Evaluating Player Experience in Cycling Exergames
Bibliographic record
Abstract
Obesity has become a worldwide problem which most countries are trying to fight. It affects many people, irrespective of age, race, gender, or religion, anyone can suffer from obesity that leads to serious problems for individuals and for society as a whole. In this study we have selected two groups of people: the basic people who rarely exercise on a weekly basis, and the average people who exercise regularly every week. We have explored the attitude of the two groups towards mixing exercises with games in order to motivate the people with basic activity levels to exercise more frequently. We have used a qualitative standard online questionnaire from AttrakDiff and we have done a quantitative study of some important factors during exercises. The results of the qualitative and quantitative studies were very encouraging, as they reveal that mixing games with exercises can transform boring exercises into entertaining ones. It can also motivate players to continue and repeat the exercises. The ANOVA test has been applied and it shows that combining games with the bike has a significant effect on the speed and the average rotation per minute of the participants.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".