In this paper, an EMG-based fuzzy-neuro control method is proposed for a three degree of freedom (3 DOF) human forearm and wrist motion assist exoskeleton robot (W-EXOS). The W-EXOS assists human forearm pronation/supination motion, wrist flexion/extension motion and ulnar/radial deviation. The paper presents the EMG-based fuzzy-neuro control method with multiple fuzzy-neuro controllers and the adaptation method of controllers. The skin surface electromyography (EMG) signals of muscles in forearm of the exoskeleton users’ and the hand force/forearm torque are used as input information for the controllers. Fuzzy-neuro control method, which is a combination of flexible fuzzy control and adaptive neural network control, has been applied to realize the natural and flexible motion assist. In the control method, multiple fuzzy-neuro controllers are applied, since the muscles activation levels change in accordance with the angles of motions. The control method is able to adapt according the changing EMG signal levels of different users. Experiments have been performed to evaluate the proposed EMG-based fuzzy-neuro control method.

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An exoskeleton system is a compact, light-weight robotic mechanism that a human can put-on for the purpose of overcoming inadequate muscle strength during the performance of physical tasks. In doing so, this integrated human-machine system offers multiple opportunities for creating assistive technologies that can be used in biomedical, industrial, aerospace and everyday life applications. The scope of the present research is to develop an advanced human-machine interface based on the electromyographic (EMG) signals. The resulting EMG control command is concerned with the detection, processing, classification and application to drive the exoskeleton system.

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A bidirectional force feedback dataglove actuated by pneumatic artificial muscles is introduced which has simpler exoskeleton structure and exerts bidirectional force only on the fingertips. Moreover, its fewer attachments can reduce the effect of the attachment looseness on the accuracy of position and force control. It meets all needs of virtual reality and the requirement of hand rehabilitation as well. Bidirectional force and movement are achieved by producing antagonistic torque with an antagonistic pair of pneumatic artificial muscles similar to the antagonistic muscles of human beings. The dataglove can support all four degrees of freedom measurement with the non-contact anisotropic magnetoresistive sensors. The cantilevered beam force sensor is integrated into the linkage of exoskeleton structure. A single PC-based control system is introduced. The dataglove can simulate the sensation of grasping rigid and elastic objects in virtual environment. At last, the clinic application in the rehabilitation of injured fingers is presented.

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Robotic gait trainers are used all over the world for the rehabilitation of stroke patients, despite relatively little is known about how the robots should be controlled to achieve the optimal improvement. Most devices control complete joint trajectories and assume symmetry between both legs by either a position or an impedance control. However we believe that the control should not be on a joint level but on a subtask level (i.e. foot clearance, balance control). To this end we have chosen for virtual model control(VMC) to define a set of controllers that can assist in each of these tasks. Thus enabling the exoskeleton to offer selective support and evaluation of each substask during rehabilitation training. The aim of this explorative pilot study was to assess the performance of a VMC of the step height and to assess if selective control of the step height left the remaining of the walking pattern unaffected. Four young healthy subjects walked on a treadmill with their legs and pelvis attached to the lopes exoskeleton in 3 different conditions: (1) providing minimal resistance, (2) control of the left step height with a low stiffness (3) control of the step height with a large stiffness. We have shown that it is possible to exert a vertical forces for the support of foot clearance during the swing phase. The higher stiffness of the VMC resulted in a greater change of the step height, which was achieved by a larger increase of the maximal hip and knee flexion compared to the low stiffness condition. The control of the step height resulted in minor changes in the cycle time and swing time. The joint angles also showed only minor changes. The preliminary results suggest that we were able to control a subtask of walking, while leaving the remaining walking trajectory largely unaffected. In the near future, control of other subtask will be implemented and evaluated in isolation and in conjunction with each other.

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Based on the human-machine intelligent robot system of lower extremity carrying exoskeleton, the new control method is provided, where the virtual torque control is improved. The exoskeleton model is built using SimMechanics in Matlab. The dynamics mathematics model is gotten by study the human walking to construct the controller. The controller in virtual torque control uses nonlinear direct force control while not PID control. The control law presented in this paper simplifies the controller design and not making use of any information about the operator or of any of the mechanical characteristics of the human-machine interface. The most important of this method is the mass properties need not be identified, which overcomes the maximum defect of the virtual torque control. Simulation results show the valid of the given method.

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