  The intensive care unit (ICU) is a specialized hospital space where
critically ill patients receive intensive care and monitoring. Comprehensive
monitoring is imperative in assessing patients conditions, in particular
acuity, and ultimately the quality of care. However, the extent of patient
monitoring in the ICU is limited due to time constraints and the workload on
healthcare providers. Currently, visual assessments for acuity, including fine
details such as facial expressions, posture, and mobility, are sporadically
captured, or not captured at all. These manual observations are subjective to
the individual, prone to documentation errors, and overburden care providers
with the additional workload. Artificial Intelligence (AI) enabled systems has
the potential to augment the patient visual monitoring and assessment due to
their exceptional learning capabilities. Such systems require robust annotated
data to train. To this end, we have developed pervasive sensing and data
processing system which collects data from multiple modalities depth images,
color RGB images, accelerometry, electromyography, sound pressure, and light
levels in ICU for developing intelligent monitoring systems for continuous and
granular acuity, delirium risk, pain, and mobility assessment. This paper
presents the Intelligent Intensive Care Unit (I2CU) system architecture we
developed for real-time patient monitoring and visual assessment.
