Contribution of Virtual Reality Environments and Artificial Intelligence for Alzheimer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Alzheimer’s Disease (AD) is one of the most crucial diseases of our century affecting millions of persons every year. Negative emotions such as anxiety, frustration, and apathy are common in AD patients which reduce their wellbeing significantly. Virtual Reality is a means of providing the patients with a sense of presence in an environment that isolates them from external factors able to induce negative emotions. In this goal we have developed several interactive virtual environments able to relax the patients and reduce negative emotions. Virtual travels, natural environments, music therapy, Zootherapy, discovering environments can be used to calm the patients. Artificial Intelligence can bring a valuable contribution if these environments can be modified dynamically according to brainwaves reactions. Neurofeedback techniques can be used to adapt the virtual environments in order to dynamically reduce negative emotions and foster positive emotions. We will present several examples of interactive virtual environments driven by the brain of Alzheimer’s patients and able to improve their cognitive capabilities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it