This paper reports on the mechanical design of an active knee orthosis, powered by pleated pneumatic artificial muscles. The orthosis is intended as a proof-of-concept rehabilitation device for the assessment of mechatronical design principles and testing of different control strategies. The general focus of this research is on the development of a step rehabilitation robot prototype consisting of a powered exoskeleton for the lower limbs.

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This paper describes the control architecture for lower limbs rehabilitation robot system and its implementation. The system hasexoskeleton structure with 10 DOF (degrees of freedom) and is pneumatically actuated. The control strategy has been specifically designed in order to ensure a proper position control guiding patient’s legs along a fixed reference gait pattern. For this purposes Fuzzy controller with additional force compensator was developed. A numerical solution of the inverse kinematics problem based on video image analysis has been used. The controller was successively implemented and tested on embedded real-time PC104 system. Laboratory experiments without patient are carried out and the results are reported and discussed.

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Haptic force feedback can be summarized as a system that provides a sense of feeling and the application of forces by a computer to a user. This type of feedback system was developed as a glove or master format in order to provide forces at the fingertips. As a core to the device, passive actuators were developed using magnetorheological fluid (MRF) and a novelexoskeleton mechanical power transmission system was designed utilizing rapid prototype parts. The fluid is a smart material that has the property of changing its viscosity when exposed to a magnetic field. By varying the intensity of electromagnets embedded within the fluid based actuators, the resistive forces of the actuators can be dynamically set. The entire system is lightweight, low power and easily portable. A completed system was tested to evaluate the usability of the device

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Directing person’s behavior is one of the roles of mind which is dominated by emotion. In order that a robot assists a person cooperatively, it is necessary to predict what behavior the person is going to do. Therefore, the purpose of this study is to construct a mind model in order to identify person’s mind for predicting or estimating person’s behavior. In a process of behavior decision from the planning to the performing, it is considered that the role of mind is modifying original desire (Desired Input) of rather selfish action according to restriction (Restrictive Input) from the external world viewed from the brain, and issuing cooperatively a reference order of action which should be taken. The external world is divided into three sub-worlds like «subject’s own body», «partner» and «concerned environment» in this research, and these sub-worlds are thought to be common situations of general cooperative work. Proposed mind model, hence, is designed to consist of three-layers corresponding to each external sub-world. Each layer of mind tries to take a balance between Desired Input and Restrictive Inputs. The role of taking the balance at each layer is expressed as an optimal control, which minimizes a performance index of quadratic integral form with the weight named «Emotion Index.» To confirm effectiveness of this mind model in a cooperative work between a person and a robot, the proposed mind model is installed in an exoskeleton power assistive system named HAL (Hybrid Assistive Leg) for walking aid. Since HAL is firmly worn on operator’s legs, HAL needs a cooperative control to play like a part of operator’s body. In order to realize the cooperative power assist, it is useful to identify mind parameters which affect operator’s behavior. Experiments of identifying the Emotion Index and the estimating Desired Input of third layer were carried out by using information from sensors built in HAL. And results of experiments show that the constructed mind model has given HAL the ability to identify and to estimate parameters of operator’s mind.

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Patient engagement and effort are thought to be important for maximizing the therapeutic benefit of robot-assisted movement exercise after neurologic injury, but little is understood about how the audio-visual feedback presented during training affects engagement and effort. For the study reported here, we hypothesized that visual distraction would decrease engagement during robot-assisted movement training, but that appropriate auditory feedback would counteract this effect. Non-disabled participants (n = 10) participated in a common therapeutic exercise in which they attempted to track a moving target presented on a computer screen as a robotic exoskeleton compliantly assisted their arm movement. Introducing a simple visual distractor significantly increased both tracking error and the interaction forces between the participants and the robot. Introducing auditory feedback of tracking error reduced the effect of the visual distractor, decreasing tracking error and interaction forces toward normative values. If tracking error is taken as a surrogate measure of participant engagement, these results indicate that the presence of even a modest level of visual distraction in the training environment may decrease engagement, and thus present a hazard to the effectiveness of training. However, providing appropriate task feedback through the auditory system can reduce the effect of visual distractors. Therefore, an integrated design of the audio, visual, and haptic environment is important for optimizing robot-assisted movement training.

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