Estimating Thorax and Shoulder Motion Using Magnetic-Free Quaternion-Based Functional Sensor-To-Segment Calibration
Bibliographic record
Abstract
Wearable rehabilitation robots rely on accurate sensing of body motion. While 9-axis inertial sensors are commonly used to measure motion, signal interpretation can be challenging for upper-limb rehabilitation due to complex and unpredictable joint movements. Other studies have addressed these inaccuracies by attaching three sensor units and using magnetometer data. However, these sensors are often used near actuators that produce ferromagnetic disturbances. Therefore, the objective of this work is to develop a methodology for estimating thorax and shoulder motion using only the orientation data from two sensors, estimated by the internal fusion of accelerometer and gyroscope data. The proposed methodology involves a functional sensor-to-segment calibration that includes performing Principal Component Analysis on the data obtained during specific functional movements to identify the primary axes of rotation. The calibration aligns the sensor coordinate system with the anatomical reference frame of the body segment. Furthermore, the methodology estimates the rotation between the global coordinate systems of the sensor units. Through an experimental evaluation and a comparison with a reference sensor system, the tracking error was $4.07^{\circ}-5.14^{\circ}$ for shoulder orientation and $1.28^{\circ}-3.88^{\circ}$ for thorax orientation. The results provide a solution for tracking thorax and shoulder motion, without using the magnetometer, supporting the development of upper-limb rehabilitation robots.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".