Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning

Published on May 1, 2019in IEEE Transactions on Neural Systems and Rehabilitation Engineering3.48
· DOI :10.1109/tnsre.2019.2907200
Cosima Prahm4
Estimated H-index: 4
(Medical University of Vienna),
Alexander Schulz36
Estimated H-index: 36
+ 5 AuthorsOskar C. Aszmann24
Estimated H-index: 24
(Medical University of Vienna)
Research on machine learning approaches for upper-limb prosthesis control has shown impressive progress. However, translating these results from the lab to patient’s everyday lives remains a challenge because advanced control schemes tend to break down under everyday disturbances, such as electrode shifts. Recently, it has been suggested to apply adaptive transfer learning to counteract electrode shifts using as little newly recorded training data as possible. In this paper, we present a novel, simple version of transfer learning and provide the first user study demonstrating the effectiveness of transfer learning to counteract electrode shifts. For this purpose, we introduce the novel Box and Beans test to evaluate prosthesis proficiency and compare user performance with an initial simple pattern recognition system, the system under electrode shifts, and the system after transfer learning. Our results show that transfer learning could significantly alleviate the impact of electrode shifts on user performance in the Box and Beans test.
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#1Benjamin Paaßen (Bielefeld University)H-Index: 6
#2Alexander Schulz (Bielefeld University)H-Index: 36
Last.Barbara Hammer (Bielefeld University)H-Index: 31
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Jun 21, 2017 in AIME (Artificial Intelligence in Medicine in Europe)
#1Cosima Prahm (Medical University of Vienna)H-Index: 4
#2Alexander Schulz (Bielefeld University)H-Index: 36
Last.Georg Dorffner (Medical University of Vienna)H-Index: 27
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#1Marco Controzzi (Sant'Anna School of Advanced Studies)H-Index: 16
#2Francesco Clemente (Sant'Anna School of Advanced Studies)H-Index: 5
Last.Christian Cipriani (Sant'Anna School of Advanced Studies)H-Index: 28
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#1Silvia Muceli (GAU: University of Göttingen)H-Index: 18
#2Ivan Vujaklija (GAU: University of Göttingen)H-Index: 9
Last.Dario Farina (GAU: University of Göttingen)H-Index: 70
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#1Cosima Prahm (Medical University of Vienna)H-Index: 4
#2Benjamin Paaßen (Citec)H-Index: 6
Last.Oskar C. Aszmann (Medical University of Vienna)H-Index: 24
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#1Janne M. Hahne (GAU: University of Göttingen)H-Index: 7
#2Dario Farina (GAU: University of Göttingen)H-Index: 70
Last.David Liebetanz (GAU: University of Göttingen)H-Index: 35
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