Explore the scholarly record.

Abstract

Abstract Physical restraint—including being held, carried, and restrained in devices—is a common feature of infants’ everyday lives. However, previous survey-based and video-based methods cannot simultaneously provide continuous, full-day accounts of infants’ restrained experiences. This study developed and validated a machine learning model to quantify infants’ restrained time moment-to-moment across the full day in the home environment using wearable sensor data. We used a dataset that includes 146 home-visit sessions from 66 infants, with 30 younger infants aged 4–7 months, and 36 older infants aged 11–14 months. We annotated infants’ restrained states in the first 1.5-h video recording of each session as ground-truth labels. The supervised machine-learning model achieved high accuracy (89%) and substantial kappa agreement (κ = .73) compared with human-coded ground truth. The model showed a slight bias toward overestimating unrestrained periods relative to restrained periods, but this bias was mitigated when we used a longer data-aggregation window. The model also showed convergent validity by corroborating prior studies that showed an age-related decrease in infants’ overall restrained time throughout the day. In short, the current study demonstrated the utility of using wearable sensors to quantify infants’ real-world restrained experiences, offering a new tool for studying how daily restraint influences early development.