Radar Gesture Recognition Using Max-Amplitude Frame Features
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Solution Overview
Problem
Doppler-radar based gesture recognition systems require high computational complexity, leading to a heavy calculation load that can interfere with the normal operation of smart devices.
Innovation Solution
A gesture recognition method that selects the cell with the maximum amplitude from each chirp to generate a sensing map, determining frame amplitudes, phases, and ranges, which are then used as input data for a neural network to classify gesture events, reducing the number of parameters needed and thus decreasing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all cells of the range Doppler image are used as input data for the neural network, then the gesture recognition accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent extracts only the maximum amplitude value from each cell of the range Doppler image, rather than using all cells as input data. This extraction process reduces the dimensionality of input data while preserving the most significant gesture information, thereby lowering computational complexity while maintaining recognition accuracy.
Solution Approach 2:
The patent transforms the raw range Doppler image data into a different parameter representation by calculating maximum amplitude values. This parameter transformation converts complex multi-dimensional cell data into simplified scalar values that are more efficient for neural network processing while retaining essential gesture characteristics.
2Adaptability or versatility
If the gesture recognition function is implemented on the smart device processor, then the system integration is improved, but the normal operation of the smart device is influenced
Solution Approach 1:
The patent extracts only the essential maximum amplitude information from the sensing data, reducing the computational burden on the smart device processor. This allows gesture recognition to be implemented on-device without significantly impacting the processor's ability to handle normal device operations.
Solution Approach 2:
The patent applies partial action by processing only the most critical features (maximum amplitude values) rather than analyzing all sensing data in full detail. This partial processing approach enables gesture recognition functionality while consuming minimal processor resources, thus avoiding interference with normal device operation.
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 decreases the calculation load on the processing unit, preventing the gesture recognition function from impacting the normal operation of smart devices and allowing for efficient integration into devices like smartphones and computers.
Implementation Method 1
a gesture recognition system is a Doppler-radar based gesture recognition system. The Doppler-radar based gesture recognition system can sense a motion of a user to generate a range Doppler image
Data Source
AI summary
A gesture recognition system executes a gesture recognition method. The gesture recognition method includes steps of: receiving a training signal; selecting one of the sensing frames of the sensing signal; generating a sensing map; selecting a cell having the max-amplitude; determining a frame amplitude, a frame phase, and a frame range of the selected one of the sensing frames; setting the frame amplitudes, the frame phases, and the frame ranges of all of the sensing frames to input data of a neural network to classify a gesture event. The present invention just uses a few data to be the input data of the neural network. Therefore, the neural network may not require high computational complexity, the gesture recognition system may decrease the calculation load of the processing unit, and the gesture recognition function may not influence a normal operation of a smart device.