Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Motion capture systems are widely used to quantify human gait. Two-dimensional (2D) video systems are simple to use, easily accessible, and affordable. However, their performance as compared to other systems (i.e. three-dimensional (3D) gait analysis) is not well established. OBJECTIVES: This work provides a comprehensive review of design specifications and performance characteristics (validity and reliability) of two-dimensional motion capture systems. STUDY DESIGN: Systematic review. METHODS: A systematic literature search was conducted in three databases from 1990 to 2019 and identified 30 research articles that met the inclusion/exclusion criteria. RESULTS: Reliability of measurements of two-dimensional video motion capture was found to vary greatly from poor to excellent. Results relating to validity were also highly variable. Comparisons between the studies were challenging due to differences in protocols, instrumentation, parameters assessed, and analyses performed. CONCLUSIONS: Variability in performance could be attributed to study design, gait parameters being measured, and technical aspects. The latter includes camera specifications (i.e. resolution and frame rate), setup (i.e. camera position), and analysis software. Given the variability in performance, additional validation testing may be needed for specific applications involving clinical or research-based assessments, including specific patient populations, gait parameters, mobility tasks, and data collection protocols. CLINICAL RELEVANCE: This review article provides guidance on the application of 2D video gait analysis in a clinical or research setting. While not suitable in all instances, 2D gait analysis has promise in specific applications. Recommendations are provided about the patient populations, gait parameters, mobility tasks, and data collection protocols.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it