File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: A Novel Outlier Detection Method for Identifying Torque-related Transient Patterns of in vivo Muscle Behavior

TitleA Novel Outlier Detection Method for Identifying Torque-related Transient Patterns of in vivo Muscle Behavior
Authors
Issue Date2014
PublisherIEEE.
Citation
The 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014), Chicago, USA, 26-30 August 2014. In the I E E E Engineering in Medicine and Biology Society. Annual Conference. Proceedings, 2014, p. 4216-4219 How to Cite?
AbstractThis paper proposed a novel outlier detection method, named l1-regularized outlier isolation and regression (LOIRE), to examine torque-related transient patterns of in vivo muscle behavior from multimodal signals, including electromyography (EMG), mechanomyography (MMG) and ultrasonography (US), during isometric muscle contraction. Eight subjects performed isometric ramp contraction of knee up to 90% of the maximal voluntary contraction, and EMG, MMG and US were simultaneously recorded from the rectus femoris muscle. Five features, including two root mean square amplitudes from EMG and MMG, muscle cross sectional area, muscle thickness and width from US were extracted. Then, local polynomial regression was used to obtain the signal-to-torque relationships and their derivatives. By assuming the signal-to-torque functions are basically quadratic, the LOIRE method is applied to identify transient torque-related patterns of EMG, MMG and US features as outliers of the linear derivative-to-torque functions. The results show that the LOIRE method can effectively reveal transient patterns in the signal-to-torque relationships (for example, sudden changes around 20% MVC can be observed from all features), providing important information about in vivo muscle behavior.
Persistent Identifierhttp://hdl.handle.net/10722/204087
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorHan, Sen_US
dc.contributor.authorChen, Xen_US
dc.contributor.authorZhong, Sen_US
dc.contributor.authorZhou, YJen_US
dc.contributor.authorZhang, Zen_US
dc.date.accessioned2014-09-19T20:04:50Z-
dc.date.available2014-09-19T20:04:50Z-
dc.date.issued2014en_US
dc.identifier.citationThe 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014), Chicago, USA, 26-30 August 2014. In the I E E E Engineering in Medicine and Biology Society. Annual Conference. Proceedings, 2014, p. 4216-4219en_US
dc.identifier.isbn9781424479290-
dc.identifier.issn1049-3565-
dc.identifier.urihttp://hdl.handle.net/10722/204087-
dc.description.abstractThis paper proposed a novel outlier detection method, named l1-regularized outlier isolation and regression (LOIRE), to examine torque-related transient patterns of in vivo muscle behavior from multimodal signals, including electromyography (EMG), mechanomyography (MMG) and ultrasonography (US), during isometric muscle contraction. Eight subjects performed isometric ramp contraction of knee up to 90% of the maximal voluntary contraction, and EMG, MMG and US were simultaneously recorded from the rectus femoris muscle. Five features, including two root mean square amplitudes from EMG and MMG, muscle cross sectional area, muscle thickness and width from US were extracted. Then, local polynomial regression was used to obtain the signal-to-torque relationships and their derivatives. By assuming the signal-to-torque functions are basically quadratic, the LOIRE method is applied to identify transient torque-related patterns of EMG, MMG and US features as outliers of the linear derivative-to-torque functions. The results show that the LOIRE method can effectively reveal transient patterns in the signal-to-torque relationships (for example, sudden changes around 20% MVC can be observed from all features), providing important information about in vivo muscle behavior.-
dc.languageengen_US
dc.publisherIEEE.-
dc.relation.ispartofIEEE Engineering in Medicine and Biology Society. Annual Conference. Proceedingsen_US
dc.rightsIEEE Engineering in Medicine and Biology Society. Annual Conference. Proceedings. Copyright © IEEE.-
dc.rights©2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleA Novel Outlier Detection Method for Identifying Torque-related Transient Patterns of in vivo Muscle Behavioren_US
dc.typeConference_Paperen_US
dc.identifier.emailHan, S: shenghan@eee.hku.hken_US
dc.identifier.emailZhang, Z: zgzhang@eee.hku.hken_US
dc.identifier.authorityZhang, Z=rp01565en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/EMBC.2014.6944554-
dc.identifier.hkuros238875en_US
dc.identifier.spage4216-
dc.identifier.epage4219-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats