Stable myoelectric control of a hand prosthesis using non-linear incremental learning

Gijsberts A, Bohra R, Gonzalez DS, Werner A, Nowak M, Caputo B, Roa MA, Castellini C (2014)

Publication Type: Journal article

Publication year: 2014

Journal

Frontiers in Neurorobotics Frontiers Research Foundation / Frontiers Media

Book Volume: 8

Article Number: 8

Journal Issue: FEB

DOI: 10.3389/fnbot.2014.00008

Abstract

Stable myoelectric control of hand prostheses remains an open problem. The only successful human-machine interface is surface electromyography, typically allowing control of a few degrees of freedom. Machine learning techniques may have the potential to remove these limitations, but their performance is thus far inadequate: myoelectric signals change over time under the influence of various factors, deteriorating control performance. It is therefore necessary, in the standard approach, to regularly retrain a new model from scratch. We hereby propose a non-linear incremental learning method in which occasional updates with a modest amount of novel training data allow continual adaptation to the changes in the signals. In particular, Incremental Ridge Regression and an approximation of the Gaussian Kernel known as Random Fourier Features are combined to predict finger forces from myoelectric signals, both finger-by-finger and grouped in grasping patterns. We show that the approach is effective and practically applicable to this problem by first analyzing its performance while predicting single-finger forces. Surface electromyography and finger forces were collected from 10 intact subjects during four sessions spread over two different days; the results of the analysis show that small incremental updates are indeed effective to maintain a stable level of performance. Subsequently, we employed the same method on-line to teleoperate a humanoid robotic arm equipped with a state-of-the-art commercial prosthetic hand. The subject could reliably grasp, carry and release everyday-life objects, enforcing stable grasping irrespective of the signal changes, hand/arm movements and wrist pronation and supination. © 2014 Gijsberts, Bohra, Sierra González, Werner, Nowak, Caputo, Roa and Castellini.

Authors with CRIS profile

Claudio Castellini

Involved external institutions

Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) / German Aerospace Center DE Germany (DE) idiap Research Institute CH Switzerland (CH)

How to cite

APA:

Gijsberts, A., Bohra, R., Gonzalez, D.S., Werner, A., Nowak, M., Caputo, B.,... Castellini, C. (2014). Stable myoelectric control of a hand prosthesis using non-linear incremental learning. Frontiers in Neurorobotics, 8(FEB). https://doi.org/10.3389/fnbot.2014.00008

MLA:

Gijsberts, Arjan, et al. "Stable myoelectric control of a hand prosthesis using non-linear incremental learning." Frontiers in Neurorobotics 8.FEB (2014).

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