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@TECHREPORT{Orabona_Idiap-RR-05-2009,
         author = {Orabona, Francesco and Castellini, Claudio and Caputo, Barbara and Fiorilla, Angelo Emanuele and Sandini, Giulio},
       projects = {Idiap, DIRAC},
          month = {3},
          title = {Model Adaptation with Least-Squares SVM for Adaptive Hand Prosthetics},
           type = {Idiap-RR},
         number = {Idiap-RR-05-2009},
           year = {2009},
    institution = {Idiap},
           note = {Accepted in ICRA09},
       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 non-natural 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.},
            pdf = {https://publications.idiap.ch/attachments/reports/2008/Orabona_Idiap-RR-05-2009.pdf}
}