HMD Component Analysis for Personalized Motion Sickness Estimation
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Solution Overview
Problem
Existing HMD technologies fail to effectively address visually induced motion sickness due to the lack of consideration for HMD components, leading to discomfort such as headaches and nausea, and existing solutions do not support various types of HMDs.
Innovation Solution
An information processing apparatus that obtains HMD configuration information, user information, and content information to estimate the induction degree of visually induced motion sickness through machine learning, using a trained model to generate notification or adjust settings to mitigate the discomfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If HMD presents video images by widely covering field of view, then immersive experience is improved, but visually induced motion sickness occurs
Solution Approach 1:
The system performs preliminary detection of HMD components and XR content characteristics before the user experiences motion sickness. By analyzing display specifications, refresh rates, and content properties in advance, the system can predict and prevent motion sickness symptoms before they occur, allowing users to maintain immersive experience without discomfort
Solution Approach 2:
The system continuously monitors usage conditions and provides feedback by detecting combinations of HMD components and content characteristics that may cause motion sickness. This feedback mechanism allows dynamic adjustment or warning to users, enabling them to modify usage patterns while preserving the immersive experience benefits
2Measurement precision
If existing technique uses learning model to detect VR content inducing discomfort, then motion sickness detection is improved, but support for various types of HMDs is limited
Solution Approach 1:
The system is designed to handle multiple types of HMDs by detecting and analyzing various display components including OLED, LCD, and micro-display specifications. The learning model processes diverse input parameters such as refresh rates, resolutions, and panel types, enabling universal application across different HMD platforms while maintaining accurate motion sickness detection
Solution Approach 2:
The system adapts to different HMD types by dynamically adjusting detection parameters based on the specific display technology being used. By modifying analysis parameters according to the detected HMD specifications (e.g., refresh rate thresholds for OLED vs. LCD), the system maintains high detection accuracy across diverse hardware configurations
Data Source
AI summary
An information processing apparatus capable of estimating an occurrence of a visually induced motion sickness from components of an HMD is provided. The information processing apparatus includes one or more processors and/or circuitry configured to execute an HMD information obtainment processing that obtains HMD configuration information indicating a relationship between components of an HMD and a visually induced motion sickness, execute an estimation processing that estimates whether or not a user using the HMD experiences the visually induced motion sickness based on an induction degree of the visually induced motion sickness obtained by using the HMD configuration information, and execute an output processing that outputs an estimation result obtained in the estimation processing.