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  • Title: Automated assessment of infant motor development to predict infant age: The determination of objective metrics of spontaneous kicking.
    Author: Fry-Hilderbrand K, Chen YP, Howard A.
    Journal: Wearable Technol; 2022; 3():e29. PubMed ID: 38486904.
    Abstract:
    Though early intervention can improve outcomes for children with motor disabilities, delays in diagnosis can impact the success of intervention programs. Prior work indicates that spontaneous kicking patterns can be used to model typical infant motor development to assist in the early detection of motor delays. However, abnormalities in spontaneous movements are not well defined or readily observable through traditional functional assessments. In this research, a method is introduced for the early detection of delays through the assessment of spontaneous kicking data gathered using a wearable sensing suit. We present formulations of kinematic features identified in the clinical space, identify which features are significant predictors of infant age, and establish normative values. Finally, we offer an analysis of preterm (PT) infant data compared to normative values derived from term infants. Term and PT infants ranging in age from 1 to 10 months were studied. We found that frequency, duration, acceleration, inter-joint coordination, and maximum joint excursion metrics had a significant correlation with age. From these features, models of typical kicking development were created using data from term, typically developing infants. When compared to normative trends, PT infants display differing developmental trends.
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