Analysis of Contact Pressure for Monitoring Body Fluid Distribution
Bibliographic record
Abstract
Fluid distribution is a key indicator for a variety of diseases that become more prevalent as we age, such as congestive heart failure (CHF) and nocturia.Current methods to monitor fluid distribution rely on infrequent and often self-report measures, which are unreliab le.This thesis aims to develop and evaluate a continuous, unobtrusive, and non-invas ive alternative method based on the use of pressure sensitive mats (PSM) to accurately measure fluid distribution within a patient.We here detail the design of an algorithmic system to monitor changes in fluid distribution over time.A series of four increasingly complex experiments were performed using PSMs.The first three experiments established that PSMs are sufficiently sensitive to detect small variations in mass typical of fluid retention observed in CHF patients, where fluid pools in the ankles while the patient is vertical and redistributes to the bladder over night while horizontal.The final experiment and corresponding algorithm aimed to differentiate when an individual exits the bed to: (1) do nothing, (2) void their bladder, or (3) drink as much water as was comfortable.A method to automatically segment a person's pressure image into regions of interest (ROI) was developed.Based on the results from a single participant in three different postures (prone, supine, and side), the system was able to identify bed exits associated with large voids and drinking events (p < 0.05).Small voids were only reliably detectable in the prone positon (p < 0.01).These results suggest that this system is effective at distinguishing between significant fluid intake and fluid output events, and emphasizes the potential for novel non-invasive fluid distribution monitoring systems to improve traditional healthcare approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".