Linked Life Data

21096217

Source:http://linkedlifedata.com/resource/pubmed/id/21096217

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2010-11-24
pubmed:abstractText
High usability myo-controlled devices require robust classification schemes during dynamic contractions. Therefore, this study investigates the impact of the training data set on the performance of several pattern recognition algorithms during dynamic contractions. It is shown that combined with a threshold to detect the onset of the contraction, current pattern recognition algorithms used on static conditions can maintain relatively high classification accuracy on dynamic situations. Moreover, the performance of the pattern recognition algorithms tested improved by optimizing the choice of the training set. Finally, the results also showed that rather simple approaches for classification of time-domain features provide results comparable to more complex classification methods of wavelet features.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1557-170X
pubmed:author
pubmed:issnType
Print
pubmed:volume
2010
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2766-9
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Surface EMG classification during dynamic contractions for multifunction transradial prostheses.
pubmed:affiliation
Aalborg Univ., Denmark.
pubmed:publicationType
Journal Article