Virtual Reality Eating Behavior Training System
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Solution Overview
Problem
Conventional treatments for eating disorders, such as anorexia and bulimia, are limited by the need for clinical settings and lack of scalable, robust solutions for training patients in beneficial eating habits, which can be physically and emotionally damaging if not effectively addressed.
Innovation Solution
A virtual reality (VR) system that uses a computing system to evaluate patient eating behaviors, generate targeted virtual scenarios, and provide real-time feedback to encourage normal eating behaviors, incorporating AI, machine learning, biofeedback, and multi-sensory outputs to simulate eating environments and track progress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional treatments are used in clinical settings, then patients receive personalized counseling and monitoring, but the treatment is not scalable and requires extensive clinical resources
Solution Approach 1:
The patent creates virtual copies of clinical treatment environments through VR simulations. Patients can practice eating behaviors in virtual restaurant settings, virtual kitchens, and other food-related scenarios that replicate real-world challenges. This allows standardized treatment protocols to be delivered consistently across multiple patients simultaneously, achieving scalability while maintaining personalized intervention through AI-driven adaptation of virtual scenarios
Solution Approach 2:
The VR platform serves multiple therapeutic functions within a single system: behavioral training through virtual scenarios, real-time monitoring of eating behaviors, AI-driven feedback provision, and progress tracking. This multi-functional approach consolidates various clinical services into one scalable digital platform that can serve numerous patients without requiring proportional increases in clinical resources
2Productivity
If VR training environments are created, then treatment scalability is improved and clinical resources are reduced, but the complexity of generating realistic virtual scenarios increases
Solution Approach 1:
The system employs dynamic scenario generation where virtual eating environments are not static but adapt in real-time based on patient behavior, preferences, and treatment progress. The AI engine dynamically adjusts virtual scenario parameters such as food types, social interactions, and environmental factors to create personalized training experiences without requiring manual creation of numerous pre-programmed scenarios
Solution Approach 2:
The VR system includes automated scenario generation capabilities where the AI engine creates and adjusts virtual training environments based on patient data and treatment goals without requiring extensive manual configuration. The system self-adapts by learning from patient responses and automatically generating appropriate virtual scenarios, reducing the complexity burden on clinicians and researchers
3Measurement precision
If AI and machine learning are integrated into the VR system, then real-time feedback accuracy is improved, but the computational requirements and system complexity increase
Solution Approach 1:
The system implements AI processing at appropriate levels of detail without over-engineering. Machine learning algorithms analyze only the most relevant behavioral parameters for eating disorder treatment, such as food selection patterns, consumption rates, and emotional responses, rather than attempting to process all possible data points. This selective approach maintains high measurement precision for clinically relevant behaviors while managing computational requirements
Data Source
AI summary
Virtual reality (VR) systems and methods for treating eating disorders or training a patient to have beneficial eating habits. Systems includes a virtual reality interface displayed to a patient and a computing system coupled to the virtual reality interface. The computing system executes instructions to evaluate a patient eating disorder based upon patient input and to construct and display a therapeutic VR environment, where the patient can be treated for the eating disorder and trained to have beneficial eating habits.