阅读说明：本技术 一种基于gsr峰值的眩晕度计算方法、装置和存储介质 (Vertigo degree calculation method and device based on GSR peak value and storage medium ) 是由 赵蕾蕾 杨铁牛 于 2019-09-18 设计创作，主要内容包括：本发明公开了一种基于GSR峰值的眩晕度计算方法、装置和存储介质,通过客户端获取GSR传感器发送的GSR信号,从GSR信号中获取GSR峰值,并在设定好的评估时间段内获取用户评分和GSR峰值发生率,计算出GSR峰值发生率和用户评分的相关性,GSR峰值发生率为客观的数值,与用户评分相结合后,相关性越高的用户评分的参考价值越高,相比起现有技术而言能够通过GSR峰值发生率对用户评分进行评价,有效提高了眩晕度参考值的参考价值。(The invention discloses a vertigo degree calculating method, a device and a storage medium based on a GSR peak value, wherein a GSR signal sent by a GSR sensor is obtained through a client, the GSR peak value is obtained from the GSR signal, a user score and a GSR peak value occurrence rate are obtained in a set evaluation time period, the correlation between the GSR peak value occurrence rate and the user score is calculated, the GSR peak value occurrence rate is an objective numerical value, and after the GSR peak value occurrence rate is combined with the user score, the higher the correlation, the higher the reference value of the user score is, compared with the prior art, the user score can be evaluated through the GSR peak value occurrence rate, and the reference value of the vertigo degree reference value is effectively improved.)

1. A vertigo degree calculation method based on GSR peak value is characterized by comprising the following steps: a client acquires a GSR signal sent by a GSR sensor, and preprocesses the GSR signal to acquire a GSR peak value;

the client acquires a preset evaluation time period, and acquires the GSR peak value number in the evaluation time period and the user score input to the client;

the client calculates the GSR peak value incidence rate in the evaluation time period, wherein the peak value incidence rate is the number of times of peak value occurrence in unit time;

the client calculates the correlation between the GSR peak rate and the user score, and sets the value of the correlation and the user score as vertigo reference values.

2. The method for calculating the vertigo degree based on the GSR peak value according to claim 1, wherein the preprocessing the GSR signal specifically comprises the following steps:

the client filters the GSR signal to obtain phase data;

the client acquires a peak value starting position and a peak value ending position from the phase data;

and the client selects a corresponding GSR curve from an initial GSR signal according to the peak value starting position and the peak value ending position, and sets the maximum value of the GSR curve as a GSR peak value.

3. The method for calculating the vertigo degree based on the GSR peak value according to claim 2, wherein the step of filtering the GSR signal by the client specifically comprises the following steps:

the client samples the GSR signal to obtain a sample set;

the client selects any sample in the sample set as a sample center, and calculates median number of surrounding samples at a fixed time interval;

and the client sets the difference value between the sample center and the median number as phase data.

4. The method of claim 2, wherein the client selects a corresponding GSR curve from an initial GSR signal according to the peak start position and the peak end position, and further comprises: the GSR curve is linearly transformed according to a min-max normalization method and the result is mapped to the [0,1] interval.

5. The vertigo degree calculation method based on the GSR peak value according to claim 1, wherein: and the client calculates the correlation between the GSR peak incidence and the user score to be Pearson correlation.

6. An apparatus for performing a vertigo degree calculation method based on a GSR peak, comprising a CPU unit for performing the steps of:

a client acquires a GSR signal sent by a GSR sensor, and preprocesses the GSR signal to acquire a GSR peak value;

the client acquires a preset evaluation time period, and acquires the GSR peak value number in the evaluation time period and the user score input to the client;

the client calculates the GSR peak value incidence rate in the evaluation time period, wherein the peak value incidence rate is the number of times of peak value occurrence in unit time;

the client calculates the correlation between the GSR peak rate and the user score, and sets the value of the correlation and the user score as vertigo reference values.

7. The apparatus for performing the GSR peak based vertigo calculation method according to claim 6, wherein said CPU unit is further configured to perform the steps of:

the client filters the GSR signal to obtain phase data;

the client acquires a peak value starting position and a peak value ending position from the phase data;

and the client selects a corresponding GSR curve from an initial GSR signal according to the peak value starting position and the peak value ending position, and sets the maximum value of the GSR curve as a GSR peak value.

8. The apparatus for performing the GSR peak based vertigo calculation method according to claim 6, wherein said CPU unit is further configured to perform the steps of:

the client samples the GSR signal to obtain a sample set;

the client selects any sample in the sample set as a sample center, and calculates median number of surrounding samples at a fixed time interval;

and the client sets the difference value between the sample center and the median number as phase data.

9. The apparatus for performing the GSR peak based vertigo calculation method according to claim 6, wherein said CPU unit is further configured to perform the steps of: the GSR curve is linearly transformed according to a min-max normalization method and the result is mapped to the [0,1] interval.

10. A computer-readable storage medium characterized by: the computer-readable storage medium stores computer-executable instructions for causing a computer to perform a GSR peak-based vertigo degree calculation method according to any one of claims 1 to 5.

Technical Field

The invention relates to the field of biological signal processing, in particular to a vertigo degree calculation method and device based on a GSR peak value and a storage medium.

