Sleep disorders are common health problems in industrialized societies and may be caused by underlying health issues. Current methods to assess the quality of sleep are invasive and not suitable for continuous monitoring in real world contexts. We have developed a smart sensing solution for non invasive sleep monitoring specifically conceived for the early identification of pre-clinical sleep disorders and insomnia in the general population. Our prototype, named the Smart-Bed, is a low-cost solution that gathers and processes data on the movement and position of the subject, physiological signals, and environmental parameters. Our tests on the prototype in controlled lab conditions highlighted that the mattress can reliably detect subject's position/motion, heart rate and breathing activity. It performs well compared to polysomnography and correctly classifies four behavioural conditions (no bed occupancy, wakefulness, non-REM sleep, and REM sleep), which are the basis for creating an objective sleep quality index.

A smart bed for non-obtrusive sleep analysis in real world context

Laurino, Marco
;
Arcarisi, Lucia;Carbonaro, Nicola;Gemignani, Angelo;Menicucci, Danilo;Tognetti, Alessandro
2020-01-01

Abstract

Sleep disorders are common health problems in industrialized societies and may be caused by underlying health issues. Current methods to assess the quality of sleep are invasive and not suitable for continuous monitoring in real world contexts. We have developed a smart sensing solution for non invasive sleep monitoring specifically conceived for the early identification of pre-clinical sleep disorders and insomnia in the general population. Our prototype, named the Smart-Bed, is a low-cost solution that gathers and processes data on the movement and position of the subject, physiological signals, and environmental parameters. Our tests on the prototype in controlled lab conditions highlighted that the mattress can reliably detect subject's position/motion, heart rate and breathing activity. It performs well compared to polysomnography and correctly classifies four behavioural conditions (no bed occupancy, wakefulness, non-REM sleep, and REM sleep), which are the basis for creating an objective sleep quality index.
2020
Laurino, Marco; Arcarisi, Lucia; Carbonaro, Nicola; Gemignani, Angelo; Menicucci, Danilo; Tognetti, Alessandro
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1035464
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