Home-Based Computer Gaming in Vestibular Rehabilitation of Gaze and Balance Impairment
Bibliographic record
Abstract
Disease or damage of the vestibular sense organs cause a range of distressing symptoms and functional problems that could include loss of balance, gaze instability, disorientation, and dizziness. A novel computer-based rehabilitation system with therapeutic gaming application has been developed. This method allows different gaze and head movement exercises to be coupled to a wide range of inexpensive, commercial computer games. It can be used in standing, and thus graded balance demands using a sponge pad can be incorporated into the program. A case series pre- and postintervention study was conducted of nine adults diagnosed with peripheral vestibular dysfunction who received a 12-week home rehabilitation program. The feasibility and usability of the home computer-based therapeutic program were established. Study findings revealed that using head rotation to interact with computer games, when coupled to demanding balance conditions, resulted in significant improvements in standing balance, dynamic visual acuity, gaze control, and walking performance. Perception of dizziness as measured by the Dizziness Handicap Inventory also decreased significantly. These preliminary findings provide support that a low-cost home game-based exercise program is well suited to train standing balance and gaze control (with active and passive head motion).
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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 | 0.000 | 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) | 0.001 | 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".