Evaluation of a Gait Assessment Module Using 3D Motion Capture Technology.
Bibliographic record
Abstract
BACKGROUND: Gait analysis is the study of human locomotion. In massage therapy, this observation is part of an assessment process that informs treatment planning. Massage therapy students must apply the theory of gait assessment to simulated patients. At Humber College, the gait assessment module traditionally consists of a textbook reading and a three-hour, in-class session in which students perform gait assessment on each other. In 2015, Humber College acquired a three-dimensional motion capture system. PURPOSE: The purpose was to evaluate the use of 3D motion capture in a gait assessment module compared to the traditional gait assessment module. PARTICIPANTS: Semester 2 massage therapy students who were enrolled in Massage Theory 2 (n = 38). RESEARCH DESIGN: Quasi-experimental, wait-list comparison study. INTERVENTION: The intervention group participated in an in-class session with a Qualisys motion capture system. MAIN OUTCOME MEASURES: The outcomes included knowledge and application of gait assessment theory as measured by quizzes, and students' satisfaction as measured through a questionnaire. RESULTS: = .13). The intervention group enjoyed the in-class session because they could visualize the content, whereas the comparison group enjoyed the interactivity of the session. The intervention group recommended adding the assessment of gait on their classmates to their experience. Both groups noted more time was needed for the gait assessment module. CONCLUSIONS: Based on the results of this study, it is recommended that the gait assessment module combine both the traditional in-class session and the 3D motion capture system.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".