Model adaptation with least-squares SVM for adaptive hand prosthetics

Francesco Orabona, Claudio Castellini, Barbara Caputo, Angelo Emanuele Fiorilla, Giulio Sandini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

72 Scopus citations

Abstract

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 human subjects. We show that, when pre-trained models are used, the number of training samples needed to reach a certain performance is reduced, and the overall performance is increased, compared to what would be achieved by starting from scratch. © 2009 IEEE.
Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation
Pages2897-2903
Number of pages7
DOIs
StatePublished - Nov 2 2009
Externally publishedYes

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