Developing Wii Balance Games to Increase Balance: A Multi-Disciplinary Approach
Bibliographic record
Abstract
Persons older than 75 years of age are more susceptible to falls and are a greater burden to the healthcare system than their younger counterparts.While devices like the Nintendo Wii can potentially increase balance control, virtual reality games available for the Wii are not designed as exercise therapy programmes for older people.Targeted therapies are more likely to increase function and decrease falls risk.An interdisciplinary approach that integrates the knowledge of engineers with clinicians can enable the development of a virtual reality home-based exercise programme with off-theshelf equipment.Two targeted exercise therapy games have been developed to encourage at home exercise using novel software with Wii balance boards.One requires stability and balance in a standing position, while the other trains stepping reactions.Ethics approval was obtained to evaluate and pilot these games with older participants.These games were shown to be effective during user testing in that five individuals have provided feedback for further improvement and are interested in participating in a longer balance and exercise programme using these games.One participant was also involved in testing the device in his home environment over the course of a few days.Initial testing in the home environment exposed issues that were not present in the laboratory environment which supports the need for user testing at a very early stage in the prototype development.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".