Validity and Reliability of Inertial Motion Unit-Based Performance Metrics During Wheelchair Racing Propulsion
Bibliographic record
Abstract
This study aims to evaluate the concurrent validity and test-retest reliability of wheelchair racing performance metrics. Thirteen individuals without disabilities and experience in wheelchair racing were evaluated twice while performing maximal efforts on a racing wheelchair. Three wheelchair athletes were also assessed to compare their performance with novice participants. The wheelchair kinematics was estimated using an inertial motion unit (IMU) positioned on the frame and a light detection and ranging (Lidar) system. The propulsion cycle (PC) duration, acceleration, average speed, speed gains during acceleration, and speed loss during deceleration were estimated for the first PC and stable PCs. The test-retest reliability was generally moderate (0.50 ≤ ICC < 0.75) to good (0.75 ≤ ICC < 0.90), while few metrics showed poor reliability (ICC < 0.50). High to very high correlations were obtained between both systems for 10 out of 11 metrics (0.78-0.99). Wheelchair athletes performed better than novice participants. Our results suggest that integrated accelerometer data could be used to assess wheelchair speed characteristics over a short distance with a known passage time. Such fine-grain analyses using methods usable in the field could allow for data-informed training in novice and elite wheelchair racing athletes.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".