REPORT Orabona_Idiap-RR-05-2009/IDIAP Model Adaptation with Least-Squares SVM for Adaptive Hand Prosthetics Orabona, Francesco Castellini, Claudio Caputo, Barbara Fiorilla, Angelo Emanuele Sandini, Giulio EXTERNAL http://publications.idiap.ch/attachments/reports/2008/Orabona_Idiap-RR-05-2009.pdf PUBLIC Idiap-RR-05-2009 2009 Idiap March 2009 Accepted in ICRA09 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.