Rehabilitative game-based system for enhancing physical and cognitive abilities of neurological disorders
Bibliographic record
Abstract
Neurological disorders affect the nervous system and can impair physical, cognitive, or emotional functions. They often result in challenges such as movement difficulties and the inability to perform daily activities. Common conditions include stroke, traumatic brain injury, and cerebral palsy. Physical therapy is a common approach to managing these disorders. Recently, virtual reality (VR), a technology that creates interactive, simulated environments, has been used in rehabilitation. This study presents a rehabilitative game-based system to improve patients' movements and cognitive abilities. Six games were designed using the Unity platform, namely, "Piano," "Connect," "Drag & Drop," "Little Intelligent," "Memory," and "Hack & Slash." The Oculus Quest 2 VR headset was used to simulate the virtual environment for gaming. A mobile application called "Recover Me" was created to facilitate communication between patients and physiotherapists. A score index was generated for each patient, indicating the performance. It enables monitoring and assessment of the patients, leading to customizing the treatment plan based on progress. The study proposed simulating monitoring and evaluation of the patients by training an artificial neural network model to predict scores for the developed games and consequently indicate the patient's actual status. A dataset of 50 patients with different injuries was used. Results indicate patient satisfaction with gaming and enjoyment. Moreover, a regression analysis was performed to detect the progress level of each patient, indicating that 60% of the tested patients had improved. A low-cost VR game-based system has proven effective in rehabilitating neurological disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".