Assessing the Validity of Attitude and Heading Reference Systems for Biomechanical Evaluation of Motions - A Methodological Proposal
Bibliographic record
Abstract
Background: Attitude and Heading Reference Systems’ (AHRS) popularity in biomechanics has been growing rapidly over the past few years. However, the limits of operation and performances of such systems for motion capture are highly dependent upon their conditions of use and the environment they operate in. The objectives of this paper are to: (1) propose a methodology for the characterization of the criterion of validity of accuracy of AHRS in a human biomechanical context; and (2) suggest a set of outcome measures to assess the accuracy of AHRS. Methods: The criterion validity of accuracy is established using an optical motion tracking gold standard under standardized human motions. Results: Global assessment of accuracy is derived by comparing the orientation data provided by the AHRS to those given by the gold standard using a coefficient of multiple correlation. Peak values and RMS difference between both sets of orientation data are also analysed to complete the accuracy portrait. The methodology proposed herein is verified for the knee during regular walk. Conclusion: The proposed methodology and analyses take into consideration the complexities and processes required to assess the accuracy of AHRS in their context of use and provide a standardized approach to report.
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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 | 0.130 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".