Adaptive Gesture Input Mapping for Accessible VR and AR Interaction
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Solution Overview
Problem
Gesture-based inputs in computing systems, particularly in VR and AR applications, face challenges due to a lack of constraint, inconsistency between users, and the burden of remembering numerous gestures, leading to frustration and inaccessibility for users with limited motion or different capabilities.
Innovation Solution
A dynamically updating input system that generates gesture mappings in real-time based on user interactions, utilizing image capture, machine learning, and context analysis to associate user gestures with intended interactions, eliminating the need for predefined mappings and user learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If gesture-based inputs are used to enable intuitive interactions in VR and AR applications, then user immersion and interaction naturalness are improved, but the lack of constraint and consistency between users leads to frustration and inaccessibility
Solution Approach 1:
The system dynamically adapts gesture mappings based on real-time analysis of user behavior, context, and device state rather than using static predefined mappings. This allows the system to adjust to individual user capabilities and preferences while maintaining consistent and reliable gesture recognition across different users and situations.
Solution Approach 2:
The system incorporates feedback mechanisms that analyze user interactions and continuously refine gesture mappings. By monitoring user behavior patterns, success rates, and contextual information, the system learns from user actions and adjusts mappings to improve both naturalness and reliability over time.
2Adaptability or versatility
If a large number of gestures are defined to cover all possible interactions, then input versatility is improved, but the burden on users to remember each gesture increases
Solution Approach 1:
The system creates universal gesture mappings that can be applied across multiple contexts and applications. By analyzing usage patterns and contextual information, a single gesture can be mapped to different functions depending on the situation, reducing the total number of gestures users need to learn while maintaining versatile input capabilities.
Solution Approach 2:
The system changes the parameters of gesture mappings dynamically based on context, user preferences, and usage frequency. Common interactions are assigned to simpler or more intuitive gestures, while less frequent interactions use more complex gestures, optimizing the balance between versatility and ease of use.
3Reliability
If predefined gesture mappings are used to provide consistent inputs, then system reliability is improved, but the inability to accommodate user-specific contexts and preferences reduces accessibility
Solution Approach 1:
The system transitions from static predefined mappings to dynamic adaptive mappings that evolve based on user behavior and context. This allows the system to maintain reliability through consistent recognition while adapting to individual user preferences, physical capabilities, and situational contexts.
Solution Approach 2:
The system automatically analyzes user interactions and adjusts gesture mappings without requiring explicit user programming or configuration. Users benefit from personalized mappings that adapt to their natural gestures and preferences, while the system maintains consistency through automated learning and refinement processes.
Data Source
AI summary
An input generation system for generating inputs in dependence upon gestures made by a user, the system comprising a user tracking unit configured to track a user's motion, a gesture identification unit configured to identify one or more gestures made by a user in dependence upon the tracked motion, an input generation unit configured to generate an input representative of the identified gesture, the input being used to provide an interaction with an application being executed by a processing device, wherein if no predefined mapping of the identified gesture to an input exists, the input generation unit is configured to generate the input in dependence upon a prediction of the user's intended interaction.