Automated neonatal sleep positioning assessment by video monitoring and machine learning

Proper positioning during sleep is critical for musculoskeletal and neurological development of preterm neonates, yet current manual assessment methods are time-intensive and subject to inter-observer variability.

In this proof-of-concept study, we retrospectively investigated body alignment (supine, prone, right lateral, left lateral, semi-prone) and positioning of infants during sleep episodes. We used the Infant Positioning Assessment Tool (IPAT) scoring from recordings obtained during daily routine care at two NICUs. Anatomical keypoints were annotated manually and used for fine-tuning the ViTPose pose estimator and as input for machine learning classifiers to predict alignment and suboptimal positioning.

A total of 1510 images from 50 stable preterm neonates during their quiet periods in closed incubators (gestational age 23-36 weeks) were assessed. Posture classification of five categories achieved a macro F1-score = 0.80 (95% CI: 0.74, 0.86). Suboptimal positioning prediction reached AUROC = 0.86 (0.79, 0.87) and AUPRC = 0.69 (0.59, 0.79). Input image to decision arrangement yielded AUROC = 0.87 (0.84, 0.92) and AUPRC = 0.70 (0.61, 0.82).

Video-based automated posture and positioning assessment is feasible, enabling continuous monitoring and early warning of positioning-related complications.

Positioning of preterm neonates is critical for musculoskeletal and neurological development; however, no automated visual assessment model is available. Visual machine learning models can assess infant positioning and predict repositioning needs. Performance saturates across architecturally diverse classifiers, indicating that further gains require improved labeling consistency rather than more complex models. Continuous 24/7 NICU incubator camera images can be used to detect positioning deterioration between routine nursing assessments.