Deep Eye-CU (DECU): Summarization of Patient Motion in the ICU

摘要

Healthcare professionals speculate about the effects of poses and pose manipulation in healthcare. Anecdotal observations indicate that patient poses and motion affect recovery. Motion analysis using human observers puts strain on already taxed healthcare workforce requiring staff to record motion. Automated algorithms and systems are unable to monitor patients in hospital environments without disrupting patients or the existing standards of care. This work introduces the DECU framework, which tackles the problem of autonomous unobtrusive monitoring of patient motion in an Intensive Care Unit (ICU). DECU combines multimodal emissions from Hidden Markov Models (HMMs), key frame extraction from multiple sources, and deep features from multimodal multiview data to monitor patient motion. Performance is evaluated in ideal and non-ideal scenarios at two motion resolutions in both a mock-up and a real ICU.