Concurrent validity of the Microsoft Kinect for Windows v2 for measuring spatiotemporal gait parameters
Bibliographic record
Abstract
This paper presents a study to evaluate the concurrent validity of the Microsoft Kinect for Windows v2 for measuring the spatiotemporal parameters of gait. Twenty healthy adults performed several sequences of walks across a GAITRite mat under three different conditions: usual pace, fast pace, and dual task. Each walking sequence was simultaneously captured with two Kinect for Windows v2 and the GAITRite system. An automated algorithm was employed to extract various spatiotemporal features including stance time, step length, step time and gait velocity from the recorded Kinect v2 sequences. Accuracy in terms of reliability, concurrent validity and limits of agreement was examined for each gait feature under different walking conditions. The 95% Bland-Altman limits of agreement were narrow enough for the Kinect v2 to be a valid tool for measuring all reported spatiotemporal parameters of gait in all three conditions. An excellent intraclass correlation coefficient (ICC2, 1) ranging from 0.9 to 0.98 was observed for all gait measures across different walking conditions. The inter trial reliability of all gait parameters were shown to be strong for all walking types (ICC3, 1 > 0.73). The results of this study suggest that the Kinect for Windows v2 has the capacity to measure selected spatiotemporal gait parameters for healthy adults.
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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 | 0.024 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".