Show simple item record

dc.contributor.author Ahmad, Siti A.
dc.contributor.author Chappell, Paul H.
dc.date.accessioned 2010-07-22T20:12:48Z
dc.date.available 2010-07-22T20:12:48Z
dc.date.issued 2008
dc.identifier.citation Proceedings of the MEC’08 conference, UNB; 2008. en_US
dc.identifier.uri http://hdl.handle.net/10161/2760
dc.description.abstract Electromyographic control systems based on pattern recognition have become an established technique in upper limb prosthetic control application. This paper describes a use of fuzzy logic to discriminate different hand grip postures by processing the surface EMG from wrist muscles. A moving data window of two hundred values is applied to the SEMG data and a new method called moving approximate entropy is used to extract information from the signals. The analyses show differences at three states of contraction (start, middle and end) where significant dips can be observed at the start and end of a muscle contraction. Mean absolute value (MAV) and kurtosis are also used in the extraction process to increase the performance of the system. The extracted features are fed to a fuzzy logic system to be classified and select the output appropriately. The preliminary experimental result demonstrates the ability of the system to classify the features related to different grip postures. en_US
dc.format.extent 244342 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.publisher Myoelectric Symposium en_US
dc.subject fuzzy logic en_US
dc.subject prosthesis control en_US
dc.title SURFACE EMG CLASSIFICATION USING MOVING APPROXIMATE ENTROPY AND FUZZY LOGIC FOR PROSTHESIS CONTROL en_US
dc.type Article en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record