Machine Learning Approaches to Predicting Energy Expenditure in Preschool Children: Insights from Accelerometry, Gyroscope Data, and Cross-National Validation
Bibliographic record
Abstract
As highlighted by the World Health Organization, physical inactivity has been recognized as a public health crisis affecting not only adults, but also children and adolescents. To address this alarming trend, it is essential to establish a reliable and robust measure of physical activity (PA) to better understand its underlying determinants. For this purpose, wearable sensors are often used, offering an indirect measure to predict/estimate the energy expenditure (EE) of PA. With the adoption of wearable sensors, numerous researchers are implementing more sophisticated machine learning approaches in their analyses that are better equipped to model complex relationships. The overarching aim of this doctoral research was to develop and refine machine learning models to predict the EE of preschool children. Across four studies, key aspects of the modeling process were explored, including model selection, preprocessing strategies, feature selection, sensor integration, the influence of metabolic equivalent (METs) definitions, and external validation. Two calibration datasets, one consisting of Canadian preschool children and the other of German preschool children, were used to develop and evaluate models using accelerometers, gyroscopes, and portable metabolic units during semi-structured activity protocols. The findings indicated that while deep learning models achieved the lowest error on the training datasets, feature-based models demonstrated superior performance in external validation. Furthermore, preprocessing techniques, specifically frequency-based filtering, and the inclusion of frequency-domain features and participant characteristics (age, sex, height, and weight) contributed to reduced prediction error. When comparing models built using gyroscope data, accelerometer data, and a combination of both, the dual-sensor models consistently outperformed single-sensor models, yielding lower error rates. Finally, after identifying the optimal feature set, the models were applied to a large cohort of Canadian children to generate and compare PA estimates based on different METs definitions. Notably, it was found that measuring the resting period, rather than estimating it using predictive approaches, resulted in higher estimates of sedentary time and lower estimates of overall PA. Collectively, this thesis advances the field of movement behavior research by contributing validated machine learning models for estimating EE in preschool children and addressing key methodological questions relevant to this domain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".