Aetiology of obesity in children

Reinehr, Thomas; Hetherington, Marion M.; Nekitsing, Chandani; McNally, Janet; Shloim, Netalie; Ekelund, Ulf

doi:10.1002/9781118857991.ch5

Cited by 1 publication

(1 citation statement)

References 121 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Physical inactivity is a modifiable risk factor that contributes to the increase in the prevalence of overweight and obesity observed in young children [ 5 , 6 ]. Consequently, public health authorities have identified the preschool years as a critical period to intervene and promote regular physical activity [ 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

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

Ahmadi

Pavey

Trost

2020

Sensors

View full text Add to dashboard Cite

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.

show abstract