The test–retest reliability of an IMU-based motion capture system for total body angular momentum range, medial–lateral margin of stability, and step width: Effects of walkway length and number of consecutive strides
Bibliographic record
Abstract
Establishing the test–retest reliability of a motion capture system is important to support repeated walking balance assessments and to discern the relevance of significant changes to measured outcomes. Reliability of walking outcomes could also be impacted by the number of consecutive strides analysed. The primary objective of this study was to evaluate the test–retest reliability of the XSens Awinda inertial sensor-based system for total body angular momentum range, margin of stability, and step width. Twenty-eight young adults (24 ± 4 years) completed two data collections >48 h apart, consisting of 10 walking trials in a laboratory (8 m) and a hallway (20 m). Total body kinematic data from 17 sensors were used to calculate ranges of total body angular momentum (H), medial–lateral margin of stability (MOS ML ), and step width (SW). Intra-class correlations (ICC ± 95 % confidence intervals) and minimum detectable change values at a 95 % confidence level (MDC 95 ) were calculated using different stride counts. ICCs indicated moderate to excellent reliability for ranges of H, MOS ML , and SW. MDC 95 levels were small for ranges of H and large for MOS ML and SW. ICCs were greater in the laboratory for MOS ML and similar between the laboratory and hallway for ranges of H and SW. MDC 95 indicated better reliability for the hallway suggesting that settings with a longer walkway may be ideal for reliable outcomes. The greatest improvements to reliability occurred within the first 10 consecutive strides, indicating at least 10 consecutive strides across multiple trials are recommended for improved reliability.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".