Quantifying the uncertainty of human activity recognition using a Bayesian machine learning method: a prediction study
Bibliographic record
Abstract
PURPOSE: Machine learning methods accurately predict physical activity outcomes using accelerometer data generated by wearable devices. Thus, they allow investigation of the impact of the built environment on population physical activity. Although traditional machine learning methods do not provide prediction uncertainty, a new method, Bayesian Additive Regression Trees (BART), can quantify such uncertainty as a posterior predictive distribution. Our objective was to evaluate the performance of BART in regard to predicting physical activity status. METHODS: We applied multinomial BART and a benchmark method, random forest, to accelerometer data at 25,424 time points generated by wearable devices worn by 37 participants. We evaluated the prediction accuracy, F1 scores, and confusion matrices using leave-one-person-out cross-validation. RESULTS: BART and random forest demonstrated comparable prediction performances. CONCLUSION: BART is a relatively novel machine learning method that can advance the incorporation of the predicted physical activity status into built environment research. Future research should evaluate the association between the environment and physical activity as predicted by BART.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".