The current research status and the problems had been summarized about the exoskeleton intelligence systems in worldwide. Human lower limbs movement data had been collected by using the VICON 460 infrared motion capture system, NOVEL pedar insole plantar pressure measure system and CASIO high speed camera. After the data had been researched, the key characteristics parameters were obtained for the human motion recognition. The results indicated that the optimal knee joint ankle was at 157°, as the assisted power was added. Foot arch GHI peak force was at 80N when hopping upward occurred, but foot arch GHI peak force was less when hopping forward occurred. The Lower Limb Exoskeleton Intelligence System model(LLEIS) had been accomplished under UG NX6.0 and exported into ADAMS R3 for the kinematics and dynamics simulation. The agreement of the final computation results with the experimental data indicates this system could be used to assist human hopping intelligently and enhance the effect of the hopping in the field of the human performance augmentation.

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The wave-filtering characteristics of the RLS self-adapting filter and the method of wavelet de-noising are studied, and the two are used as the methods of filtering noises in the fact signals tested from the lower extremity exoskeleton. In order to get better effects of filtering noises, different methods are used to different signals. The noise-filtering method based on changing the threshold of small wave transformation is used to filter white noise in digital signals. The self-adapting filter is used to other noises whose statistic characteristics are uncertain, or the both are combined to filter noises in such signal. For the wavelet de-noising of changeable threshold quality, there are two pivotal questions need to be settled. One is the choice of the threshold quality, the other is the determination of level-decomposition. In this paper, the adaptable determination method of level-decomposition is used, and the threshold quantity selection based on 3 sigma theorem is applied. The simulations of the fact signals tested from the lower limbs’ exoskeleton validated the superiority of noise-eliminating methods given in this paper.

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This paper deals with the optimal design and control of an exoskeletal robot. First, the motion data from the fingers 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. A comparison between Recurrent Neural Network and Multi Layer Perceptron for classifying and mapping the EMG to finger position was also carried out.

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Many kinds of exoskeleton robots have been developed to assist the motion in rehabilitation and daily living of physically weak persons. The concept of perception-assist has been proposed to assist not only the daily motion but also the interaction with an environment, by applying the modification force to the user’s motion. In this paper, task-oriented perception-assist is proposed to assist the daily task of physically weak persons with an upper-limb power-assist exoskeleton. The effectiveness of the proposed method has been evaluated by performing experiments.

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Over 450,000 Americans suffer from degenerative muscle diseases characterized by loss of strength and dexterity in the human hand. An assistive hand exoskeleton was designed to amplify residual muscle strength and restore functionality by assisting pinching and grasping motions. The device featured three movable digits: thumb, index, and middle-ring-small (MRS) digit. Adjustable straps wrapped around the exterior of the finger links and secured the user’s fingers inside the device. A microcontroller processed force sensing resistor (FSR) data and commanded articulating motors. The exoskeleton was lightweight, flexible, portable and accessible to a wide range of user finger diameters.

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