Reproduction of In Vivo Motion Using a Parallel Robot
Bibliographic record
Abstract
Although alterations in knee joint loading resulting from injury have been shown to influence the development of osteoarthritis, actual in vivo loading conditions of the joint remain unknown. A method for determining in vivo ligament loads by reproducing joint specific in vivo kinematics using a robotic testing apparatus is described. The in vivo kinematics of the ovine stifle joint during walking were measured with 3D optical motion analysis using markers rigidly affixed to the tibia and femur. An additional independent single degree of freedom measuring device was also used to record a measure of motion. Following sacrifice, the joint was mounted in a robotic/universal force sensor test apparatus and referenced using a coordinate measuring machine. A parallel robot configuration was chosen over the conventional serial manipulator because of its greater accuracy and stiffness. Median normal gait kinematics were applied to the joint and the resulting accuracy compared. The mean error in reproduction as determined by the motion analysis system varied between 0.06 mm and 0.67 mm and 0.07 deg and 0.74 deg for the two individual tests. The mean error measured by the independent device was found to be 0.07 mm and 0.83 mm for the two experiments, respectively. This study demonstrates the ability of this system to reproduce in vivo kinematics of the ovine stifle joint in vitro. The importance of system stiffness is discussed to ensure accurate reproduction of joint motion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".