Reference Free Incremental Deep Learning Model Applied for Camera-Based Respiration Monitoring

Abstract

The article describes a reference and training set free incrementally trained deep learning algorithm for camera-based respiration monitoring systems. The algorithm uses a model based discriminator to find salient areas having respiration like periodic motion. It stores the first principle component of the found waveforms into two slowly growing set along with negative, uncorrelated motion patterns. Using these samples, it trains a deep neural network classifier incrementally to recognize respiration from sudden and motion intensive situations. The classifier had no forgetting mechanism and it is able to adapt quickly the changing respiration patterns and conditions. The algorithm has been validated in a total of 24 hours diverse recording captured in the neonatal intensive care unit (NICU) of the I