Wearable health monitoring with seismocardiography and generative modeling
Bibliographic record
Abstract
Cardiovascular disease (CVD) remains a leading cause of death and disability worldwide, underscoring the critical need for early detection and continuous monitoring.Wearable technologies have emerged as a promising solution, offering non-invasive, real-time assessment of physiological signals in daily life.Among these, seismocardiography (SCG)-a technique that NIBP, ICG, and PPG, respectively.This result demonstrates the feasibility of using a single motion sensor to estimate rich, multimodal cardiac information, offering a simplified and scalable alternative to traditional multi-sensor systems.Finally, we explore the use of synthetic SCG data to improve cross-domain pulmonary hypertension (PH) detection.We leverage generative models and introduce a dataset selection method to optimize the composition of synthetic and real training data.Our approach improves the out-of-distribution PH detection performance, increasing the area under the ROC curve from 0.51 to 0.86.These results highlight the potential of generative SCG modelling in a clinically relevant scenario with limited training data.Collectively, these contributions advance SCG as a viable and scalable modality for wearable cardiac monitoring.By improving signal robustness, addressing data scarcity, enabling multimodal estimation, and demonstrating clinical relevance, this work expands the role of SCG in both research and real-world applications.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".