CN110584650A

CN110584650A - HRV-based driving comfort quantification method and device and storage medium

Info

Publication number: CN110584650A
Application number: CN201910878755.4A
Authority: CN
Inventors: 赵蕾蕾; 杨铁牛
Original assignee: Wuyi University
Current assignee: Wuyi University
Priority date: 2019-09-18
Filing date: 2019-09-18
Publication date: 2019-12-20

Abstract

The invention discloses a HRV-based driving comfort quantification method, a device and a storage medium, which are used for simultaneously acquiring sensing data sent by an electrocardio sensor and scoring data sent by pickup equipment, calculating an HRV time domain index according to the sensing data and a read abnormal period, wherein the HRV time domain index comprises a normal heartbeat interval standard difference and a heartbeat number of an adjacent normal heartbeat interval value difference in all RR intervals in the electrocardio data, the heartbeat number of the abnormal period is larger than the heartbeat number of the abnormal period, calculating the correlation between the HRV time domain index and the scoring data, and introducing the HRV time domain index as objective reference data, so that the authenticity of the scoring data is supported by the objective data, the reference value of the scoring data is further improved, and the accurate quantification of the driving comfort is realized.

Description

HRV-based driving comfort quantification method and device and storage medium

Technical Field

The invention relates to the field of biological signal processing, in particular to a driving comfort quantification method and device based on HRV and a storage medium.

Background

At present, an automobile is one of the most important travel vehicles, and driving comfort is very important for a driver, so that the driving comfort needs to be quantified in a development process, and a data basis is provided for improving the driving comfort. Although the conventional method mostly depends on the trial driver to dictate the driving feeling, the method can obtain some reference data, but the evaluation made by the trial driver in the driving process is possibly influenced by physical conditions, such as a car sickness condition during long-term violent driving, and the judgment cannot be objective, so that the quantized result of the driving degree obtained by the prior art has poor reference value.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention aims to provide a driving comfort quantification method, a driving comfort quantification device and a storage medium based on an HRV (Heart rate variability), which can be combined with the HRV and the scoring data of a driver to be tested, so as to improve the reference value of the driving comfort quantification.

The technical scheme adopted by the invention for solving the problems is as follows: in a first aspect, the invention provides a driving comfort quantification method based on an HRV, comprising the following steps:

the client side obtains sensing data sent by the electrocardio sensor and grading data sent by the pickup equipment, and preprocesses the sensing data to obtain electrocardio data;

the client side obtains a preset abnormal period, and an HRV time domain index is calculated according to the abnormal period and the electrocardio data, wherein the HRV time domain index comprises a normal heartbeat interval standard deviation and the number of heartbeats of which the adjacent normal heartbeat interval value difference in all RR intervals in the electrocardio data is larger than the abnormal period;

and the client calculates the Pearson correlation between the HRV time domain index and the scoring data to obtain a scoring correlation, and sets the scoring correlation and the scoring data as quantitative data.

Further, the sensing data is electrocardiosignals acquired through a sampling frequency of 1000 Hz.

Further, the preprocessing the sensing data comprises the following steps:

the client acquires a preset first cut-off frequency and a preset second cut-off frequency, and carries out high-pass filtering on the sensing data by using the first cut-off frequency to obtain a first filtering signal;

the client performs low-tube filtering on the first filtering signal at the second cut-off frequency to obtain a second filtering signal;

and the client performs baseline removal processing on the second filtering signal to obtain the electrocardiogram data.

Further, the sampling frequency of the electrocardio data is 250 Hz.

Further, the abnormal period is 50 milliseconds.

In a second aspect, the present invention provides an apparatus for performing a HRV-based driving comfort quantification method, comprising a CPU unit for performing the steps of:

Further, the CPU unit is further configured to perform the steps of:

In a third aspect, the present invention provides an apparatus for performing a HRV-based driving comfort quantification method, comprising at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform the HRV-based driving comfort quantification method as described above.

In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the HRV-based driving comfort quantification method as described above.

In a fifth aspect, the present invention also provides a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the HRV-based driving comfort quantification method as described above.

One or more technical schemes provided in the embodiment of the invention have at least the following beneficial effects: the embodiment of the invention simultaneously acquires the sensing data sent by the electrocardio sensor and the grading data sent by the pickup equipment, calculates the HRV time domain index according to the sensing data and the read abnormal period, calculates the correlation between the HRV time domain index and the grading data, and introduces the HRV time domain index as objective reference data, so that the authenticity of the grading data is supported by the objective data, the reference value of the grading data is further improved, and the accurate quantification of the driving comfort is realized.

