Method and system for early warning of two stools based on adversarial generative network
An early warning system and network technology, applied in the fields of medical technology and data processing, can solve problems such as incontinence and long-term care difficulties for the elderly, and achieve the effects of saving social costs, solving long-term care difficulties, and high prediction accuracy
Active Publication Date: 2022-04-22
SOUTH CHINA UNIV OF TECH
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[0008] The purpose of the present invention is to provide a two-fee early warning system and method based on the confrontation generation network using the gastric electroacquisition system, aiming at solving the problem of fecal incontinence caused by the loss of excretion-related functions of the long-term bedridden elderly through real-time monitoring and early warning. long term care problems
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[0040] An early-warning system based on adversarial generative networks, including electrogastric sensors, signal preprocessing modules, feature extractors, two-fee early warning model training modules, and embedded microprocessors;
[0041] Among them, the electrogastric sensor collects the electrogastric signal data and sends it to the signal preprocessing module for preprocessing, and the signal preprocessing module sends the preprocessed electrogastric signal data to the feature extractor for feature extraction; the second stool early warning model training module will extract The characteristic samples of the feature samples are divided into training set and test set, and the two-poop early-warning model is constructed and trained by using the generative confrontation network to obtain the trained two-poop early-warning model; the trained two-poop early-warning model is migrated on the embedded microprocessor, and the After receiving the gastric electrical signal data coll...
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The invention discloses an early warning method and system for defecation and defecation based on an adversarial generation network. The method comprises the following steps: collecting the electrogastric signal data of several volunteers; preprocessing the collected electrogastric signal data; using a feature extractor to extract features from the preprocessed data; dividing the extracted feature samples into The training set and the test set, construct the two-fee early warning model and use the generative confrontation network to train it, and obtain the trained two-fee early warning model; migrate the trained two-fee early warning model to the embedded microprocessor, connect the EGG gastric Sensors, EGG sensor collects gastric signal data in real time and inputs them into the embedded microprocessor; the embedded microprocessor preprocesses the real-time collected gastric signal data and performs pre-warning and judgment based on the preprocessed data. The invention can solve the difficulty of long-term care for the disabled elderly, save social costs, and has certain practical value and social benefits.
Description
technical field [0001] The invention relates to medical technology and data processing technology, in particular to an early warning system and method based on confrontation generation network. Background technique [0002] my country's population aging problem has become increasingly prominent since entering the 21st century. A large number of elderly people need to be taken care of. If all of them are taken care of manually, there will be many inconveniences. How to realize the semi-automatic or even fully automatic monitoring of the disabled elderly and solve the care needs of the disabled elderly has aroused widespread concern. my country's population aging problem is becoming more and more serious, and the number of disabled elderly people accounts for a considerable proportion. Long-term bed rest for the disabled elderly will lead to various complications, including mobility or loss of defecation-related functions, which will affect the normal excretion of urine and d...
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IPC IPC(8): A61B5/392A61B5/00G06K9/00G06K9/62G06N3/04G06N3/08
CPCA61B5/392A61B5/7267A61B5/746A61B5/7257G06N3/08G06N3/045G06F2218/08G06F2218/12G06F18/241
Inventor 邹焱飚吴盛红张铁
Owner SOUTH CHINA UNIV OF TECH