AR Environment Adaptation for Cognitive State Control
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Solution Overview
Problem
Current augmented reality technologies do not effectively modify environments to achieve specific cognitive states for users, limiting their interactive and digitally manipulable experiences.
Innovation Solution
A system that receives environment and physiological information to simulate a target environment in augmented reality, determining the user's cognitive state and modifying the environment to achieve a desired mental state by adjusting visual and auditory elements based on collected data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If augmented reality technology is used to simulate environments, then user immersion and interaction are improved, but the ability to dynamically modify the environment to achieve specific cognitive states is insufficient
Solution Approach 1:
The AR environment is transformed from a static simulation to a dynamic system that continuously adapts to user cognitive states. The system modifies environmental parameters (visual, auditory, haptic) in real-time based on physiological feedback, making the environment responsive and flexible rather than fixed and rigid.
Solution Approach 2:
A closed-loop feedback mechanism is implemented where physiological sensors continuously monitor user cognitive states and feed this information back to the AR system. The system then adjusts environmental parameters based on this feedback, creating a self-regulating cycle that achieves desired cognitive states while managing complexity through automated control.
2Measurement precision
If physiological information is continuously monitored to determine cognitive state, then cognitive state accuracy is improved, but information processing load and system complexity increase
Solution Approach 1:
The complex task of cognitive state determination is segmented into multiple independent physiological measurements (heart rate, skin conductance, temperature, respiration). Each sensor provides a discrete data stream that is processed separately and then integrated, breaking down the complex analysis into manageable components that reduce overall system complexity.
Solution Approach 2:
The physiological data collection system is designed to serve multiple functions: it not only determines cognitive states for AR environment modification but also provides health monitoring, performance tracking, and safety monitoring capabilities. This multi-functionality justifies the information processing load by delivering multiple valuable outputs from the same sensing infrastructure.
3Productivity
If the AR environment is dynamically modified to achieve target cognitive states, then user performance and training effectiveness are improved, but energy consumption and computational resources increase
Solution Approach 1:
Instead of continuous full-scale environmental modification, the system employs periodic adjustments based on detected cognitive state deviations from targets. The AR environment remains relatively stable during appropriate cognitive states and only undergoes modifications when needed, reducing unnecessary energy consumption while maintaining training effectiveness through timely interventions.
Solution Approach 2:
The system modifies environmental parameters (lighting intensity, sound volume, haptic feedback strength) rather than fundamentally restructuring the entire AR environment. These parameter-level adjustments achieve cognitive state transitions with lower computational overhead and energy consumption compared to complete environment reconfiguration, while still delivering effective training outcomes.
Data Source
AI summary
In one embodiment of the present invention, environment information corresponding to a user is received. A target environment is simulated in augmented reality based on the environment information. Physiological information corresponding to the user is received. A cognitive state of the user is determined based on the physiological information. In response to determining that the cognitive state of the user is not a target cognitive state, the target environment is modified in augmented reality to achieve the target cognitive state.