Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods.

Journal: Medicine and science in sports and exercise

PMID: 29443824

Abstract

PURPOSE: This study aimed to improve estimates of sitting time from hip-worn accelerometers used in large cohort studies by using machine learning methods developed on free-living activPAL data.

Authors

Jacqueline Kerr

University of California San Diego.
Jordan Carlson

Children's Mercy Kansas City, Kansas City, MO.
Suneeta Godbole
Lisa Cadmus-Bertram

Department of Kinesiology, University of Wisconsin-Madison, Madison, WI.
John Bellettiere

Department of Family Medicine and Public Health, UCSD, La Jolla, CA.
Sheri Hartman

Moores Cancer Center, UCSD, La Jolla, CA.

Keywords

Accelerometry Aged Algorithms Breast Neoplasms Cross-Sectional Studies Exercise Female Hip Humans Machine Learning Middle Aged Monitoring, Ambulatory Pilot Projects Sitting Position Survivors Thigh

External Resources

View on PubMed Access via DOI PubMed (29443824)

Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods.

Abstract

Authors

Keywords

External Resources

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