Heart Rate and Heart Rate Variability Estimation in the Presence of Motion Artifacts
Bibliographic record
Abstract
Vital signs such as heart rate and heart rate variability can be acquired using a variety of equipment, such as ECG, pulse oximeter and even video camera. ECG using wet electrodes is considered the gold standard, but it is not suitable for long-term patient monitoring. Dry electrodes (e.g. a wearable chest-strap) could solve this problem, but the motion of the sensor relative to the skin affects measurements. Non-contact modalities (e.g. heart rate detected from a video of patient's face) could offer further advancements in patient care, but again motion artifacts, caused by changing illumination conditions, affect measurements. Mainstream processing techniques typically assume ideal conditions and fail under realistic conditions. This thesis pinpoints the failure mechanisms of a few commonly used heart rate estimation methods under realistic conditions and proposes mitigation techniques, hoping to contribute to the effort of increasing adoption rate of modern and convenient sensor technologies for patient care.
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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) | 0.000 | 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".