Load Distribution Analysis for Weight and Ballistocardiogram Measurements of Heart Failure Patients using a Bed Scale
Bibliographic record
Abstract
Ballistocardiogram (BCG) is an emerging tool with the potential to monitor heart failure (HF) patients. A close association of the weight to the BCG as an intermediate signal source requires a careful design, where events such as saturation of the weight signal can result in the loss of the BCG. This work closely examined the factors around the weight while load cells placed under each support of a bed collected the BCG (e.g., body weight, distribution over the four supports of the bed). Following the calibration of weights based on the location of the polls, the study examined the ratios of loads in head-foot and lateral directions. The head-foot ratio was also correlated to the height. Twelve non-obese HF patients were recruited, and the weight and BCG were appropriately measured, where the average error of the weight measurements was 0.45 ± 0.30%. The mean ratio of the loads between head to foot sensors was 3.2 ± 0.7 with a maximum ratio of 4.5, showing that the head-ward sensors supported greater body weight. The ratio of the loads between the right to left sensors was 1.2 ± 0.1. The height and the head-to-foot ratio had an inverse correlation (r = 0.52). Based on the analysis, the head-ward sensors should have a higher capacity of up to three times that of the foot-ward sensors to prevent any signal saturation. Mobility issues were observed in some subjects, attributing to the lateral imbalance. These novel findings based on the end-users (i.e., HF population) may allow better allocation of conditioning resources to obtain the BCG (e.g., optimally adjusted sensitivity).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".