Low-cost and disposable pressure sensor mat for non-invasive sleep and movement monitoring applications
Bibliographic record
Abstract
Sleep has profound effects on the physical and mental well-being of an individual. The National Institutes of Health (NIH) Sleep Disorder Research Plan gives particular emphasis to non-invasive sleep monitoring methods. Older adults experience sleep fragmentation due to sleep disorders. Unobtrusive non-contact monitoring can be the only realistic solution for long term home-based sleep monitoring. The demand for a low-cost and non-invasive sleep monitoring system for in-home use is more than before due to an increasingly stressful life style. Cost and complexity of current sensor elements hinder the development of low-cost sleep monitoring devices for in-home use. This paper presents the design, development and implementation of a low-cost and disposable pressure sensor mat that could be useful for in-home sleep and movement monitoring applications. The sensor mat design is based on a compressible foam sandwiched between two orthogonal arrays of cPaper capacitance sensors. A low-cost conducting paper has been developed for use as the capacitance sensor electrode. Typical mat design uses a 3 mm thick foam with 5 mm row/column grid array shows that it has a measurement resolution of 0.1 PSI pressure. The resolution can be controlled by both modifying properties of the conducting paper and the foam. Since this pressure mat design is based on low-cost paper, the sensor electrodes are disposable or semi-durable and hence it is ideal for the use in point-of-care physiological monitoring, pervasive healthcare and consumer electronic devices.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".