This paper examines how features extracted from full-day data recorded by wearable sensors are able to differentiate between infants with typical development and those with or at risk for developmental delays. Wearable sensors were used to collect full-day (8–13 h) leg movement data from infants with typical development ( $n=12$ ) and infants at risk for developmental delay ( $n = 24$ ). At 24 months, at-risk infants were assessed as having good ( $n = 10$ ) or poor ( $n = 9$ ) developmental outcomes. With this limited size dataset, our statistical analysis indicated that accelerometer features collected earlier in infancy differentiated between at-risk infants with poor and good outcomes at 24 months, as well as infants with typical development. This paper also tested how these features performed on a subset of the data for which the infant movement was known, i.e., 5-min intervals more representative of clinical observations. Our results on this limited dataset indicated that features for full-day data showed more group differences than similar features for the 5-min intervals, supporting the usefulness of full-day movement monitoring.

中文翻译：

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使用可穿戴传感器识别婴儿发育迟缓：全天腿部运动统计特征分析

本文研究了从可穿戴传感器记录的全天数据中提取的特征如何能够区分具有典型发育的婴儿和具有发育迟缓或有发育迟缓风险的婴儿。可穿戴传感器用于收集具有典型发育的婴儿的全天（8-13 小时）腿部运动数据（ $n=12$）和有发育迟缓风险的婴儿（ $n = 24$）。24 个月时，高危婴儿被评估为具有良好（ $n = 10$）或较差（ $n = 9$) 发展成果。利用这个有限大小的数据集，我们的统计分析表明，在婴儿期早期收集的加速度计特征可以区分 24 个月时结局不佳和良好的高危婴儿以及具有典型发育的婴儿。本文还测试了这些特征如何在已知婴儿运动的数据子集（即更能代表临床观察的 5 分钟间隔）上执行。我们在这个有限数据集上的结果表明，全天数据的特征比 5 分钟间隔的相似特征显示出更多的群体差异，支持全天运动监测的有用性。