Mobile Sign Language Recognition With Offline Vision Processing
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Solution Overview
Problem
Existing sign language interpretation systems face challenges in real-time accuracy, handling variations in signing styles and environmental conditions, computational complexity on mobile devices, and lack of privacy and security, limiting their practical utility and accessibility.
Innovation Solution
A computer vision system using a machine-learned model with a pretrained ResNet-50 model, dynamic programming, and CNNs for efficient sign language recognition on mobile devices, incorporating end-to-end encryption for privacy, and offline processing to avoid latency and cloud dependency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional computer vision techniques are used for sign language recognition, then the system can process basic gestures, but it struggles with variations in lighting, camera angles, and user movements
Solution Approach 1:
The patent transforms sign language video data into multiple parameter representations including optical flow fields, gradient orientations, and histogram of oriented gradients (HOG). These parameter transformations enable the system to capture essential gesture characteristics while being invariant to lighting changes, camera angles, and user movements, thereby resolving the contradiction between adaptability and precision
Solution Approach 2:
The patent segments the sign language recognition process into distinct feature extraction stages: optical flow computation, gradient calculation, HOG feature generation, and temporal pattern recognition. This segmentation allows each component to specialize in handling specific aspects of variation (spatial, temporal, lighting), improving overall recognition accuracy across diverse conditions
2Productivity
If cloud processing is used for sign language interpretation, then computational power is sufficient, but it introduces latency and privacy concerns
Solution Approach 1:
The patent extracts the complete sign language interpretation processing capability from cloud-based systems and implements it locally on mobile devices. By taking out the computational processing from the cloud environment and embedding it in the edge device, the system eliminates network latency while maintaining interpretation functionality, directly resolving the time loss contradiction
Solution Approach 2:
The patent enables mobile devices to perform self-service sign language interpretation through locally deployed machine learning models. The device independently processes video input, extracts features, and generates text output without requiring external cloud services, thereby eliminating network dependency and latency while preserving privacy
3Measurement precision
If complex machine learning models are deployed on mobile devices, then recognition accuracy improves, but computational resources and battery life are limited
Solution Approach 1:
The patent implements a hierarchical feature extraction approach where simpler features (optical flow, gradients) are computed first, and only essential higher-level features are processed further. This partial action strategy maintains recognition accuracy by focusing computational resources on the most discriminative features while reducing overall computational burden and energy consumption
Solution Approach 2:
The patent segments the computational workload into distinct processing stages: lightweight optical flow computation, gradient calculation, HOG feature extraction, and temporal pattern recognition. This segmentation allows the system to distribute computational demands across different resource profiles, maintaining accuracy while managing mobile device energy constraints effectively
Data Source
AI summary
In one aspect, a computerized process useful for managing a hybrid motion sensing framework includes the step of providing a motion capture framework worn by a user to measure a user posture and motion by measuring an external source signal and an inertial property of the motion capture framework. The motion capture framework comprises a set of motion sensing units (MSUs) and an electromagnetic field generator (EFG). The MSU is a hybrid sensing system using a combination of sensors to measure position and orientation. The EFG generates an alternating electromagnetic field with a specified frequency. The method includes the step of calculating the user posture and motion based on the measuring an external source signal and an inertial property of the motion capture framework using a sensor fusion algorithm. The method includes the step of visualizing the position and orientation of the user.