Hand Gesture Code Injection in IDE Using Depth Camera
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Solution Overview
Problem
Traditional human-machine interfaces rely on mediator hardware, making them less natural and less efficient for intense human-computer interaction, and existing methods struggle to provide a straightforward, intuitive way for users to interact with computers using hand gestures without requiring extensive machine learning or computer vision processing.
Innovation Solution
A system and method that allow programmers to inject code into a graphical user interface (GUI) of an integrated development environment (IDE) by capturing and analyzing hand gestures using a camera, which are defined by discrete pose and motion values, allowing for the creation of code segments that can be used to interact with computerized machines, reducing the need for complex processing and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional human-machine interfaces use mediator hardware devices, then interaction is achieved through established input/output devices, but the interface becomes less natural and less efficient for intense human-computer interaction
Solution Approach 1:
The patent extracts the hand gesture recognition capability from complex computer vision systems and isolates it into a discrete, manageable interface. By taking out only the essential gesture detection function and representing it through discrete values, the system eliminates the need for extensive mediator hardware while maintaining natural interaction.
Solution Approach 2:
The patent changes the parameter representation from continuous computer vision data to discrete gesture values. This parameter transformation simplifies the interface by converting complex visual information into a finite set of discrete states, reducing hardware complexity while preserving natural gesture-based operation.
2Measurement precision
If existing methods use extensive machine learning and computer vision processing for hand gesture recognition, then gesture recognition accuracy may improve, but the processing complexity and computational requirements increase significantly
Solution Approach 1:
The patent segments the gesture recognition process into discrete, predefined gesture states. Instead of using continuous machine learning models, the system divides gesture space into distinct categories with specific discrete values, allowing accurate recognition without the computational burden of extensive processing.
Solution Approach 2:
The patent uses lightweight, discrete gesture representations instead of heavy machine learning models. These simple discrete values act as 'cheap' computational objects that can be processed quickly and efficiently, providing accurate gesture recognition without requiring expensive, complex computational resources.
3Loss of information
If continuous computer vision processing is used for hand gesture analysis, then detailed gesture information can be captured, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-defining discrete gesture states and their corresponding values before runtime processing. This allows the system to quickly match observed gestures against predefined categories without performing complex real-time analysis, reducing processing time while maintaining gesture information integrity.
Data Source
AI summary
A system of injecting a code section to a code edited by a graphical user interface (GUI) of an integrated development environment (IDE), comprising: a memory storing a dataset associating each code segment with one hand pose feature or hand motion feature; an imager adapted to capture images of a hand while an IDE being executed on a client terminal; and processor for executing code of an application, comprising: code instructions to identify at least one of the features and at least one discrete value of the identified features from an analysis of the images; code instructions to select at least one of the code segments associated with the identified features; and code instructions to add automatically a code section generated based on the code segments and the discrete value to a code presented by a code editor of the IDE.