Predicting the near-future impact of daily activities on heart rate for at-risk populations
Bibliographic record
Abstract
In this paper we demonstrate the ability to predict changes to heart rate due to changes in levels of activity, up to an hour into the future. Activity levels are calculated from data collected by a worn accelerometer for a person performing daily activities outside a laboratory environment. People with congestive heart failure must take care not to excessively stress their heart. This can be a challenge due to the difficulty of predicting how much stress an activity is exerting on the heart. We propose to model the relationship between motion and heart rate and thus to enable the prediction of heart rate changes prior to performing an activity. We explored three methods to predict current and future heart rate from activity level: a continuous state Kalman Filter, two simple linear models, and a nonlinear model given in the literature [5]. The results from healthy subjects and subjects with congestive heart failure show that using the proposed models, the heart rate can be predicted an hour into the future using accelerometer data.
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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) | 0.000 | 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) | 0.001 | 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 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".