Bibliographic record
Abstract
As ambient systems proliferate, there is an increased need for data-level fusion methods that reflect the challenges of unknown environments, non-deterministic signal behaviours, and movement artifact.Cushioning between the sensor and bed occupant decreases signal to noise power and signal availability, while non-linear signals can masquerade as delayed and reversed signals.The main contributions of this thesis are to study how these challenges affect extraction of a breathing signal from bed-based sensors and to propose more robust fusion techniques.New trend analysis methods effectively corrected polarity reversals, increasing the number of good quality signals by 9% and reducing mean respiratory rate error by 24%.To fuse these signals, selection combining, weighted summation, and blind source separation methods were innovated and compared.None performed best all of the time; some were generally good with some weaknesses, while others had specialized strengths.Contextual ensemble fusion selected the best fusion method in degraded conditions in 55% of records, compared to 36% for the top individual fusion method, providing clinical applications with more reliable data.While sleep medicine is an important application, ambient monitoring is also suited to cognitive medicine and palliative care.Developed methods were applied to monitor patients in palliative care, marking the first long-term, continuous monitoring of this population.Breathing patterns observed in the last weeks of life included Cheyne-Stokes respiration and tachypnea, while breathing variability was associated with survival time.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".