Biometric Display Management for Adaptive XR Viewing
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Solution Overview
Problem
Existing display management systems struggle with manual adjustments being inconvenient and inefficient, leading to suboptimal display settings and premature shutdowns, especially in head-mounted extended reality devices, due to user unawareness of optimal parameters and varying user preferences.
Innovation Solution
Implementing machine learning models that utilize user biometrics to automatically adjust display parameters based on real-time conditions and individual user preferences, continuously learning and optimizing settings through biometric data feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual display management is implemented, then user control over display settings is achieved, but convenience and efficiency deteriorate due to frequent user awareness and adjustment requirements
Solution Approach 1:
The system enables automatic display management where the display device autonomously adjusts its parameters based on detected biometric data and environmental conditions, eliminating the need for manual user intervention. The machine learning model continuously learns from user responses and automatically optimizes display settings, allowing the system to serve itself rather than requiring constant user control.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where biometric sensors continuously monitor user physiological responses (such as eye tracking, pupil dilation, blink rate), the machine learning model processes this data along with environmental sensor inputs, and the display parameters are automatically adjusted in response. This real-time feedback loop enables the system to adapt to changing user needs and conditions without manual intervention.
2Ease of operation
If automatic display management is implemented, then convenience is improved, but adaptability to individual user preferences deteriorates due to generic algorithms
Solution Approach 1:
The system performs preliminary calibration by collecting biometric data and user feedback during initial usage periods to establish baseline preferences and characteristics for each individual user. This preliminary action creates a personalized profile that the machine learning model uses as a foundation for automatic adjustments, ensuring that the system is pre-adapted to each user's specific preferences before full automatic management begins.
Solution Approach 2:
The machine learning model is designed to be dynamic and continuously evolving, adapting its parameters and decision-making logic based on ongoing biometric feedback and environmental conditions. The system does not rely on static, generic algorithms but continuously learns and adjusts to capture individual user preferences, making the adaptability dynamic rather than fixed.
3Reliability
If display parameters are continuously adjusted, then viewing quality is improved, but energy consumption increases
Solution Approach 1:
The system applies partial adjustment by making display parameter changes only when biometric feedback indicates that quality improvements are actually needed, rather than continuously adjusting regardless of condition. The machine learning model analyzes multiple sensor inputs and determines the minimum necessary adjustments to maintain optimal viewing quality, avoiding unnecessary energy-consuming changes when the display is already appropriately configured for current conditions.
Data Source
AI summary
Display management modeling based on user biometrics is described herein. In one implementation, a device sets a display parameter used to display visual content to a user. The display parameter is set to a first value determined using a machine learning model in response to an occurrence of a condition associated with a context in which the device displays the visual content. In association with the setting of the display parameter to the first value, biometric data from the user is detected as the device displays the visual content to the user. Based on this biometric data from the user, the machine learning model is updated. Then, in response to a reoccurrence of the condition, the display parameter is set to a second value that is different from the first value and is determined using the updated machine learning model. Corresponding methods, systems, and media are also disclosed.