(EN)Disclosed is a wearable motion assist device (10) provided with a motion assist glove (20) that comprises finger insertion portions (21) into which fingers of a wearer are inserted, drive units (40) that are disposed on the back side of the motion assist glove (20) and drive the finger insertion portions (21), linear members (50) that are disposed along the extension directions of the finger insertion portions (21) so as to transmit the drive force of the drive units (40) to the finger insertion portions (21), a biosignal detection unit (60) that detects a biosignal for operating the fingers of the wearer, and a control unit (70) that outputs a drive control signal to the drive units (40) on the basis of the biosignal generated by the biosignal detection unit (60), the wearable motion assist device (10) being characterized in that the drive units (40) operate the liner members (50) in the extending directions or bending directions of the finger insertion portions (21) on the basis of the drive control signal from the control unit (70).
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System for controlling an exoskeleton haptic device for rehabilitation purposes, and corresponding exoskeleton haptic device.

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A technique for deflecting an actuator belt includes applying a variable deflection force to the actuator belt. The technique may be used to construct actuators for active orthotics, robotics or other applications. Versions with passive clutches may also be used to construct variable-ratio motor gearheads, or may be scaled up to build continuously variable transmissions for automobiles, bicycles, or other vehicles.

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Калифорнийская компания Berkeley Bionics представила публике экзоскелет eLEGS, позволяющий ходить людям с парализованными нижними конечностями.

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The next generation of tools for rehabilitation robotics requires advanced human-robot interfaces able to activate the device as soon as patient’s motion intention is raised. This paper investigated the suitability of Support Vector Machine (SVM) classifiers for identification of locomotion intentions from surface electromyography (sEMG) data. A phase-dependent approach, based on foot contact and foot push off events, was employed in order to contextualize muscle activation signals. Good accuracy is demonstrated on experimental data from three healthy subjects. Classification has also been tested for different subsets of EMG features and muscles, aiming to identify a minimal setup required for the control of an EMG-based exoskeleton for rehabilitation purposes.

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