Linked Life Data

17946392

Source:http://linkedlifedata.com/resource/pubmed/id/17946392

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-10-23
pubmed:abstractText
Joint stiffness is defined as the dynamic relationship between the position of the joint and torque acting about it. Joint stiffness is composed of two components: intrinsic and reflex stiffness. Measuring the two stiffness components cannot be done simply because the two components appear and change together. A number of approaches have been used to estimate the components, but all those approaches are inherently off-line. We have developed novel algorithm that separates and estimates the two components in real-time. Intrinsic stiffness was estimated by finding the cross-correlations between the position, its derivatives and the torque. Reflex stiffness was estimated by finding the IRF between the half-wave rectified velocity and the estimated reflex torque. A novel position perturbation, consisting of pseudo random series of pulses of different lengths, was used to eliminate covariance of intrinsic and reflex stiffness estimates. Using simulated data, the real-time estimates were shown to be estimated accurately. The real-time estimation algorithm was validated by comparing the real-time estimates with estimates generated by the parallel-cascade identification, an established off-line intrinsic and reflex stiffness identification algorithm, using simulated and experimental data. The estimates produced by the two algorithms were in agreement for both simulated and experimental data.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1557-170X
pubmed:author
pubmed:issnType
Print
pubmed:volume
1
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
292-5
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Real-time estimation of intrinsic and reflex stiffness.
pubmed:affiliation
Dept. of Biomedical Engineering, McGill University, Montreal, QC, Canada. daniel.ludvig@mail.mcgill.ca
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't