An Augmented Full-Body Model that Improves Upper Body Tracking and Reduces Dynamic Inconsistency in Complex Motion
Bibliographic record
Abstract
PURPOSE: In recent years, the applications of musculoskeletal simulations have been expanded from simple walking to complex movements in various kinds of sports. The goal of this study was to augment the capability of the currently widely used full-body model (Rajagopal (2016) IEEE Trans. Biomed. Eng. 63:2068-2079) to improve the tracking of the kinematics of the head, shoulder, arms, and torso during complex full-body motion. METHODS: Based on the testing of different modeling choices of neck, shoulder, and torso segments, the original Rajagopal full-body model was augmented by adding three joints in the spine and two sternoclavicular joints. The inverse kinematics and inverse dynamics of sports-related movements from 16 collegiate athletes were compared between the original Rajagopal and augmented full-body model. RESULTS: Our results showed that the augmented full-body model had significant improvements in tracking errors of the markers on the head, arm, torso, and pelvis during inverse kinematics, which led to reduced dynamic inconsistency in inverse dynamics, compared to the Rajagopal model. CONCLUSION: With a significant improvement in tracking the kinematics of the upper body, the augmented full-body model is a more suitable model to perform simulations involving complex full-body movements and is available for research use upon request from simtk.org.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".