Decomposition of the vertical ground reaction forces during gait on a single force plate.
Bibliographic record
Abstract
UNLABELLED: Davis and Cavanagh (1993) have proposed a solution to avoid the footstep targeting by using a large force plate but several points of Davis and Cavanagh's method remain unclear and hardly computable. OBJECTIVE: to develop a method that decomposes left and right GRF profiles from the GRF profile recorded on a single platform. This method aims to include a systematic detection of the single to double stand-phase-instants in order to lead to accurate measurement of the vertical GRF component in typically developing children. METHODS: Six children were asked to walk without targeting their footsteps on a set-up composed of independent force platforms. The vertical GRF component, independently measured on the different platforms, was numerically summed to obtain the corresponding global vertical GRF, to which the decomposition method was applied. Then, the validation consisted in comparing the vertical GRF computed from this decomposition to the independently measured vertical GRF. RESULTS: the mean relative error between the computed vertical GRF and the corresponding measured vertical GRF of 36 double stances (6 double stances x 6 children) is equal to 3.8±2.6%. CONCLUSION: implemented a new method to assess with known accuracy the vertical GRF component under each foot using a unique large force platform.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".