To automatically align exoskeleton axes to human anatomical axes, we propose to decouple the joint rotations from the joint translations. Decoupling can reduce setup times and painful misalignment forces, at the cost of increased mechanical complexity and movement inertia. The decoupling approach was applied to the Dampace and Limpact exoskeletons.

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The effect of a twist dimension on the dexterity of double pointing systems is studied. It is found that as the twist increases from 0° the dexterity of the double pointing system is continually reduced. However a twist dimension of up to 81° allows for a dexterous exoskeleton shoulder joint workspace large enough to accommodate the range of shoulder joint motion in the typical human. This amount of twist greatly simplifies the design of the shoulder joint exoskeleton, allowing room for hardware such as gearing

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Naval Aeronautical Engineering Institute Exoskeleton Suit (NAEIES) is a kind of human-machine robot which combines the human’s intelligence with the powerful energy of mechanism. It can help people to carry heavy load, walking on kinds of terrains. A prototype of NAEIES is proposed in this paper. When human walking, his (her) forearm has a similar motion trajectory with knee joint. So the forearm motion is measured by a potentiometer as the control signal of the knee joint. The hip joint motion is realized by a gas spring. This is the simplest method to realize the motion of NAEIES. The principle of the method was illustrate in the paper and for the further study a control techniques based on the feedback of angle acceleration is proposed and simulated with the one-Dof and two-Dof model of NAEIES leg.

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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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