AR Hand-Tracking Input Using Structured Gesture Components
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Solution Overview
Problem
AR systems on head-worn devices lack effective user input modalities, making it difficult for users to indicate intent and invoke actions due to the absence of physical input devices like touchscreens or keyboards, limiting interaction capabilities.
Innovation Solution
Implementing computer vision-based hand-tracking with Direct Manipulation of Virtual Objects (DMVO) and gesture recognition frameworks to enable users to interact with AR systems through natural hand movements and gestures, decomposing these into structured gesture components to enhance input modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If physical input devices like touchscreens or keyboards are added to head-worn AR devices, then user input capability is improved, but device weight and complexity increase
Solution Approach 1:
The patent replaces physical mechanical input devices (touchscreens, keyboards) with a computer vision-based hand-tracking system. The system uses cameras to capture hand movements and machine learning algorithms to interpret gestures as input commands, eliminating the need for physical input hardware while maintaining or improving ease of operation.
Solution Approach 2:
The patent introduces an intermediary software layer (gesture recognition framework and machine learning model) that translates physical hand gestures into digital input commands. This intermediary system bridges the gap between the user's natural movements and the digital interface without requiring physical input devices to be integrated into the head-worn device.
2Ease of operation
If physical input devices like touchscreens or keyboards are added to head-worn AR devices, then user input capability is improved, but device weight increases
Solution Approach 1:
The patent replaces physical mechanical input devices (touchscreens, keyboards) with a computer vision-based hand-tracking system. The system uses cameras to capture hand movements and machine learning algorithms to interpret gestures as input commands, eliminating the need for physical input hardware while maintaining or improving ease of operation.
3Adaptability or versatility
If gesture recognition framework is implemented, then user interaction range is expanded, but computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the gesture recognition process into distinct modular components: hand detection module, landmark identification module, gesture classification module, and input generation module. This segmentation allows for optimized processing at each stage and enables the system to handle multiple gesture types without proportionally increasing overall computational complexity.
Solution Approach 2:
The patent implements a hierarchical gesture recognition approach where the system first identifies basic hand presence, then progressively recognizes more complex gestures only when needed. This partial action approach allows the system to maintain low computational overhead for simple cases while expanding interaction capabilities for complex tasks.
Data Source
AI summary
A hand-tracking platform generates gesture components for use as user inputs into an application of an Augmented Reality (AR) system. In some examples, the hand-tracking platform generates real-world scene environment frame data based on gestures being made by a user of the AR system using a camera component of the AR system. The hand-tracking platform recognizes a gesture component based on the real-world scene environment frame data and generates gesture component data based on the gesture component. The application utilizes the gesture component data as user input in a user interface of the application.