Enhancing motor screening efficiency: Toward an empirically derived abridged version of the Alberta Infant Motor Scale

Abstract

Use of machine learning (ML) in the early detection of developmental delay is possible through the analysis of infant motor skills, though the large number of potential indicators limits the speed at which the system can be trained. Body joint obstructions, the inability to infer aspects of movement such as muscle tone and volition, and the complexities of the home environment – confound machine learning's ability to distinguish between some motor items. To train the system efficiently requires using an excerpted list of validated items, a salient set, which uses only those motor items that are the ‘easiest’ to see and identify, while being the most highly correlated to a low/qualifying score. This work describes the examination of motor items, selection of 15 items that comprise the salient set, and the ability of the set to reliably screen for motor delay in the first-year infant.