Adaptive Gesture Triggering for Repeated XR Selection Attempts
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Solution Overview
Problem
Existing XR environments face challenges in accurately tracking and managing hand gestures, leading to accidental rejection of user input actions, particularly in failed indirect selection attempts where a user's input gesture is associated with selecting a user interface component.
Innovation Solution
A technique is introduced to modify gesture recognition by increasing the target region around interface elements and making gesture detection parameters more permissive on subsequent attempts, ensuring that repeated gestures are recognized as valid inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gesture detection parameters are made strict to avoid false positives, then input accuracy is improved, but user input gestures are accidentally rejected
Solution Approach 1:
The patent applies dynamics by modifying gesture detection parameters dynamically based on attempt history. On repeated attempts of the same gesture type, the system automatically relaxes detection thresholds and expands target regions, transforming a static detection system into an adaptive one that responds to user behavior patterns.
Solution Approach 2:
The system implements feedback by monitoring whether gestures are successfully recognized and adjusting detection parameters accordingly. When a gesture is rejected, the system learns from this failure and modifies subsequent detection behavior, creating a closed-loop control system that improves recognition over time.
2Reliability
If target region is increased to improve gesture recognition, then repeated gestures are more likely to be recognized, but precision of gesture targeting deteriorates
Solution Approach 1:
The target region size is made dynamic rather than static. The system automatically adjusts the target region expansion based on the attempt number, applying larger expansions on repeated attempts while maintaining normal precision on first attempts. This temporal differentiation resolves the contradiction between reliability and precision.
Solution Approach 2:
The patent changes detection parameters (target region size, threshold values) based on the gesture attempt history. By modifying these parameters dynamically according to whether it's a first or repeated attempt, the system achieves both high precision on initial attempts and high reliability on repeated attempts.
3Reliability
If gesture detection is made permissive to reduce rejections, then user interaction accuracy is improved, but false positive gestures increase
Solution Approach 1:
The patent segments the gesture detection process into different phases: first attempts use strict detection to avoid false positives, while repeated attempts use permissive detection to ensure recognition. This temporal segmentation allows the system to optimize for different goals at different times, resolving the contradiction between reliability and precision.
Solution Approach 2:
The system performs preliminary detection on first attempts with strict criteria to establish a baseline. When gestures fail initially, the system then applies permissive detection on repeated attempts. This preliminary strict detection prevents false positives while allowing subsequent permissive detection to catch legitimate repeated gestures.
Data Source
AI summary
Processing gesture input includes detecting a first instance of a gesture based on hand tracking data, determining that the first instance of the gesture fails to satisfy an input action trigger based on a first set of heuristics for the gesture, and detecting a second instance of the gesture based on additional hand tracking data. In accordance with a determination that the second instance of the gesture is detected within a threshold time from the first instance of the gesture, a determination is made as to whether the second instance of the gesture satisfies the input action trigger based on a second set of heuristics for the gesture.