Decomposition of three-dimensional ground-reaction forces under both feet during gait.
Bibliographic record
Abstract
Three-dimensional ground reaction forces (3D-GRF) are essential for functional evaluation for rehabilitation. A platform path is required to obtain the 3D-GRF. The main shortcoming of these platform paths is that during double stance phases of gait, both feet can be placed on the same force platform causing the need for decomposing the 3D-GRF under each foot. Despite the high number of studies on force decomposition, there is still no method on the decomposition of 3D-GRF based on data from platforms. OBJECTIVE: This study aims to present an automatic method using parametric curve fitting modeling to increase the accuracy of decomposition of 3D-GRF during double stances under each foot. METHODS: The decomposition method was applied to the global 3D-GRF using 3rd order polynomial, sine, and sine-sigmoid functions. The computed 3D-GRF was compared to the 3D-GRF independently recorded by force platforms for each subject. RESULTS: The relative average error between the computed 3D-GRF and the recorded 3D-GRF were equal to 3.3±1.6%. In details for the vertical, antero-posterior, and medio-lateral GRF, these errors were 2.9±1.6%, 6.3±4.3%, and, 9.5±3.6%, respectively, for 30 subjects. CONCLUSION: The global error on the GRF is the best one in the literature. This method can be validated on various populations with musculoskeletal disorders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".