Adaptive Gain Regulation of Sensor Fusion Algorithms for Orientation Estimation with Magnetic and Inertial Measurement Units
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Bibliographic record
Abstract
Magnetic and inertial measurement units (MIMUs) proved to be an accurate alternative for optic, electromagnetic, or acoustic measurement technologies. While the orientation of the MIMU could be estimated using accelerometer, gyroscope, or magnetometer sensors alone, many studies proposed sensor fusion algorithms (SFAs) to overcome the drawbacks that appear when each sensor is used individually. However, the performance of such SFAs highly depends on their gains, and poor initialization or incorrect adjustment of the gains would degrade the SFAs’ performance. Therefore, this article proposes a general framework to find the optimal adaptive gain tuning scheme for Kalman filters and complementary filters to achieve accurate and robust orientation estimation with MIMUs. To this end, we proposed an innovative optimization framework to find the fixed optimal gain of an SFA or the optimal adaptive gain regulation scheme. Also, we demonstrated that the designed adaptive gain regulation scheme (a hard switch with two or three levels or a fuzzy inference system) is essential for orientation tracking with various SFAs. We measured the thigh, shank, and foot motion of nine participants while performing various activities using MIMUs and a camera motion-capture system to calculate the MIMUs’ error in 3-D angle estimations. Gain regulation by hard switch was significantly ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p < 0.05$ </tex-math></inline-formula> ) more accurate and robust than it was for innovation adaptive estimation. Also, for all tested SFAs, hard switching for shank and foot MIMUs was significantly more accurate or robust than that for fixed optimal gain. Our experimental results showed that the adaptive gain tuning of SFAs using optimized gains is crucial, regardless of the algorithm structure or complexity.
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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 | 0.000 | 0.000 |
| 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 it