Comparison of Cylindrical Wrapping Geometries to Via Points for Modeling Muscle Paths in the Estimation of Sit-to-Stand Muscle Forces
Bibliographic record
Abstract
To better understand the complexities of rising from a seated to a standing position, a model of the human has been created. Sit-to-stand kinematics as well as ground reaction forces were measured experimentally and are used in an inverse dynamics analysis to estimate nine muscle forces during motion. Calculated muscle forces are sensitive to assumptions made when modeling muscle paths. Changes in the line of action of a muscle due to interaction with anatomical constraints are often accounted for by including fixed via points in a model. Here an alternate approach of representing anatomical constraints using three-dimensional cylindrical geometries is derived and presented. In this mathematical model the course of the muscle is determined as the minimum-length path where the muscle is allowed to wrap freely over the surface of the cylinder. Muscle forces are estimated for sit-to-stand by resolving net joint torques using an objective function giving preference to solutions minimizing both muscle stresses and abrupt changes in muscle forces. This is the first time muscle forces have been presented for sit-to-stand using a musculoskeletal model with included anatomical constraints represented using cylindrical wrapping geometries alone. A comparison of calculated muscle force patterns using fixed via points and wrapping points versus three-dimensional wrapping surfaces is made with reference to electromyographic phase data. For the sit-to-stand motion, the inclusion of anatomical constraints as three-dimensional cylindrical geometries results in calculation of muscle forces more true to the experimental data and more consistent with the belief that gradual motions are created by gradual changes in muscle force over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".