Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics
Bibliographic record
Abstract
This study aims to validate a commercially available inertial sensor based motion capture system, Xsens MVN BIOMECH using its native protocols, against a camera-based motion capture system for the measurement of joint angular kinematics. Performance was evaluated by comparing waveform similarity using range of motion, mean error and a new formulation of the coefficient of multiple correlation (CMC). Three dimensional joint angles of the lower limbs were determined for ten healthy subjects while they performed three daily activities: level walking, stair ascent, and stair descent. Under all three walking conditions, the Xsens system most accurately determined the flexion/extension joint angle (CMC > 0.96) for all joints. The joint angle measurements associated with the other two joint axes had lower correlation including complex CMC values. The poor correlation in the other two joint axes is most likely due to differences in the anatomical frame definition of limb segments used by the Xsens and Optotrak systems. Implementation of a protocol to align these two systems is necessary when comparing joint angle waveforms measured by the Xsens and other motion capture systems.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".