Minimum-Order Kalman Filter With Vector Selector for Accurate Estimation of Human Body Orientation
Bibliographic record
Abstract
This paper describes a new quaternion-based Kalman filter (KF) for estimating human body orientation using an inertial/magnetic sensor. The proposed algorithm is comprised of a quaternion measurement step and a KF step that are connected in feedback relationship. This allows the algorithm to have a minimum-order structure (i.e., fourth order) that is computationally very efficient. Furthermore, to offer more reliable information to the quaternion measurement step, a vector selector scheme is adopted, which effectively adds the gyro measurement to the so-called Wahba's problem that conventionally uses only the accelerometer and magnetometer measurements. This protects the algorithm against undesirable conditions such as fast movements and temporary magnetic disturbances, enabling it to compute an accurate orientation estimate. Due to the computational efficiency of the algorithm, it is suitable for real-time ambulatory human motion tracking applications that require multiple and untethered inertial/magnetic sensors with low-cost onboard processing.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".