Segmenting accelerometer data from daily life with unsupervised machine learning
Segmenting accelerometer data from daily life with unsupervised machine learning
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Publisher
United States: Public Library of Science
Journal title
Language
English
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Publisher
United States: Public Library of Science
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Scope and Contents
Contents
Accelerometers are increasingly used to obtain valuable descriptors of physical activity for health research. The cut-points approach to segment accelerometer data is widely used in physical activity research but requires resource expensive calibration studies and does not make it easy to explore the information that can be gained for a variety of...
Alternative Titles
Full title
Segmenting accelerometer data from daily life with unsupervised machine learning
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TN_cdi_plos_journals_2165648109
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_2165648109
Other Identifiers
ISSN
1932-6203
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0208692
