BBAuthors: Prahm, C.; Advisor: -; Participants: Schulz, A.; Paaßen, B.; Schoisswohl, J.; Kaniusas, E.; Dorffner, G.; Hammer, B.; Aszmann, O. (2019)
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 nov... 
