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. |
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| Projects |
Idiap DIRAC |
| Authors | |
| Added by: | [ADM] |
| Total mark: | 0 |
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