Infant's spontaneous movements mirror integrity of brain networks, and thus also predict the future development of higher cognitive functions. Early recognition of infants with compromised motor development holds promise for guiding early therapies to improve lifelong neurocognitive outcomes. It has been challenging, however, to assess motor performance in ways that are objective and quantitative. Novel wearable technology has shown promise for offering efficient, scalable and automated methods in movement assessment. Here, we describe the development of an infant wearable, a multi-sensor smart jumpsuit that allows mobile data collection during independent movements. A deep learning algorithm, based on convolutional neural networks (CNNs), was then trained using multiple human annotations that incorporate the substantial inherent ambiguity in movement classifications. We also quantify the substantial ambiguity of a human observer, allowing its transfer to improving the automated classifier. Comparison of different sensor configurations and classifier designs shows that four-limb recording and end-toend CNN classifier architecture allows the best movement classification. Our results show that quantitative tracking of independent movement activities is possible with a human equivalent accuracy, i.e. it meets the human inter-rater agreement levels in infant posture and movement classification.

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 their spontaneous play. A novel gross motor description scheme was used for human visual classification of postures and movements at a second-level time resolution. A deep learning -based classifier was then trained to mimic human annotations, and aggregated recording-level outputs were used to provide posture- and movement-specific developmental trajectories, which enabled more holistic assessments of motor maturity. Results Recordings were technically successful in all infants, and the algorithmic analysis showed human-equivalent-level accuracy in quantifying the observed postures and movements. The aggregated recordings were used to train an algorithm for predicting a novel neurodevelopmental measure, Baba Infant Motor Score (BIMS). This index estimates maturity of infants’ motor abilities, and it correlates very strongly (Pearson’s r = 0.89, p < 1e-20) to the chronological age of the infant. Conclusions The results show that out-of-hospital assessment of infants’ motor ability is possible using a multi-sensor wearable. The algorithmic analysis provides metrics of motility that are transparent, objective, intuitively interpretable, and they link strongly to infants’ age. Such a solution could be automated and scaled to a global extent, holding promise for functional benchmarking in individualized patient care or early intervention trials.

This is an open access article under the terms of the Creat ive Commo ns Attri bution-NonCo mmercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.