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

Abstract

The invention discloses a method, device and storage medium for quantifying driving comfort based on HRV, which simultaneously acquires the sensing data sent by the electrocardiogram sensor and the score data sent by the sound pickup device, and according to the sensing data and the read abnormal cycle Calculate the HRV time domain index, the HRV time domain index includes the standard deviation of the normal heartbeat interval and the heartbeat number of the adjacent normal heartbeat interval value difference in all RR intervals in the ECG data greater than the abnormal cycle, calculate The correlation between HRV time-domain indicators and scoring data, introducing HRV time-domain indicators as objective reference data, makes the authenticity of scoring data supported by objective data, further improves the reference value of scoring data, and realizes the improvement of driving comfort. accurate quantification.

Description

A method, device and storage medium for quantifying driving comfort based on HRV

technical field

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

Background technique

At present, automobiles are one of the most important means of travel, and driving comfort is very important to drivers. Therefore, in the development process, it is necessary to quantify driving comfort to provide a data basis for improving driving comfort. Most of the existing methods rely on the test driver’s oral driving experience. Although this method can obtain some reference data, the evaluation made by the test driver during the driving process may be affected by physical conditions, such as motion sickness during long-term intense driving, Judgment cannot be objective, so the reference value of the quantitative driving results obtained by the prior art is relatively poor.

Contents of the invention

In order to overcome the deficiencies in the prior art, the object of the present invention is to provide a method, device and storage medium for quantifying driving comfort based on HRV, which can combine scoring data of HRV (Heart rate variability, heart rate variability) and test drivers, Improve the reference value of driving comfort quantification.

The technical scheme adopted by the present invention to solve its problem is: first aspect, the present invention provides a kind of method for quantifying driving comfort based on HRV, comprising the following steps:

The client obtains the sensing data sent by the ECG sensor and the scoring data sent by the sound pickup device, and preprocesses the sensing data to obtain the ECG data;

The client obtains the preset abnormal cycle, and calculates the HRV time-domain index according to the abnormal cycle and the ECG data, and the HRV time-domain index includes the standard deviation of the normal heartbeat interval and all RR intervals in the ECG data. The difference between adjacent normal heartbeat intervals is greater than the heartbeat number of the abnormal cycle;

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 scoring data as quantitative data.

Further, the sensing data is an electrocardiographic signal collected with a sampling frequency of 1000 Hz.

Further, the preprocessing of the sensor data includes the following steps:

The client acquires a preset first cut-off frequency and a second cut-off frequency, performs high-pass filtering on the sensing data with the first cut-off frequency, and obtains a first filtered signal;

The client performs low-barrel filtering on the first filtered signal with the second cutoff frequency to obtain a second filtered signal;

The client performs debaseline processing on the second filtered signal to obtain ECG data.

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

Further, the abnormal period is 50 milliseconds.

In a second aspect, the present invention provides a device for performing an HRV-based driving comfort quantification method, including a CPU unit, the CPU unit is used to perform the following steps:

The client obtains the sensing data sent by the ECG sensor and the scoring data sent by the sound pickup device, and preprocesses the sensing data to obtain the ECG data;

The client obtains the preset abnormal cycle, and calculates the HRV time-domain index according to the abnormal cycle and the ECG data, and the HRV time-domain index includes the standard deviation of the normal heartbeat interval and all RR intervals in the ECG data. The difference between adjacent normal heartbeat intervals is greater than the heartbeat number of the abnormal cycle;

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 scoring data as quantitative data.

Further, the CPU unit is also used to perform the following steps:

The client acquires a preset first cut-off frequency and a second cut-off frequency, performs high-pass filtering on the sensing data with the first cut-off frequency, and obtains a first filtered signal;

The client performs low-barrel filtering on the first filtered signal with the second cutoff frequency to obtain a second filtered signal;

The client performs debaseline processing on the second filtered signal to obtain ECG data.

In a third aspect, the present invention provides a device for performing an HRV-based driving comfort quantification method, including at least one control processor and a memory for communicating with at least one control processor; the memory stores information that can be used by at least Instructions executed by a control processor, the instructions are executed by at least one control processor, so that the at least one control processor can execute the HRV-based driving comfort quantification method as described above.

In a fourth aspect, the present invention provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and the computer-executable instructions are used to cause a computer to execute the method for quantifying driving comfort based on HRV as described above.

In a fifth aspect, the present invention also provides a computer program product, the computer program product includes a computer program stored on a computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer , causing the computer to execute the method for quantifying driving comfort based on HRV as described above.

