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 ...

• Type: Conference
• Year: 2009
• Where: ICRA
• Authors: Francesco Orabona, Claudio Castellini, Barbara Caputo, Angelo Emanuele Fiorilla, Giulio Sandini
• Keywords: Dexterous Hand Prostheses, ICRA 2009, Main Control Interface, Pre-trained Models, Robotics