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Proceedings Paper

Surface EMG and intra-socket force measurement to control a prosthetic device
Author(s): Joe Sanford; Rita Patterson; Dan Popa
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Paper Abstract

Surface electromyography (SEMG) has been shown to be a robust and reliable interaction method allowing for basic control of powered prosthetic devices. Research has shown a marked decrease in EMG-classification efficiency throughout activities of daily life due to socket shift and movement and fatigue as well as changes in degree of fit of the socket throughout the subject's lifetime. Users with the most severe levels of amputation require the most complex devices with the greatest number of degrees of freedom. Controlling complex dexterous devices with limited available inputs requires the addition of sensing and interaction modalities. However, the larger the amputation severity, the fewer viable SEMG sites are available as control inputs. Previous work reported the use of intra-socket pressure, as measured during wrist flexion and extension, and has shown that it is possible to control a powered prosthetic device with pressure sensors. In this paper, we present data correlations of SEMG data with intra-socket pressure data. Surface EMG sensors and force sensors were housed within a simulated prosthetic cuff fit to a healthy-limbed subject. EMG and intra-socket force data was collected from inside the cuff as a subject performed pre-defined grip motions with their dominant hand. Data fusion algorithms were explored and allowed a subject to use both intra-socket pressure and SEMG data as control inputs for a powered prosthetic device. This additional input modality allows for an improvement in input classification as well as information regarding socket fit through out activities of daily life.

Paper Details

Date Published: 18 June 2015
PDF: 9 pages
Proc. SPIE 9494, Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX, 94940C (18 June 2015); doi: 10.1117/12.2177399
Show Author Affiliations
Joe Sanford, The Univ. of Texas at Arlington (United States)
Rita Patterson, Univ. of North Texas Health Science Ctr. at Fort Worth (United States)
Dan Popa, The Univ. of Texas at Arlington (United States)

Published in SPIE Proceedings Vol. 9494:
Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX
Misty Blowers; Dan Popa; Muthu B. J. Wijesundara, Editor(s)

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