Comparability of Methods for Remotely Assessing Gait Quality
Bibliographic record
Abstract
Advancements in remote gait analysis technologies enable efficient, cost-effective, and personalized real-time assessments at home. This study aims to contribute evidence as to the comparability of gait quality metrics of three methods of remote gait assessment in individuals with Parkinson’s disease (PD): (1) observation, (2) a wearable sensor, and (3) pose estimation. A cross-sectional, multiple case series study was conducted remotely. Twenty participants submitted videos performing a modified TUG test with the Heel2ToeTM wearable. Each video was analysed by six raters using the checklist specific to PD developed for this study and the MediaPipe Pose Landmarker task estimation library. The observational ratings agreed with the Heel2ToeTM on detecting heel strike 64% of the time and 28.5% of the time on detecting push-off. The difference in the ranks of paired observations based on the Wilcoxon signed rank sum test between the pairs of methods compared was significant for all parameters, except for push-off when estimates from MediaPipe were compared to the ratings from the Observational Checklist, W = 86 (p = 0.498). A combination of digital technologies for remote gait analysis, such as wearable sensors and pose estimation, can detect subtle nuances in gait impairments that may be overlooked by the human eye.
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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 | 0.029 | 0.095 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".