Data and code from: Healthy young adults use distinct gait strategies to enhance stability when walking on mild slopes and when altering arm swing
Bibliographic record
Abstract
This repository contains the Julia code, Jupyter notebook, and data used in the study “Healthy young adults use distinct gait strategies to enhance stability when walking on mild slopes and when altering arm swing” by MacDonald et al. Instructions To run this analysis on your computer, both Julia and Jupyter Notebook must be installed. A version of Julia appropriate for your OS can be downloaded from the Julia website, and Jupyter can be installed from within Julia (in the REPL) with ] add IJulia Alternate instructions for installing Jupyter can be found on the IJulia github or the Jupyter homepage (not recommended). From within the main repository directory, start Julia and then start Jupyter in the Julia REPL using IJulia notebook(;dir=pwd()) or if using a system Jupyter installation, start Jupyter from your favorite available shell (e.g. Powershell on Windows, bash on any *nix variant, etc.). In Jupyter, open the notebooks/analysis.ipynb notebook. Running all cells will reproduce the results for this paper. Description of data The data directory contains all the data used in the production of the results which were statistically tested. Each .mat file contains events and data generated in Visual3D: Events LTO/RTO (Left/right toe-off) LHS/RHS (Left/right heel-strike) HIST/HIEN (Hilly start/end) ROST/ROEN (Rocky start/end) MLST/MLEN (ML translation start/end) Data LFootPos/RFootPos (Left/right foot COM position) TrunkPos/TrunkVel/TrunkAcc (Trunk COM position, velocity, and acceleration) HeadPos/HeadVel/HeadAcc (Head COM position, velocity, and acceleration) COG (Whole-body COM/COG) The .csv files contain system state of the CAREN system produced by D-Flow software, which includes various system and software settings, most pertinent of which is the treadmill speed. The .c3d files contain the raw motion capture data from Vicon Nexus. The results of the notebooks/analysis.ipynb notebook are found in the results folder. Please see the paper for a list of the dependent variables and statistical analyses.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.405 | 0.303 |
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".