A Commercially Available Capacitive Stretch-Sensitive Sensor for Measurement of Rotational Neck Movement in Healthy People: Proof of Concept
Bibliographic record
Abstract
Freedom of neck range of motion has been identified for decades as an important indicator of neck health. In the past, neck motion has been measured in clinical settings using straight-plane movements that do not represent real-world 'ecological' performance. The tools currently used are low-fidelity analog or digital tools that rely greatly on the orientation of the person with respect to gravity, or the evaluator's ability to accurately align protractor arms with key surface markers for angle measurement. A possible solution lies in the use of wearable sensors for tracking the motion of the neck without clinical instruction. For this purpose, the focus of this paper is on the assessment of a commercially available stretch sensitive sensor, C-Stretch® against a gold standard for motion tracking. The sensor's accuracy and agreement for measuring neck rotations were evaluated. The results show that the stretch sensitive sensor was accurate with an average RMSE of 5.86° (SD=$4.38^{\circ}, \mathrm{n}=2$) and highly correlated $r=0.88-0.99,(p\lt0.01)$ with Aurora, an electromagnetic tracking system. This work may lead to using wearable sensors as a cost-effective, lightweight, and safe alternative to assess real-world neck range of motion for clinical application.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".