A high sample rate, wireless instrumented wheel for measuring 3D pushrim kinetics of a racing wheelchair
Bibliographic record
Abstract
In wheelchair racing, measuring pushrim kinetics such as propulsion forces and moments is paramount for improving performance and preventing injuries. However, there is currently no instrumented racing wheel that records 3D pushrim kinetics wirelessly and at a high sample rate, which is necessary for accurately analysing wheelchair racing biomechanics. In this work, we present an instrumented wheel that measures 3D kinetics at 2500 Hz. Bidirectional wireless communication is used to interface the wheel through a smart phone. The wheel was tested with a world-class racing athlete who propelled at maximal acceleration and maximal speed on a training roller. During acceleration, the peak total force increased continuously from 186 N to 484 N while the peak tangential force was constant at 171 N ± 15 N. At higher speeds, a counterproductive tangential force was measured during the first 15% and the last 25% of the push phase, peaking at -78 N. This wheel may be of great value for both coaches and athletes to help with planning and validating training programs and adaptations to the wheelchair such as positioning. This wheel also has very high potential for further research on wheelchair racing biomechanics and on preventing shoulder pathologies associated with this sport.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".