3D Skeletal Movement Recognition System for Sign Language Translation
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Solution Overview
Problem
Current systems for interpreting human body movements, such as in sign language, require specialized knowledge and are not capable of real-time, accurate translation into written or spoken language, particularly for hearing-impaired individuals who face linguistic barriers in daily life and emergencies.
Innovation Solution
An automatic body movement recognition and association system utilizing three-dimensional skeletal data from a stand-alone depth-sensing image capture device, which identifies and processes body motions to translate them into written or spoken words, incorporating a 'live testing' engine and 'off-line' training component to improve accuracy and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized knowledge (trained psychologists, medical professionals, sign language interpreters) is used to interpret body movements, then interpretation accuracy is improved, but system complexity and operational difficulty increase
Solution Approach 1:
The system performs automatic body movement recognition and interpretation without requiring specialized human operators. The computer vision system and machine learning algorithms autonomously capture, process, and translate body movements into meaningful information, eliminating the need for trained psychologists, medical professionals, or sign language interpreters to manually analyze movements.
Solution Approach 2:
The patent replaces the mechanical system of human expert analysis with an automated computer vision system. Depth-sensing cameras capture three-dimensional skeletal data, which is then processed by algorithms to recognize and interpret body movements, substituting human cognitive processing with computational analysis.
2Measurement precision
If manual interpretation by trained professionals is used, then accurate understanding of body movements is achieved, but real-time processing capability deteriorates
Solution Approach 1:
The system replaces manual interpretation processes with automated computer vision and machine learning algorithms that can process body movement data in real-time. The depth-sensing cameras continuously capture skeletal information, and the system immediately processes and interprets the movements without the time delays inherent in manual analysis by professionals.
Solution Approach 2:
The system enables continuous, uninterrupted processing of body movement data. The depth-sensing cameras continuously capture three-dimensional skeletal information, and the automated recognition system processes this data in real-time without the interruptions that occur when multiple professionals need to analyze movements sequentially.
3Reliability
If traditional body movement interpretation systems are used, then specialized knowledge requirements are met, but accessibility for non-experts deteriorates
Solution Approach 1:
The system provides automated body movement interpretation that is accessible to anyone without requiring specialized knowledge. Users can simply perform body movements while the system automatically captures, recognizes, and interprets them, making the technology usable by the general public including hearing-impaired individuals who use sign language, without needing trained operators.
Solution Approach 2:
The patent introduces an automated computer vision system as an intermediary between body movements and their interpretation. This intermediary system handles the complex analysis and translation processes, bridging the gap between physical movements and meaningful information in a way that is accessible to non-experts while maintaining reliability.
4Measurement precision
If comprehensive training data is collected for building classifiers, then recognition accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of training data by extracting relevant features and creating standardized representations before building classifiers. This preprocessing step organizes the comprehensive training data in a way that accelerates the classifier training process while maintaining high recognition accuracy, reducing the time needed to process large datasets.
Data Source
AI summary
An automatic body movement recognition and association system that includes a preprocessing component and a “live testing” engine component. The system further includes a transition posture detector module and a recording module. The system uses three dimensional (3D) skeletal joint information from a stand-alone depth-sensing capture device that detects the body movements of a user. The transition posture detector module detects the occurrence of a transition posture and the recording module stores a segment of body movement data between occurrences of the transition posture. The preprocessing component processes the segments into a preprocessed movement that is used by a classifier component in the engine component to produce text or speech associated with the preprocessed movement. An “off-line” training system that includes a preprocessing component, a training data set, and a learning system also processes 3D information, off-line from the training data set or from the depth-sensing camera, to continually update the training data set and improve a learning system that sends updated information to the classifier component in the engine component when the updated information is shown to improve accuracy.