelectricity signal «c», respectively, based on the command signal «d» generated by the optional control unit 4, and supplies the currents to the actuator 201.

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Current brain–computer interfaces (BCIs) that make use of EEG acquisition techniques require unpleasant electrode gel causing skin abrasion during the stan-dard preparation procedure. Electrodes that require tap water instead of electrolytic electrode gel would make both daily setup and clean up much faster, easier and comfortable. This paper presents the results from ten subjects that controlled an SSVEP-based BCI speller system using two EEG sensor modalities: water-based and gel-based surface electrodes. Subjects performed in copy spelling mode using conventional gel-based electrodes and water-based electrodes with a mean information transfer rate (ITR) of 29.68 ± 14.088 bit min−1 and of 26.56 ± 9.224 bit min−1, respectively. A paired t-test failed to reveal significant dif-ferences in the information transfer rates and accuracies of using gel- or water-based electrodes for EEG acquisition. This promising result confirms the operational readiness of water-based electrodes for BCI
applications.

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