We have been developing robotic exoskeletons to assist motion of physi-cally weak persons such as elderly, disabled, and injured persons. The robotic exoskeleton is controlled basically based on the electromyogram (EMG) signals, since the EMG signals of human muscles are important signals to understand how the user intends to move. Even though the EMG signals contain very important in-formation, however, it is not very easy to predict the user’s upper-limb motion (elbow and shoulder mo-tion) based on the EMG signals in real-time because of the difficulty in using the EMG signals as the controller input signals. In this paper, we propose a robotic exoskeleton for human upper-limb motion as-sist, a hierarchical neuro-fuzzy controller for the robotic exoskeleton, and its adaptation method.

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We have been developing robotic exoskeletons to assist motion of physically weak persons such as elderly, disabled, and injured persons. The robotic exoskeleton is controlled basically based on the electromyogram (EMG) signals, since the EMG signals of human muscles are important signals to understand how the user intends to move. Even though the EMG signals contain very important information, however, it is not very easy to predict the user’s upper-limb motion (elbow and shoulder motion) based on the EMG signals in real-time because of the difficulty in using the EMG signals as the controller input signals. In this paper, we propose a robotic exoskeleton for human upper-limb motion assist, a hierarchical neuro-fuzzy controller for the robotic exoskeleton, and its adaptation method.

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An exoskeleton is an external structural mechanism with joints and links corresponding to those of the human body. With applications in rehabilitation medicine and virtual reality simulation, exoskeletons offer benefits for both disabled and healthy populations. A pilot databa-se defining the kinematics and dynamics of the upper limb during daily living activities was one among several factors guiding the development of an anthropomorphic, 7-DOF, powered arm exoskeleton. Additional design inputs include anatomical and physiological considerations, workspace analyses, and upper limb joint ranges of motion. The database was compiled from 19 arm activities of daily living. The cable-actu-ated dexterous exoskeleton for neurorehabilitation (CADEN)-7 offers remarkable opportunities as a versatile human–machine interface and as a new generation of assistive technology. Proximal placement of motors and distal placement of cable-pulley reductions were incorporated into the design, leading to low inertia, highstiffness links, and backdrivable transmissions with zero backlash. The design enables full glenohumeral, elbow, and wrist joint functionality. Potential applications of the exoskeleton as a wearable robot include: 1) a therapeutic and diagnostics device for physiotherapy, 2) an assistive (orthotic) device for human power amplifications, 3) a haptic device in virtual reality simulation, and 4) a master device for teleoperation.

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The present invention relates to electroactive polymers that are pre-strained to improve conversion from electrical to mechanical energy. When a voltage is applied to electrodes contacting a pre-strained polymer, the polymer deflects. This deflection may be used to do mechanical work. The pre-strain improves the mechanical response of an electroactive polymer. The present invention also relates to actuators including an electroactive polymer and mechanical coupling to convert deflection of the polymer into mechanical work. The present invention further relates to compliant electrodes that conform to the shape of a polymer. The present invention provides methods for fabricating electromechanical devices including one or more electroactive polymers.

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We have been developing robotic exoskeletons to assist motion of physically weak persons such as elderly, disabled, and injured persons. The robotic exoskeleton is controlled basically based on the electromyogram (EMG) signals, since the EMG signals of human muscles are important signals to understand how the user intends to move. Even though the EMG signals contain very important information, however, it is not very easy to predict the user’s upper-limb motion (elbow and shoulder motion) based on the EMG signals in real-time because of the difficulty in using the EMG signals as the controller input signals. In this paper, we propose a robotic exoskeleton for human upper-limb motion assist, a hierarchical neuro-fuzzy controller for the roboticexoskeleton, and its adaptation method.

(далее…)

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