Development of a microcontrolled bioinstrumentation system for active control of leg prostheses

Delis, Alberto Lopez, da Rocha, Adson Ferreira, dos Santos, Icaro, Sene, Iwens Gervasio, Jr., Salomoni, Sauro and Borges, Geovany Araujo (2008). Development of a microcontrolled bioinstrumentation system for active control of leg prostheses. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, Canada, (2393-2396). 20-24 August 2008. doi:10.1109/IEMBS.2008.4649681


Author Delis, Alberto Lopez
da Rocha, Adson Ferreira
dos Santos, Icaro
Sene, Iwens Gervasio, Jr.
Salomoni, Sauro
Borges, Geovany Araujo
Title of paper Development of a microcontrolled bioinstrumentation system for active control of leg prostheses
Conference name 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Conference location Vancouver, Canada
Conference dates 20-24 August 2008
Proceedings title 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society   Check publisher's open access policy
Journal name IEEE Engineering in Medicine and Biology Society. Conference Proceedin   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2008
Sub-type Fully published paper
DOI 10.1109/IEMBS.2008.4649681
ISBN 9781424418145
9781424418152
ISSN 1557-170X
Start page 2393
End page 2396
Total pages 4
Language eng
Abstract/Summary This article describes the design of a microcontrolled bioinstrumentation system for active control of leg prostheses, using 4-channel electromyographic signal (EMG) detection and a single-channel electrogoniometer. The system is part of a control and instrumentation architecture in which a master processor controls the tasks of slave microcontrollers, through a RS-485 interface. Several signal processing methods are integrated in the system, for feature extraction (Recursive Least Squares), feature projection (Self Organizing Maps), and pattern classification (Levenberg-Marquardt Neural Network). The acquisition of EMG signals and additional mechanical information could help improving the precision in the control of leg prostheses.
Keyword Electromyographic signals
Prosthesis control
Artificial leg
EMG
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Conference Paper
Collection: School of Health and Rehabilitation Sciences Publications
 
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