Surface EMG in advanced hand prosthetics

Castellini C, Van Der Smagt P (2009)

Publication Type: Journal article

Publication year: 2009

Journal

Biological Cybernetics Springer Verlag (Germany)

Book Volume: 100

Pages Range: 35-47

Journal Issue: 1

DOI: 10.1007/s00422-008-0278-1

Abstract

One of the major problems when dealing with highly dexterous, active hand prostheses is their control by the patient wearing them. With the advances in mechatronics, building prosthetic hands with multiple active degrees of freedom is realisable, but actively controlling the position and especially the exerted force of each finger cannot yet be done naturally. This paper deals with advanced robotic hand control via surface electromyography. Building upon recent results, we show that machine learning, together with a simple downsampling algorithm, can be effectively used to control on-line, in real time, finger position as well as finger force of a highly dexterous robotic hand. The system determines the type of grasp a human subject is willing to use, and the required amount of force involved, with a high degree of accuracy. This represents a remarkable improvement with respect to the state-of-the-art of feed-forward control of dexterous mechanical hands, and opens up a scenario in which amputees will be able to control hand prostheses in a much finer way than it has so far been possible. © 2008 Springer-Verlag.

Authors with CRIS profile

Claudio Castellini

Involved external institutions

University of Genova / Università degli Studi di Genova

Italy (IT) Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) / German Aerospace Center

Germany (DE)

How to cite

APA:

Castellini, C., & Van Der Smagt, P. (2009). Surface EMG in advanced hand prosthetics. Biological Cybernetics, 100(1), 35-47. https://dx.doi.org/10.1007/s00422-008-0278-1

MLA:

Castellini, Claudio, and Patrick Van Der Smagt. "Surface EMG in advanced hand prosthetics." Biological Cybernetics 100.1 (2009): 35-47.

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