Drawings

The invention is further illustrated with reference to the following figures and examples.

Fig. 1 is a flowchart of a driving comfort quantifying method based on HRV according to a first embodiment of the present invention;

fig. 2 is a flowchart of preprocessing sensed data in a driving comfort quantifying method based on HRV according to a first embodiment of the present invention;

fig. 3 is a schematic diagram of an apparatus for performing a driving comfort quantifying method based on HRV according to a second embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

It should be noted that, if not conflicted, the various features of the embodiments of the invention may be combined with each other within the scope of protection of the invention. Additionally, while functional block divisions are performed in apparatus schematics, with logical sequences shown in flowcharts, in some cases, steps shown or described may be performed in sequences other than block divisions in apparatus or flowcharts.

Referring to fig. 1, a first embodiment of the present invention provides a HRV-based driving comfort quantifying method, including the steps of:

step S100, a client acquires sensing data sent by an electrocardio sensor and grading data sent by pickup equipment, and preprocesses the sensing data to obtain electrocardio data;

step S200, the client side obtains a preset abnormal period, and an HRV time domain index is calculated according to the abnormal period and the electrocardio data, wherein the HRV time domain index comprises a normal heartbeat interval standard deviation and the number of heartbeats of which the adjacent normal heartbeat interval value difference in all RR intervals in the electrocardio data is larger than the abnormal period;

step S300, the client calculates the Pearson correlation between the HRV time domain index and the scoring data to obtain the scoring correlation, and the scoring correlation and the scoring data are set as quantized data.

It should be noted that the sensing data may be obtained by an electrocardiograph sensor, or may be obtained by any form of device, and the electrocardiograph signal of the driver during driving may be obtained. It should be noted that the sound pickup device in this embodiment may be any common type on the market, and the voice score input of the test driver may be obtained, which is not described herein again. Note that, the score data of the present embodiment is preferably a numerical score, which can more intuitively express driving comfort.

It should be noted that the HRV time domain index may be any value, and in this embodiment, it is preferable that the standard deviation during the normal heartbeat period and the number of heartbeats in the abnormal period are used, so that the abnormal heartbeat can be quantified. It can be understood that the heart rate may be a heart rate in any period, in this embodiment, preferably, the difference between adjacent normal heartbeat intervals in the RR period is greater than the heart rate in an abnormal period, the RR period is a time of a cardiac cycle, and a difference between the normal heartbeat intervals in two adjacent RR periods is greater, which means that the heartbeat frequency of the test driver varies.

It should be noted that the standard deviation during normal heartbeat can be calculated by using any formula, and the following formula is adopted in this embodiment:wherein SDNN is the standard deviation, NN, of the normal heartbeat period_iThe number of beats in the ith normal heartbeat period is N, and the number of the normal heartbeat periods in the electrocardiogram data is N.

Further, in another embodiment of the present invention, the sensing data is an electrocardiographic signal acquired with a sampling frequency of 1000 Hz.

It should be noted that, the electrocardiographic signals of this embodiment are collected through the 33 th lead of anteegomrt at a sampling frequency of 1000Hz, which can ensure the accuracy of the electrocardiographic signals and improve the reference value of the later-stage calculation.

Referring to fig. 2, further, in another embodiment of the present invention, preprocessing the sensed data includes the steps of:

step S110, the client acquires a preset first cut-off frequency and a preset second cut-off frequency, and carries out high-pass filtering on the sensing data by using the first cut-off frequency to obtain a first filtering signal;

step S120, the client performs low-tube filtering on the first filtering signal at a second cut-off frequency to obtain a second filtering signal;

and step S130, the client performs baseline removal processing on the second filtering signal to obtain the electrocardiogram data.

In this embodiment, the first cut-off frequency is 0.4Hz, and the second cut-off frequency is 0.003Hz, which can also be adjusted according to actual requirements, and will not be described herein again. It should be noted that the baseline removal processing is an algorithm in the prior art, which is not an improvement related to the present embodiment, and the effect can be achieved, and is not described herein again.

Further, in another embodiment of the present invention, the sampling frequency of the electrocardiographic data is 250 Hz.

It should be noted that the sampling frequency of the electrocardiographic data is reduced from the initial 1000Hz to 250Hz, so that the computational complexity caused by the overlarge sampling frequency is avoided, and the computational time can be saved.

