Design of a six degree of freedom motion simulator for advancing sports bra comfort research
Bibliographic record
Abstract
Evaluating sports bra comfort in an objective manner is challenging because it is difficult to reproduce experiments consistently with human subjects. Using a mannequin with ap propriate breast prostheses and a motion simulator can overcome this limitation. However, commercially available motion simulators are not optimized for reproducing the specific combination of range of movement, speed and high acceleration observed during athletic activity. Achieving this requires a purpose-built motion simulation platform with the right combination of travel range, speed, and dynamic performance. A Stewart’s platform motion simulator is particularly well suited for this task. To meet the specific demands of torso motion simulation, a custom six-degree-of-freedom (6-DOF), rotary-actuated Stewart’s platform was designed. A parametric torso motion trajectory was synthesized from motion capture and inertial measurement unit (IMU) data collected in collaboration with Lululemon and the Human Motion Biomechanics Lab (HuMBL). The system was optimized from the ground up to replicate the most demanding portions of this trajectory using impedance matching in the design phase, and the control system was tuned for maximum dynamic performance. The resulting platform enables repeatable reproduction of high intensity torso motion and provides a cost-effective, high performance solution for objective comfort testing of sports bras.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".