Quantifying the Uncertainty of Human Activity Recognition Using a Bayesian Machine Learning Method: A Prediction Study
Bibliographic record
Abstract
Abstract Background Machine learning methods accurately predict physical activity outcomes using accelerometer data generated by wearable devices, thus allowing the investigation of the impact of built environment on population physical activity. While traditional machine learning methods do not provide prediction uncertainty, a new method, Bayesian Additive Regression Trees (BART) can quantify such uncertainty as posterior predictive distribution. We evaluated the performance of BART in predicting physical activity status. Methods We applied multinomial BART and the benchmark method, random forest, to accelerometer data in 25,424 time points, which were generated by wearable devices attached to 37 participants. We evaluated prediction accuracies and confusion matrix using leave-one-out cross-validation. Results BART and random forest demonstrated comparable accuracies in prediction. Conclusions BART is a relatively novel ML method and will advance the incorporation of predicted physical activity status into built environment research. Future research includes the evaluation of the association between the built environment and predicted physical activity with and without accounting for prediction uncertainty.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".