Linked Life Data

18002349

Source:http://linkedlifedata.com/resource/pubmed/id/18002349

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-11-16
pubmed:abstractText
The ability to rise from a chair is considered to be important to achieve functional independence and quality of life. This sit-to-stand task is also a good indicator to assess condition of patients with chronic diseases. We developed a wavelet based algorithm for detecting and calculating the durations of sit-to-stand and stand-to-sit transitions from the signal vector magnitude of the measured acceleration signal. The algorithm was tested on waist worn accelerometer data collected from young subjects as well as geriatric patients. The test demonstrates that both transitions can be detected by using wavelet transformation applied to signal magnitude vector. Wavelet analysis produces an estimate of the transition pattern that can be used to calculate the transition duration that further gives clinically significant information on the patients condition. The method can be applied in a real life ambulatory monitoring system for assessing the condition of a patient living at home.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1557-170X
pubmed:author
pubmed:issnType
Print
pubmed:volume
2007
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1884-7
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Wavelet based approach for posture transition estimation using a waist worn accelerometer.
pubmed:affiliation
E-Health Research Centre, CSIRO ICT Centre, Lvl 20:300, Adelaide, Brisbane, QLD, Australia. niranjan.bidargaddi@csiro.au
pubmed:publicationType
Journal Article