OpenOFM: an open-source implementation of the multi-segment Oxford Foot Model
Bibliographic record
Abstract
The Oxford Foot Model (OFM) is a widely-used multi-segment foot model for the evaluation of foot motion. To date, custom code based on the original scientific publications have failed to reproduce results available through the Vicon plug-in (ViconOFM). This highlights a lack of transparency, affecting the accessibility and understanding of the model. Therefore, the aims of this study are to (1) replicate ViconOFM using Python for open-source distribution (openOFM v1.0) and (2) reproduce the original scientific description of the OFM in a second version (openOFM v1.1), highlighting differences between both versions. A dataset comprising one healthy adult and a set of five patients with heterogeneous foot pathologies was used for analyses. Evaluation was conducted using the normalised root mean square error (NRMSE) between the inter-segment angles and arch heights of both implementations. The openOFM v1.1 was developed based on the original OFM publications. The average NRMSE between ViconOFM and openOFM v1.0, using both healthy and pathological gait, was of 0.0012. Based on our openOFM v1.1 implementation, differences between ViconOFM and the original OFM description from the literature are due to an integrated smoothing and gap filling function and changes in segment definitions. The negligible differences between ViconOFM and openOFM v1.0 in healthy and pathological gait supports the concurrent validity of openOFM. Providing users with both openOFM versions enables informed use of either model and allows further investigation into the implications of these differences. The open-source nature of the project promotes further development.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.014 |
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".