Article(id=1242117687099330962, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242117680468131883, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1252857600000, receivedDateStr=2009-09-14, revisedDate=1255881600000, revisedDateStr=2009-10-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1258041600000, onlineDateStr=2009-11-13, pubDate=1258041600000, pubDateStr=2009-11-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1258041600000, onlineIssueDateStr=2009-11-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774073534029, creator=sys-migrate, updateTime=1774073534029, updator=sys-migrate, issue=Issue{id=1242117680468131883, tenantId=1146029695717560320, journalId=1146031591421210625, year='2009', volume='27', issue='0921', pageStart='1', pageEnd='140', issueExtLink='null', onlineDate='null', pubDate='1258041600000', pubDateStr='2009-11-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774073532452, creator='sys-migrate', updateTime=1774073532452, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=77, endPage=81, ext={EN=ArticleExt(id=1242117690945507765, articleId=1242117687099330962, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=EMG Analysis Based On The Entropy Theory And Complexity Measure, columnId=1242116080374710456, journalTitle=Science & Technology Review, columnName=Articles, runingTitle=null, highlight=null, articleAbstract=Electromyography(EMG) finds more and more application areas, such as the workload evaluation and the robotic control. The key is the feature analysis and extraction. Various methods for the analysis of the EMG can be grouped into the linear methods and the nonlinear methods. The methods based the entropy theory and the complexity are among the nonlinear methods. The entropy methods include various entropy calculation algorithms, and the Renyi Entropy and Wavelet Entropy are the typical methods. In this paper the EMG are obtained from two volunteers while they are doing the push-up exercise, and the measures of the Renyi entropy, Wavelet entropy and LZC complexity are calculated. In the algorithm, the segments of the EMG are made to achieve better analysis results. The complexity of the EMG is reflected by these measures, as is validated by analysis. The value of the wavelet entropy implies the power of the EMG, and a higher power is obtained for the muscle fiber concentrated discharge, and so the complexity is decreased. The results from the Renyi entropy and LZC are the same. With these methods, the EMG is divided into several segments based on the muscle fatigue and EMG complexity. The results show that with these methods the EMG is a promising research field in the future, and these measures can also be applied to muscle fatigue., authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=TOeFKiDb0u2/keM/vtZmxg==, pdfFileSize=552055, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242117689775292516, articleId=1242117687099330962, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=基于熵理论和复杂度的肌电信号分析, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=传统信号处理方法对肌电信号分析存在一定局限，不能很好地描述肌电信号的复杂性；而基于熵理论和复杂度等非线性分析方法越来越多地应用于肌电信号等生理信号的处理。熵理论和复杂度对于肌电信号的处理具有运算速度快、数值特征明显，并且能够很好地描述其复杂性等特点。本文以做俯卧撑的上肢肌电信号为分析对象，通过计算其Renyi熵、小波熵和复杂度刻画不同阶段肌电信号的复杂性，并验证以此测度进行肌电信号不同区域划分的合理性，以及应用该方法分析肌电信号的有效性，取得了较好的效果。试验数据分析结果表明，小波熵值较大的部分对应于肌电信号能量较高的区域。从生理意义而言，这些区域正是肌肉纤维集中放电的过程。肌电信号成分单一，是复杂度较低的区域，而Renyi熵和复杂度值越大，对应的肌电信号成分复杂度越高，这与理论分析吻合得比较好，同时三者也得到了相互验证。由此表明该方法对于肌电信号的分析是可行的，非线性分析方法可能是未来肌电信号等生理信号的发展方向，该方法还可以应用于体力疲劳评价。, authors=肖毅¹,²,陈善广²,王春慧², authorsList=肖毅;陈善广;王春慧, authorCompany=1. 装备指挥技术学院光电装备系，北京1014162. 中国航天员科研训练中心，北京 100193, correspAuthors=null, authorNote=null, correspAuthorsNote=肖毅, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=NLs0b/p4rPeab0qbUP3uIw==, pdfFileSize=552055, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, 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