Skip navigation
  • Home
  • Browse
    • Communities
      & Collections
    • Browse Items by:
    • Publication Date
    • Author
    • Title
    • Subject
    • Department
  • Sign on to:
    • My MacSphere
    • Receive email
      updates
    • Edit Profile


McMaster University Home Page
  1. MacSphere
  2. Open Access Dissertations and Theses Community
  3. Open Access Dissertations and Theses
Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/14151
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorTorre, Della E.en_US
dc.contributor.authorde, Bruin Huibregten_US
dc.date.accessioned2014-06-18T17:06:28Z-
dc.date.available2014-06-18T17:06:28Z-
dc.date.created2009-08-19en_US
dc.date.issued1976-07en_US
dc.identifier.otheropendissertations/898en_US
dc.identifier.other1700en_US
dc.identifier.other951645en_US
dc.identifier.urihttp://hdl.handle.net/11375/14151-
dc.description.abstract<p>Skeletal muscles produce detectable electrical currents and voltages when they contract from any cause. The electrical potentials or electromyographic (EMG) signal is recorded from the muscle using suitable electrodes. The research presented in this thesis is concerned with the analysis and processing of electromyographic signals with a view to their use as a source of control for environmental control or other rehabilitation devices. An application of myo-electric control of a simple communication device for a cerebral palsied patient is presented. A model of the myo-electric source which can be used to stimulate EMG signals in real-time is proposed. The model algorithm has been tested for two different electrode systems and the results compared with real signals recorded using these electrode systems. A number of statistical parameters of the surface recorded EMG signal have been examined to determine which parameter is most suitable for myo-electric control. Finally, a pattern recognition algorithm is proposed which attempts to extract the motor unit recruitment and discharge frequency information present in the surface recorded EMG signal. The statistical parameters and the algorithm have been tested for six normal subjects, under isometric conditions.</p>en_US
dc.subjectElectrical and Electronicsen_US
dc.subjectElectrical and Electronicsen_US
dc.titleAspects of Analysis and Processing of Electromyographic Signalsen_US
dc.typethesisen_US
dc.contributor.departmentElectrical Engineeringen_US
dc.description.degreeDoctor of Philosophy (PhD)en_US
Appears in Collections:Open Access Dissertations and Theses

Files in This Item:
File SizeFormat 
fulltext.pdf
Open Access
4.68 MBAdobe PDFView/Open
Show simple item record Statistics


Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.

Sherman Centre for Digital Scholarship     McMaster University Libraries
©2022 McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4L8 | 905-525-9140 | Contact Us | Terms of Use & Privacy Policy | Feedback

Report Accessibility Issue