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http://hdl.handle.net/11375/6023
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DC Field | Value | Language |
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dc.contributor.advisor | Bruin, H. de | en_US |
dc.contributor.advisor | Kitai, R. | en_US |
dc.contributor.author | Marsh, Amanda Eva Mary | en_US |
dc.date.accessioned | 2014-06-18T16:33:55Z | - |
dc.date.available | 2014-06-18T16:33:55Z | - |
dc.date.created | 2009-07-28 | en_US |
dc.date.issued | 1981-09 | en_US |
dc.identifier.other | opendissertations/136 | en_US |
dc.identifier.other | 1478 | en_US |
dc.identifier.other | 913080 | en_US |
dc.identifier.uri | http://hdl.handle.net/11375/6023 | - |
dc.description.abstract | <p>Several techniques used by researchers in the area of human locomotion to process and analyse normal and pathological gait electromyographs (EMG) are discussed. Basic elements of neuromuscular organization are described.</p> <p>The thesis reports original work in several topic. The spectral analysis of dynamic EMG acquired during the locomotion of a normal subject was done to confirm the selected sampling frequency, and to determine a suitable low pass filter cutoff for smoothing EMG prior to data analysis.</p> <p>Results of using two filters for smoothing EMG, a second order Butterworth low pass filter, and a mid-point moving window average filter are compared.</p> <p>The cross correlation function is used in analysing EMG, since EMG signals are random. The results of cross correlation are compared with clinical observations in assessing the state of a patient following a stroke. Results for five normal and fourteen hemiplegic subjects are reported.</p> <p>The conclusion is that cross correlation analysis quantifies the state of the patient and assesses post stroke recovery according to the neurological picture of central nervous system control.</p> | en_US |
dc.subject | Electrical and Electronics | en_US |
dc.subject | Electrical and Electronics | en_US |
dc.title | Human Locomotion: Techniques for Processing and Analysis of EMG Data | en_US |
dc.type | thesis | en_US |
dc.contributor.department | Electrical Engineering | en_US |
dc.description.degree | Master of Engineering (ME) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Size | Format | |
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fulltext.pdf | 3.14 MB | Adobe PDF | View/Open |
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