Validity, reliability, and user perspectives of the newly developed joint angle measurement system: a preliminary study
Bibliographic record
Abstract
We aimed to evaluate the applicability of a newly developed joint angle measurement system comprising six-axis inertial measurement unit sensors and tablet-based application for estimating joint angles from angular velocity and acceleration data. The application calculated orientation angles from single sensor data, with relative angles calculated using multiple sensors. In experiment 1, validity and reliability were examined using a test device. In experiment 2, static angles of five joints were calculated in four healthy participants using attached sensors and compared with universal goniometer values. In experiment 3, usability and satisfaction were evaluated using the System Usability Scale (SUS) and Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST)-like scale. In experiment 1, mean difference and root mean squared error (RMSE) between the developed system and test device were < 0.2° and < 1.0°, respectively, across all axes. In experiment 2, when data from all joints were pooled, mean difference and RMSE were 0.2° and 3.8°, respectively. Mean difference and RMSE across each joint were < 5°, indicating the system is comparable to universal goniometer. In experiment 3, median SUS and QUEST-like scores were 73.8 and 4.0, respectively, indicating good usability and satisfaction. The developed system has high accuracy and sufficient validity for human joint angles, with good usability and 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 | 0.026 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| 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".