Social and Behavioral Sciences Commons^™

Prediction Of Activity Type In Preschool Children Using Machine Learning Techniques, M. Hagenbuchner, Dylan P. Cliff, Stewart Trost, Van Tuc Nguyen, Gregory E. Peoples

Faculty of Science, Medicine and Health - Papers: part A

Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3-6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised …