Driving student management method and system based on electrocardiosignal

A technology of electrocardiographic signal and management method, applied in the direction of human identification, data processing applications, instruments, etc., can solve the problems of unsatisfactory identity verification, inability to guarantee the identity of the same person, lack of identity verification methods, etc., to avoid Substitute classes, facilitate reasonable allocation and arrangement of study time, and ensure the effect of reliability and safety

Inactive Publication Date: 2018-08-31
SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But this strictness is mainly focused on the difficulty of the exam, and it is not satisfactory for the identity verification
Nowadays, fingerprint recognition and iris recognition are commonly used identity verification methods, but there is no guarantee that the person who verifies the identity is the same person who is practicing the car or taking the test
Now the driver's test is organized by the regional vehicle management office. Due to the lack of a reasonable identity verification method, this leaves room for the substitute test

Method used

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  • Driving student management method and system based on electrocardiosignal
  • Driving student management method and system based on electrocardiosignal
  • Driving student management method and system based on electrocardiosignal

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0032] When a student driver registers for driving practice at a driving school or registers for a test at a vehicle management office, he needs to perform electrocardiographic registration. The steps of electrocardiographic registration are as follows: figure 1 shown, including:

[0033] Step 101, adjust the client to the ECG registration state;

[0034] Step 102, collecting the ECG information of the student driver through the ECG acquisition module;

[0035] Step 201, preprocessing the collected ECG information;

[0036] Step 202, detecting the R wave position of the preprocessed ECG signal, intercepting the QT band, using the autocorrelation transformation algorithm to perform feature extraction, and obtaining the ECG autocorrelation sequence; fitting the acquired ECG autocorrelation sequence through an orthogonal polynomial Regression performs dimensionality reduction and generates feature templates;

[0037] Step 203, selecting and evaluating the optimal ECG feature t...

Embodiment 2

[0060] The difference from Embodiment 1 is that steps 202 and 402 are: detecting the position of the R wave in the preprocessed ECG signal, intercepting the QT wave, and generating sparse features using a distinguishing dictionary learning algorithm for sparse representation to form a feature template.

[0061] The acquisition using a distinguishing dictionary learning algorithm for sparse representation is specifically the following formula, Among them, J (D,C) is the solved dictionary D and sparse features C, Verif(X i ,X j ,D,C i ,C j ) is the feature distinguishing attribute, λ is the sparsity degree coefficient, α is the regularization coefficient, and the value range of λ and α is between 0 and 1.

[0062] x i with X j represent the i-th and j-th QT waves respectively, C i and C j Respectively represent and X i and x j The corresponding sparse features, where i≠j.

[0063]

[0064] Among them, dm is the minimum distance between different classes set, label...

Embodiment 3

[0068] The difference from Example 1 is that, as image 3 As shown, steps 202 and 402 are: detect each reference point in the ECG signal to extract the quasi-periodic heartbeat signal as the original ECG feature, perform segmental waveform correction on the heartbeat, and then use PCA to reduce the dimensionality and The coefficient feature is extracted as the final ECG feature to generate a feature template.

[0069] Firstly, each reference point in the ECG signal is detected to extract the quasi-periodic heartbeat as the original ECG feature. The ECG signal is a quasi-periodic signal, but not the components in the entire cardiac cycle are specific, in which the P wave, QRS complex and T wave in each cardiac cycle contain most of the ECG specific information. The wave bands in each cardiac cycle are cut out from the continuous ECG signals as the original ECG features. To do this, a reference point for the heartbeat is located. In addition, in the follow-up waveform correc...

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Abstract

The invention provides a driving student management method and system based on electrocardiosignal, which involve driving student identity recognition and monitor. The method includes the following steps of: A1. collecting electrocardio information of a driving student in real time through an electrocardio acquisition module arranged in a client; A2. processing information collected by the clientthrough a CPU central processor, and performing electrocardio registration or electrocardio certification on the driving student; A3. synchronizing the information collected by the client of step A1 and/or the processed information of step A2 in the client and an information management system through a communication module; and A4. achieving driving practice or examination management of the driving student by the information management system according to driving student information. The method and system of the invention can accurately verify and monitor in real time driving student identities, are more complete than original single identity recognition modes such as fingerprint recognition, have a stronger anti-cheating function, and effectively avoid the problems of swiping card for others, covering for the others, only swiping card, free of practicing and replacing the others to take an examination.

Description

technical field [0001] The present invention relates to a management method and system for driving schools or vehicle management offices, in particular to a management method and system for driving students based on electrocardiographic signals. Background technique [0002] This research work has been funded by the National Natural Science Foundation of China (Project Approval Number: 61571268). [0003] At present, the number of vehicles in China is increasing rapidly, and at the same time, the demand for driver training is also increasing. Driving has now become a basic skill. Faced with this situation, although the number of driving schools and drivers can barely cope, the quality of driving school training and driver examinations is not satisfactory. [0004] First of all, in terms of driving school training, the current driving school training is too rough. According to the requirements, the students who sign up for the driving school must undergo the prescribed trai...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/117G06F21/32G06Q50/20
CPCA61B5/117G06F21/32G06Q50/205
Inventor 张跃时光博雷夏飞冯治蒙张拓
Owner SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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