From Theory to practice: monitoring mechanical power output dur-ing wheelchair field and court sports using inertial measurement units
Bibliographic record
Abstract
An important performance determinant in wheelchair sports is the power exchanged between the athlete-wheelchair combination and the environment, in short, mechanical power. To monitor the mechanical power during wheelchair sports practice, inertial measurement units (IMUs) might be used. However, a well-founded and unambiguous theoretical framework that follows the dynamics of manual wheelchair propulsion is required to validly apply IMUs for mechanical power assessment in wheelchair sports. Such a framework does not yet exist. Therefore, this research has two goals. First, to present a theoretical framework that supports the use of IMUs to estimate power output via power balance equations. Second, to create a set of guidelines on how to use IMUs to monitor mechanical power during wheelchair propulsion supported by experimental data. After verifying the theoretical framework, an IMU model was defined. Subsequently, the validity of the IMU model and underlying assumptions was determined. Therefore, power was estimated from IMU data during wheelchair propulsion and was subsequently compared to gold standard optical motion capture data. Data was collected from eleven participants without wheelchair experience propelled an all-court sports wheelchair on a large treadmill. At the same time, kinematics were measured using two IMUs and an optical motion capture system. The results reveal that, with a proper drag or deceleration test, one IMU on the wheelchair frame and one IMU on the wheel axis, decent power estimations can be obtained in daily wheelchair (sports) practice. To conclude, the theoretical framework and the resulting IMU-based power is thus well suitable to estimate mechanical power during straight-line wheelchair propulsion in wheelchair court sports and daily wheelchair practice, and it is an important first step towards feasible power estimations in all wheelchair (sports) situations.
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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 | 0.022 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".