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Method and system for implementing respiration detection through CGAN and multi-scale convolutional neural network

A technology of convolutional neural network and breathing detection, which is applied in the field of CGAN and multi-scale convolutional neural network to realize breathing detection and system, can solve the problems of low detection accuracy and large environmental noise interference, and achieve the goal of ensuring cleanliness and detection accuracy less demanding effect

Active Publication Date: 2022-01-11
SICHUAN UNIV
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Problems solved by technology

[0006] In view of the problems of the above research, the purpose of the present invention is to provide a method and system for realizing breath detection by CGAN and multi-scale convolutional neural network, so as to solve the problem that the existing detection is greatly disturbed by environmental noise, thus making the detection accuracy low

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  • Method and system for implementing respiration detection through CGAN and multi-scale convolutional neural network

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[0070] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0071] The present invention proposes a CSI signal with human breathing impact collected by ordinary Wi-Fi equipment, denoises the CSI through CGAN, and then uses a multi-scale convolutional neural network to extract signal periods for human breathing detection, and at the same time can Real-time monitoring of abnormal breathing can be widely used in home hospitals and other environments.

[0072] The main process of the present invention includes: 1) acquisition of CSI data with respiratory impact; 2) generation of noise-free power spectrum; 3) noise reduction processing of channel CSI data; 4) establishment of human respiratory CSI model; 5) multi-scale neural network Extracting the CSI periodically varying angular velocity; 6) estimating the respiration rate according to the periodically varying angular velocity. The specific implementation...

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Abstract

The invention discloses a method and system for implementing respiration detection through a CGAN and a multi-scale convolutional neural network, belongs to the field of medical health and the field of deep learning, and solves the problem of low detection precision caused by the fact that existing detection is interfered a lot by environmental noise. The method comprises the steps: 1, acquiring CSI data with breathing impact; 2, generating a noiseless power spectrum chart; 3, carrying out channel CSI data noise reduction processing; 4, establishing a human body respiration CSI model; 5, extracting a CSI periodic change angular velocity by a multi-scale neural network; and 6, estimating the respiratory rate according to the periodic change angular velocity. The method and the system are used for human respiration detection.

Description

technical field [0001] A method and system for realizing breath detection by CGAN and multi-scale convolutional neural network are used for human breath detection and belong to the fields of medical health and deep learning. Background technique [0002] Breathing is one of the most important life activities of human beings. Respiratory frequency is an important indicator for judging human health and an early manifestation of physiological deterioration. Real-time detection of respiratory frequency can provide timely and effective feedback on the health status of the human body and prevent the occurrence of diseases. Especially for some people suffering from respiratory diseases, real-time detection of respiratory conditions is necessary. Obtaining the breathing information of the human body to prevent the occurrence of dangerous situations often requires professional breathing detection equipment. In the past, respiratory detection equipment was generally used for real-tim...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/08A61B5/00G06N3/04G06N3/08
CPCA61B5/0816A61B5/7203A61B5/7267G06N3/08G06N3/045
Inventor 魏骁勇吴柳繁张栩禄杨震群
Owner SICHUAN UNIV
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