Inertial Measures of Motion for Clinical Biomechanics: Comparative Assessment of Accuracy under Controlled Conditions – Changes in Accuracy over Time
Bibliographic record
Abstract
BACKGROUND: Interest in 3D inertial motion tracking devices (AHRS) has been growing rapidly among the biomechanical community. Although the convenience of such tracking devices seems to open a whole new world of possibilities for evaluation in clinical biomechanics, its limitations haven't been extensively documented. The objectives of this study are: 1) to assess the change in absolute and relative accuracy of multiple units of 3 commercially available AHRS over time; and 2) to identify different sources of errors affecting AHRS accuracy and to document how they may affect the measurements over time. METHODS: This study used an instrumented Gimbal table on which AHRS modules were carefully attached and put through a series of velocity-controlled sustained motions including 2 minutes motion trials (2MT) and 12 minutes multiple dynamic phases motion trials (12MDP). Absolute accuracy was assessed by comparison of the AHRS orientation measurements to those of an optical gold standard. Relative accuracy was evaluated using the variation in relative orientation between modules during the trials. FINDINGS: Both absolute and relative accuracy decreased over time during 2MT. 12MDP trials showed a significant decrease in accuracy over multiple phases, but accuracy could be enhanced significantly by resetting the reference point and/or compensating for initial Inertial frame estimation reference for each phase. INTERPRETATION: The variation in AHRS accuracy observed between the different systems and with time can be attributed in part to the dynamic estimation error, but also and foremost, to the ability of AHRS units to locate the same Inertial frame. CONCLUSIONS: Mean accuracies obtained under the Gimbal table sustained conditions of motion suggest that AHRS are promising tools for clinical mobility assessment under constrained conditions of use. However, improvement in magnetic compensation and alignment between AHRS modules are desirable in order for AHRS to reach their full potential in capturing clinical outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".