Inertial measurement unit-based pose estimation: Analyzing and reducing sensitivity to sensor placement and body measures
Bibliographic record
Abstract
INTRODUCTION: Inertial measurement units have been proposed for automated pose estimation and exercise monitoring in clinical settings. However, many existing methods assume an extensive calibration procedure, which may not be realizable in clinical practice. In this study, an inertial measurement unit-based pose estimation method using extended Kalman filter and kinematic chain modeling is adapted for lower body pose estimation during clinical mobility tests such as the single leg squat, and the sensitivity to parameter calibration is investigated. METHODS: The sensitivity of pose estimation accuracy to each of the kinematic model and sensor placement parameters was analyzed. Sensitivity analysis results suggested that accurate extraction of inertial measurement unit orientation on the body is a key factor in improving the accuracy. Hence, a simple calibration protocol was proposed to reach a better approximation for inertial measurement unit orientation. RESULTS: , without the need for any other calibration. CONCLUSIONS: Only a small subset of kinematic and sensor parameters contribute significantly to pose estimation accuracy when using body worn inertial sensors. A simple calibration procedure identifying the inertial measurement unit orientation on the body can provide good pose estimation performance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".