Unobtrusive neonatal patient monitoring using video and pressure data

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Our research aims to improve patient care in the neonatal intensive care unit (NICU) through the development of novel unobtrusive sensors and algorithms. Over the past 2.5 years, with NSERC CRD support, we have collected a unique, rich, multi-sensor patient dataset from the NICU at the Children's Hospital of Eastern Ontario (CHEO). Through continued collaboration with IBM's Centre for Advanced Studies and clinicians at CHEO, we will now mine these data using machine learning and computer vision to create novel non-contact patient monitoring systems to augment patient care in the NICU. Our original NSERC CRD grant funded the collection of a unique dataset from 35 neonates spanning different patient masses and levels of care (bed types). Data were collected from a pressure-sensitive mat (PSM) - a first for the neonatal population; an Intel RealSense RGB-D camera that measures colour, near-infrared, and depth data; and gold-standard physiologic data from the patient monitor with wired electrodes. In addition, we have developed a custom tablet app such that a bed-side researcher was able to annotate all events of clinical interest simultaneously with the sensor data collection. These data are unique, in that we have multiple sensor data streams collected simultaneously with gold standard physiologic data and clinical event annotations. The present research will develop and evaluate machine learning and computer vision algorithms for estimating physiologic signals, such as respiration rate, from non-contact sensors. Our Temporal Event Annotation (TEA) framework will be extended and released as an open-source project to enable other research groups to automatically generate native data collection apps based solely on a description of the event types to be captured. Lastly, patient movement will also be detected and characterized, with the ultimate goal to reduce false alarms resulting from motion artifacts.