Sleep classification from wrist-worn accelerometer data using random forests

Sleep classification from wrist-worn accelerometer data using random forests

Abstract Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are both low in price and provide reasonably accurate estimates of movement. Techniques to classify sleep from the high-resolu...

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Autores principales: Kalaivani Sundararajan, Sonja Georgievska, Bart H. W. te Lindert, Philip R. Gehrman, Jennifer Ramautar, Diego R. Mazzotti, Séverine Sabia, Michael N. Weedon, Eus J. W. van Someren, Lars Ridder, Jian Wang, Vincent T. van Hees
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/97baecca4ff44f33ac66c0224412daae
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https://doaj.org/article/97baecca4ff44f33ac66c0224412daae

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