One or more technical solutions provided in the embodiments of the present invention have at least the following beneficial effects: the embodiment of the present invention acquires the sensing data sent by the ECG sensor and the score data sent by the sound pickup device at the same time, and according to the sensing data and reading The HRV time-domain index is calculated from the abnormal cycle taken, and the HRV time-domain index includes the standard deviation of the normal heartbeat interval and the heartbeat whose value difference between adjacent normal heartbeat intervals in all RR intervals in the ECG data is greater than the abnormal cycle Calculate the correlation between HRV time-domain indicators and scoring data, and introduce HRV time-domain indicators as objective reference data, so that the authenticity of scoring data is supported by objective data, further improving the reference value of scoring data, and realizing Accurate quantification of driving comfort.

Description of drawings

The present invention will be further described below in conjunction with accompanying drawing and example.

Fig. 1 is a flow chart of a method for quantifying driving comfort based on HRV provided by the first embodiment of the present invention;

Fig. 2 is a flow chart of preprocessing sensory data in an HRV-based driving comfort quantification method provided by the first embodiment of the present invention;

Fig. 3 is a schematic diagram of a device for implementing the HRV-based driving comfort quantification method provided by the second embodiment of the present invention.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

It should be noted that, if there is no conflict, various features in the embodiments of the present invention may be combined with each other, and all of them are within the protection scope of the present invention. In addition, although the functional modules are divided in the schematic diagram of the device, and the logical order is shown in the flowchart, in some cases, the division of modules in the device or the sequence shown in the flowchart can be performed in different ways. or the steps described.

Referring to Fig. 1, the first embodiment of the present invention provides a method for quantifying driving comfort based on HRV, comprising the following steps:

Step S100, the client acquires the sensing data sent by the ECG sensor and the scoring data sent by the sound pickup device, and preprocesses the sensing data to obtain the ECG data;

Step S200, the client obtains the preset abnormal cycle, and calculates the HRV time domain index according to the abnormal cycle and the ECG data. The HRV time domain index includes the standard deviation of the normal heartbeat interval and the adjacent RR intervals in the ECG data. The difference between the normal heartbeat interval value is greater than the heartbeat number of the abnormal cycle;

In step S300, the client calculates the Pearson correlation between the HRV time-domain index and the scoring data, obtains the scoring correlation, and sets the scoring correlation and the scoring data as quantitative data.

Among them, it should be noted that the sensing data can be obtained by an electrocardiographic sensor, or by any form of equipment. It only needs to be able to obtain the driver's electrocardiographic signal during driving. The sensors in this embodiment are all existing The products in the technology do not involve the improvement of hardware equipment, so I won't repeat them here. It should be noted that the sound pickup device in this embodiment can be any common model on the market, and it only needs to be able to obtain the voice score input of the test driver, and will not be repeated here. It should be noted that the score data in this embodiment is preferably a digital score, which can express the driving comfort more intuitively.

Wherein, it should be noted that the HRV time-domain index can be any value, and in this embodiment, the standard deviation during the normal heartbeat and the heartbeat number of the abnormal cycle are preferred, so that the abnormal heartbeat can be quantified. It can be understood that the number of heartbeats can be the number of heartbeats in any period. In this embodiment, the value difference between adjacent normal heartbeat intervals in the RR period is greater than the heartbeat number of the abnormal cycle. The RR period is the time of one cardiac cycle , the difference between the normal heartbeat periods of two adjacent RR periods is large, which means that the heartbeat frequency of the test driver changes, and if the scoring data changes correspondingly during the change, the calculated correlation will also be relatively strong , the score data is highly referential, that is, as the driving comfort decreases, the standard deviation SDNN of the normal heartbeat interval of the HRV time domain index is larger, and the difference between adjacent normal heartbeat intervals in all RR intervals is greater than that of the abnormal cycle. The heart rate is greater.

It should be noted that the standard deviation during a normal heartbeat can be calculated by any formula, and the following formula is used in this embodiment: Among them, SDNN is the standard deviation of the normal heartbeat period, NN _i is the heartbeat number of the i-th normal heartbeat period, and N is the number of normal heartbeat periods in the ECG data.

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

Among them, it should be noted that the electrocardiographic signal of this embodiment is collected by the 33rd lead of ANTeegoTMrt at a sampling frequency of 1000 Hz, which can ensure the accuracy of the electrocardiographic signal and improve the reference value for later calculation.

Referring to FIG. 2, further, in another embodiment of the present invention, preprocessing the sensing data includes the following steps:

Step S110, the client obtains the preset first cut-off frequency and second cut-off frequency, performs high-pass filtering on the sensing data with the first cut-off frequency, and obtains a first filtered signal;

Step S120, the client performs low-barrel filtering on the first filtered signal with a second cutoff frequency to obtain a second filtered signal;

In step S130, the client performs debaseline processing on the second filtered signal to obtain ECG data.

