XR Eyewear Feedback Control for Adaptive Therapeutic Settings
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Solution Overview
Problem
Users of extended reality eyewear need to manually adjust settings for sensory, cognitive, or psychological changes, and there is a lack of systems to automatically adapt these settings for therapeutic interventions.
Innovation Solution
The eyewear includes a Neural-loop Computational Model that collects user data and adjusts settings using a machine learning algorithm based on trends in visual, auditory, and cognitive metrics, eliminating the need for manual adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of settings is required for sensory, cognitive, or psychological changes, then user control over settings is maintained, but user burden and time consumption increase
Solution Approach 1:
The system automatically monitors sensory, cognitive, and psychological metrics and adjusts settings without user intervention. The processor continuously analyzes metric data and modifies eyewear parameters autonomously, eliminating the need for manual adjustments while maintaining optimal performance based on real-time user state
Solution Approach 2:
The system implements a closed-loop feedback mechanism where sensory, cognitive, and psychological metrics are continuously measured, analyzed by the processor, and used to automatically adjust settings. This feedback loop enables the system to adapt to changing user conditions dynamically without requiring user input or manual intervention
2Extent of automation
If automated adjustment systems are implemented, then user intervention is minimized, but system complexity increases
Solution Approach 1:
The processor serves multiple functions: it monitors diverse metrics (sensory, cognitive, psychological), analyzes trend data, determines optimal settings, and controls various eyewear parameters. By consolidating these functions into a single processing unit, the system achieves high automation without proportionally increasing overall complexity
Solution Approach 2:
The system combines metric collection, data analysis, setting determination, and execution into an integrated automated process. The processor merges multiple computational tasks (monitoring trends, comparing baseline data, adjusting settings) into a unified system that operates seamlessly without requiring separate manual intervention points
3Manufacturing precision
If multiple metrics are collected and analyzed, then setting optimization is improved, but data processing requirements increase
Solution Approach 1:
The system processes metrics continuously but focuses computational resources on analyzing trends and deviations from baseline rather than processing every raw data point with equal intensity. By prioritizing trend analysis and threshold-based triggering, the system achieves precise setting optimization while managing energy consumption through selective processing intensity
Data Source
AI summary
In general, one aspect disclosed features a computer-implemented method comprising: obtaining a first metric of a physiological aspect of a patient, the first metric obtained at a first time by a head-wearable apparatus while the head-wearable apparatus is worn by the patient; obtaining a second metric of the physiological aspect of a patient, the second metric obtained at a second time by the head-wearable apparatus while the head-wearable apparatus is worn by the patient, wherein the second time is later than the first time; determining a value for a parameter of the head-wearable apparatus based on the first metric and the second metric; and setting the parameter in the head-wearable apparatus to the determined value for the parameter.