Repeatability of measuring knee flexion angles with wearable inertial sensors
Bibliographic record
Abstract
BACKGROUND: As assessment with inertial-measurement-units (IMUs) increases in research and in clinics, it is important to be aware of the repeatability of these sensors. The objectives of this experiment were to evaluate the measurement repeatability of IMU joint angles using a repeatable robot controller and an anthropomorphic leg phantom and to determine effects of joint speed and sensor positioning on the angles collected by these sensors. Comparisons to an electro-goniometer and three-dimensional (3D) motion capture cameras were also completed. METHODS: Two dual-IMU setups (posterior and lateral) were tested concurrently with an electro-goniometer and 3D motion capture cameras using a repeatable robot controller and a leg phantom. All modalities were attached to the phantom, which was flexed 10 times using a pre-programmed motion pathway during each test. Mean angles were compared across tests. Effects of joint speed, sensor re-positioning, and anatomical placement of the sensors on repeatability were assessed. RESULTS: Re-positioning caused greater deviation to the maximum and minimum angles than differences in speed. Overall, the means ± standard deviations, and 95% confidence intervals of the maximum angles across all tests for the 3D camera markers, electro-goniometer, posterior IMUs, and lateral IMUs were 119.4 ± 0.3° (119.4, 119.5), 112.4 ± 0.5° (112.3, 112.5), 116.2 ± 2.4° (115.7, 116.7), and 118.3 ± 1.1° (118.1, 118.6). CONCLUSIONS: Both posterior and lateral IMU setups demonstrated acceptable repeatability in measurement of range of motion that was advantageous to manual goniometer methods. Posterior and lateral IMU setups demonstrated overlapping standard deviations about their means.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".