Abstract
The purpose of this study was to determine the feasibility and validity of using three-dimensional (3D) video data and computer vision to estimate physical activity intensities in young children. Families with children (2–5-years-old) were invited to participate in semi-structured 20-minute play sessions that included a range of indoor play activities. During the play session, children’s physical activity (PA) was recorded using a 3D camera. PA video data were analyzed via direct observation, and 3D PA video data were processed and converted into triaxial PA accelerations using computer vision. PA video data from children (n = 10) were analyzed using direct observation as the ground truth, and the Receiver Operating Characteristic Area Under the Curve (AUC) was calculated in order to determine the classification accuracy of a Classification and Regression Tree (CART) algorithm for estimating PA intensity from video data. A CART algorithm accurately estimated the proportion of time that children spent sedentary (AUC = 0.89) in light PA (AUC = 0.87) and moderate-vigorous PA (AUC = 0.92) during the play session, and there were no significant differences (p > 0.05) between the directly observed and CART-determined proportions of time spent in each activity intensity. A computer vision algorithm and 3D camera can be used to estimate the proportion of time that children spend in all activity intensities indoors.
Highlights
The health-enhancing benefits of physical activity (PA) in early childhood have been widely reported [1,2] and evidence suggests that social and contextual factors, such as proximity to others, may influence PA behaviors in young children [3,4,5]
Children for whom engaging in moderate-vigorous physical activity would present any concerns for safety due to existing medical conditions were excluded from participating in the study
Paralleling prior wearable sensor studies that have investigated physical activity intensity classification in resistance training paradigms [28], we found that when young children were pushing, or walking while carrying heavier objects in the play space, that these episode were mislabeled in our study as light physical activity (LPA) rather than being correctly classified as moderate-vigorous physical activity (MVPA) [20]
Summary
The health-enhancing benefits of physical activity (PA) in early childhood (ages 2–5-years-old) have been widely reported [1,2] and evidence suggests that social and contextual factors, such as proximity to others, may influence PA behaviors in young children [3,4,5]. As an alternative method for dynamically measuring PA and social-contextual signals, studies have shown that video data can be processed using computer vision algorithms to extract information about physical activity behaviors within a given context [6]. While a small number of studies have used custom computer vision algorithms to convert video-recorded PA behaviors into quantifiable PA signals [6,8,9,10,11], no study has validated such a method for estimating PA volumes and intensities in young children from these signals.
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