Configurable Panel Radar for 3D Gesture Reconstruction
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Solution Overview
Problem
Traditional radar-based human activity detection and 3D reconstruction systems are limited by their reliance on a single radar to capture the entire human body, which affects accuracy and complexity in gesture recognition and classification.
Innovation Solution
The use of a configurable panel radar system with two radars to individually capture the upper and lower parts of the human body, combined with machine learning classification and pattern matching techniques for 3D reconstruction, enabling precise identification and reconstruction of human gestures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single radar is used to capture the entire human body, then the device complexity is reduced, but the measurement precision and reliability of gesture recognition deteriorate
Solution Approach 1:
The patent divides the human body into multiple segments (upper body and lower body) and assigns different radars to capture each segment. The system processes radar data from multiple radars separately and then fuses the results, enabling precise classification of gestures performed by different body parts while maintaining manageable system complexity through modular architecture
2Device complexity
If a single radar is used to capture the entire human body, then the device complexity is reduced, but the reliability of gesture classification deteriorates
Solution Approach 1:
The system segments the gesture recognition task by assigning different radars to monitor different body regions. Each radar independently classifies gestures within its monitoring zone, and the results are fused to produce a reliable overall classification, thereby improving gesture classification reliability without significantly increasing system complexity
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors radar data, compares classified gestures against expected patterns, and adjusts classification parameters accordingly. This feedback loop enhances the reliability of gesture classification by correcting errors and adapting to varying gesture characteristics
3Measurement precision
If multiple radars are used to individually capture body parts, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the monitoring task across multiple radars, each focused on specific body parts. This segmentation improves measurement precision for each gesture type while managing complexity through a structured modular architecture where each radar operates semi-independently
Solution Approach 2:
The system employs universal processing algorithms that can handle data from any radar configuration. The gesture classification framework is designed to work with multiple radars monitoring different body parts, allowing the same processing pipeline to universally handle various gesture types and radar arrangements without requiring separate specialized systems
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly improves the accuracy of human gesture recognition and 3D reconstruction, achieving a high level of precision and efficiency in classifying and reconstructing human motions, as demonstrated by performance results showing a 93.3% accuracy compared to traditional single-radar systems.
Implementation Method 1
Radar-based gesture recognition systems can interact with applications or an operating system of computing devices, or remotely through a communication network by transmitting input responsive to recognizing gestures
Data Source
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AI summary
This disclosure relates generally to radar based human activity detection, and, more particularly to, systems and methods from radar based human activity detection and three-dimensional (3D) reconstruction of human gestures using configurable panel radar system. Traditional systems and methods may not provide for a separate capturing of top and bottom parts of the human body. Embodiment of the present disclosure overcome the limitations faced by the traditional systems and methods by identifying a user that performed a gesture; detecting each gesture performed by the identified user; generating, by simulating a set of gesture labels, a sensor data and the generated metadata, a two-dimensional (2D) reference database of different speeds of the detected gestures; computing a displacement and a time of the detected gestures via a pattern matching technique; and reconstructing a video of the identified user performing the detected gestures in 3D.