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ICRA
2009
IEEE

Model adaptation with least-squares SVM for adaptive hand prosthetics

10 years 5 months ago
Model adaptation with least-squares SVM for adaptive hand prosthetics
— The state-of-the-art in control of hand prosthetics is far from optimal. The main control interface is represented by surface electromyography (EMG): the activation potentials of the remnants of large muscles of the stump are used in a nonnatural way to control one or, at best, two degrees-of-freedom. This has two drawbacks: first, the dexterity of the prosthesis is limited, leading to poor interaction with the environment; second, the patient undergoes a long training time. As more dexterous hand prostheses are put on the market, the need for a finer and more natural control arises. Machine learning can be employed to this end. A desired feature is that of providing a pre-trained model to the patient, so that a quicker and better interaction can be obtained. To this end we propose model adaptation with least-squares SVMs, a technique that allows the automatic tuning of the degree of adaptation. We test the effectiveness of the approach on a database of EMG signals gathered from ...
Francesco Orabona, Claudio Castellini, Barbara Cap
Added 23 May 2010
Updated 23 May 2010
Type Conference
Year 2009
Where ICRA
Authors Francesco Orabona, Claudio Castellini, Barbara Caputo, Angelo Emanuele Fiorilla, Giulio Sandini
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