Personalization of Wearable-Based Exergames with Continuous Player Modeling
Bibliographic record
Abstract
In recent years, sedentary behavior has been recognized as a major lifestyle-related health risk.Meanwhile, exergames and gamification of physical activities are effective tools to motivate behavior change, particularly to promote daily physical activities.Research has shown that persuasive technologies and gamification can be utilized to increase physical activity.On the other hand, studies have suggested that a "one-size-fitsall" approach does not work well for persuasive game design.At the same time, player modeling and recommender systems are increasingly used for personalizing contents and services.However, there is limited existing work on how to build comprehensive player models for personalizing gamified systems and recommending daily physical activities, and on the long-term effectiveness of gamified exercise-promoting systems.To fill these gaps, a new approach for gamified 24/7 fitness recommendation systems is introduced in this research.It uses wearable activity trackers combined with continuous player modeling to provide personalized activity recommendations and generates gamified content targeted to each user.Preliminary results show the feasibility of using wearable activity trackers for gamification of physical activities, and the effectiveness of using player modeling for generating personalized exercise recommendations.We show that personalizing recommendations using player modeling and gamification with wearables will improve users' engagement and motivation towards fitness activities over time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".