Validity and reliability of GAITWell portable modular system for gait analysis
Bibliographic record
Abstract
Gait analysis systems are essential for rehabilitation but are often time-consuming and less accessible in low- and middle-income countries. GAITWell was developed to address these challenges with its portable and modular design for automated gait data collection and analysis. This study evaluates its methodological properties. GAITWell uses discrete binary sensors on interconnected plates to capture gait data, analyzed using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, which identifies key reference points like foot contact and toe-off. DBSCAN detects clusters of arbitrary shapes and sizes, separates noise from data, and identifies natural patterns within the data space without prior group knowledge. Each plate measures 44 cm × 37 cm and has an 11 × 7 sensor array with 4 cm spacing. Test-retest reliability was evaluated using the intraclass correlation coefficient (ICC), standard error of the mean (SEM), and Bland-Altman plots. Concurrent validity was assessed by comparing GAITWell measurements to those from the Qualisys Pro-Reflex system. Results: 38 healthy adults participated (average age 33.2 years, SD 13.0). Correlations between GAITWell and Qualisys ranged from moderate (right step length) to very high (gait speed, cycle time, right and left step time, left step length, stance time, swing time, right and left cadence, and base of support). Moderate to good agreement was found for gait speed, cycle time, stride length, right and left step length, right and left step time, and stance and swing time, but poor agreement was observed for double support times, right and left cadence, and base of support. Preliminary analysis suggests that increasing sensor resolution could reduce measurement error by 70%. Conclusions: GAITWell is a promising tool, with future research focusing on enhancing sensor accuracy for improved reliability.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".