A Magnetometer Calibration Method Using Single-Axis Motion Trajectory and Unscented Kalman Filter for Body Motion Capture Applications
Bibliographic record
Abstract
Magnetometer calibration is a pre-processing step in the Attitude and Heading Reference Systems (AHRS) which has an essential role in many applications. The main purpose of this article is to derive an innovative and precise calibration approach for a magnetometer set installed on human body. To execute this calibration method, all the error parameters of multiple magnetometers are considered in an Unscented Kalman Filter (UKF) model for accurate estimation of calibration parameters. As achieving a precise estimation in Kalman filters-based algorithms needs an accurate and complete observation model, a special single-axis rotation trajectory for Inertial Measurement Unit (IMU) is performed to increase the observability rank of the calibration model. To evaluate the proposed method, five body-mounted sensors were experimented in the laboratory at the same time for applying in the body motion capture system. The results showed that all five sensors were well-calibrated without any need to be detached from the body and using any rotational robot arm. The resolution and precision of the proposed calibration method are assessed by the ellipsoid-fitting representation method. Consequently, all the body-mounted magnetometers were calibrated, on average, by about 1% uncertainty. The method can be used in every motion capture and AHRS applications due to its feasibility and simplicity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".