Linked Life Data

19027325

Source:http://linkedlifedata.com/resource/pubmed/id/19027325

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2009-12-16
pubmed:abstractText
This paper studies the time-dependent power spectral density (PSD) estimation of nonstationary surface electromyography (SEMG) signals and its application to fatigue analysis during isometric muscle contraction. The conventional time-dependent PSD estimation methods exhibit large variabilities in estimating the instantaneous SEMG parameters so that they often fail to identify the changing patterns of short-period SEMG signals and gauge the extent of fatigue in specific muscle groups. To address this problem, a time-varying autoregressive (TVAR) model is proposed in this paper to describe the SEMG signal, and then the recursive least-squares (RLS) and basis function expansion (BFE) methods are used to estimate the model coefficients and the time-dependent PSD. The instantaneous parameters extracted from the PSD estimation are evaluated and compared in terms of reliability, accuracy, and complexity. Experimental results on synthesized and real SEMG data show that the proposed TVAR-model-based PSD estimators can achieve more stable and precise instantaneous parameter estimation than conventional methods.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1873-5711
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
89-101
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Time-dependent power spectral density estimation of surface electromyography during isometric muscle contraction: methods and comparisons.
pubmed:affiliation
Department of Orthopaedics and Traumatology, The University of Hong Kong, Duchess of Kent Children's Hospital, Hong Kong, China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't