Post-trial anatomical frame alignment procedure for comparison of 3D joint angle measurement from magnetic/inertial measurement units and camera-based systems
Bibliographic record
Abstract
Magnetic and inertial measurement units (MIMUs) have been widely used as an alternative to traditional camera-based motion capture systems for 3D joint kinematics measurement. Since these sensors do not directly measure position, a pre-trial anatomical calibration, either with the assistance of a special protocol/apparatus or with another motion capture system is required to establish the transformation matrices between the local sensor frame and the anatomical frame (AF) of each body segment on which the sensors are attached. Because the axes of AFs are often used as the rotational axes in the joint angle calculation, any difference in the AF determination will cause discrepancies in the calculated joint angles. Therefore, a direct comparison of joint angles between MIMU systems and camera-based systems is less meaningful because the calculated joint angles contain a systemic error due to the differences in the AF determination. To solve this problem a new post-trial AF alignment procedure is proposed. By correcting the AF misalignments, the joint angle differences caused by the difference in AF determination are eliminated and the remaining discrepancies are mainly from the measurement accuracy of the systems themselves. Lower limb joint angles from 30 walking trials were used to validate the effectiveness of the proposed AF alignment procedure. This technique could serve as a new means for calibrating magnetic/inertial sensor-based motion capture systems and correcting for AF misalignment in scenarios where joint angles are compared directly.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.009 | 0.003 |
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".