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Clinical observations indicate that during critical care at the hospitals,patients sleep positioning and motion affect recovery. Unfortunately, there isno formal medical protocol to record, quantify, and analyze patient motion.There is a small number of clinical studies, which use manual analysis of sleepposes and motion recordings to support medical benefits of patient positioningand motion monitoring. Manual processes are not scalable, are prone to humanerrors, and strain an already taxed healthcare workforce. This study introducesDECU (Deep Eye-CU): an autonomous mulitmodal multiview system, which addressesthese issues by autonomously monitoring healthcare environments and enablingthe recording and analysis of patient sleep poses and motion. DECU uses threeRGB-D cameras to monitor patient motion in a medical Intensive Care Unit (ICU).The algorithms in DECU estimate pose direction at different temporalresolutions and use keyframes to efficiently represent pose transitiondynamics. DECU combines deep features computed from the data with a modifiedversion of Hidden Markov Model to more flexibly model sleep pose duration,analyze pose patterns, and summarize patient motion. Extensive experimentalresults are presented. The performance of DECU is evaluated in ideal (BC:Bright and Clear/occlusion-free) and natural (DO: Dark and Occluded) scenariosat two motion resolutions in a mock-up and a real ICU. The results indicatethat deep features allow DECU to match the classification performance ofengineered features in BC scenes and increase the accuracy by up to 8% in DOscenes. In addition, the overall pose history summarization tracing accuracyshows an average detection rate of 85% in BC and of 76% in DO scenes. Theproposed keyframe estimation algorithm allows DECU to reach an average 78%transition classification accuracy.

Summarization of ICU Patient Motion from Multimodal Multiview Videos