Brain-Aware XR Engagement Detection for Low-Power Sensing
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Solution Overview
Problem
XR devices face power management challenges due to continuous operation of power-intensive systems and reliance on explicit user interactions, leading to excessive power consumption and suboptimal user experience, while existing brain-computer interfaces struggle with lengthy response times, variability in brain wave patterns, and complex training requirements.
Innovation Solution
A two-stage Brain Computer Interface system that continuously monitors brain activity in a low-power state, detecting engagement signals through endogenous brain patterns like alpha waves, and activates additional sensors and contextual analysis only when meaningful engagement is detected, integrating passive brain monitoring with multi-modal contextual analysis to determine user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If XR devices continuously operate power-intensive systems (cameras, displays, sensors), then user experience and system availability are improved, but power consumption increases excessively
Solution Approach 1:
The system performs preliminary brain activity monitoring in a low-power state to detect engagement signals before activating full XR functionality. This preliminary detection determines whether the user is naturally engaged with the environment, allowing the system to activate additional sensors and display only when needed, thus reducing overall power consumption while maintaining good user experience.
Solution Approach 2:
The system dynamically adjusts its power consumption based on detected user engagement levels. By continuously monitoring brain activity and adapting the operational state of sensors and displays according to real-time engagement detection, the system optimizes the balance between power consumption and user experience, transitioning between low-power and high-performance modes as required.
2Measurement precision
If XR devices rely on explicit user interactions (buttons, touch controls), then system control is precise, but user experience is interrupted and power consumption increases
Solution Approach 1:
The system uses brain activity monitoring to automatically detect user engagement and intent without requiring explicit interactions. The XR system serves itself by interpreting neural signals to determine when and what content to display, eliminating the need for buttons or touch controls and providing a more intuitive, hands-free user experience.
Solution Approach 2:
The patent replaces mechanical user input mechanisms (buttons, touchscreens) with a neural-based interaction system. By substituting physical interaction with brain signal interpretation, the system achieves more natural and convenient control while reducing the need for explicit user actions that would interrupt flow and consume additional power.
3Measurement precision
If brain-computer interfaces continuously monitor brain activity, then user engagement detection is accurate, but power consumption increases
Solution Approach 1:
The system implements periodic brain activity monitoring rather than continuous high-power operation. By monitoring brain waves at optimized intervals and using efficient signal processing, the system maintains accurate engagement detection while significantly reducing power consumption compared to continuous monitoring approaches.
Solution Approach 2:
The system changes the monitoring parameters dynamically based on operational state. By adjusting the frequency and intensity of brain activity sampling according to whether the system is in a low-power or full-function mode, it maintains detection accuracy when needed while minimizing power consumption during idle or low-engagement periods.
4Measurement precision
If existing brain-computer interfaces use complex processing to handle variability in brain wave patterns, then user-specific accuracy is improved, but response time increases and training requirements increase
Solution Approach 1:
The system changes the approach to handling brain wave variability by using optimized signal processing parameters that balance accuracy and speed. By adjusting processing thresholds and detection algorithms, the system achieves sufficient user-specific accuracy without the excessive computational delay associated with more complex analysis methods.
Solution Approach 2:
The patent replaces complex, computationally intensive brain wave processing with more efficient neural signal interpretation methods. By substituting heavy computational algorithms with optimized processing techniques, the system reduces response time while maintaining the ability to accurately detect user engagement and intent.
Data Source
AI summary
An extended Reality (XR) system is provided that monitors neurological signals to determine an engagement of a user with a real-world environment. The XR system continuously monitors neurological signals of a user through a processor operating in a low-power mode. The XR system generates an engagement signal by analyzing endogenous brain patterns in the neurological signals. In response to the engagement signal, the XR system activates environmental sensors to capture real-world environment data. The XR system generates contextual data from the captured environment data and determines XR content to provide to the user based on the contextual data. The XR system selectively activates XR capabilities to display the determined XR content.