Predictive 3D Virtual Touch Detection for Responsive XR Interaction
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Solution Overview
Problem
Existing user interaction systems in extended reality (XR) environments face challenges in accurately detecting and responding to user interactions, such as touches and gestures, due to system latency and inaccuracies in tracking user movements, leading to inconsistent and delayed responses.
Innovation Solution
Implementing a predictive model that uses position and velocity information to anticipate user interactions, such as touches, by setting fuzzy boundaries and adjusting detection sensitivity based on interaction types, to account for system latency and improve responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If predictive modeling is used to anticipate user interactions, then responsiveness is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by predicting future user interactions before they occur. The predictive model analyzes current position and velocity data to forecast upcoming touch events, allowing the system to prepare and respond more quickly. This principle directly addresses the responsiveness improvement while managing complexity through targeted prediction rather than comprehensive system overhaul.
Solution Approach 2:
The patent replaces traditional mechanical touch detection (waiting for physical contact) with a computational prediction system. Instead of relying solely on sensor data from actual finger-surface contact, the system uses algorithms to predict interaction events based on motion trajectories, substituting mechanical detection with intelligent computation to achieve faster response times.
2Measurement precision
If fuzzy boundaries are used to account for tracking inaccuracies, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system applies parameter changes by introducing fuzzy boundary thresholds to the touch detection process. Instead of using fixed, precise coordinates for touch targets, the system defines flexible boundary regions that account for tracking inaccuracies. This allows the system to adapt to variations in user movement and tracking precision without requiring more complex hardware.
3Speed
If prediction is made based on position and velocity information, then responsiveness is improved, but measurement precision may deteriorate
Solution Approach 1:
The predictive system incorporates feedback mechanisms to maintain accuracy while improving responsiveness. The model continuously compares predicted interaction events with actual sensor data, adjusting its predictions based on discrepancies. This feedback loop ensures that the system maintains measurement precision even as it responds faster to user interactions.
Data Source
AI summary
Various implementations interpret user activity as user interactions with virtual elements (e.g., user interface elements) positioned within in a 3D space such as an XR environment. Some implementations enable a user to provide input using a direct input modality in which the user interacts with virtual content by virtually touching the virtual content. As examples, a user may move their finger to directly tap, pinch, swipe, or otherwise interact with a user interface (UI) element within a 3D space. Some implementations perform touch detection (e.g., detecting when a user's finger virtually makes, continues, and breaks virtual contact with a UI element in 3D space) using position and velocity information about the user.