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Segmenting accelerometer data from daily life with unsupervised machine learning
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Dafne van Kuppevelt, Joe Heywood, Mark Hamer, Séverine Sabia, Emla Fitzsimons, Vincent van Hees
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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Crossref Metadata (via API)
Worldcat
SHERPA/RoMEO (journal policies)
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CORE.ac.uk
Semantic Scholar
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