The importance of position and path repeatability on force at the knee during six-DOF joint motion
Bibliographic record
Abstract
Mechanical devices, such as robotic manipulators have been designed to measure joint and ligament function because of their ability to position a diarthrodial joint in six degrees-of-freedom with fidelity. However, the precision and performance of these testing devices vary. Therefore, the objective of this study was to determine the effect of systematic errors in position and path repeatability of two high-payload robotic manipulators (Manipulators 1 and 2) on the resultant forces at the knee. Using a porcine knee, the position and path repeatability of these manipulators were determined during passive flexion-extension with a coordinate measuring machine. The position repeatability of Manipulator 1 was 0.3 mm in position and 0.2 degrees in orientation while Manipulator 2 had a better position repeatability of 0.1 mm in position and 0.1 degrees in orientation throughout the range of positions examined. The corresponding variability in the resultant force at the knee for these assigned positions was 32+/-33 N for Manipulator 1 and 4+/-1 N for Manipulator 2. Furthermore, the repeatability of the trajectory of each manipulator while moving between assigned positions (path repeatability) was 0.8 mm for Manipulator 1 while the path repeatability for Manipulator 2 was improved (0.1 mm). These path discrepancies produced variability in the resultant force at the knee of 44+/-24 and 21+/-8 N, respectively, for Manipulators 1 and 2 primarily due to contact between the articular surfaces of the tibia and femur. Therefore, improved position and path repeatability yields lower variability in the resultant forces at the knee. Although position repeatability has been the most common criteria for evaluating biomechanical testing devices, the current study has clearly demonstrated that path repeatability can have an even larger effect on the variability in resultant force at the knee. Consequently, the repeatability of the path followed by the joint throughout its prescribed trajectory is as important as the repeatability of the joint at reaching positions making up its trajectory, particularly when joint contact occurs.
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".