Systematic Development of a Simple Human Gait Index
Bibliographic record
Abstract
Human gait analysis aims to assess gait mechanics and to identify the deviations from "normal" gait patterns by using meaningful parameters extracted from gait data. As each parameter indicates different gait characteristics, a proper combination of key parameters is required to perform an overall gait assessment. Therefore, in this study, we introduced a simple gait index derived from the most important gait parameters (walking speed, maximum knee flexion angle, stride length, and stance-swing phase ratio) to quantify overall gait quality. We performed a systematic review to select the parameters and analyzed a gait dataset (120 healthy subjects) to develop the index and to determine the healthy range (0.50 - 0.67). To validate the parameter selection and to justify the defined index range, we applied a support vector machine algorithm to classify the dataset based on the selected parameters and achieved a high classification accuracy (∼95%). Also, we explored other published datasets that are in good agreement with the proposed index prediction, reinforcing the reliability and effectiveness of the developed gait index. The gait index can be used as a reference for preliminary assessment of human gait conditions and to quickly identify abnormal gait patterns and possible relation to health issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".