Designing and evaluating digital games for frail elderly persons
Bibliographic record
Abstract
Game research increasingly addresses human factors of gaming. Though more and more seniors become players, game design for frail elderly has rarely been explored. This paper addresses game design for senior citizens experiencing age-related changes, especially cognitive and physical limitations. We introduce and evaluate the case study SilverPromenade, which is specifically aimed at providing institutionalized frail elderly with a new leisure activity. SilverPromenade allows players to go on virtual walks while accounting for special needs regarding game complexity, and simplistic interaction paradigms are provided using Nintendo's Wii Remote and the Balance Board for game control. Evaluation results suggest that despite age-related impairments, the game was generally accessible to elderly persons. Yet, differences between inexperienced and experienced players were observed which suggest that interaction problems may be reduced by engaging with games over a longer time. Findings also indicate that the engagement of elderly players transcends into their everyday life, and their social interaction increases among one another. Most importantly, the evaluation showed that games were perceived as enjoyable leisure activity, supporting the approach of applying digital games to raise the quality of life among frail elderly by fostering activity.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".