Further, in another embodiment of the present invention, the exception period is 50 milliseconds.

It should be noted that the abnormal period may be any value, preferably 50ms in this embodiment, and also provides a basis for the commonly used stroke number NN50 in the prior art, that is, the stroke number in this embodiment is the stroke number NN50 in which the difference between adjacent normal heartbeat intervals in all RR intervals is greater than 50ms, and the calculation formula is as follows: NN50 ═ COUNT (| NN)_i+1-NN_i|) is more than or equal to 50 ms; wherein, NN_iThe number of beats in the ith normal heartbeat interval.

Referring to fig. 3, the second embodiment of the present invention further provides an apparatus for implementing a HRV-based driving comfort quantification method, where the apparatus is a smart device, such as a smart phone, a computer, a tablet computer, and the like, and can have a processor and implement a corresponding function, and the present embodiment takes the computer as an example for description.

In the computer 3000 for executing the HRV-based driving comfort quantifying method, a CPU unit 3100 is included, the CPU unit 3100 being configured to perform the steps of:

the client acquires a preset abnormal period, and calculates an HRV time domain index according to the abnormal period and the electrocardiogram data, wherein the HRV time domain index comprises a normal heartbeat interval standard deviation and the number of heartbeats of which the difference of adjacent normal heartbeat interval values in all RR intervals in the electrocardiogram data is greater than the abnormal period;

the client calculates the Pearson correlation between the HRV time domain index and the scoring data to obtain the scoring correlation, and the scoring correlation and the scoring data are set as quantitative data.

Further, in another embodiment of the present invention, CPU unit 3100 is further configured to perform the steps of:

the client performs low-tube filtering on the first filtering signal at a second cut-off frequency to obtain a second filtering signal;

The computer 3000 and the CPU unit 3100 may be connected via a bus or other means, and the computer 3000 further includes a memory as a non-transitory computer-readable storage medium for storing non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the apparatus for performing the HRV-based driving comfort quantifying method according to the embodiment of the present invention. The computer 3000 controls the CPU unit 3100 to execute various functional applications for executing the HRV-based driving comfort quantifying method and data processing, i.e., to implement the HRV-based driving comfort quantifying method of the above-described method embodiments, by running non-transitory software programs, instructions, and modules stored in the memory.

The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the CPU unit 3100, and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from CPU unit 3100, which may be connected to computer 3000 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The one or more modules are stored in the memory and, when executed by the CPU unit 3100, perform the HRV-based driving comfort quantification method in the above-described method embodiment.

An embodiment of the present invention further provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and the computer-executable instructions are executed by the CPU 3100, so as to implement the HRV-based driving comfort quantifying method described above.

The above-described embodiments of the apparatus are merely illustrative, and the apparatuses described as separate components may or may not be physically separate, may be located in one place, or may be distributed over a plurality of network apparatuses. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

It should be noted that, since the apparatus for executing the HRV-based driving comfort quantification method in the present embodiment is based on the same inventive concept as the HRV-based driving comfort quantification method described above, the corresponding contents in the method embodiment are also applicable to the present apparatus embodiment, and are not described in detail herein.

Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by software plus a general hardware platform. Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.

While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the foregoing and various other changes, omissions and deviations in the form and detail thereof may be made without departing from the scope of this invention.

Claims

1. A HRV-based driving comfort quantification method is characterized by comprising the following steps: the client side obtains sensing data sent by the electrocardio sensor and grading data sent by the pickup equipment, and preprocesses the sensing data to obtain electrocardio data;

2. The HRV-based driving comfort quantification method of claim 1, wherein: the sensing data is electrocardiosignals acquired through a sampling frequency of 1000 Hz.

3. A HRV-based driving comfort quantification method according to claim 1, wherein the preprocessing of the sensing data comprises the following steps:

4. A HRV-based driving comfort quantification method according to claim 3, characterized by: the sampling frequency of the electrocardio data is 250 Hz.

5. The HRV-based driving comfort quantification method of claim 1, wherein: the exception period is 50 milliseconds.

6. An apparatus for performing a HRV-based driving comfort quantification method, comprising a CPU unit for performing the steps of:

7. An apparatus for performing a HRV-based driving comfort quantification method as claimed in claim 6 wherein the CPU unit is further configured to perform the steps of:

8. A computer-readable storage medium characterized by: the computer-readable storage medium stores computer-executable instructions for causing a computer to perform a HRV-based driving comfort quantification method as recited in any one of claims 1-5.