Customizable Gesture Commands for Video Conferencing
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Solution Overview
Problem
Video conferencing systems lack the ability to recognize and respond to user-customized video gestures, limiting user control inputs and varying between different users.
Innovation Solution
A method and system that constructs gesture containers, trains machine learning models to detect video gestures, and executes commands assigned to these gestures, allowing for customizable control inputs in video conferencing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If video conferencing systems use hidden user commands to prevent interface overload, then user interface simplicity is improved, but user control flexibility deteriorates
Solution Approach 1:
The patent introduces video gestures as an intermediary control mechanism between the user and the video conferencing system. These gestures are detected through computer vision technology and mapped to specific commands, allowing users to control the system without directly interacting with the hidden command interface. This resolves the contradiction by providing flexible user control through natural gestures while maintaining the simplicity of the underlying command structure.
Solution Approach 2:
The patent replaces traditional mechanical input devices (buttons, switches, menu selections) with video-based gesture recognition. The mechanical interaction is substituted by capturing and analyzing video streams of user gestures, enabling more intuitive and flexible control while keeping the system interface simple and hidden.
2Adaptability or versatility
If video conferencing systems support a wide variety of user commands, then system functionality is improved, but user interface complexity deteriorates
Solution Approach 1:
Video gestures serve as an intermediary layer that simplifies access to diverse system functions. Instead of presenting users with a complex menu structure, the system recognizes natural gestures and maps them to appropriate commands, enabling full system functionality through simple, intuitive physical actions.
Solution Approach 2:
The gesture recognition system provides a universal control interface that can trigger multiple different commands depending on the gesture detected. A single gesture recognition mechanism handles diverse functions (muting, camera control, screen sharing, etc.), eliminating the need for separate controls for each function and reducing overall interface complexity.
3Adaptability or versatility
If video conferencing systems recognize video gestures, then user control inputs are improved, but gesture recognition accuracy deteriorates due to user variability
Solution Approach 1:
The system performs preliminary actions by capturing multiple training images of user gestures before actual recognition begins. These training images are used to create personalized gesture models for each user, allowing the system to adapt to individual gesture styles and improve recognition accuracy before live conferencing starts.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously learns from gesture recognition outcomes. When gestures are successfully recognized, the system reinforces those patterns; when recognition fails, it adjusts its interpretation, improving accuracy over time while maintaining adaptability to different user control inputs.
Data Source
AI summary
A method implements customizable gesture commands. The method includes constructing a set of gesture containers and training a machine learning model, for a gesture container of the set of gesture containers, to detect a performance of a video gesture. The method further includes detecting the performance of the video gesture, from a gesture container of the set of gesture containers, in a video stream using the machine learning model. The method further includes executing, in response to detecting the video gesture, a command assigned to the video gesture by the gesture container.