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Article: Novel calibration technique for precision walker dynamometer system based on artificial neural network

TitleNovel calibration technique for precision walker dynamometer system based on artificial neural network
Authors
KeywordsArtificial Neural Network
Calibration
Walker
Issue Date2009
Citation
Nami Jishu Yu Jingmi Gongcheng/Nanotechnology And Precision Engineering, 2009, v. 7 n. 3, p. 245-248 How to Cite?
AbstractA back-propagation artificial neural network model with three layers was developed for the calibration of precision walker dynamometer system. This model adopted the output voltages from 12-channel strain gauge bridges in the dynamometer system as the network input vector and the six load components as the network output vector. The neuron number of the single hidden layer in this model was optimized by comparing the absolute error summations under the target error. Relevant error check results showed that, after the calibration using this neural network, the maximal system precision error with single-direction force was 7.78% and the maximal crosstalk was 7.49%. In comparison with traditional linear calibration method, the proposed technique can effectively increase the measurement precision of walker loads and greatly decrease the crosstalk error, which might be helpful for accurately monitoring and evaluating the rehabilitation training effect of walker-assisted walking in the future.
Persistent Identifierhttp://hdl.handle.net/10722/170142
ISSN
2015 SCImago Journal Rankings: 0.253
References

 

DC FieldValueLanguage
dc.contributor.authorMing, Den_US
dc.contributor.authorZhang, Xen_US
dc.contributor.authorDai, YGen_US
dc.contributor.authorZhou, ZXen_US
dc.contributor.authorWan, BKen_US
dc.contributor.authorHu, Yen_US
dc.contributor.authorWang, WJen_US
dc.date.accessioned2012-10-30T06:05:35Z-
dc.date.available2012-10-30T06:05:35Z-
dc.date.issued2009en_US
dc.identifier.citationNami Jishu Yu Jingmi Gongcheng/Nanotechnology And Precision Engineering, 2009, v. 7 n. 3, p. 245-248en_US
dc.identifier.issn1672-6030en_US
dc.identifier.urihttp://hdl.handle.net/10722/170142-
dc.description.abstractA back-propagation artificial neural network model with three layers was developed for the calibration of precision walker dynamometer system. This model adopted the output voltages from 12-channel strain gauge bridges in the dynamometer system as the network input vector and the six load components as the network output vector. The neuron number of the single hidden layer in this model was optimized by comparing the absolute error summations under the target error. Relevant error check results showed that, after the calibration using this neural network, the maximal system precision error with single-direction force was 7.78% and the maximal crosstalk was 7.49%. In comparison with traditional linear calibration method, the proposed technique can effectively increase the measurement precision of walker loads and greatly decrease the crosstalk error, which might be helpful for accurately monitoring and evaluating the rehabilitation training effect of walker-assisted walking in the future.en_US
dc.languageengen_US
dc.relation.ispartofNami Jishu yu Jingmi Gongcheng/Nanotechnology and Precision Engineeringen_US
dc.subjectArtificial Neural Networken_US
dc.subjectCalibrationen_US
dc.subjectWalkeren_US
dc.titleNovel calibration technique for precision walker dynamometer system based on artificial neural networken_US
dc.typeArticleen_US
dc.identifier.emailHu, Y:yhud@hku.hken_US
dc.identifier.authorityHu, Y=rp00432en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-66349097819en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-66349097819&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume7en_US
dc.identifier.issue3en_US
dc.identifier.spage245en_US
dc.identifier.epage248en_US
dc.identifier.scopusauthoridMing, D=9745824400en_US
dc.identifier.scopusauthoridZhang, X=23986626500en_US
dc.identifier.scopusauthoridDai, YG=26640197400en_US
dc.identifier.scopusauthoridZhou, ZX=7406097013en_US
dc.identifier.scopusauthoridWan, BK=7102316798en_US
dc.identifier.scopusauthoridHu, Y=7407116091en_US
dc.identifier.scopusauthoridWang, WJ=7501755807en_US

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