Wherein, in this embodiment, the first cut-off frequency is 0.4 Hz, and the second cut-off frequency is 0.003 Hz, which can also be adjusted according to actual needs, and will not be repeated here. It should be noted that the de-baseline processing is an algorithm in the prior art, and is not an improvement involved in this embodiment, it only needs to achieve the effect, and will not be repeated here.

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

Wherein, it should be noted that the sampling frequency of ECG data is reduced from the initial 1000 Hz to 250 Hz, which avoids calculation complexity caused by excessive sampling frequency and saves calculation time.

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

Among them, it should be noted that the abnormal period can be any value, and this embodiment is preferably 50 milliseconds, which also provides a basis for the heartbeat number NN50 commonly used in the prior art, that is, the heartbeat number in this embodiment is the number of all RR intervals The heartbeat number NN50 whose difference between adjacent normal heartbeat intervals is greater than 50ms, its calculation formula is: NN50=COUNT(|NN _i+1 -NN _i |)≥50ms; where, NN _i is the ith normal heartbeat interval period heart rate.

Referring to Fig. 3, the second embodiment of the present invention also provides a device for implementing the HRV-based driving comfort quantification method, the device is a smart device, such as a smart phone, a computer and a tablet computer, etc. It only needs to realize corresponding functions, and this embodiment uses a computer as an example for illustration.

In the computer 3000 for implementing the HRV-based driving comfort quantification method, a CPU unit 3100 is included, and the CPU unit 3100 is used to perform the following steps:

The client obtains the sensing data sent by the ECG sensor and the scoring data sent by the pickup device, and preprocesses the sensing data to obtain the ECG data;

The client obtains the preset abnormal cycle, and calculates the HRV time-domain index based on the abnormal cycle and the ECG data. The HRV time-domain index includes the standard deviation of the normal heartbeat interval and the adjacent normal heartbeat intervals among all RR intervals in the ECG data. The difference in expected value is greater than the number of heartbeats in the abnormal cycle;

The client calculates the Pearson correlation between HRV time-domain indicators and scoring data, obtains the scoring correlation, and sets the scoring correlation and scoring data as quantitative data.

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

The client acquires a preset first cutoff frequency and a second cutoff frequency, and performs high-pass filtering on the sensing data at the first cutoff frequency to obtain a first filtered signal;

The client performs low-barrel filtering on the first filtered signal with a second cutoff frequency to obtain a second filtered signal;

The client performs debaseline processing on the second filtered signal to obtain ECG data.

The computer 3000 and the CPU unit 3100 can be connected through a bus or other means, and the computer 3000 also includes a memory, which is a non-transitory computer-readable storage medium and can be used to store non-transitory software programs, non-transitory Computer-executable programs and modules, such as the program instructions/modules corresponding to the device for implementing the HRV-based driving comfort quantification method in the embodiment of the present invention. The computer 3000 controls the CPU unit 3100 to execute various functional applications and data processing for implementing the HRV-based driving comfort quantification method by running the non-transitory software programs, instructions and modules stored in the memory, that is, to realize the implementation of the above method. Example of HRV-based driving comfort quantification method.

The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function; the data storage area may store data created according to usage of the CPU unit 3100, and the like. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include memory located remotely from the CPU unit 3100, and these remote memories may be connected to the computer 3000 via a network. Examples of the aforementioned 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, the method for quantifying driving comfort based on HRV in the above method embodiment is executed.

The embodiment of the present invention also provides a computer-readable storage medium, the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by the CPU unit 3100 to realize the aforementioned HRV-based driving comfort quantification method.

The device embodiments described above are only illustrative, and the devices described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network devices. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

It should be noted that since the device for implementing the HRV-based driving comfort quantification method in this embodiment is based on the same inventive concept as the above-mentioned HRV-based driving comfort quantification method, the corresponding content in the method embodiment It is also applicable to this embodiment of the device, and will not be described in detail here.

Through the above description of the implementation manners, those skilled in the art can clearly understand that each implementation manner can be implemented by means of software plus a general hardware platform. Those skilled in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing related hardware through computer programs. The programs can be stored in computer-readable storage media, and the programs can be executed when executed , may include the flow of the embodiment of the above-mentioned method. Wherein, the storage medium may be a magnetic disk, an optical disk, a read-only memory (ReadOnly Memory, ROM) or a random access memory (Random Access Memory, RAM), etc.

The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above-mentioned implementation, and those skilled in the art can also make various equivalent deformations or replacements without violating the spirit of the present invention. Equivalent modifications or replacements are all within the scope defined by the claims of the present application.

Claims (8)

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;

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.

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:

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.

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:

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.

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:

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.

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.