Sleep-Wake Cycles
Understanding Sleep
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Tommaso Banfi1,2,3, Nicolò Valigi4, Marco di Galante4,5
1The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy. tommaso.banfi@santannapisa.it.
This study introduces a new deep learning algorithm for accurate, on-device sleep tracking using wearable accelerometers. The lightweight, generalizable method efficiently detects sleep/wake patterns without manual feature extraction.
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2026-06-19T13:38:59.810344+00:00