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Model Adaptation with Least-Squares SVM for Adaptive Hand Prosthetics
Type of publication: Idiap-RR
Citation: Orabona_Idiap-RR-05-2009
Number: Idiap-RR-05-2009
Year: 2009
Month: 3
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.
Projects Idiap
Authors Orabona, Francesco
Castellini, Claudio
Caputo, Barbara
Fiorilla, Angelo Emanuele
Sandini, Giulio
Added by: [ADM]
Total mark: 0
  • Orabona_Idiap-RR-05-2009.pdf (MD5: fc8d41867f73fe2cbc05659a1436e9c8)