Optimization of Inertial Sensor-Based Motion Capturing for Magnetically Distorted Field Applications
Bibliographic record
Abstract
Inertial measurement units (IMU) are gaining increasing importance for human motion tracking in a large variety of applications. IMUs consist of gyroscopes, accelerometers, and magnetometers which provide angular rate, acceleration, and magnetic field information, respectively. In scenarios with a permanently distorted magnetic field, orientation estimation algorithms revert to using only angular rate and acceleration information. The result is an increasing drift error of the heading information. This article describes a method to compensate the orientation drift of IMUs using angular rate and acceleration readings in a quaternion-based algorithm. Zero points (ZP) were introduced, which provide additional heading and gyroscope bias information and were combined with bidirectional orientation computation. The necessary frequency of ZPs to achieve an acceptable error level is derived in this article. In a laboratory environment the method and the effect of varying interval length between ZPs was evaluated. Eight subjects were equipped with seven IMUs at trunk, head and upper extremities. They performed a predefined course of box handling for 40 min at different motion speeds and ranges of motion. The orientation estimation was compared to an optical motion tracking system. The resulting mean root mean squared error (RMSE) of all measurements ranged from 1.7 deg to 7.6 deg (roll and pitch) and from 3.5 deg to 15.0 deg (heading) depending on the measured segment, at a mean interval-length of 1.1 min between two ZPs without magnetometer usage. The 95% limits of agreement (LOA) ranged in best case from -2.9 deg to 3.6 deg at the hip roll angle and in worst case from -19.3 deg to 18.9 deg at the forearm heading angle. This study demonstrates that combining ZPs and bidirectional computation can reduce orientation error of IMUs in environments with magnetic field distortion.
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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 | 0.000 | 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) | 0.001 | 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 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".