An EM transfer learning algorithm with applications in bionic hand prostheses

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Titel
An EM transfer learning algorithm with applications in bionic hand prostheses
Verfasser
Paaßen, Benjamin ; Schulz, Alexander ; Hahne, Janne ; Hammer, Barbara
Herausgeber
Verleysen, Michel
Erschienen
2017
Sprache
Englisch
Dokumenttyp
Konferenzband
ISBN
978-2-87587-038-4
URN
urn:nbn:de:0070-pub-29093690

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Abstract

Modern bionic hand prostheses feature unprecedented functionality, permitting motion in multiple degrees of freedom (DoFs). However, conventional user interfaces allow for contolling only one DoF at a time. An intuitive, direct and simultaneous control of multiple DoFs requires machine learning models. Unfortunately, such models are not yet sufficiently robust to real-world disturbances, such as electrode shifts. We propose a novel expectation maximization approach for transfer learning to rapidly recalibrate a machine learning model if disturbances occur.
In our experimental evaluation we show that even if few data points are available which do not cover all classes, our proposed approach finds a viable transfer mapping which improves classification accuracy significantly and outperforms all tested baselines.

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