VR Image Presentation Apparatus for Personalized Motion Sickness Reduction
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Solution Overview
Problem
Existing VR technologies fail to effectively address motion sickness in users due to individual differences, as current methods do not account for personal characteristics such as eyesight and age.
Innovation Solution
An image presentation apparatus that uses machine learning to analyze user data, including physiological information and virtual space interaction data, to adjust the presentation of virtual space images and provide personalized anti-motion sickness processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generic anti-motion sickness methods are applied to all users, then implementation simplicity is maintained, but effectiveness varies due to individual differences in users
Solution Approach 1:
The system performs preliminary machine learning processing to create user-specific motion sickness models before actual VR usage. By pre-acquiring user data and generating personalized processing parameters in advance, the system prepares customized anti-motion sickness strategies for each user, improving effectiveness without adding complexity during actual VR operation
Solution Approach 2:
The system automatically adapts to each user by self-learning from user-specific data during VR usage. The machine learning model continuously acquires user responses and adjusts processing parameters autonomously, eliminating the need for manual configuration or complex user setup while achieving personalized anti-motion sickness processing
2Ease of operation
If continuous field of view movement is provided in virtual reality, then user immersion and engagement are improved, but motion sickness in users increases
Solution Approach 1:
The system dynamically adjusts VR field of view movement based on real-time user state detection and machine learning predictions. By continuously adapting movement parameters to match each user's tolerance and response patterns, the system maintains high immersion for users with high tolerance while reducing motion sickness triggers for sensitive users
Solution Approach 2:
The system changes multiple VR presentation parameters including field of view movement speed, acceleration profiles, and transition smoothness based on user-specific machine learning models. By adjusting these parameters dynamically according to detected user state and predicted motion sickness risk, the system optimizes the balance between immersion and comfort for each individual user
Data Source
AI summary
Provided is an image presentation apparatus configured to present a virtual space image to a user. The image presentation apparatus receives acquisition information that is acquired during the presentation of the image and a notification from the user that indicates that the user has entered a predetermined condition. The image presentation apparatus learns a relationship between the acquisition information and content of the notification by machine learning. A result of the machine learning is used for predetermined processing during the presentation of the virtual space image to the user.