Study objectivesTo develop a non-invasive and practical wearable method for long-term tracking of infants' sleep.MethodsAn infant wearable, NAPping PAnts (NAPPA), was constructed by combining a diaper cover and a movement sensor (triaxial accelerometer and gyroscope), allowing either real-time data streaming to mobile devices or offline feature computation stored in the sensor memory. A sleep state classifier (wake, N1/REM, N2/N3) was trained and tested for NAPPA recordings (N=16649 epochs of 30 seconds), using hypnograms from co-registered polysomnography (PSG) as a training target in 33 infants (age 2 weeks to 18 months; Mean=4). User experience was assessed from an additional group of 16 parents.ResultsOvernight NAPPA recordings were successfully performed in all infants. The sleep state classifier showed good overall accuracy (78%; Range 74 - 83%) when using a combination of five features related to movement and respiration. Sleep depth trends derived were generated from the classifier outputs to visualise sleep state fluctuations, which closely aligned with PSG-derived hypnograms in all infants. Consistently positive parental feedback affirmed the effectiveness of the NAPPA-design.ConclusionsNAPPA offers a practical and feasible method for out-of-hospital assessment of infants' sleep behaviour. It can directly support large-scale quantitative studies and development of new paradigms in scientific research and infant healthcare. Moreover, NAPPA provides accurate and clinically informative computational measures for body positions, respiration rates, and activity levels, each with their respective clinical and behavioural value.

NAPping PAnts (NAPPA): An Open Wearable Solution for Monitoring Infant’s Sleeping Rhythms, Respiration and Posture

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