Poster Open Access

# A Case Study On Classification Of Foot Gestures via Surface Electromyography

Lyons, Kenneth R.; Joshi, Sanjay S.

### DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<identifier identifierType="DOI">10.5281/zenodo.569072</identifier>
<creators>
<creator>
<creatorName>Lyons, Kenneth R.</creatorName>
<givenName>Kenneth R.</givenName>
<familyName>Lyons</familyName>
</creator>
<creator>
<creatorName>Joshi, Sanjay S.</creatorName>
<givenName>Sanjay S.</givenName>
<familyName>Joshi</familyName>
</creator>
</creators>
<titles>
<title>A Case Study On Classification Of Foot Gestures via Surface Electromyography</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2015</publicationYear>
<subjects>
<subject>electromyography</subject>
<subject>prosthetics</subject>
</subjects>
<dates>
<date dateType="Issued">2015-06-11</date>
</dates>
<resourceType resourceTypeGeneral="Text">Poster</resourceType>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/569072</alternateIdentifier>
</alternateIdentifiers>
<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">&lt;p&gt;A fundamental problem for individuals with amputations proximal to the elbow is the lack of methods for controlling an upper limb prosthetic. Myoelectric control is limited in flexibility when few residual muscle sites are available, and noninvasive approaches to this problem are needed when surgical techniques such as targeted muscle reinnervation are not suitable. Controlling an upper limb prosthetic with the leg is one such approach. In this case study, we investigate recognition of foot gestures via surface electromyography using methods commonly employed in upper limb gesture recognition research. For comparison with the current standard in gesture recognition, electromyographic sensors recorded muscle activity from the forearm while participants performed gestures with the dominant hand. Then, participants performed an analogous set of foot gestures with sensors on the lower leg. Participants found the mapping between the hand and foot to be intuitive, and offline results show that classification accuracy for the two cases is comparable, motivating further work in applying this idea to upper limb prosthetic control.&lt;/p&gt;</description>
</descriptions>
</resource>

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