Bibliographic record
Abstract
This research represents the first documented investigation into the biomechanics and neural motor control of Paralympic wheelchair curling. A multibody biomechanical model of the wheelchair curling delivery was developed in reference to a Team Canada Paralympic athlete with a spinal cord injury. Subject-specific body segment parameters were quantified via dual-energy x-ray absorptiometry. The angular joint kinematics throughout the wheelchair curling delivery were experimentally measured using an inertial measurement unit system; the translational kinematics of the curling stone were additionally evaluated with optical motion capture. The experimental kinematics were optimized to satisfy the kinematic constraints of the multibody biomechanical model. The optimized kinematics were subsequently used to compute the resultant joint moments through inverse dynamics analysis. The neural motor control of the Paralympic athlete was modeled using forward dynamic optimization. The predicted kinematics from different optimization objective functions were compared with those experimentally measured throughout the wheelchair curling delivery. Of the optimization objective functions under consideration, minimizing angular joint accelerations resulted in the most accurate predictions of the kinematic trajectories and the shortest optimization computation time. The implications of these findings are discussed in relation to musculoskeletal modeling and optimal equipment design through predictive simulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".