Bibliographic record
Abstract
Advances in Internet connectivity and personal multimedia computing have created opportunities for integrating simple motion analysis into clinical practice. The Macromedia Shockwave environment provides tools for creating media-rich software that runs within a Web browser. For this project, clinical motion analysis software was created using Shockwave that can load digital video clips of a client's motion, step/shuttle/play through the clip, superimpose a grid over the video image, measure relative joint angles, scale to a linear factor, measure distances, and measure average velocities. After installing the Shockwave and Quicktime video plug-ins, the Motion Analysis Tools-Shockwave program runs directly from a Web page hyperlink. Program testing involved comparing angle measurements, linear distances, stride length, and walking speed among six video clips. The first three clips were of a transtibial prosthesis being carried through the field of view (640 x 480, 320 x 240, 320 x 240 enlarged to 640 x 480). The second set of three clips was of a metal square carried through the field of view. Average root mean square errors were 2.0 degrees for angle measures and 1.2 cm for length measures. Stride length standard deviation was 4.6 cm (mean length = 212.1 cm). Average walking speed standard deviation was 0.015 m/s (mean speed = 1.15 m/s). The test results were consistent with video motion analysis results and within an acceptable range for clinical design-making. This Web-based motion analysis approach provides a useful tool for ubiquitous, quantitative, clinical gait analysis.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".