Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living
Bibliographic record
Abstract
Abstract Currently, oxygen uptake ("Equation missing") is the most precise means of investigating aerobic fitness and level of physical activity; however, "Equation missing" can only be directly measured in supervised conditions. With the advancement of new wearable sensor technologies and data processing approaches, it is possible to accurately infer work rate and predict "Equation missing" during activities of daily living ( ADL ). The main objective of this study was to develop and verify the methods required to predict and investigate the "Equation missing" dynamics during ADL . The variables derived from the wearable sensors were used to create a "Equation missing" predictor based on a random forest method. The "Equation missing" temporal dynamics were assessed by the mean normalized gain amplitude ( MNG ) obtained from frequency domain analysis. The MNG provides a means to assess aerobic fitness. The predicted "Equation missing" during ADL was strongly correlated ( r = 0.87, P < 0.001) with the measured "Equation missing" and the prediction bias was 0.2 ml·min −1 ·kg −1 . The MNG calculated based on predicted "Equation missing" was strongly correlated ( r = 0.71, P < 0.001) with MNG calculated based on measured "Equation missing" data. This new technology provides an important advance in ambulatory and continuous assessment of aerobic fitness with potential for future applications such as the early detection of deterioration of physical health.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".