Multivariate approach for assessing electrode positioning in surface electromyography

dc.contributor.advisorMacIsaac, Dawn T.
dc.contributor.advisorEnglehart, Kevin
dc.contributor.authorNsofor, Chitom Clare
dc.date.accessioned2024-02-09T14:57:53Z
dc.date.available2024-02-09T14:57:53Z
dc.date.issued2023-08
dc.description.abstractPlacing electrodes near or over an innervation zone has been shown to affect the quality and integrity of the recorded signals. The aim of this work was to investigate whether using pattern recognition to analyze electromyography data could lead to an automated approach for estimating the graded effect of an innervation zone on surface electromyography signal. Using a set of features from simulated electromyography signals as input, classification and regression algorithms were explored to predict the graded effect of an innervation zone. The regression technique was observed to be best suited for the application. The effects of physiological parameter variability between the training and test data sets were also investigated. Some physiological parameters, especially the innervation zone distribution and conduction velocity, were found to have the most impact on the performance of the regressor. Regression is a promising approach for subsequent research, especially with recorded data.
dc.description.copyright© Chitom Clare Nsofor, 2023
dc.format.extentxi, 131
dc.format.mediumelectronic
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/37698
dc.language.isoen
dc.publisherUniversity of New Brunswick
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineElectrical and Computer Engineering
dc.titleMultivariate approach for assessing electrode positioning in surface electromyography
dc.typemaster thesis
oaire.license.conditionother
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.Sc.E.

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