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BACKGROUND: Early development of gross motor skills is foundational for the upcoming neurocognitive performance. Here, we studied whether at-home wearable measurements performed by the parents could be used to quantify and track infants' developing motor abilities. METHODS: Unsupervised at-home measurements of the infants' spontaneous activity were made repeatedly by the parents using a multisensor wearable suit (altogether 620 measurements from 134 infants at age 4-22 months). Machine learning-based algorithms were developed to detect the reaching of gross motor milestones (GMM), to measure times spent in key postures, and to track the overall motor development longitudinally. Parental questionnaires regarding GMMs were used for developing the algorithms, and the results were benchmarked with the interrater agreement levels established by World Health Organization (WHO). A total of 97 infants were used for the algorithm development and cross-validation, whereas an external validation was done using 37 infants from an independent recruitment in the same hospital. RESULTS: The algorithms detected the reaching of GMMs very accurately (cross-validation: accuracy, 90.9%-95.5%; external validation, 92.4%-96.8%), which compares well with the human experts in the WHO reference study. The wearable-derived postural times showed strong correlation to parental assessments (ρ = .48-.81). Individual trajectories of motor maturation showed strong correlation to infants' age (ρ = .93). CONCLUSIONS: These findings suggest that infants' gross motor skills can be quantified reliably and automatically from unsupervised at-home wearable recordings. Such methodology could be used in health care practice and in all developmental studies for gaining real-world quantitation and tracking of infants' motor abilities.
Airaksinen et al. (Fri,) studied this question.