Cuffless Blood Pressure Estimation for Activities of Daily Living
Why this work is in the frame
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Bibliographic record
Abstract
This work presents a modelling approach to predict the blood pressure (BP) waveform time series during activities of daily living without the use of a traditional pressure cuff. A nonlinear autoregressive model with exogenous inputs (NARX) is implemented using artificial neural networks and trained to predict the BP waveform time series from electrocardiography (ECG) and forehead photoplethysmography (PPG) input signals. To broaden the range of blood pressures present in the training set, a protocol was implemented that included sitting, standing, walking, Valsalva manoeuvers, and static handgrip exercise. A five-minute interval of data in the sitting position at the end of the day was also used for training. The efficacy of the cuffless BP method for continuous BP estimation over 4.67 hours was evaluated on 3 participants for varying training data segments. A mean absolute error of 6.3 and 5.2 mmHg were achieved for systolic BP and diastolic BP estimates, respectively. Including static handgrips and Valsalva manoeuvers in the training dataset leads to better estimation of the higher ranges of BP observed throughout the day. The proposed method shows potential for estimating the range of BP experienced during activities of daily living.Clinical Relevance- Establishes a method for cuffless continuous blood pressure estimation during activities of daily living that can be used for continuous monitoring and acute hypertension detection.
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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) | 0.000 | 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 it