Orabona F, Caputo B, Fiorilla AE, Sandini G, Castellini C (2009)
Publication Type: Conference contribution
Publication year: 2009
Pages Range: 2897-2903
Conference Proceedings Title: Proceedings - IEEE International Conference on Robotics and Automation
Event location: JPN
ISBN: 9781424427895
DOI: 10.1109/ROBOT.2009.5152247
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 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. © 2009 IEEE.
APA:
Orabona, F., Caputo, B., Fiorilla, A.E., Sandini, G., & Castellini, C. (2009). Model adaptation with least-squares SVM for adaptive hand prosthetics. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 2897-2903). JPN.
MLA:
Orabona, Francesco, et al. "Model adaptation with least-squares SVM for adaptive hand prosthetics." Proceedings of the 2009 IEEE International Conference on Robotics and Automation, ICRA '09, JPN 2009. 2897-2903.
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