Calibration of instrumented treadmills using an instrumented pole; a modified version to use relatively smaller forces
Bibliographic record
Abstract
Abstract The instrumented treadmills’ quality of the generated Ground Reaction Forces (GRF) may degrade over time, as the original calibration matrix may not accurately represent the exerted forces. A cost-effective alternative to manufacturer recalibration is to use an instrumented pole for calibration. Collins et al. presented a simple method to collect multiple data points by exerting forces in various directions. The sensor on the instrumented pole provides instantaneous force magnitudes, while motion capture records the pole’s instantaneous directions. They recommended a relatively large force magnitude (1000N), requiring at least two individuals. Using an optimization method, the new calibration may be estimate by relating the exerted forces (pole) to the treadmill signals. Here, we attempted to simplify the process further, allowing a single individual to perform force exertion with additional force exertion direction. Thus, the calibrating forces were reduced to one-third of the prior recommendation. This maintained the structural integrity of the pole and helped avoid inducing bending moments that could affect calibration results. The cross-validation score for test data (prediction score) was at least 0.92. Additionally, comparing GRFs in posterior/anterior and vertical directions with a benchmark treadmill for even walking revealed an average cross-correlation of 0.97.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".