ICRA'09 Paper Abstract


Paper FrD6.1

Orabona, Francesco (Idiap Research Institute), Castellini, Claudio (University of Genova), Caputo, Barbara (IDIAP Research Institute), Fiorilla, Angelo Emanuele (UniversitÓ di Genova), Sandini, Giulio (Italian Institute of Technology)

Model Adaptation with Least-Squares SVM for Adaptive Hand Prosthetics

Scheduled for presentation during the Regular Sessions "Learning and Adaptive Systems - IV" (FrD6), Friday, May 15, 2009, 15:30−15:50, Room: 404

2009 IEEE International Conference on Robotics and Automation, May 12 - 17, 2009, Kobe, Japan

This information is tentative and subject to change. Compiled on January 21, 2022

Keywords Learning and Adaptive Systems, Neurorobotics, Rehabilitation Robotics


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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