Efficient embedded sleep wake classification for open-source actigraphy

Abstract This study presents a thorough analysis of sleep/wake detection algorithms for efficient on-device sleep tracking using wearable accelerometric devices. It develops a novel end-to-end algorithm using convolutional neural network applied to raw accelerometric signals recorded by an open-sour...

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Autores principales: Tommaso Banfi, Nicolò Valigi, Marco di Galante, Paola d’Ascanio, Gastone Ciuti, Ugo Faraguna
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/bc563d4fe26d448399528de23fc2ff5b
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