An exoskeleton hand robotic training device is specially designed for persons after stroke to provide training on their impaired hand by using an exoskeleton robotic hand which is actively driven by their own muscle signals. It detects the stroke person’s intention using his/her surface electromyography (EMG) signals from the hemiplegic side and assists in hand opening or hand closing functional tasks. The robotic system is made up of an embedded controller and a robotic hand module which can be adjusted to fit for different finger length. Eight chronic stroke subjects had been recruited to evaluate the effects of this device. The preliminary results showed significant improvement in hand functions (ARAT) and upper limb functions (FMA) after 20 sessions of robot-assisted hand functions task training. With the use of this light and portable robotic device, stroke patients can now practice more easily for the opening and closing of their hands at their own will, and handle functional daily living tasks at ease. A video is included together with this paper to give a demonstration of the hand robotic system on chronic stroke subjects and it will be presented in the conference.

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Using the wavelet neural networks, an adaptive control system, with two wavelet neural networks as controller and dynamics model identifier respectively, is developed for lower extreme carrying exoskeleton robot. Because the wavelet neural networks have the ability to approximate nonlinear functions and good advantage of time-frequency localization properties, this system can identify nonlinear system dynamic characters more precisely, and can map more complex control strategies. Results show that this control system is more effective than those based on normal controller, where the exoskeleton tracking precision is high and the operator feels very little torque.

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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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