To assist a large number of physically disabled people who no longer are in full possession of their body motion, we have been developing an exoskeleton robot (ExoRob) to rehabilitate and to ease upper limb motion since movement of shoulder, elbow, and wrist play a vital role in the performance of essential daily activities. The proposed ExoRob will be comprised of seven degrees of freedom to enable naturalistic movements of the human upper-limb. This paper focuses on the modeling and control of the proposed ExoRob. A kinematical model of ExoRob has been developed based on modified Denavit-Hartenberg notations. To achieve the dynamic simulation of the developed model, a nonlinear computed torque control technique is employed. In the simulation, the trajectory tracking performance of the controller is evaluated with the developed dynamic model. Simulation results show that the controller is able to drive the ExoRob efficiently to track the desired trajectories, which in this case consisted in passive arm movements. Such movements are used in therapy and could be performed efficiently with the developed model and the controller. This paper also focused on the development of a 7DOF upper-limb prototype (lower scaled) master exoskeleton arm (mExoArm) which corresponds to the proposed ExoRob. The developed mExoArm will be used to maneuver the proposed ExoRob (in manual control mode) especially to provide `passive mode of rehabilitation’.

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This paper deals with the optimal design and control of an exoskeleton robot. First, the motion data from the thumb of a normal subject was captured by a vision system. As the human finger joints cannot be modeled by single revolute joints due to changing instantaneous centre of rotation, we have used 4-bar mechanisms to model each joint. Optimal 4-bars have been designed using genetic algorithms, by minimizing the error between a coupler point and points traced by the finger links. It is shown that the designed 4-bars can accurately track the motion of the human fingers. The exoskeleton is controlled by using the EMG signals obtained from the subject’s muscles. The relation between the EMG and finger motion is first learned, using a neural net. Based on the learned parameters, the subjects EMG signal is used to control a simulation of the exoskeleton joint motion.

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We have been developing exoskeleton robots for assisting the motion of physically weak individuals such as elderly or slightly disabled in daily life. In this paper we propose an EMG-based control of a three degree of freedom (3-DOF) exoskeleton robot for the forearm pronation/supination motion, wrist flexion/extension motion and ulnar/radial deviation. The paper describes the hardware design of the exoskeleton robot and the control method. The skin surface electromyographic (EMG) signals of muscles in forearm of the exoskeleton’s user and the hand force/forearm torque are used as input information for the proposed controller. By applying the skin surface EMG signals as main input signals to the controller, automatic control of the robot can be realized without manipulating any other equipment. Fuzzy control method has been applied to realize the natural and flexible motion assist. An experiment has been performed to evaluate the proposed exoskeleton robot.

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Force feedback from the virtual world can greatly enhance the sense of immersion even for simple applications. The ISU force reflecting exoskeleton enables the user to interact dynamically with simulated environments by providing an electro-magnetic haptic interface between the human and the environment. This paper describes the force and trajectory control interface for the PUMA 560 manipulator that supports the ISU force reflecting exoskeleton hand tracking system. The combined exoskeleton-PUMA system allows the application of virtual forces to the digits of the human finger. Two different typical synthetic environments are programmed and tested using the ISU force reflecting exoskeleton haptic interface device. The experimental results shows that the magnetic interface gives adequate force levels for perception of virtual objects, enhancing the feeling of immersion in the virtual environment, and that the PUMA 560 provides adequate tracking and free motion capabilities for the ISUexoskeleton system

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A human wearing an exoskeleton-type assistive device results in a parallel control system that includes two controllers: anexoskeleton controller and a human brain that includes the spinal cord and the cerebrum. Unknown and complicated characteristics of the brain dynamically interact with the exoskeleton controller, which makes the controller design challenging. In this paper, the motion control system of a human is regarded as a feedback control loop that consists of the brain, muscles, and the dynamics of the extended human body. The brain is modeled as a control algorithm amplified by a fictitious gain (FG). The FG is adjusted to compensate for characteristic changes in the muscle and human body dynamics due to physical impairment, varying load, or any other causes. In this paper, an exoskeleton controller that realizes the FG is designed, and its performance and robustness are discussed.

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