NAPping PAnts (NAPPA): An open wearable solution for monitoring Infant's sleeping rhythms, respiration and posture.

Study Objectives: To develop a non-invasive and practical wearable method for long-term tracking of infants' sleep.

Methods: An 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 ( = 16649 epochs of 30 s), using hypnograms from co-registered polysomnography (PSG) as a training target in 33 infants (age 2 weeks to 18 months; Mean = 4).

The impact of stability in appearance on the development of facial representations.

The way faces become familiar and what information is represented as familiarity develops has puzzled researchers in the field of human face recognition for decades. In this paper, we present three experiments serving as proof of concept for a cost-efficient mechanism of face learning describing how facial representations form over time and accounting for recognition errors. We propose that the encoding of facial information is dynamic and modulated by the intrinsic stability in individual faces' appearance.