Hand Tracking Mode Switching for XR Gesture Input
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Solution Overview
Problem
Existing Extended Reality (XR) systems face challenges in accurately tracking hand gestures, leading to accidental input recognition, which reduces user interaction efficiency and increases accidental inputs.
Innovation Solution
Implementing active and inactive modes for hand tracking, where different parameters are used to recognize gestures, with active mode being more permissive and inactive mode being more restrictive, to differentiate intentional and unintentional gestures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If gesture recognition parameters are made more permissive to improve responsiveness, then user input detection is improved, but accidental input recognition increases
Solution Approach 1:
The system dynamically adjusts gesture recognition parameters based on the detected state of the hand (active vs. inactive). When an active state is detected, permissive parameters are applied for rapid response. When an inactive state is detected, restrictive parameters are applied to prevent accidental input. This dynamic parameter adjustment resolves the contradiction by making the system responsive when needed and reliable when not needed.
Solution Approach 2:
The system changes recognition parameters (such as gesture threshold, detection sensitivity, and gesture duration requirements) based on the hand state. In active mode, parameters are set to be more permissive with lower thresholds. In inactive mode, parameters are set to be more restrictive with higher thresholds. This parameter change strategy allows the system to optimize between responsiveness and reliability based on contextual state.
2Reliability
If gesture recognition parameters are made more restrictive to reduce accidental inputs, then reliability is improved, but responsiveness to intentional gestures deteriorates
Solution Approach 1:
The system dynamically switches between restrictive and permissive parameter sets based on hand state detection. When the hand is in an inactive state, restrictive parameters are applied to reduce accidental inputs. When the hand transitions to an active state, the system switches to permissive parameters to restore responsiveness. This dynamic switching resolves the contradiction by applying the appropriate parameter strictness based on contextual need.
Solution Approach 2:
The system implements two distinct parameter sets: one for active state with permissive thresholds, and one for inactive state with restrictive thresholds. The parameter changes include adjustments to gesture detection sensitivity, minimum gesture duration, and activation thresholds. This parameter differentiation allows the system to be reliable during inactive periods while remaining responsive during active periods.
3Measurement precision
If hand tracking is continuously monitored with high sensitivity, then gesture detection accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary detection of hand presence and active state using less computationally intensive methods. Only when an active state is detected does the system engage full high-sensitivity gesture recognition processing. This preliminary action filters out inactive periods where full processing would be wasteful, reducing overall processing time while maintaining detection accuracy when needed.
Solution Approach 2:
The system applies partial processing during inactive states (basic hand presence detection) and full processing during active states (comprehensive gesture recognition). This partial action during inactive periods reduces computational resource consumption and processing time, while ensuring full accuracy is applied when gesture detection is actually needed during active periods.
Data Source
AI summary
Processing gesture input includes obtaining hand tracking data for a first hand based on one or more camera frames, detecting a first input gesture by the first hand based on the hand tracking data, and determining whether the first hand is in an active state. An input action associated with the first gesture is initiated in accordance with a determination that the first hand is in the active state. If, while the hand is in an active state, a determination is made that the inactive criterion is satisfied, then the first hand is transitioned to an inactive state.