RF Living Body Posture Detection via Feature Selection
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Solution Overview
Problem
Existing non-contact sensing technologies face challenges in accurately and efficiently detecting the posture of a living body using RF signals, requiring advanced analysis algorithms to differentiate physiological movements and posture changes.
Innovation Solution
A living body detection method and system that employs machine learning to process RF signal data, featuring an RF signal processing circuit, storage circuit, and processor to extract initial training features, establish classification prediction models, and select preferred features for reduced computational complexity and rapid detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced analysis algorithms are used to accurately detect posture changes from RF signals, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex analysis task into two distinct stages: a training stage where a classification prediction model is built using labeled data, and a detection stage where the trained model is applied to determine posture. This segmentation transforms the complex algorithmic challenge into a manageable machine learning pipeline, improving measurement precision while controlling device complexity through model reuse.
Solution Approach 2:
The patent performs preliminary action by pre-training a classification prediction model using labeled training data that includes various posture labels. This pre-computed model encapsulates the complex analysis logic, allowing the actual posture detection to proceed with simpler inference operations rather than complex real-time analysis, thus resolving the contradiction between precision and complexity.
2Measurement precision
If comprehensive feature extraction is performed on RF signal data, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs feature extraction and model training in advance during an offline training phase. The classification prediction model is pre-computed using comprehensive features from labeled training data, storing the results in a ready-to-use format. During actual posture detection, the pre-trained model is applied directly to new data without repeating the extensive feature extraction and analysis, thus maintaining high measurement precision while significantly reducing the time loss during operational detection.
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
Enables accurate determination of human body posture with reduced computational requirements, maintaining classification accuracy and facilitating efficient machine learning-based detection.
Implementation Method 1
If the human body is irradiated with electromagnetic waves by a radar, according to the Doppler effect, these contraction and extension movements of the human muscle will cause phase changes in the electromagnetic waves upon reflection.
Data Source
AI summary
A living body detection method and a living body detection system are provided. A radio-frequency signal reflected by an experiment living body is received, and raw sampling data of the RF signal are obtained. A feature extraction process is performed to generate initial training features of sampling datasets, wherein the initial training features respectively correspond to feature generation rules. A classification prediction model is established according to a posture of the experiment living body and the initial training features, and correlation feature weightings respectively corresponding to the initial training features are obtained. Preferred features corresponding to at least one of the feature generation rules are selected from the initial training features according to the correlation feature weightings. Another classification prediction model configured for determining a posture of a detection living body is established according to the posture of the experiment living body and the preferred features.