Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children.

ABSTRACT: Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows comprised of multiple activity types by using shorter windows, including temporal features such as standard deviation in lag and lead windows, and using multiple sensors may improve the classification accuracy under free-living conditions. The objective of this study was to evaluate the accuracy of Random Forest (RF) activity classification models for preschool-aged children trained on free-living accelerometer data. Thirty-one children (mean age = 4.0 ± 0.9 years) completed a 20 min free-play session while wearing an accelerometer on their right hi

SUBMITTER: Ahmadi MN 

PROVIDER: S-EPMC7472058 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature