Passive Physiological Monitoring via Ambient Sensors Embedded in a Home Environment
Bibliographic record
Abstract
In Canada, heart failure (HF) costs $2.8 billion per year. Self-care or self-management of HF includes activities such as daily weight measurement, medication taking, dietary and fluid restriction, and exercise. Despite its benefits however, the efficacy of self-care is limited due to the high rate of non-adherence caused by age-related symptoms, cognitive factors, and social issues. As such, there is a need for an intelligent solution that shifts the physical and cognitive burden from the patients to the technology. This dissertation presents passive ambient solutions for vital sign monitoring of HF patients that require zero-effort from the user. The first study examined the feasibility of detecting the heart rate of healthy adults using an instrumented floor tile while standing and sitting. The instrumented floor tile was successful in collecting electrocardiogram (ECG) from the feet of healthy seated subjects with 89% agreement with the gold-standard measurement. The heart rate obtained by ballistocardiogram (BCG) while the subject was standing had 1.8 ± 4.3% error. The second study was about an unobtrusive algorithm that determines if the body experienced greater than 20mmHg of change of the systolic blood pressure (SBP) using a feature called RJ-interval, which is the time difference between the peaks of ECG and BCG. The algorithm detected increased SBP with 89.3% true positive rate (TPR) and decreased SBP with 92.3% TPR. The third study expanded the concept of the instrumented tile to a chair and used it to measure BCG of older adults with HF and younger healthy adults. The study showed potential in using the chair to address some of the limitations of using standing form factor by successfully collecting clean BCG of a HF clinical population. As an early stage investigation, the technology presented showed a significant potential to unobtrusively monitor older adults with HF to assist them self-manage their condition better.
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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) | 0.000 | 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) | 0.001 | 0.001 |
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".