Two-Handed Gesture Interpretation in XR Systems
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Solution Overview
Problem
Current hand-based input systems for extended reality (XR) environments struggle to accurately interpret two-handed gestures and preserve user privacy, as they often require extensive input recognition capabilities within applications, which can be complex and invasive.
Innovation Solution
The system employs an operating system process that interprets user activity data from two hands in a 3D coordinate system, distinguishing between user-centric, app-centric, and hybrid gestures using motion and context data, and provides limited data to applications to maintain privacy, allowing for accurate input recognition without extensive input recognition capabilities within the applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If applications implement extensive input recognition capabilities to accurately interpret hand gestures, then gesture interpretation accuracy improves, but application complexity increases
Solution Approach 1:
The system divides the input recognition functionality into separate modules: hand tracking components that capture raw hand data, gesture recognition components that interpret gestures, and application-specific processing components. This segmentation allows accurate gesture interpretation while keeping individual application complexity low, as the heavy lifting is done by dedicated system-level components.
Solution Approach 2:
The patent introduces an intermediary layer between the hand tracking system and applications - a gesture recognition service that processes raw hand data and translates it into meaningful gestures. This intermediary handles the complex interpretation logic centrally, allowing applications to receive simplified gesture events without implementing their own extensive recognition capabilities.
2Measurement precision
If applications receive extensive user activity data to improve gesture recognition, then input accuracy improves, but user privacy is compromised
Solution Approach 1:
The system extracts and processes sensitive user activity data in a secure, centralized location rather than exposing it to applications. Raw hand tracking data and intermediate gesture interpretations are processed by the system-level gesture recognition service, which filters and anonymizes the data before passing relevant information to applications, thereby maintaining input accuracy while protecting user privacy.
Solution Approach 2:
A privacy-preserving intermediary layer processes user activity data before making it available to applications. This intermediary performs necessary processing for accurate gesture recognition while removing or anonymizing personally identifiable information and sensitive context, allowing applications to benefit from accurate input recognition without direct access to raw user data.
3Measurement precision
If the system processes all user activity data to distinguish gesture types, then gesture differentiation accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary processing of user activity data by pre-computing hand tracking information, motion vectors, and basic gesture features as data is collected. This preliminary action prepares the data in advance, so when gesture type differentiation is needed, the system can quickly compare pre-computed features against known gesture patterns without processing all raw data from scratch, thereby maintaining high accuracy while reducing processing time.
Solution Approach 2:
The system implements a multi-stage processing approach where only the necessary portion of user activity data is processed at each stage. For common gestures, the system uses simplified processing with key features, while reserving full processing power for ambiguous cases that require more comprehensive analysis. This partial processing strategy maintains gesture differentiation accuracy for critical cases while reducing overall processing time.
Data Source
AI summary
Devices, systems, and methods that interpret user activity, such as two hand gestures, as user interactions with virtual elements positioned within a three-dimensional (3D) space. For example, an example process may include receiving data corresponding to user activity involving two hands in a 3D coordinate system. The process may further include identifying actions performed by the two hands based on the data corresponding to the user activity, each of the two hands performing one of the identified actions. The process may further include determining whether the identified actions satisfy a criterion for a gesture type based on the data corresponding to the user activity. The process may further include interpreting the identified actions based on a reference element corresponding to the gesture type, wherein different gesture types correspond to different reference elements in accordance with determining that the identified actions satisfy the criterion for the gesture type.