Eyewear Sign Language Translation via CNN Gesture Recognition
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Solution Overview
Problem
Current portable eyewear devices, such as smart glasses, lack effective solutions for real-time object recognition, translation of sign language, and assistance for visually impaired users, as they do not efficiently convert visual information into audible feedback or text.
Innovation Solution
The eyewear device employs a convolutional neural network (CNN) to identify hand gestures and objects within the user's field of view, converting this information into speech through a camera-based system, enabling real-time translation of sign language and reading of visual content for users with disabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If portable eyewear devices integrate cameras and see-through displays, then the device functionality is enhanced, but the ability to provide real-time sign language translation and object recognition is insufficient
Solution Approach 1:
The patent introduces an intermediary processing system that captures video input, processes it through neural networks for sign language recognition and object detection, and outputs translated text or speech. This intermediary layer bridges the gap between basic camera/display integration and sophisticated real-time translation capabilities, resolving the contradiction by adding specialized processing functionality without requiring complete system redesign.
Solution Approach 2:
The eyewear device is designed to perform multiple functions including sign language translation, object recognition, and visual information-to-audio conversion within a single platform. By making the device universal and multi-functional, the patent enhances adaptability while maintaining reliable core translation capabilities through integrated processing.
2Measurement precision
If the eyewear device uses CNN to identify hand gestures and objects in real-time, then translation accuracy is improved, but processing speed and computational load increase
Solution Approach 1:
The system performs preliminary actions by pre-processing video frames, pre-training neural network models for specific gesture recognition tasks, and pre-establishing translation dictionaries. This preliminary preparation enables faster real-time processing while maintaining high accuracy, as the computationally intensive work is done in advance rather than during real-time interaction.
Solution Approach 2:
The patent segments the complex gesture recognition task into distinct components: hand detection, gesture classification, and translation generation. By dividing the processing into separate stages with specialized algorithms for each, the system achieves both high accuracy through focused processing and improved speed by parallelizing independent tasks.
3Adaptability or versatility
If the device converts visual information to audible feedback, then accessibility for visually impaired users is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical or hardware-based visual-to-audio conversion systems with software-based neural network processing and digital signal processing. This substitution maintains the accessibility function while reducing physical complexity, as the conversion is achieved through algorithmic processing rather than mechanical components.
Solution Approach 2:
An intermediary software layer is introduced that handles the conversion from visual input to audible output through text-to-speech synthesis. This mediator simplifies the overall system architecture by creating a clear processing pipeline: camera captures visual information, neural network processes it, and speech synthesis converts it to audio, making the system more manageable despite its multifunctionality.
Data Source
AI summary
An eyewear having an electronic processor configured to identify a hand gesture including a sign language, and to generate speech that is indicative of the identified hand gesture. The electronic processor uses a convolutional neural network (CNN) to identify the hand gesture by matching the hand gesture in the image to a set of hand gestures, wherein the set of hand gestures is a library of hand gestures stored in a memory. The hand gesture can include a static hand gesture, and a moving hand gesture. The electronic processor is configured to identify a word from a series of hand gestures.