Segmenting accelerometer data from daily life with unsupervised machine learning release_b5cboupmrbd2zocye6qr6ws75u

by Dafne van Kuppevelt, Joe Heywood, Mark Hamer, Séverine Sabia, Emla Fitzsimons, Vincent van Hees

Published in PLoS ONE by Public Library of Science (PLoS).

2019   Volume 14, Issue 1, e0208692

Abstract

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 raw data metrics. To address these limitations, we present a data-driven approach for segmenting and clustering the accelerometer data using unsupervised machine learning.
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Date   2019-01-09
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