Surface EMG in advanced hand prosthetics |
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Authors: | Claudio Castellini Patrick van der Smagt |
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Institution: | (1) LIRA-Lab, University of Genova, viale F. Causa, 13, 16145 Genova, Italy;(2) Institute of Robotics and Mechatronics, German Aerospace Center/DLR, Oberpfaffenhofen, Germany |
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Abstract: | One of the major problems when dealing with highly dexterous, active hand prostheses is their control by the patient wearing
them. With the advances in mechatronics, building prosthetic hands with multiple active degrees of freedom is realisable,
but actively controlling the position and especially the exerted force of each finger cannot yet be done naturally. This paper
deals with advanced robotic hand control via surface electromyography. Building upon recent results, we show that machine
learning, together with a simple downsampling algorithm, can be effectively used to control on-line, in real time, finger
position as well as finger force of a highly dexterous robotic hand. The system determines the type of grasp a human subject
is willing to use, and the required amount of force involved, with a high degree of accuracy. This represents a remarkable
improvement with respect to the state-of-the-art of feed-forward control of dexterous mechanical hands, and opens up a scenario
in which amputees will be able to control hand prostheses in a much finer way than it has so far been possible.
This work is partially supported by the project NEURObotics, FP6-IST-001917. |
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Keywords: | Learning and adaptive systems Rehabilitation robotics Physical human-robot interaction |
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