A framework for the analysis and synthesis of 3D dynamic human gait
Bibliographic record
Abstract
SUMMARY A comprehensive framework for the analysis and synthesis of 3D human gait is presented. The framework consists of a realistic morphological representation of the human body involving 40 degrees of freedom and 17 body segments. Through the analysis of human gait, the joint reaction forces/moments can be estimated and parameters associated with postural stability can be quantified. The synthesis of 3D human gait is a complicated problem due to the synchronisation of a large number of joint variables. Herein, the framework is employed to reconstruct a dynamically balanced gait cycle and develop sets of reference trajectories that can be used for either the assessment of human mobility or the control of mechanical ambulatory systems. The gait cycle is divided into eight postural configurations based on particular gait events. Gait kinematic data is used to provide natural human movements. The balance stability analysis is performed with various ground reference points. The proposed reconstruction of the gait cycle requires two optimisation steps that minimise the error distance between evaluated and desired gait and balance constraints. The first step (quasi-static motion) is used to approximate the postural configurations to a region close to the second optimisation step target while preserving the natural movements of human gait. The second step (dynamic motion) considers a normal speed gait cycle and is solved using the spacetime constraint method and a global optimisation algorithm. An experimental validation of the generated reference trajectories is carried out by comparing the paths followed by 19 optical markers of a motion tracking system with the paths of the corresponding node points on the model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".