Dynamically consistent inverse kinematics framework using optimizations for human motion analysis
Bibliographic record
Abstract
Human motion analysis is of crucial importance in countless applications such as human-robot interaction or during the design of assistive devices. Human motion should be estimated as accurately as possible at both kinematics and dynamics levels. Accurately estimating joint trajectories, inter-segmental loads, geometric parameters and body segment inertial parameters specific to each subject will allow most of the indexes used to quantify/analyze human motion to be reconstructed. In this study, a multi-objective optimization problem is formulated to estimate the joint angles, velocities, and accelerations. Moreover two constraint quadratic problems are used to determine geometric parameters and body segment inertial parameters. Contrary to state of the art methods that rely solely on marker data to perform inverse kinematics, the proposed approach relies on force-plate data to obtain dynamically consistent joint trajectories. The proposed approach is evaluated on a squat exercise, performed by 8 subjects, and shows an improved accuracy in joint kinematics and inertial parameter estimation over the classical methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".