Alignment-free, self-calibrating elbow angles measurement using inertial sensors
Bibliographic record
Abstract
Due to their relative ease of handling and low-cost, inertial measurement unit (IMU) based joint angle measurements are used for a widespread range of applications. These include sports performance, gait analysis and rehabilitation (e.g. Parkinson's disease monitoring or post-stroke assessment). However, a major downside of current algorithms recomposing human kinematics from IMU data is that they require calibration motions and/or the careful alignment of the IMUs respective to their body segment. In this article, we propose a new method, which is alignment free and self-calibrated using the arbitrary movements of the user and an initial zero reference arm pose. The proposed method utilizes real time optimization to identify the two dominant axes of rotation of the elbow joint. Using a two degree of freedom joint mimicking the human elbow, the performance of the algorithm was assessed by comparing the angles obtained from two IMUs to the ones obtained from a marker-based optical tracking system. The self-calibration proved to converge within seconds and the RMS errors with respect to the optical reference system were below 5°. Our method can be particularly useful in the field of telerehabilitation, where precise manual sensor to segment alignment as well as precise, predefined calibration movements are impractical.
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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 | 0.001 |
| Research integrity | 0.001 | 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".