An investigation of transition-informed classifier adaptation for myoelectric control

dc.contributor.advisorMacIsaac, Dawn
dc.contributor.advisorScheme, Erik
dc.contributor.authorMeneley, Julia
dc.date.accessioned2024-04-24T18:35:37Z
dc.date.available2024-04-24T18:35:37Z
dc.date.issued2023-12
dc.description.abstractMyoelectric prostheses use pattern recognition of surface electromyography (SEMG) to interpret a user’s intent. Over time, changes in the SEMG worsen the usability of these prostheses, requiring cumbersome retraining. Adaptive learning, although able to update the classifier, suffers from mislabelling errors during unsupervised use. This study aimed to overcome this by investigating the impact of transitions between classes, often associated with elevated misclassification, on the adaptation process. Several adaptation techniques, some based on explicitly avoiding transitions and others based on leveraging awareness of transitions to improve decision stream labelling, were explored. Finally, these transition-informed adaptation techniques were tested on two datasets that included sequences of transitions between known classes. Results suggest that an awareness of transience in the SEMG can inform the data selection process and improve the labelling of unsupervised data for adaptation. A resulting LC-SSL technique yielded significant (p¡0.05) improvement to several offline classifier performance metrics.
dc.description.copyright© Julia Meneley, 2023
dc.format.extentxiii, 84
dc.format.mediumelectronic
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/37795
dc.language.isoen
dc.publisherUniversity of New Brunswick
dc.relationNatural Sciences and Engineering Research Council of Canada (NSERC)
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineElectrical and Computer Engineering
dc.titleAn investigation of transition-informed classifier adaptation for myoelectric control
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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