Assessing the Applicability of the Nintendo Wii as a Device for Advancing Clinical Practice and Functional Independence
Bibliographic record
Abstract
The purpose of this review was to assess the potential of the Nintendo Wii to collect clinical data such as center of pressure and weight. The use of this system as an assistive device for individuals with disability was also reviewed. Keywords including "Wii," "Nintendo Wii," "rehabilitation," and "instrumentation" were entered into the search engines Pubmed, CINAHL, and Google Scholar. Articles in English, and focusing on the use of the Nintendo Wii were reviewed. Articles focusing on the use of this system in a rehabilitation setting were excluded. In total, 260 articles were identified. After reviewing the abstracts, 13 were considered suitable for the current review (12 from Pubmed and CINAHL, 1 from Google Scholar). The Nintendo Wii, when paired with customized software, is able to collect data regarding center of pressure, weight-bearing asymmetry, and posture similar in quality to data collected by laboratory equipment. The literature is equivocal regarding the validity and reliability of data that the device can collect its own, suggesting the need for further research. When paired with custom software, the Nintendo Wii may act as an assistive device allowing individuals with disability to interact with, and respond to, environmental stimuli.
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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".