Bibliographic record
Abstract
Pressure sensitive mats (PSM) have been widely used in research for measuring contact pressure.In the field of non-obtrusive patient monitoring, PSM have been used in a number of applications including finding the center of pressure for patients in different positions, monitoring heart and breathing rates of a patient laying on a bed, and monitoring and controlling the amount of time in which a patient remains in the same position, thus avoiding pressure ulcers.However, the cost of such devices remains high, and the deployment of PSMs in actual clinical practice is rare.This thesis presents the design of a new pressure sensitive mat technology using infrared proximity sensors and evaluates its performance relative to the state of the art.A flexible printed circuit board with sixteen solid-state infrared proximity sensors is prototyped and interfaced with a development LaunchPad microcontroller board.Graphical user interfaces are developed in MATLAB and as a native Microsoft Windows application to acquire data from the prototype board.A silicone rubber layer comprising multiple cavities is designed and manufactured, such that applied pressure reduces the distance from the cavity's reflective surface to the proximity sensor, thereby transducing pressure into displacement, into optical intensity, and into an electrical signal.The linearity and metrological properties of the implemented prototype are evaluated and compared with existing technologies, where improved performance was observed.The prototype was positioned beneath a subject's back while breathing at a controlled rate and it is demonstrated that the breathing rate can be robustly estimated from the recorded pressure signal.The results suggest that the novel PSM technology can be used in clinical settings I would like to thank my wife, Eli Angela Dalla Valle, for all the support and patience throughout my master's study.Without her unconditional help and support it would not be possible to even start this important step in my career.Special thanks to Dr. James R. Green, who guided me through the program and provided all the important insights for the master's program and also during my stay at Carleton University.The support in our meetings and by email was essential to the advancement of this research.Finally, thank you for considering international students
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".