Effect of Calibration Method on Tekscan Sensor Accuracy
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Bibliographic record
Abstract
Tekscan pressure sensors are used in biomechanics research to measure joint contact loads. While the overall accuracy of these sensors has been reported previously, the effects of different calibration algorithms on sensor accuracy have not been compared. The objectives of this validation study were to determine the most appropriate calibration method supplied in the Tekscan program software and to compare its accuracy to the accuracy obtained with two user-defined calibration protocols. We evaluated the calibration accuracies for test loads within the low range, high range, and full range of the sensor. Our experimental setup used materials representing those found in standard prosthetic joints, i.e., metal against plastic. The Tekscan power calibration was the most accurate of the algorithms provided with the system software, with an overall rms error of 2.7% of the tested sensor range, whereas the linear calibrations resulted in an overall rms error of up to 24% of the tested range. The user-defined ten-point cubic calibration was almost five times more accurate, on average, than the power calibration over the full range, with an overall rms error of 0.6% of the tested range. The user-defined three-point quadratic calibration was almost twice as accurate as the Tekscan power calibration, but was sensitive to the calibration loads used. We recommend that investigators design their own calibration curves not only to improve accuracy but also to understand the range(s) of highest error and to choose the optimal points within the expected sensing range for calibration. Since output and sensor nonlinearity depend on the experimental protocol (sensor type, interface shape and materials, sensor range in use, loading method, etc.), sensor behavior should be investigated for each different application.
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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 it