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dc.contributor.author Young, Aaron en_US
dc.contributor.author Hargrove, Levi en_US
dc.date.accessioned 2011-09-21T13:13:35Z
dc.date.available 2011-09-21T13:13:35Z
dc.date.issued 2011 en_US
dc.identifier.citation Proceedings of the MEC’11 conference, UNB; 2011. en_US
dc.identifier.uri http://hdl.handle.net/10161/4705
dc.description.abstract Standard myoelectric control systems use carefully placed bipolar electrode pairs to provide independent myoelectric signals (MESs) for prosthesis control. Because myoelectric pattern recognition systems do not require isolated MESs, the two electrode poles used for each MES channel may be placed longitudinally along individual muscles or transversely across multiple muscles. In addition, each electrode pole can be combined with a number of additional poles to form multiple channels. However, practical issues limit the number of poles that can be used in clinical settings. In this study, we investigated classification error reduction and controllability improvements provided by a combination of transverse and longitudinal MES channels in two conditions: (1) a constant number of electrode poles, and (2) a constant number of MES channels. In both cases, we also investigated performance when the electrodes were slightly shifted from their original positions to evaluate sensitivity to electrode shift. We found that a combination of two transverse and two longitudinal electrode channels constructed from four poles significantly outperformed the individual performances of either two transverse or two longitudinal channels each constructed from four poles (p<0.01). Using eight poles, we found that the best channel subset was always comprised of a combination of transverse and longitudinal channels. These results are important because the number and arrangement of poles and channels is a practical consideration for successful clinical implementation of myoelectric pattern recognition control. en_US
dc.publisher Myoelectric Symposium en_US
dc.title Toward Optimizing Electrode Configurations To Improve Myoelectric Pattern Recognition en_US

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