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

Gijsberts, Arjan and Bohra, Rashida and Sierra González, David and Werner, Alexander and Nowak, Markus and Caputo, Barbara and Roa, Maximo A. and Castellini, Claudio (2014) Stable myoelectric control of a hand prosthesis using non-linear incremental learning. Frontiers in Neurorobotics, 8. Frontiers Media S.A.. doi: 10.3389/fnbot.2014.00008. ISSN 1662-5218.

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Official URL: http://journal.frontiersin.org/Journal/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.

Item URL in elib:https://elib.dlr.de/88375/
Document Type:Article
Title:Stable myoelectric control of a hand prosthesis using non-linear incremental learning
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Gijsberts, ArjanIDIAP, SwitzerlandUNSPECIFIED
Sierra González, Daviddavid.sierragonzalez (at) dlr.deUNSPECIFIED
Werner, Alexanderalexander.werner (at) dlr.deUNSPECIFIED
Nowak, Markusmarkus.nowak (at) dlr.deUNSPECIFIED
Caputo, BarbaraIDIAP, SwitzerlandUNSPECIFIED
Roa, Maximo A.maximo.roa (at) dlr.deUNSPECIFIED
Castellini, Claudioclaudio.castellini (at) dlr.deUNSPECIFIED
Journal or Publication Title:Frontiers in Neurorobotics
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.3389/fnbot.2014.00008
Publisher:Frontiers Media S.A.
Keywords:rehabilitation robotics, prosthetics, teleoperation, electromyography, machine learning, adaptive sistems
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Deposited By: Castellini, Dr. Claudio
Deposited On:11 Jun 2014 16:14
Last Modified:08 Mar 2018 18:32

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