Autoencoder-based myoelectric controller for prosthetic hands

• Published in: Frontiers in bioengineering and biotechnology Vol. 11; p. 1134135
• Main Authors: Portnova-Fahreeva, Alexandra A, Rizzoglio, Fabio, Mussa-Ivaldi, Ferdinando A, Rombokas, Eric
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 26.06.2023
• Subjects: autoencoders; Bioengineering and Biotechnology; controller; dimensionality reduction; hand; myoelectric; prosthetics; autoencoders; controller; dimensionality reduction; myoelectric; prosthetics; hand
• ISSN: 2296-4185; 2296-4185
• DOI: 10.3389/fbioe.2023.1134135

In the past, linear dimensionality-reduction techniques, such as Principal Component Analysis, have been used to simplify the myoelectric control of high-dimensional prosthetic hands. Nonetheless, their nonlinear counterparts, such as Autoencoders, have been shown to be more effective at compressing and reconstructing complex hand kinematics data. As a result, they have a potential of being a more accurate tool for prosthetic hand control. Here, we present a novel Autoencoder-based controller, in which the user is able to control a high-dimensional (17D) virtual hand via a low-dimensional (2D) space. We assess the efficacy of the controller via a validation experiment with four unimpaired participants. All the participants were able to significantly decrease the time it took for them to match a target gesture with a virtual hand to an average of and three out of four participants significantly improved path efficiency. Our results suggest that the Autoencoder-based controller has the potential to be used to manipulate high-dimensional hand systems via a myoelectric interface with a higher accuracy than PCA; however, more exploration needs to be done on the most effective ways of learning such a controller.