Publications

Clinical observations indicate that during critical care at the hospitals, patients sleep positioning and motion affect recovery. Unfortunately, there is no formal medical protocol to record, quantify, and analyze patient motion. There is a small number of clinical studies, which use manual analysis of sleep poses and motion recordings to support medical benefits of patient positioning and motion monitoring. Manual processes are not scalable, are prone to human errors, and strain an already taxed healthcare workforce. This study introduces DECU (Deep Eye-CU): an autonomous mulitmodal multiview system, which addresses these issues by autonomously monitoring healthcare environments and enabling the recording and analysis of patient sleep poses and motion. DECU uses three RGB-D cameras to monitor patient motion in a medical Intensive Care Unit (ICU). The algorithms in DECU estimate pose direction at different temporal resolutions and use keyframes to efficiently represent pose transition dynamics. DECU combines deep features computed from the data with a modified version of Hidden Markov Model to more flexibly model sleep pose duration, analyze pose patterns, and summarize patient motion.