Inertial Measures of Motion for Clinical Biomechanics: Comparative Assessment of Accuracy under Controlled Conditions - Effect of Velocity
Bibliographic record
Abstract
BACKGROUND: Inertial measurement of motion with Attitude and Heading Reference Systems (AHRS) is emerging as an alternative to 3D motion capture systems in biomechanics. The objectives of this study are: 1) to describe the absolute and relative accuracy of multiple units of commercially available AHRS under various types of motion; and 2) to evaluate the effect of motion velocity on the accuracy of these measurements. METHODS: The criterion validity of accuracy was established under controlled conditions using an instrumented Gimbal table. AHRS modules were carefully attached to the center plate of the Gimbal table and put through experimental static and dynamic conditions. Static and absolute accuracy was assessed by comparing the AHRS orientation measurement to those obtained using an optical gold standard. Relative accuracy was assessed by measuring the variation in relative orientation between modules during trials. FINDINGS: Evaluated AHRS systems demonstrated good absolute static accuracy (mean error < 0.5(o)) and clinically acceptable absolute accuracy under condition of slow motions (mean error between 0.5(o) and 3.1(o)). In slow motions, relative accuracy varied from 2(o) to 7(o) depending on the type of AHRS and the type of rotation. Absolute and relative accuracy were significantly affected (p<0.05) by velocity during sustained motions. The extent of that effect varied across AHRS. INTERPRETATION: Absolute and relative accuracy of AHRS are affected by environmental magnetic perturbations and conditions of motions. Relative accuracy of AHRS is mostly affected by the ability of all modules to locate the same global reference coordinate system at all time. CONCLUSIONS: Existing AHRS systems can be considered for use in clinical biomechanics under constrained conditions of use. While their individual capacity to track absolute motion is relatively consistent, the use of multiple AHRS modules to compute relative motion between rigid bodies needs to be optimized according to the conditions of operation.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".