Enhancing Automatic Inertial Sensor Calibration Algorithm for Accurate Joint Angle Estimation in High Flexion Postures
Bibliographic record
Abstract
Inertial measurement units (IMUs) offer an appealing solution to the measurement of joint kinematics across a wide range of settings; however, the interpretation of their data requires complex alignment between these sensors and the anatomical axes of each joint. This study sought to evaluate the Seel joint axis (SJA) algorithm and the proposed iterative Seel spherical axis (ISSA) extension to this algorithm for sensor-to-segment alignment and the estimation of ankle and hip angles in the sagittal plane during high flexion movements eliciting increased soft tissue movement in the thigh and shank which are commonly adopted occupationally. These algorithms were validated in nine such movements as well as in gait across 50 participants. The mean root-mean-squared error (RMSE) between the ISSA algorithm extension and optical protocols were 6.61° ± 2.96° and 14.64° ± 6.73° for the ankle and hip, respectively. The ISSA algorithm-based estimates, however, demonstrated strong correlations with gold-standard optical-based angles for both joints in all high flexion movements except for moderate correlations reported for the ankle when sitting on a stool or child-sized chair (CCS). The proposed extension to the algorithm is recommended when measuring postures, which may elicit high flexion angles and increased soft tissue movement.
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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 | 0.004 |
| 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) | 0.002 | 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".