Physical Activity Classification in Youth Using Raw Accelerometer Data from the Hip

ABSTRACT

This study developed and evaluated machine learning algorithms to predict children’s physical activity category from raw accelerometer data collected at the hip. Fifty participants (mean age = 13.9 ± 3.0 y) completed 12 activity trials that were categorized into 5 categories: sedentary (SED), light household activities and games (LHHAG), moderate-vigorous games and sports (MVGS), walking (WALK), and running (RUN). Random Forest (RF) and Logistic Regression (LR) classifiers were trained with features extracted from the vector magnitude using 10 s non-overlapping windows. Classification accuracy was evaluated using leave-one-subject-out cross validation. Overall accuracy for the RF and LR classifiers was 95.7% and 94.3%, respectively. Classification accuracy was excellent for SED (96.3% – 98.1%), LHHAG (92.3% – 95.2%), WALK (94.5% – 97.1%), RUN (99.5% – 99.6%); and MVGS (87.5% – 92.7%). The results indicate that classifiers trained on features in the raw acceleration from the hip can be used for activity recognition in young people.

Abbreviations: VM: Vector Magnitude; RF: Random Forest; LR: Logistic Regression; LOSO: Leave-One-Subject-Out

KEYWORDS:

• Physical activity
• objective measurement
• accelerometry
• children
• adolescents

Acknowledgments

We thank the families for their participation and the data collectors.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This study was supported by funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD R01 55400)