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Non-contact relapse monitoring method and system for drug addicts

A non-contact, monitoring system technology, applied in blood flow measurement, pulse rate/heart rate measurement, diagnostic recording/measurement, etc., can solve the problem of inability to timely understand the changes in the physiological health of drug addicts, the detection process is complicated, and it is difficult to avoid relapse events and other issues

Pending Publication Date: 2020-10-23
赵永翔
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although these contact detection methods are accurate, the detection process is complex, the detection cycle is long, and the detection cost is high. It is impossible to carry out remote real-time tracking and detection for each drug user multiple times a day, so it is impossible to timely understand the daily status of drug users. Physiological health changes, it is difficult to avoid the occurrence of relapse events

Method used

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  • Non-contact relapse monitoring method and system for drug addicts
  • Non-contact relapse monitoring method and system for drug addicts
  • Non-contact relapse monitoring method and system for drug addicts

Examples

Experimental program
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Effect test

Embodiment 1

[0066] see figure 1 , a non-contact method for drug addicts relapse monitoring, comprising the following steps:

[0067] Acquisition of physiological characteristics, through non-contact sensor components, directly collect or indirectly calculate the physiological characteristics of the detected person;

[0068] Establish a drug relapse probability assessment model, obtain drug addicts’ relapse symptoms and clinical signs data as training samples, conduct training through statistical learning methods, machine learning methods or deep learning methods, and generate a drug relapse probability assessment model. Statistical learning methods, Machine learning methods and deep learning methods include various statistical analysis methods, support vector machines, neural networks, genetic algorithms, etc.;

[0069] Suspicious judgment, input the physiological characteristics of the tested person into the drug relapse probability assessment model, output the probability value of the ...

Embodiment 2

[0072] Further, see figure 2 , in this embodiment, the probability of drug relapse of the subject is judged by the following steps:

[0073] Step S100: the sensor assembly includes an optical sensor, an acoustic sensor, and an infrared sensor; the face video data of the detected person is obtained through the optical sensor, and the optical sensor can use an ordinary color camera, an infrared night vision camera, a high-speed camera, a Optical camera, webcam, IP camera, laser camera, etc. with one or more optical filters; the sound sensor can be an ordinary laptop built-in microphone, mobile phone built-in microphone, camera built-in microphone, desktop external microphone, Omni-directional stereo microphone or microphone array, etc.; the infrared sensor can be an infrared single-point temperature measurement sensor, an infrared dot matrix temperature measurement sensor, an infrared thermal imaging camera, an infrared thermal imaging camera, an infrared thermometer, etc.;

...

Embodiment 3

[0087] see image 3 , a non-contact drug addict relapse monitoring system, comprising: a physiological feature acquisition module, a risk assessment module and a judgment module;

[0088] The physiological characteristic acquisition module is used to directly collect or indirectly calculate the physiological characteristics of the detected person through the non-contact sensor component;

[0089] The risk assessment module is used to obtain the data of relapse symptoms and clinical signs of drug addicts as training samples, and conduct training through statistical learning methods, machine learning methods or deep learning methods to generate drug relapse probability assessment models, statistical learning methods, machine learning methods Learning methods and deep learning methods include various statistical analysis methods, support vector machines, neural networks, genetic algorithms, etc.;

[0090] The judgment module is used to input the physiological characteristics of ...

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Abstract

The invention relates to a non-contact relapse monitoring method and system for drug addicts. The monitoring method comprises the following steps: physiological characteristic acquisition: directly collecting or indirectly calculating physiological characteristics of a testee through a non-contact sensor assembly; drug relapse probability evaluation model establishment: acquiring relapse symptomsand clinical sign data of a drug addict as training samples, and performing training through a statistical learning method, a machine learning method or a deep learning method to generate the drug relapse probability evaluation model; and suspect judgment: inputting the physiological characteristics of the testee to the drug relapse probability evaluation model, and outputting the probability of drug relapse of the testee.

Description

technical field [0001] The invention relates to a non-contact drug addict relapse monitoring method and system, and relates to the technical field of drug control monitoring. Background technique [0002] According to the definition of the World Health Organization, drug abuse and drug addiction are chronic recurrent diseases that occur in the brain. Therefore, drug abusers and drug abusers are patients just like patients with other physical diseases. Excessively high drug relapse rate has always been a huge problem facing drug rehabilitation. According to surveys, the drug relapse rate of drug addicts in western developed countries is as high as 90%, and the drug relapse rate of drug addicts in my country also remains high. [0003] So far, the detection of drug relapse of drug addicts mainly uses contact detection methods such as urine test, blood test, saliva test and hair test. Although these contact detection methods are accurate, the detection process is complicated,...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/00A61B5/01A61B5/024A61B5/026A61B5/11A61B5/145
CPCA61B5/00A61B5/024A61B5/02405A61B5/145A61B5/01A61B5/1103A61B5/0261A61B5/1101A61B5/7264
Inventor 赵永翔
Owner 赵永翔
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