Efficient embedded sleep wake classification for open-source actigraphy

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-source wrist-...

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Detalles Bibliográficos
Autores principales: Banfi, Tommaso, Valigi, Nicolò, di Galante, Marco, d’Ascanio, Paola, Ciuti, Gastone, Faraguna, Ugo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7801620/
https://www.ncbi.nlm.nih.gov/pubmed/33431918
http://dx.doi.org/10.1038/s41598-020-79294-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7801620/
https://www.ncbi.nlm.nih.gov/pubmed/33431918
http://dx.doi.org/10.1038/s41598-020-79294-y

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