Eye Movement Analysis Using Virtual Avatar for Telemedicine
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Solution Overview
Problem
Current methods for diagnosing dizziness through eye movement analysis in telemedicine face challenges such as information security issues and diagnostic difficulties due to direct exposure of the patient's face, and the inability to accurately measure eye movement at the time of dizziness, which is crucial for diagnosis.
Innovation Solution
A method and apparatus that analyze eye movement remotely using a deep learning algorithm to interpret and visualize eye movement direction, automatically exclude inaccurate measurements, and provide auditory feedback to ensure proper patient focus during testing, without exposing the patient's face, utilizing an image capturing device and eye movement analysis apparatus to track and display eye movement as a virtual avatar in a metaverse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If eye movement is observed directly using a nystagmograph, then measurement precision is improved, but patient's face must be exposed causing information security issues
Solution Approach 1:
The patent uses a camera to capture images of the patient's eye and generates a virtual avatar that copies and represents the patient's eye movement. This virtual avatar serves as a substitute for direct observation, allowing remote diagnosis without exposing the patient's actual face, thus resolving the contradiction between measurement precision and information security.
2Reliability
If eye movement is tested using a nystagmograph, then diagnostic accuracy is improved, but large amount of training is required and device complexity increases
Solution Approach 1:
The patent replaces the complex mechanical nystagmograph system with a camera-based image capture system combined with virtual avatar generation. This substitution simplifies the device while maintaining diagnostic accuracy, as the virtual avatar automatically processes and displays eye movement patterns without requiring extensive operator training.
3Reliability
If eye movement is measured at the time of dizziness, then diagnostic reliability is improved, but patient must self-measure and save data which increases operation complexity
Solution Approach 1:
The patent enables patients to perform self-measurement by capturing their own eye movement images using a camera and generating their virtual avatar. The system automatically processes the images and extracts eye movement data, allowing patients to conduct diagnostics independently at the time of dizziness without requiring complex manual operations or data saving procedures.
Data Source
AI summary
The present disclosure relates to an eye movement analysis method performed in an eye movement analysis apparatus, the eye movement analysis method including: receiving a facial image; obtaining an eye image that corresponds to a part of an eye that determines a midpoint position of the eye in the facial image; analyzing movement of the eye based on movement of the midpoint position in the facial image; and visually displaying a direction of the movement of the eye.