What are the joint models used in multibody kinematic optimisation for the estimation of human joint kinematics? a review
Bibliographic record
Abstract
Human movement analysis is frequently accomplished through multibody kinematic optimisation which enforcesjoint constraints between adjacent segments. Despite the popularity of the approach, an array of different jointmodels have been applied in the literature which is known to affect the model-derived kinematics. The purpose ofthis study was to perform a literature review to determine the different joint models used when applying multibodykinematic optimisation to the full body, upper and lower limbs.Embase, Medline, Scopus, PubMed, and Web of Science were systematically searched and a total of 66 relevantarticles were identified and included in the review. The main finding was that the upper and lower limb joints were typically modelled as spherical, revolute or universal joints with the purpose of removing non-physiological joint dislocation effects due to soft tissue artefact that would otherwise be predicted with single-body optimisation. A diversity of joint models used in multibody kinematic optimisation was found in the literature. The most frequently used models were the simple, idealised models which, however, have also been criticised for not accurately replicating the detailed joint mechanics. On the other hand, more advanced joint models have emerged, such as parallel mechanisms or coupling equations between the joint degrees of freedom. Nevertheless, no consensus exists on how the joints should be defined to accurately estimate the overall joint kinematics with multibody kinematic optimisation.The review also revealed that the method has also been referred to under other names such as ‘global optimisation’and ‘inverse kinematics’, which can be misleading since these names also have other, and more accepted,meanings. The authors, therefore, recommend that, in the future, the process is termed multibody kinematicoptimisation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".