Prosthesis control using a nearest neighbor electromyographic pattern classifier

A prosthesis control strategy using a nearest neighbor electromyographic pattern classifier was investigated with both a real time microprocessor-based controller and offline computational facilities. Four active electrodes for myoelectric signal amplitude detection were interfaced with a microcomputer for data logging and pattern classification. A nearest neighbor algorithm correctly identified arm motions as belonging to one of six pattern classes from 72 percent to 100 percent of the time. There were five vectors for each class in the look-up table. The nearest neighbor pattern classifier was compared to a minimum error rate Bayes classifier under the assumption that the probability densities were distributed as a multivariate normal distribution. Comparable error rates were obtained with the same data vectors. A condensed nearest neighbor classifier was constructed to determine what minimum number of vectors was necessary in the look-up table. This minimum number of vectors ranged from two to six for the majority of the classes. Larger numbers of vectors were placed in the look-up table for classes that were more difficult to classify.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Electrical Engineering

Department dc:contributor.department

Electrical Engineering

Grantor dc:publisher

Virginia Polytechnic Institute and State University

Year dc:date.issued

1982

Author dc:creator

• Dening, David Charles

Chair dc:contributor.committeechair

• Gray, F. Gail

Committee members dc:contributor.committeemember

• Beex, Aloysius A.
• Haralick, Robert M.
• Leinhardt, T.E.
• Lumia, R.

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/10919/80263

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/80263