Continuous and unconstrained vital signs monitoring with ballistocardiogram sensors in headrest position
Bibliographic record
Abstract
Unobtrusive and long-term monitoring of human vital signs are essential requirements for early diagnosis and prophylaxis due to many reasons, one of the most important being improving the quality of life. Currently, vital signs are continuously monitored through sensors attached to the body, such as multiple electrodes for measuring electrical activity of the heart. Such methods may be undesirable, especially for elderly, infants and other groups of people. In this paper, we introduce an improved technique for measuring heart rate from noisy ballistocardiogram signals acquired from 50 human volunteers in a sitting position using a massage chair. The signals are unobtrusively collected from a microbend fiber optic sensor embedded within the headrest of the chair, and then transmitted to a computer through a Bluetooth connection. The heart rate is computed using the multiresolution analysis of the maximal overlap discrete wavelet transform. The error between the proposed method and the reference ECG is estimated in beats per minute using the mean absolute error, where the system achieved relatively good results (7.31 ± 1.60) despite the large amount of motion artifacts produced owing to the frequent body movements and/or vibrations of the massage chair during stress relief massage. Unlike the complete ensemble empirical mode decomposition algorithm, previously employed for heart rate estimation, the suggested system is much faster. Hence, it can be used in real-time applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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 source (direct Gemma or distilled Codex), 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".