Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants

Background: Early neurodevelopmental care needs better, effective and objective solutions for assessing infants’ motor abilities. Novel wearable technology opens possibilities for characterizing spontaneous movement behavior. This work seeks to construct and validate a generalizable, scalable, and effective method to measure infants’ spontaneous motor abilities across all motor milestones from lying supine to fluent walking. Methods: A multi-sensor infant wearable was constructed, and 59 infants (age 5–19 months) were recorded during spontaneous play. A novel gross motor description scheme was used for human classification of postures and movements at a second-level time resolution. A deep learning–based classifier was trained to mimic human annotations; aggregated recording-level outputs provided posture- and movement-specific developmental trajectories enabling holistic assessments of maturity. Results: All recordings were technically successful, and algorithmic analysis showed human-equivalent accuracy in quantifying postures and movements. Aggregated recordings were used to train an algorithm for predicting a novel neurodevelopmental measure, Baba Infant Motor Score (BIMS), estimating maturity of infants’ motor abilities; BIMS correlated very strongly with chronological age (Pearson’s r = 0.89, p < 1e-20). Conclusions: Out-of-hospital assessment of infants’ motor ability is possible using a multi-sensor wearable. The algorithmic analysis provides transparent, objective, interpretable motility metrics that link strongly to infants’ age. Such a solution could be automated and globally scaled, holding promise for benchmarking in individualized patient care and early intervention trials.