Adaptive VR Training System for Dynamic User Assistance
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Solution Overview
Problem
Current training methods for users often lack adaptability and effectiveness in providing personalized assistance based on user state and environmental stimuli, leading to inefficient learning and safety risks.
Innovation Solution
A computer-based method that detects user state and environmental stimuli to provide variable levels of assistance, including immersive virtual reality, augmented reality, and sparsely augmented reality, adjusting the level of support based on user skill and alerting users to critical stimuli, while also offering personalized safety training and risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional training methods using actual machines or fixed training environments are used, then users can learn practical skills, but the training lacks adaptability and personalization based on user state and environmental stimuli
Solution Approach 1:
The system dynamically adjusts the level of virtual reality assistance based on detected user state and environmental stimuli. The assistance level transitions between immersive virtual reality, augmented virtual reality, and sparsely augmented reality modes, creating a dynamic and adaptive training experience that responds to real-time conditions rather than using a fixed training environment
Solution Approach 2:
The system changes the parameter of virtual reality immersion level based on detected user state and environmental factors. By monitoring user state (e.g., skill level, performance metrics) and environmental stimuli, the system adjusts the degree of virtual assistance provided, transforming static training into adaptive training with variable parameters
2Adaptability or versatility
If fixed training environments are designed for specific activities, then training can be standardized, but the training cannot provide personalized assistance based on individual user needs
Solution Approach 1:
The system continuously monitors user state and environmental stimuli, using this feedback to adjust the level of assistance provided. This closed-loop feedback mechanism enables personalization of training by adapting to individual user needs in real-time, providing more help when needed and reducing assistance when the user demonstrates competence, thereby optimizing training time
Solution Approach 2:
The system prepares multiple levels of assistance (immersive, augmented, and sparsely augmented reality modes) in advance, allowing it to quickly transition between different personalization levels based on user performance without requiring extended training sessions to adapt to each user's needs
3Reliability
If traditional training without real-time environmental monitoring is used, then the training process is simpler, but safety risks cannot be detected and addressed promptly
Solution Approach 1:
The system uses multi-functional detection capabilities to monitor both training performance and environmental safety conditions simultaneously. The same sensors and detection mechanisms that track user state for personalization also detect environmental stimuli and abnormalities, providing safety monitoring without requiring entirely separate detection systems
Solution Approach 2:
The virtual reality assistance acts as an intermediary between the user and environmental hazards. By detecting environmental stimuli and abnormalities, the system can alert users to potential safety risks through the virtual interface, providing an additional layer of protection without directly interfering with the physical training environment
Data Source
AI summary
In one embodiment, a computer-based method includes detecting a state of a user and detecting a stimulus in an environment of the user. The computer-based method then provides a variable level of assistance to the user based on the detected state of the user and the detected stimulus in the environment. In another embodiment, detecting the stimulus in the environment of the user may include detecting a visual stimulus, an auditory stimulus, a physical stimulus, a vibratory stimulus, an electro-magnetic stimulus, an olfactory stimulus, a temperature stimulus, or a movement stimulus. Detecting the state of the user may include detecting natural language spoken by the user. The resulting variable level of assistance utilizes a range of immersive virtual reality, augmented reality and sparsely augmented reality. 3D models of the user's situation (the environment) may be employed in the variable levels of assistance.