Development of a video-based technique for ambulatory monitoring of foot placement with an instrumented rollator
Bibliographic record
Abstract
OBJECTIVE: To develop a video-based system, mounted on a rollator to quantify the step width values of rollator users in the community. SUBJECTS: A total of 5 able-bodied young adults, age range 24-28 years. METHODS: A digital video camera system was mounted on the rollator frame to capture the position of the participant's feet during overground walking. A method of estimating step width from the video data was developed and evaluated against the output from a concurrently recording Vicon MX motion capture system. RESULTS: Mean step widths of the rollator and motion capture systems were 14.40 cm (standard deviation (SD) 4.64) and 14.37 cm (SD 4.34), respectively, revealing a strong level of agreement; intra-class correlation coefficient 0.999 (95% confidence interval (95% CI) 0.987-1.000; and root-mean-square difference 0.70 cm. CONCLUSION: The video-based system mounted on a rollator to collect foot placement data enabled accurate measurement of step width during rollator use. The ability to record foot placement measurements outside the laboratory setting, characterizing foot placement patterns occurring in the community, will enable research into how these assistive devices influence mobility during everyday use.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".