Evaluating the Effects of Load Area and Sensor Configuration on the Performance of Pressure Sensors at Simulated Body-Device Interfaces
Bibliographic record
Abstract
Force and pressure sensors are used at the body-device interface to monitor fit and inform designers, researchers, and clinicians in various biomedical applications. Commercial sensor manufacturers state that consistent actuation and a rigid surface beneath the sensor is required to ensure repeatable measurement results. However, the body-device interface is subjected to dynamically changing actuation, and the presence of rigid substrates can cause pain and affect the pressure distribution. A benchtop testing protocol was designed to determine the effects of load area and sensor configuration (i.e., elastomer load puck and rigid backing) on two common commercially available thin-film force sensitive resistors. Testing was performed on an apparatus designed to simulate the body-device interface. The effects of two calibration techniques on sensor performance were also examined. The coefficient of variance (CV) was used to evaluate sensor repeatability, where a CV of 10% and under is deemed acceptable for clinical use. All configuration variables (area, puck, backing) were found to significantly affect repeatability of measurements. The results suggest sensors should be used with an elastomer load puck and without a backing to ensure optimal results. One commercial sensor was found to consistently provide a CV of less than 10%. The results indicate a simplified calibration technique can be warranted (i.e., calibration settings approximate, but not match, the experimental settings) if a loading puck is used.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".