A quaternion-based tilt angle correction method for a hand-held device using an inertial measurement unit
Bibliographic record
Abstract
A gyro-based orientation sensor is prone to orient drift due to an integration step. However, a triaxial accelerometer does not require any integration step to calculate the tilt angles, and the calculated tilt angles do not drift over time. In order to find the tilt angles from a triaxial accelerometer, the sensor should be in an acceleration-free condition. In this paper, an expert system is proposed to identify the stationary state of an inertial measurement unit (IMU). A Kalman filter is designed to reduce the noises of the sensors to make the expert system more reliable. To validate the tilt angle correction method, two different tests are conducted: static and dynamic. When an IMU remains stationary for 30 seconds, almost no angular error is observed: The yaw angle stayed at almost 0deg for 30 seconds, and the roll and pitch angles are derived from the accelerations measured by accelerometers. For the dynamic test, the IMU is moved and then returned to the original orientation. The roll and pitch angles are almost perfectly corrected but the yaw angle exhibits no significant improvement.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".