The Deep Eye-CU (DECU) project integrates temporal motion information with the multimodal multiview network to monitor patient sleep poses. It uses deep features, which slightly improved the patient sleep pose classification accuracy (when compared to the performance of engineered features such as Hu-moments and Histogram of Oriented Gradients (HOG)). The DECU also uses principles from Hidden Markov Models (HMMs) a popular technique in speech process. It leverages pose time-series data and assumes that patient motion can be modeled using a “state-machine” approach. However, HHMs are limited in their ability to model state duration. In a high level analysis, state duration is used to distinguish between poses and pseudo poses, which are transitory poses seen when patients move from one position to another. The DECU framework (system and algorithms) are currently deployed in a real medical ICU at Santa Barbara Cottage Hospital where study volunteers have consented to the study.