  Currently, many critical care indices are repetitively assessed and recorded
by overburdened nurses, e.g. physical function or facial pain expressions of
nonverbal patients. In addition, many essential information on patients and
their environment are not captured at all, or are captured in a non-granular
manner, e.g. sleep disturbance factors such as bright light, loud background
noise, or excessive visitations. In this pilot study, we examined the
feasibility of using pervasive sensing technology and artificial intelligence
for autonomous and granular monitoring of critically ill patients and their
environment in the Intensive Care Unit (ICU). As an exemplar prevalent
condition, we also characterized delirious and non-delirious patients and their
environment. We used wearable sensors, light and sound sensors, and a
high-resolution camera to collected data on patients and their environment. We
analyzed collected data using deep learning and statistical analysis. Our
system performed face detection, face recognition, facial action unit
detection, head pose detection, facial expression recognition, posture
recognition, actigraphy analysis, sound pressure and light level detection, and
visitation frequency detection. We were able to detect patient's face (Mean
average precision (mAP)=0.94), recognize patient's face (mAP=0.80), and their
postures (F1=0.94). We also found that all facial expressions, 11 activity
features, visitation frequency during the day, visitation frequency during the
night, light levels, and sound pressure levels during the night were
significantly different between delirious and non-delirious patients
(p-value<0.05). In summary, we showed that granular and autonomous monitoring
of critically ill patients and their environment is feasible and can be used
for characterizing critical care conditions and related environment factors.

Human-Computer Interaction;Artificial Intelligence;Computer Vision and Pattern Recognition;Signal Processing