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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This paper is aimed to perform optimization of a multifunctional actuator utilizing magnetorheological (MR) fluids. In the design of the MR fluid based rotary devices, the inner coils are mainly in interior and exterior forms. However, the effect of these two forms of inner coils on output torque was not discussed. In this research, models with different inner coils are built, analyzed and compared. Among them, the model with interior inner coil is the best option in terms of output torque. Based on the analysis, optimization of the multifunctional actuator is carried out. The corresponding prototype is then fabricated and each function of the developed multifunctional actuator is tested.

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Different gait patterns of stroke patients cannot be derived satisfactorily by traditional treadmill training robots. This paper presents a method to generate adaptive trajectories for controlling a lower extremity rehabilitation exoskeleton designed to help patients recover or improve walking ability. The model-based adaptation mechanism that consists of an inverse dynamic model, a trajectory generator and a fuzzy adaptation algorithm is proposed to minimize patient’s discomfort when active muscle contractions are not easily to be obtained. The effectiveness of the fuzzy adaptation algorithm, which accounts for the experience of a rehabilitation doctor, is demonstrated numerically with an illustrative example in this paper.

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This paper outlines a detailed description of the design on a 3-DOF hip robotic device for kinematically establishing animal model of avascular necrosis (AVN) of the femoral head. In order to realize the hip extension/flexion and abduction/adduction movements of rabbit subjects, a novel 2-DOF hip motion apparatus based on a pair of cardan joints is investigated. 2 electric grounded motors are used to drive rabbits passively in terms of predefined trajectories repeatedly with an additional periodical impact loading on the femoral head during motions. It is ideal to simulate usual forceful and repeated actions in sports, such as gymnastics and acrobatics, and initiated a new way to establish animal femoral head AVN models by means of kinematically simulating the real motion procedure rather than traditional medical or physiological operation method. With sets of experiments, the characterization of the exoskeleton system is discussed.

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