A computer model simulating the electrical activity of muscles of the upper
arm during elbow motion is presented. The output of the model is an
Electromyographic (EMG) signal. System identification is performed on the EMG
signals using autoregressive moving average (ARMA) modelling. The calculated
ARMA coefficients are then used as the feature set for pattern recognition.
Pattern recognition is performed on the EMG signals to attempt to identify which
of four possible motions is producing the signal. The results of pattern recognition
are compared with results from pattern recognition of real EMG signals. The
model is shown to be useful in predicting general trends found in the real data, but
is not robust enough to predict accurate quantitative results. Simplifying
assumptions about the filtering effects of body tissue, and about the size and
position of muscles, are conjectured to be the most likely reasons the model is not
quantitatively accurate. / Graduation date: 1992
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/37016 |
| Date | 05 December 1991 |
| Creators | Lerman, David |
| Contributors | Saugen, John L. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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