Mixed Reality Gesture Interpretation for Sign Language
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Solution Overview
Problem
There is no globally accepted standardized sign language, making it challenging for hearing-impaired individuals to communicate effectively with those who are not fluent in sign language.
Innovation Solution
A system using a machine-learned model that receives information about gestures or signs via a mixed reality device, calculates probabilities of their meanings, and interprets them in real-time, providing the interpretation to another user, potentially through text or audio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sign language interpretation is performed manually by human interpreters, then communication accuracy is improved, but communication efficiency and accessibility are worsened due to lack of real-time interpretation and limited availability
Solution Approach 1:
The patent replaces the mechanical system of manual human interpretation with an automated machine learning-based interpretation system. The system uses computer vision to capture gestures, machine learning models to interpret them, and automatically generates translations, eliminating the need for human interpreters while maintaining interpretation accuracy and enabling real-time communication.
Solution Approach 2:
The patent introduces an automated interpretation system as an intermediary between sign language users and non-sign language users. This intermediary system captures gestures, interprets them through machine learning models, and translates them into spoken or written language, facilitating communication without requiring direct human interpretation.
2Speed
If real-time gesture interpretation is implemented, then communication speed is improved, but system complexity and computational requirements are worsened
Solution Approach 1:
The patent segments the gesture interpretation system into distinct functional modules: gesture capture module, machine learning interpretation module, and translation output module. This segmentation allows each module to be optimized independently and enables distributed processing across multiple devices, reducing the complexity burden on any single device while maintaining real-time performance.
Solution Approach 2:
The patent implements preliminary action by pre-training machine learning models with extensive gesture datasets before deployment. This pre-processing of interpretation capabilities allows the system to perform real-time translations with reduced computational complexity during actual use, as the heavy lifting of pattern recognition has already been established during the training phase.
3Adaptability or versatility
If comprehensive gesture recognition is provided for all sign languages, then adaptability is improved, but model complexity and training data requirements are worsened
Solution Approach 1:
The patent implements a universal gesture recognition framework that can handle multiple sign languages through a common machine learning architecture. The system uses language-specific adaptation layers that can be configured for different sign languages without requiring completely separate models, enabling multi-language support while controlling overall system complexity through shared processing components.
Data Source
AI summary
Described herein is are systems and methods for interpreting gesture(s) and/or sign(s) using a machine-learned model. Information regarding gesture(s) and/or sign(s) is received from a first user. The information can be received via a mixed reality device of the first user and/or a second user. Probabilities that the gesture(s) or sign(s) have particular meanings are calculated using a machine-trained model. The gesture(s) and/or sign(s) are interpreted in accordance with the calculated probabilities. Information regarding the interpreted gesture(s) and/or sign(s) are provided (e.g., displayed as visual text and/or an audible output) to the second user.