Image-Based Hand Touch Detection for Precise XR Input
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Solution Overview
Problem
Extended Reality (XR) systems face challenges in accurately interpreting user gestures, particularly in environments requiring precise and intuitive user inputs, often relying on physical controllers or less precise gesture recognition, which affects user experience and restricts their use in professional and creative settings.
Innovation Solution
A hand touch detection process that interprets detailed hand touch interactions directly from captured images, integrating haptic feedback without separate haptic components, and uses machine learning to recognize subtle variations in touch pressure and gesture dynamics, enhancing gesture recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical controllers are used for user input, then gesture recognition accuracy is improved, but device complexity and user convenience deteriorate
Solution Approach 1:
The patent replaces physical controllers with an optical sensing system that uses cameras to capture hand images and machine learning models to recognize gestures. This substitutes mechanical input devices with a vision-based system, eliminating the need for physical controllers while maintaining gesture recognition accuracy.
Solution Approach 2:
The system creates a virtual copy of the hand's position and gesture from captured images, allowing the user to interact with the XR environment using natural hand movements rather than physical controllers. The machine learning model processes image data to generate gesture recognition results that mirror real-world hand actions.
2Measurement precision
If physical controllers are used for user input, then gesture recognition accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The system uses the user's own hand as the input interface, eliminating the need to learn and adapt to external physical controllers. The machine learning model is trained to recognize natural hand gestures, allowing users to interact intuitively with their own body movements rather than requiring separate control devices.
3Device complexity
If traditional gesture recognition technologies are used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system changes the parameter of gesture detection from simple motion tracking to detailed image analysis with machine learning classification. By processing image data through trained models, the system achieves high gesture recognition accuracy while maintaining relatively simple device architecture using existing camera hardware.
Data Source
AI summary
An XR system is provided. This system captures images including images of a first hand of a user and a second hand of the user using one or more cameras. The XR system generates cropped images using the images, each cropped image including a surface of the first hand. The XR system detects a hand touch of the surface of the hand by a digit of the second hand using the cropped images. The hand touch is used as an input into an XR user interface of the XR system. The surface of the hand can be palmar surface or a hand dorsal surface.