Bibliographic record
Abstract
Electromagnetic (EM) sensors and inertial sensors (gyros and accelerometers) are two types of motion tracking sensors that could be used as a wearable technology to extract real-time localization values of a teleoperator's controlling appendage and translate them into joint coordinates for a robot arm with the same degrees of freedom. The EM sensor measures time-independent position and orientation, while the gyro and accelerometer measure time-dependent rotational velocity and linear acceleration, respectively. This paper proposes a low-computational dynamically weighted discrete sensor fusion algorithm, based on the complementary filter, to fuse the orientation measurements from these two types of sensor by drawing on the advantages of one to compensate for the flaws of the other one. This algorithm uses the inertial sensor's gyroscopic velocity and the EM sensor's orientation and compares each sensor's higherorder derivatives in order to readjust the filter's weight during each iteration. This allows for the gyro to continuously either validate or correct the EM sensor's orientation. Experimental results using controlled rotations of the sensors performed by an industrial robot show that this new filtration concept can return an accurate orientation value by adequately counteracting the EM sensor's errors from field distortions while also avoiding the inertial sensor's temporal drift.
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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 | 0.001 |
| 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".