Evaluation of an electronic video game for improvement of balance
Bibliographic record
Abstract
Virtual environments have been investigated for fitness and medical rehabilitation. In this study, the Sony EyeToy ® and PlayStation 2 ® were used with the AntiGrav™ game to evaluate their potential for improving postural balance. The game required lateral head, body, and arm movements. The performance on balance tests of subjects who trained for 3 weeks with this game was compared to the performance of controls who were not trained. Training subjects showed improvement for two of the three tests (each testing a different facet of balance), suggesting specificity of training, while control subjects did not show significant improvement on any test. Simulator sickness questionnaire results showed a variety of mild symptoms, which decreased over the training sessions. Motor learning analysis of the game scores showed that mastery had been achieved on the easier level in the game, but not on the second level of difficulty. This reflects the potential for continued learning and training through advanced levels within a game. A model parameter using the time constants of game score improvement was developed, which could be used to quantify the difficulty for any video game design. The results suggest that this video game could be used for some aspects of balance training.
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.
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) | 0.000 | 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 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".