Towards Bridging the Gap between Biomechanics and Motor Control for Virtual Ergonomics Applications
Bibliographic record
Abstract
Posture prediction algorithms, particularly those that consider comfort (or discomfort), are typically based on gross movements rather than on the finer movements of the upper extremity. However, understanding these finer movements, particularly during goal-directed reaching tasks, is critical to accurately predicting postures, and the associated potential injury risks, when using virtual ergonomic tools. Furthermore, it is expected that these finer movements will be highly sensitive to the planning processes used to perform sequential tasks, particularly when start and terminal orientation, terminal precision, and terminal force are manipulated. Thus, the purpose of this proposed study is to challenge the theory of the end-state comfort effect under conditions of varying terminal precision and exertion force requirements during both discrete and sequential goal-directed reaching tasks. It is expected that each of these manipulations (i.e. start and terminal orientation, terminal precision, terminal force, and number of movement sequences) will influence the chosen movement patterns. Understanding how each of these variables affects movement patterns will provide important information for the development of posture prediction algorithms for use with virtual ergonomics tools.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".