Neonatal Face Tracking for Non-Contact Continuous Patient Monitoring

Abstract

Noncontact video-based patient monitoring promises several advantages over wearable sensors, particularly for patients in the NICU who have fragile skin. However, such approaches often require definition of a region-of-interest (ROI), such as the patient’s forehead. For example, a number of neonatal monitoring studies have estimated heart rate and respiration from video by first manually cropping the face of the patient before performing analyses within that region. Relying on a static ROI can fail due to patient motion or during clinical interventions, thereby demanding additional manual ROI selection over the course of the monitoring period. Widely used face detection algorithms tend to fail in a neonatal context. We therefore propose a semi-automated method where the ROI is automatically and repeatedly reinitialized to ensure robustness of the ROI for continuous monitoring. Factors such as the displacement of the patient and the change in patient poses are addressed using multiple computer vision techniques before selecting a comprehensive method for ROI tracking. Results were obtained from three patients admitted at the NICU using 20-minute videos including periods of rest, motion, and occlusion events. Compared to a static ROI, the proposed method achieves significantly improved tracking of the patient’s face, as demonstrated by an area under the curve > 0.63 across all patients.