An EM transfer learning algorithm with applications in bionic hand prostheses

Paaßen B, Schulz A, Hahne J, Hammer B (2017)
In: Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Verleysen M (Ed); Bruges: i6doc.com: 129-134.

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Konferenzbeitrag | Veröffentlicht | Englisch
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Abstract / Bemerkung
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.
Erscheinungsjahr
Titel des Konferenzbandes
Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017)
Seite
129-134
Konferenz
25th European Symposium on Artificial Neural Networks (ESANN 2017)
Konferenzort
Bruges
Konferenzdatum
2017-04-26 – 2017-04-28
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Paaßen B, Schulz A, Hahne J, Hammer B. An EM transfer learning algorithm with applications in bionic hand prostheses. In: Verleysen M, ed. Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Bruges: i6doc.com; 2017: 129-134.
Paaßen, B., Schulz, A., Hahne, J., & Hammer, B. (2017). An EM transfer learning algorithm with applications in bionic hand prostheses. In M. Verleysen (Ed.), Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017) (pp. 129-134). Bruges: i6doc.com.
Paaßen, B., Schulz, A., Hahne, J., and Hammer, B. (2017). “An EM transfer learning algorithm with applications in bionic hand prostheses” in Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017), Verleysen, M. ed. (Bruges: i6doc.com), 129-134.
Paaßen, B., et al., 2017. An EM transfer learning algorithm with applications in bionic hand prostheses. In M. Verleysen, ed. Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Bruges: i6doc.com, pp. 129-134.
B. Paaßen, et al., “An EM transfer learning algorithm with applications in bionic hand prostheses”, Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017), M. Verleysen, ed., Bruges: i6doc.com, 2017, pp.129-134.
Paaßen, B., Schulz, A., Hahne, J., Hammer, B.: An EM transfer learning algorithm with applications in bionic hand prostheses. In: Verleysen, M. (ed.) Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). p. 129-134. i6doc.com, Bruges (2017).
Paaßen, Benjamin, Schulz, Alexander, Hahne, Janne, and Hammer, Barbara. “An EM transfer learning algorithm with applications in bionic hand prostheses”. Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Ed. Michel Verleysen. Bruges: i6doc.com, 2017. 129-134.
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OA Open Access
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2017-04-27T17:06:44Z

Material in PUB:
Populärwissenschaftliche Version
Linear Supervised Transfer Learning Toolbox
Paaßen B, Schulz A (2017) : Bielefeld University. doi:10.4119/unibi/2912671.
Spätere Version
Expectation maximization transfer learning and its application for bionic hand prostheses
Paaßen B, Schulz A, Hahne J, Hammer B (2018)
Neurocomputing 298: 122-133.

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