VR Discomfort Classification via Physiological Pattern Matching
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Solution Overview
Problem
Virtual reality (VR) content often induces discomfort or sickness in users due to incompatible sensory cues, making it difficult to predict user susceptibility and improving the VR experience.
Innovation Solution
A deep learning engine is used to build a model that predicts user discomfort by analyzing physiological measurements and interaction data from testers, allowing for the classification of VR content and users based on discomfort levels, enabling targeted content delivery and development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If VR content is designed to provide complete immersion and sensory stimulation, then user engagement and realism are improved, but user discomfort and sickness increase
Solution Approach 1:
The system performs preliminary classification of VR content into discomfort levels (first, second, third levels) before user interaction. This allows users to be pre-informed about potential discomfort, enabling them to make informed decisions about content selection and thereby reducing actual discomfort experienced while maintaining engagement through appropriate content matching.
Solution Approach 2:
The system incorporates user feedback mechanisms where users report their discomfort experiences during or after VR content interaction. This feedback is used to continuously refine and update the discomfort level classifications, creating a closed-loop system that improves accuracy over time while maintaining high user engagement through personalized content recommendations.
2Object-affected harmful factors
If VR content classification is implemented to reduce user discomfort, then user comfort is improved, but system complexity increases
Solution Approach 1:
The classification system is segmented into discrete, manageable discomfort levels (first, second, and third levels), each with specific characteristics and mitigation strategies. This segmentation simplifies the overall complexity by breaking down the continuous problem of discomfort into discrete categories that are easier to manage and implement.
Solution Approach 2:
The system uses parameter changes in the VR content itself (such as adjusting visual motion parameters, auditory stimulation levels, or interaction complexity) to mitigate discomfort while maintaining engagement. By modifying content parameters rather than building entirely new classification systems, the solution reduces overall system complexity.
3Measurement precision
If physiological measurements are collected and analyzed to predict user discomfort, then prediction accuracy is improved, but data processing requirements increase
Solution Approach 1:
The system extracts and analyzes only the most relevant physiological measurement features that correlate with discomfort, rather than processing all available data. This selective extraction maintains high prediction accuracy while significantly reducing the computational energy required for data processing.
Solution Approach 2:
The system implements partial monitoring of physiological parameters, focusing on key indicators of discomfort (such as heart rate variability, skin conductance, or eye movement patterns) rather than continuously monitoring all physiological signals. This partial monitoring approach maintains adequate prediction accuracy while reducing overall data processing requirements and energy consumption.
Data Source
AI summary
A method for method for classification of virtual reality (VR) content for use in head mounted displays (HMDs). The method includes accessing a model that identifies a plurality of learned patterns associated with the generation of corresponding baseline VR content that is likely to cause discomfort. The method includes executing a first application to generate first VR content. The method includes extracting data associated with simulated user interactions with the first VR content, the extracted data generated during execution of the first application. The method includes comparing the extracted data to the model to identify one or more patterns in the extracted data matching at least one of the learned patterns from the model such that the one or more patterns are likely to cause discomfort.