A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG

Simple item page
dc.contributor.advisorVerrinder, Robynen_ZA
dc.contributor.advisorJohn, Len_ZA
dc.contributor.authorMulligan, Shaun Ren_ZA
dc.date.accessioned2015-06-29T07:40:05Z
dc.date.available2015-06-29T07:40:05Z
dc.date.issued2014en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractThe patent developed by Dr. L. John [1] allows for the the detection of deep muscle activation through the combination of specially positioned monopolar surface Electromyography (sEMG) electrodes and a Blind Source Separation algorithm. This concept was then proved by Morowasi and John [2] in a 12 electrode prototype system around the bicep. This proof of concept showed that it was possible to extract the deep tissue activity of the brachialis muscle in the upper arm, however, the effect of surface electrode positioning and effectual number of electrodes on signal quality is still unclear. The hope of this research is to extend this work. In this research, a genetic algorithm (GA) is implemented on top of the Fast Independent Component Analysis (FastICA) algorithm to reduce the number of electrodes needed to isolate the activity from all muscles in the upper arm, including deep tissue. The GA selects electrodes based on the amount of significant information they contribute to the ICA solution and by doing so, a reduced electrode set is generated and alternative electrode positions are identified. This allows a near optimal electrode configuration to be produced for each user. The benefits of this approach are: 1.The generalized electrode array and this algorithm can select the near optimal electrode arrangement with very minimal understanding of the underlying anatomy. 2. It can correct for small anatomical differences between test subjects and act as a calibration phase for individuals. As with any design there are also disadvantages, such as each user needs to have the electrode placement specifically customised for him or her and this process needs to be conducted using a higher number of electrodes to begin with.en_ZA
dc.identifier.apacitationMulligan, S. R. (2014). <i>A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/13149en_ZA
dc.identifier.chicagocitationMulligan, Shaun R. <i>"A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2014. http://hdl.handle.net/11427/13149en_ZA
dc.identifier.citationMulligan, S. 2014. A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Mulligan, Shaun R AB - The patent developed by Dr. L. John [1] allows for the the detection of deep muscle activation through the combination of specially positioned monopolar surface Electromyography (sEMG) electrodes and a Blind Source Separation algorithm. This concept was then proved by Morowasi and John [2] in a 12 electrode prototype system around the bicep. This proof of concept showed that it was possible to extract the deep tissue activity of the brachialis muscle in the upper arm, however, the effect of surface electrode positioning and effectual number of electrodes on signal quality is still unclear. The hope of this research is to extend this work. In this research, a genetic algorithm (GA) is implemented on top of the Fast Independent Component Analysis (FastICA) algorithm to reduce the number of electrodes needed to isolate the activity from all muscles in the upper arm, including deep tissue. The GA selects electrodes based on the amount of significant information they contribute to the ICA solution and by doing so, a reduced electrode set is generated and alternative electrode positions are identified. This allows a near optimal electrode configuration to be produced for each user. The benefits of this approach are: 1.The generalized electrode array and this algorithm can select the near optimal electrode arrangement with very minimal understanding of the underlying anatomy. 2. It can correct for small anatomical differences between test subjects and act as a calibration phase for individuals. As with any design there are also disadvantages, such as each user needs to have the electrode placement specifically customised for him or her and this process needs to be conducted using a higher number of electrodes to begin with. DA - 2014 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG TI - A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG UR - http://hdl.handle.net/11427/13149 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/13149
dc.identifier.vancouvercitationMulligan SR. A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2014 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/13149en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.titleA Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMGen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
thesis_ebe_2014_mulligan_s 2.pdf
Size:
2.86 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Masters