Puzzling Patterns: Assessing Neck Range of Motion Using a Mobile Puzzle Exergame
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cervical range of motion (ROM) is a crucial aspect of assessment following a neck injury and prior to cervical rehabilitation. We explored using an exergame with a head-tracker to predict the degree of cervical ROM. Using head movement, users moved a cursor over a picture-reveal puzzle to remove tiles and reveal an underlying picture. In a within-subjects user study, we controlled mobility restriction by fitting participants with either a rigid cervical collar (severe restriction), a soft cervical collar (moderate restriction), or no collar (no restriction). We also controlled task difficulty through two levels each of number of tiles (13 × 10, 7 × 5) and gain (high, low). Selection rate by mobility restriction ranged from ≈ 30% for severe to ≈ 95% with none, and ≈ 50% for moderate. Results suggest the following ascending ranks for difficulty based on number of tiles and gain: (1) 7×5, high gain, (2) 7×5, low gain, (3) 13×10, high gain, and (4) 13×10, low gain. This ascending difficulty order is recommended for presenting the puzzles to people with cervical conditions to avoid overexertion. The collected data were also used in machine learning with a Random Forest model. Mobility restriction category (severe, moderate, none) was correctly predicted in 80.6% of 36 samples. The results are a first step in using an exergame and machine learning to automatically categorize patients according to their cervical ROM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it