Using Peak Vicon data to drive Classic JACK animation for the comparison of low back loads experienced during para-rowing
Bibliographic record
Abstract
The use of kinematic data collected from subjects in a Vicon laboratory can be used to generate human motion within the Classic JACK simulation environment. This feature is primarily used in the ergonomic design of equipment that the human must interact with. In this case, the research was evaluating the estimated low-back joint force while rowing in one of three para-rowing setups. This paper describes the method by which Vicon data were recorded, smoothed, imported and applied to the JACK manikin to produce realistic rowing animations. It also describes how the kinetic information from a load cell placed in-line with the ergometer chain was recorded, conditioned and applied to the virtual hands of the JACK manikin to improve the force estimates of the lower back. The low back compressive forces estimated by the Classic JACK program are compared to the NIOSH occupational limits as a point of reference. Findings suggest that males, but not females, rowing in the trunk and arms category were significantly above the NIOSH action limit. Females rowing in the arm and shoulder category were significantly below the NIOSH action limit. Future work will evaluate how design features of the para-rowing setup might be altered to reduce joint loading.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".