AR Session Adaptation via Eye Behavior Monitoring
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Solution Overview
Problem
Rehabilitation therapies for Alzheimer's patients are often hindered by memory issues and the need for repetitive activities, which can be inconsistent due to staffing and scheduling challenges.
Innovation Solution
A computer-implemented method and system that generates augmented reality sessions based on eye behavior, detecting changes in eye characteristics to determine a reduced cognitive state and modifying the AR session to maintain or boost patient attentiveness and cognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If repetitive rehabilitation activities are used for Alzheimer's patients, then therapy consistency is improved, but patient engagement and cognitive stimulation deteriorate due to monotony
Solution Approach 1:
The system dynamically adjusts AR session parameters including complexity, duration, and content based on real-time eye behavior monitoring. This creates variable rehabilitation experiences that maintain consistency in therapeutic goals while adapting to patient cognitive state, preventing monotony and sustaining engagement.
Solution Approach 2:
The system changes multiple parameters of the rehabilitation therapy including AR session complexity, visual stimulus characteristics, and interaction requirements based on detected eye behavior patterns. This allows the therapy to remain consistent in its rehabilitative purpose while varying in execution to maintain patient interest.
2Productivity
If AR session complexity is increased to boost cognitive stimulation, then patient engagement is improved, but patient fatigue and cognitive overload worsen
Solution Approach 1:
The system continuously monitors eye behavior metrics such as fixation duration, saccade frequency, and pupil dilation as feedback signals. Based on this feedback, the AR session complexity is dynamically adjusted - increasing stimulation when engagement is low but reducing complexity when signs of fatigue appear, maintaining optimal cognitive challenge without overload.
Solution Approach 2:
The rehabilitation protocol transitions from static to dynamic adjustment of session parameters. The system modulates AR content complexity, visual stimulus intensity, and interaction requirements in real-time based on patient cognitive state, ensuring optimal stimulation while preventing fatigue.
3Adaptability or versatility
If manual monitoring of patient cognitive state is used, then therapy personalization is improved, but staffing requirements and operational complexity worsen
Solution Approach 1:
The system performs self-monitoring of patient cognitive state through automated eye behavior analysis. The AR device and computing system automatically detect, analyze, and respond to cognitive state changes without requiring manual observation or intervention by therapists, enabling personalized therapy with reduced staffing demands.
Solution Approach 2:
The system replaces manual monitoring and assessment mechanisms with automated computational analysis of eye behavior data. Computer vision algorithms and machine learning models substitute for human therapist observation, providing continuous objective measurement of cognitive state without additional staffing requirements.
4Adaptability or versatility
If eye behavior monitoring is implemented to detect cognitive state changes, then therapy responsiveness is improved, but system complexity and processing requirements worsen
Solution Approach 1:
The system extracts and focuses on specific, salient eye behavior features (fixation duration, saccade patterns, pupil response) rather than analyzing all possible ocular parameters. This selective extraction of critical signals enables effective cognitive state detection while reducing computational complexity and processing requirements.
Solution Approach 2:
The system implements monitoring of a subset of key eye behavior metrics rather than comprehensive analysis of all ocular functions. By focusing on the most informative parameters for cognitive state detection, the system achieves responsive therapy adjustment without excessive computational burden.
Data Source
AI summary
A method, system, and computer program product for generating augmented reality sessions based on eye behavior of a user is provided. The method detects a set of eye characteristics of a user. In response to detecting the set of eye characteristics, presentation of an augmented reality session is initiated using an augmented reality device. The method detects a change to at least one eye characteristic of the set of eye characteristics of the user. In response to detecting the change, the method determines the change to the at least one eye characteristic indicates a reduced cognitive state of the user. The augmented reality state being presented to the user is modified in response to determining the change indicates the reduced cognitive state.