AR VR Decision Simulation System with Dynamic Feedback
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Solution Overview
Problem
Current simulations for stressful situations lack dynamic interaction and fail to provide realistic feedback, necessitating the development of augmented or virtual reality-based decision-making simulations that can mimic real-world scenarios effectively.
Innovation Solution
A system comprising a controller, AR/VR device, processor, and memory that receives user input, displays simulated environments, monitors parameters, and evaluates user decisions using machine learning, with features like heart rate monitoring and head movement sensing to adjust the simulation dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If basic models with static injuries and human actors are used in mass trauma simulations, then the simulation setup is simple and低成本, but the simulation lacks dynamic interaction and realistic feedback
Solution Approach 1:
The patent uses virtual reality to create a digital copy of the trauma simulation environment, replacing physical actors and static injury models with immersive virtual representations. This allows for dynamic interaction and realistic feedback without the complexity of coordinating multiple human actors and physical props.
Solution Approach 2:
The simulation transitions from static injury models to dynamic virtual environments where injury conditions, victim responses, and environmental factors can change in real-time based on user actions. The system dynamically adjusts scenario parameters to provide realistic feedback loops.
2Adaptability or versatility
If augmented reality or virtual reality devices are used to create immersive simulations, then dynamic interaction and realistic feedback are achieved, but the device complexity and cost increase
Solution Approach 1:
The patent designs the AR/VR simulation system to serve multiple training purposes across different scenarios (mass trauma, active shooter, natural disasters, etc.). A single versatile platform replaces the need for multiple specialized training systems, justifying the complexity through broad applicability.
Solution Approach 2:
The system implements comprehensive feedback mechanisms that monitor user actions, physiological responses (heart rate, galvanic skin response), and decision-making processes. This feedback is processed by machine learning algorithms to provide adaptive guidance and performance evaluation, enhancing the training value despite increased system complexity.
3Measurement precision
If machine learning is used to evaluate user performance in simulations, then comprehensive and adaptive evaluation is achieved, but the processing requirements and system complexity increase
Solution Approach 1:
The machine learning system automatically evaluates user performance by analyzing simulation data, physiological responses, and decision patterns. The system self-calibrates and adapts evaluation criteria based on accumulated data, reducing the need for manual evaluation protocols and expert intervention despite the computational complexity.
Data Source
AI summary
A system for display for an augmented/virtual reality-based decision-making simulation includes a controller configured to receive input from a user, at least one of an augmented reality or a virtual reality device configured to display a simulated environment, a processor, and a memory coupled to the processor. The memory stores one or more computer-readable instructions, which, when executed by the processor, cause the system to: receive input by the user from the controller, indicating a selected scenario; display, on the at least one of the augmented reality or the virtual reality device, the selected scenario; receive input from the user, from the controller, to interact with the selected scenario; monitor one or more parameters associated with the execution of tasks in the selected scenario using the controller; and evaluate the user based on the monitored one or more parameters.