Eye Gaze Scroll Control via ML Pattern Recognition
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Solution Overview
Problem
Traditional scrolling mechanisms are cumbersome, inefficient, and limiting for people with disabilities, as they require manual interaction and do not adapt to individual reading habits.
Innovation Solution
A system and method for hands-free scrolling using eye gaze tracking, where an eye tracker detects eye movement data, samples it, and uses machine learning to establish eye gaze patterns, determining when to initiate computerized actions based on the user's gaze within a computing environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional scrolling mechanisms are used, then manual interaction is required, but this reduces accessibility and efficiency for users with disabilities
Solution Approach 1:
The patent replaces manual mechanical scrolling interactions with an automated eye-tracking system. The eye tracker detects gaze patterns and triggers scrolling actions automatically, substituting the mechanical hand-scrolling process with an optical detection and automated response system, thereby improving accessibility for users with motor disabilities
Solution Approach 2:
The system learns and adapts to each user's natural reading patterns through machine learning, enabling the scrolling mechanism to serve itself by automatically adjusting to individual user behaviors without requiring manual configuration or intervention, making the system inherently adaptive to user needs
2Adaptability or versatility
If traditional scrolling mechanisms are used, then uniform scrolling behavior is applied, but this does not adapt to individual reading habits
Solution Approach 1:
The system continuously monitors eye gaze patterns and uses this feedback to dynamically adjust scrolling behavior. The machine learning model processes real-time eye tracking data to understand user reading preferences and automatically modifies scrolling speed and timing, creating a closed-loop adaptive system that evolves with user behavior
Solution Approach 2:
The system performs preliminary learning of user reading patterns during initial usage periods, establishing baseline behaviors before full adaptive scrolling is activated. This preliminary action phase allows the system to pre-adapt to user habits, reducing the time required for meaningful adaptation while maintaining natural reading experiences
3Ease of operation
If eye gaze tracking is implemented, then hands-free operation is enabled, but this increases system complexity
Solution Approach 1:
The eye tracking system is integrated into existing computing devices and can serve multiple functions beyond scrolling, including cursor control, text selection, and accessibility features. This multi-functionality justifies the added complexity by providing universal benefits across various user interactions and applications
Solution Approach 2:
The machine learning model acts as an intermediary layer between the raw eye tracking data and the scrolling control system. It processes and interprets gaze patterns, translating complex eye movement data into simple scrolling commands, thereby managing system complexity through intelligent abstraction
Data Source
AI summary
Systems and methods for initializing computerized actions using eye gaze and eye gestures are disclosed. An example method begins with detecting, via an eye tracker of a computing device, eye gaze data. The detected eye gaze data points are sampled and collected. A machine learning model is trained using the sampled eye gaze data points. Then, a context of a computing environment is determined. The machine learning model determines a location of the plurality of eye gaze data points of the user in the computing environment. When the plurality of eye gaze data points of the user and an object of the computing environment are both located within a bounding box, a rectangular area or a zone of the computing environment, a computerized action associated with the object is initiated.