Monitoring method and system for safe driving, automobile and readable storage medium

A technology for safe driving and data, applied in the measurement of pulse rate/heart rate, diagnostic record/measurement, medical science, etc., which can solve the problems of image recognition method detection accuracy easily affected by ambient light, low accuracy, and capture signal interference. , to reduce the amount of data processing, ensure driving safety, and reduce the difficulty of analysis

Inactive Publication Date: 2017-10-17
深圳市卡迪赛克科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the influence of ambient light, there is a large interference to the captured signal, and the image recognition method has the disadvantage that the detection accuracy is easily affected by ambient light.
[0004] In recent years, there has also been a method of judging fatigue driving based on heart rate parameters, t...

Method used

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  • Monitoring method and system for safe driving, automobile and readable storage medium
  • Monitoring method and system for safe driving, automobile and readable storage medium
  • Monitoring method and system for safe driving, automobile and readable storage medium

Examples

Experimental program
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Embodiment 1

[0068] Please refer to figure 1 , is a monitoring system for safe driving, the system includes: a signal acquisition module 10, a signal processing module 20, a data processing module 60 and a data analysis module 30, in some embodiments, also includes: a data storage module 40 and an alarm module 50, which will be explained in detail below.

[0069] The signal acquisition module 10 includes: a cardiopulmonary signal acquisition unit 11 and an auxiliary physiological signal acquisition unit 12 .

[0070] The cardiopulmonary signal collection unit 11 is used to collect ECG waveforms and respiratory waveforms, including: cardiopulmonary sensing electrodes for collecting cardiopulmonary activity potential signals, cardiopulmonary signal conditioning circuits and analog-to-digital conversion circuits for amplification and filtering.

[0071] The auxiliary signal acquisition unit 12 is used to collect body temperature, blood pressure, and blood oxygen signal waveforms, including: ...

Embodiment 2

[0099] refer to Figure 4 , is a data analysis method provided by the present application, and this method can be applied in step 103 of the first embodiment. The method includes:

[0100] Step 201, modeling: use HMM hidden Markov model to establish cardiac model X and respiratory model Y respectively; HMM hidden Markov model divides fatigue into four states: state I means normal, state II means mild fatigue , state III means moderately fatigued but alert, and state IV means drowsy.

[0101] Step 202, model training: select the cardiac interval sequence data of n time units of the sample to be tested as the observation value sequence Ox to train the cardiac model X, learn the cardiac model Y to obtain hidden Markov model parameters; select n time units of the sample to be tested The continuous respiration rate data is used as the observation value sequence Oy to train the respiration model Y, and learn the hidden Markov model parameters of the respiration model Y, and n is a...

Embodiment 3

[0119] refer to Image 6 , to provide another data analysis method for this application, and this method can be applied in step 103 of the first embodiment. The method includes:

[0120] Step 301, the first acquisition step: Obtain the cardiac interval sequence data of public samples from the public database, and select the cardiac interval sequence data of a large number of normal people, sub-healthy people, patients with related cardiovascular diseases and patients with myocardial infarction in the public Value, calculate the corresponding heart rate variability and heart rate deceleration force.

[0121] Step 302, the second acquisition step: obtain the cardiac interval sequence data of the sample to be tested for a period of m time units, and calculate its heart rate variability and heart rate deceleration force; the length of the cardiac interval sequence data of the public sample and the sample to be tested is the same, both are m time unit, m is a positive integer gre...

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Abstract

A monitoring system for safe driving comprises a signal collection module, a signal processing module, a data processing module and a data analyzing module, wherein the signal processing module extracts heartbeat intervening sequence data and breath intervening sequence data, and corresponding cardiopulmonary reference datum such as heart rate, heart rate variability, heart rate declarative force, breath rate and breath rate variability can be calculated based on the two data via the data processing module; a series of feature values of an original waveform can be acquired and data processing amount can be reduced, so analyzing difficulty can be reduced; heart rate parameters and breath parameters are comprehensively considered via the data analyzing module, so driver fatigue degree and risk of spasmodic diseases can be respectively predicted; safe driving rises can be accurately monitored in real time, so drinking safety can be ensured; relatively, the invention further provides a safe driving monitoring method, an automobile including safe driving monitoring system and a storage medium.

Description

technical field [0001] The invention relates to the technical field of vehicle safety, in particular to a method and system for monitoring safe driving, a vehicle, and a readable storage medium. Background technique [0002] The driver needs to concentrate during the driving process. The driver is highly stressed for a long time, the brain is prone to fatigue, and the probability of sudden diseases (such as myocardial infarction and acute heart failure, etc.) is also relatively high. Therefore, it is necessary to protect the driver Driving safely is of the utmost importance. [0003] In order to ensure the driver's safe driving, the existing technology often reflects the risk of safe driving by predicting the driver's fatigue level. Traditional fatigue driving detection often uses image recognition methods to record the driver's facial expressions through the camera, or analyze facial expressions such as blinking. Features, eye signals, head movement, etc. infer the driver'...

Claims

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Application Information

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IPC IPC(8): A61B5/024A61B5/08A61B5/18
CPCA61B5/02405A61B5/08A61B5/18A61B5/7264A61B5/7271A61B5/746
Inventor 林科李秋平李冉
Owner 深圳市卡迪赛克科技有限公司
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