Process monitoring in the intensive care unit: Assessing patient mobility through activity analysis with a non-invasive mobility sensor

Abstract

Throughout a patient’s stay in the Intensive Care Unit (ICU),accurate measurement of patient mobility,as part of routine care,is helpful in understanding the harmful effects of bedrest [1]. However,mobility is typically measured through observation by a trained and dedicated observer,which is extremely limiting. In this work,we present a video-based automated mobility measurement system called NIMS: Non-Invasive Mobility Sensor. Our main contributions are: (1) a novel multi-person tracking methodology designed for complex environments with occlusion and pose variations,and (2) an application of human-activity attributes in a clinical setting. We demonstrate NIMS on data collected from an active patient room in an adult ICU and show a high inter-rater reliability using a weighted Kappa statistic of 0.86 for automatic prediction of the highest level of patient mobility as compared to clinical experts.