Zoo Therapy for Alzheimer’s Disease with Real-Time Speech Instruction and Neurofeedback System
Bibliographic record
Abstract
There is an increasing number of people with Alzheimer’s disease. Negative emotions are not only one of the symptoms of AD, but also the accelerator of the disease. Animal therapy can have a positive impact on the negative emotions of patients, but it has strict requirements for both environments and animals. In this study, we aim to explore the effectiveness of using virtual animals and their impact on the reduction of patients’ negative emotions to improve the user’s cognitive functions. This approach has been implemented in the Zoo Therapy project, which presents an immersive 3D virtual reality animal environment, where the impact on the patient’s emotion is measured in real-time by using electroencephalography (EEG). In addition to creating highly realistic virtual animals, the innovation of Zoo Therapy is also in its communication mechanism as it implements bidirectional human-computer interaction supported by 3 interaction methods: 3D buttons, speech instruction, and Neurofeedback. Patients can actively interact with virtual animals through 3D buttons or speech instructions. The Neurofeedback system will guide the animal to actively interact with the patients according to their real-time emotional changes to reduce their negative emotions. Experiments and preliminary results show that it is possible to interact with virtual animals in Zoo Therapy, and the Neurofeedback system can intervene in Zoo VR environment when the emotional value goes down and might reduce patients’ negative emotions.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".