Mitigating Thermal Expansion Effects in Silicone-Coated Pelvic Floor Muscle Dynamometer
Bibliographic record
Abstract
Objective: Various types of sensors, such as dynamometers, have been developed to assist in strengthening the pelvic floor muscles, aiming to improve the quality of life for women affected by urinary incontinence. This paper presents silicone encapsulation method for portable vaginal dynamometers that minimizes force measurement drift caused by thermal expansion mismatch between the silicone and the dynamometer housing due to body temperature. Methods: The encapsulation process involves two steps. First, based on the size and shape of the dynamometer, a mold is created to form a cured silicone sleeve slightly larger than the sensor. This sleeve is then slid over the dynamometer. A second silicone layer is subsequently applied over the sleeve and any exposed surfaces of the dynamometer. The dynamometers were tested in air and water at 40 °C to simulate thermal conditions and assess the force measurement drift at body temperature. Results: The proposed method limited the force drift to 0.014 N -a significant reduction compared to the 5 N observed when the silicone was directly applied to the dynamometer surface. This demonstrates the effectiveness of the two-layer encapsulation in mitigating the impact of thermal expansion on the measured force. Significance: This may pave the way to accurate personal pelvic dynamometers for at-home and personalized pelvic muscle training.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
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 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".