Rolling from Home: A Methodology for Classifying Infant Rolling Movements from At-Home Video Analysis
Dr. Erin Mannen
Rolling from a supine to prone position marks an important developmental milestone for infants. While previous studies have recorded infant rolling patterns, these are conducted in complex laboratory settings with limitations. Research laboratories are an unfamiliar environment for infants which may not simulate how infants roll at home. Identifying these rolling patterns also requires expensive motion capture equipment which is not readily available for all researchers or clinicians. Accurately classifying infant rolling patterns throughout development is crucial in the early diagnosis of possible developmental delays or disorders. Therefore, using video techniques could be a promising approach for studying infant rolling movements throughout development that is readily available for both researchers and clinicians. In this study, we aim to present a methodology that allows researchers to accurately and consistently categorize infant rolling patterns via at-home video analysis. The methodology consists of three steps: 1) identifying roll direction, 2) identifying stationary and moving limbs, and 3) determining synchronicity of moving limbs. Detailed descriptions and illustrations of each coordinated movement were presented to aid the viewer in categorizing videos into six different movement types. Three reviewers were tasked with categorizing 45 videos of infants achieving a roll using the methodology. Fleiss’ Kappa statistical analysis was used to evaluate inter- and intra-rater reliability. The overall inter-rater reliability score was 0.694 and the overall intra-rater reliability score was 0.801, both classifying as substantial agreement. These results suggest that this methodology can produce consistent results when evaluating infant roll patterns through video.
Ogle, Melissa; Mannen, Erin; and Siegel, Danielle, "Rolling from Home: A Methodology for Classifying Infant Rolling Movements from At-Home Video Analysis" (2023). 2023 Undergraduate Research Showcase. 45.