ASIS: A Smart Alarm Clock Based on Deep Learning for the Safety of Night Workers

Sleep inertia is the condition that a person experiences upon awakening, which can vary depending on the sleep stage at the time of awakening. It is characterized by reduced alertness, drowsiness, and difficulties in cognitive and motor functions. This can pose risks in professions that require immediate high levels of attention after awakening. This study presents the Anti Sleep Inertia System (ASIS). The ASIS aims to determine the best time to wake up using an alarm clock within a specified time window. This is done using a system composed of two machine learning (ML) models that classify and predict each segment of the electroencephalogram (EEG) signal recorded during sleep. Despite the complexity of the field, the model has shown its efficacy reaching an overall accuracy of 0.73, despite its lightweight architecture.

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Published
2024-10-21
DOI
https://doi.org/10.1109/metroxraine62247.2024.10796738
Primary Topic
Healthcare Technology and Patient Monitoring
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article
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article

ASIS: A Smart Alarm Clock Based on Deep Learning for the Safety of Night Workers

Pasquale Caianiello, Giuseppe Placidi, Daniele Lozzi, A Cipriani et al.
Healthcare Technology and Patient Monitoring
article

ASIS: A Smart Alarm Clock Based on Deep Learning for the Safety of Night Workers

Pasquale Caianiello, Giuseppe Placidi, Daniele Lozzi, A Cipriani, Filippo Mignosi, Alessandro Di Matteo, Enrico Mattei
article en

Abstract

Sleep inertia is the condition that a person experiences upon awakening, which can vary depending on the sleep stage at the time of awakening. It is characterized by reduced alertness, drowsiness, and difficulties in cognitive and motor functions. This can pose risks in professions that require immediate high levels of attention after awakening. This study presents the Anti Sleep Inertia System (ASIS). The ASIS aims to determine the best time to wake up using an alarm clock within a specified time window. This is done using a system composed of two machine learning (ML) models that classify and predict each segment of the electroencephalogram (EEG) signal recorded during sleep. Despite the complexity of the field, the model has shown its efficacy reaching an overall accuracy of 0.73, despite its lightweight architecture.

Università Cattolica del Sacro Cuore (IT), University of L'Aquila (IT)
Gender equality
Openalex Percentile: Top 56%
Healthcare Technology and Patient Monitoring
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ASIS: A Smart Alarm Clock Based on Deep Learning for the Safety of Night Workers — Pasquale Caianiello, Giuseppe Placidi, et al. · (2024) | TGRS Research Map | TGRS