Validity, reliability, and user perspectives of the newly developed joint angle measurement system with inertial measurement unit sensors
Bibliographic record
Abstract
Abstract Purpose We aimed to evaluate the applicability of the newly developed joint angle measurement system consisting of six-axis inertial measurement unit sensors and tablet-based application that measure and store angular velocity and acceleration data for estimating joint angles. Materials and Methods The tablet-based application was used to calculate the orientation angles from angular velocity and acceleration data measured using a single sensor. The relative angles were calculated using the data from multiple sensors. In experiment 1, the validity and reliability of calculated angles was examined using a test device. In experiment 2, the angles of five joints were calculated in four healthy participants using attached inertial measurement unit sensors; the angles were compared with universal goniometer-measured values. In experiment 3, usability and satisfaction were evaluated with the System Usability Scale (SUS) and a Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST)-like scale. Results In experiment 1, the mean difference of the time-series data between those obtained by the developed system and test device was ˂0.2° for all axes. In experiment 2, the mean difference of the integrated data was 0.2°. The mean difference for all joints was ˂5°, indicating that the measurement system is comparable to the universal goniometer. In experiment 3, the median SUS and QUEST-like scale scores were 81 and 4.0, respectively, indicating high usability and satisfaction. Conclusion The newly developed joint angle measurement system has high accuracy in measuring angles and sufficient validity in application to human joint angles, with high usability and user satisfaction.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".