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 hip and non-dominant wrist. Video-based direct observation was used to categorize the children's movement behaviors into five activity classes. The models were trained using prediction windows of 1, 5, 10, and 15 s, with and without temporal features. The models were evaluated using leave-one-subject-out-cross-validation. The F-scores improved as the window size increased from 1 to 15 s (62.6%-86.4%), with only minimal improvements beyond the 10 s windows. The inclusion of temporal features increased the accuracy, mainly for the wrist classification models, by an average of 6.2 percentage points. The hip and combined hip and wrist classification models provided comparable accuracy; however, both the models outperformed the models trained on wrist data by 7.9 to 8.2 percentage points. RF activity classification models trained with free-living accelerometer data provide accurate recognition of young children's movement behaviors under real-world conditions.

Keywords: accelerometer; assessment; classification; early childhood; measurement; physical activity; supervised learning.

Figure 1

Interaction plots summarizing the effect of window size and feature set on the adjusted F-scores for models trained on wrist, hip, and combined hip and wrist accelerometer data. + Denotes significantly different from the base model at a given window size p < 0.05; * Denotes significantly different from the previous window size for a given feature set p < 0.05.

Figure 2

Confusion matrices for physical activity classification from the wrist, hip, and combined hip and wrist placement for lag/lead 10 and 15 s window models. The columns represent observed; rows represent predictions; bold represents correct predictions; SED = sedentary; LIGHT_AG = light physical activity and games; MV_AG = moderate to vigorous physical activity and games; WALK = walking; RUN = running.