Immersive virtual reality and affective computing for gaming, fear and anxiety management
Bibliographic record
Abstract
Video game developers are enthusiastic about creating novel interaction approaches that yield a better gaming experience; such interactions are usually built with physical and emotional immersion in mind. Technologies such as Wii®, Kinect®, and Playstation Move® focus on the physical movement of play to encourage seamless and natural behaviors during gameplay. On the other hand, technologies such as biofeedback are not yet being utilized to any large degree in the commercial industry and could be used to gain further knowledge of player's behavior and emotions. Biofeedback refers to technologies that provide awareness of human physiological functions through signals in order to control a system or improve those functions. This technology was primarily developed for clinical purposes to treat diseases such as headaches, high blood pressure, and epilepsy. The patients obtain the skill to control functions associated with aforementioned diseases by being exposed to equipment that measures and displays their bodily functions such as brain waves, heart rate, and galvanic skin response (GSR). This enables them to observe those senses through visualization and exert control over their physiological response over time.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".