The Trade-Off Between Torque and Power with Speed: A Study of Shoulder Performance During an Isokinetic and Multiplanar Task
Bibliographic record
Abstract
Abstract The human shoulder likely evolved under selective pressures favouring diverse tasks that require high mobility, speed, and torque. For example, humans are uniquely adept at high-speed and accurate throwing. Prior work has aimed to quantify the kinematics and kinetics of upper limb movements in isometric or uniplanar motions. However, we still do not fully understand the trade-offs of shoulder torque and power with angular velocity during functional tasks that are reflective of demands that may be relevant to the shoulder’s evolution. We developed a novel approach for upper limb 3D inverse dynamic calculations by integrating motion capture with an instrumented cable machine. Twenty-five participants performed a crossbody, isokinetic upper limb motion at various cable speeds in a rigid and free torso condition (self-imposed). Shoulder torque decreased significantly (p < 0.05) with increasing angular velocity in 19 and 16 participants for the constrained and unconstrained conditions, respectively. Shoulder power increased significantly (p < 0.05) with angular velocity for 6 and 11 participants for constrained and unconstrained, respectively. T-tests revealed no statistical difference between the torso conditions for torque and power against angular velocity. Our findings suggest that despite having a trade-off in torque and velocity, the shoulder may be tuned to produce power over a wide range of velocities independent of energy transfer from the lower extremities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".