Background

When a passenger gets a car, the car sickness occurs to some passengers, so the cause of the car sickness needs to be researched, and the research is carried out on the premise that how to quantitatively calculate the dizziness degree of the passengers, and the dizziness degree is converted into a visual number from an abstract feeling. In the prior art, the vertigo degree of different conditions is quantified by mostly depending on the oral dictation score of a subject, but the method has strong subjectivity, and the subject cannot necessarily ensure rational thinking even under uncomfortable conditions. Gsr (galvanic skin response) sensors are commonly used to detect electrical skin signals that can be manifested differently when subjects experience differences, and there is a need for a method that can improve the reference value of vertigo.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention aims to provide a vertigo degree calculating method, a vertigo degree calculating device and a storage medium based on a GSR peak value, wherein the vertigo degree can be calculated after a skin electric signal is acquired to obtain a quantitative result.

The technical scheme adopted by the invention for solving the problems is as follows: in a first aspect, the present invention provides a vertigo degree calculation method based on a GSR peak, comprising the steps of:

a client acquires a GSR signal sent by a GSR sensor, and preprocesses the GSR signal to acquire a GSR peak value;

the client acquires a preset evaluation time period, and acquires the GSR peak value number in the evaluation time period and the user score input to the client;

the client calculates the GSR peak value incidence rate in the evaluation time period, wherein the peak value incidence rate is the number of times of peak value occurrence in unit time;

the client calculates the correlation between the GSR peak rate and the user score, and sets the value of the correlation and the user score as vertigo reference values.

Further, the preprocessing the GSR signal specifically includes the following steps:

the client filters the GSR signal to obtain phase data;

the client acquires a peak value starting position and a peak value ending position from the phase data;

and the client selects a corresponding GSR curve from an initial GSR signal according to the peak value starting position and the peak value ending position, and sets the maximum value of the GSR curve as a GSR peak value.

Further, the filtering, by the client, the GSR signal specifically includes the following steps:

the client samples the GSR signal to obtain a sample set;

the client selects any sample in the sample set as a sample center, and calculates median number of surrounding samples at a fixed time interval;

and the client sets the difference value between the sample center and the median number as phase data.

Further, after the client selects a corresponding GSR curve from an initial GSR signal according to the peak start position and the peak end position, the method further includes: the GSR curve is linearly transformed according to a min-max normalization method and the result is mapped to the [0,1] interval.

Further, the client calculates the correlation between the GSR peak incidence and the user score as a Pearson correlation.

In a second aspect, the present invention provides an apparatus for performing a vertigo degree calculation method based on a GSR peak, comprising a CPU unit for performing the steps of:

a client acquires a GSR signal sent by a GSR sensor, and preprocesses the GSR signal to acquire a GSR peak value;

the client acquires a preset evaluation time period, and acquires the GSR peak value number in the evaluation time period and the user score input to the client;

the client calculates the GSR peak value incidence rate in the evaluation time period, wherein the peak value incidence rate is the number of times of peak value occurrence in unit time;

the client calculates the correlation between the GSR peak rate and the user score, and sets the value of the correlation and the user score as vertigo reference values.

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

the client filters the GSR signal to obtain phase data;

the client acquires a peak value starting position and a peak value ending position from the phase data;

and the client selects a corresponding GSR curve from an initial GSR signal according to the peak value starting position and the peak value ending position, and sets the maximum value of the GSR curve as a GSR peak value.

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

the client samples the GSR signal to obtain a sample set;

the client selects any sample in the sample set as a sample center, and calculates median number of surrounding samples at a fixed time interval;

and the client sets the difference value between the sample center and the median number as phase data.

Further, the CPU unit is further configured to perform the steps of: the GSR curve is linearly transformed according to a min-max normalization method and the result is mapped to the [0,1] interval.

In a third aspect, the present invention provides an apparatus for performing a GSR peak based vertigo calculation 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 GSR peak based vertigo degree calculation method as described above.

In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the GSR peak-based vertigo degree calculating 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 that, when executed by a computer, cause the computer to perform the GSR peak-based vertigo degree calculation method as described above.

One or more technical schemes provided in the embodiment of the invention have at least the following beneficial effects: according to the method, the client side obtains the GSR signal sent by the GSR sensor, the GSR peak value is obtained from the GSR signal, the user score and the GSR peak value occurrence rate are obtained in the set evaluation time period, the correlation between the GSR peak value occurrence rate and the user score is calculated, the GSR peak value occurrence rate is an objective numerical value, and after the GSR peak value occurrence rate is combined with the user score, the higher the correlation is, the higher the reference value of the user score is, compared with the prior art, the user score can be evaluated through the GSR peak value occurrence rate, and the reference value of the vertigo reference value is effectively improved.

Drawings

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

Fig. 1 is a flowchart of a vertigo degree calculating method based on a GSR peak value according to a first embodiment of the present invention;

fig. 2 is a flowchart of preprocessing a GSR signal in a giddiness degree calculation method based on a GSR peak value according to a first embodiment of the present invention;

fig. 3 is a flowchart illustrating filtering of a GSR signal by a client in a GSR peak-based vertigo calculation method according to a first embodiment of the present invention;

fig. 4 is a schematic diagram of an apparatus for performing a vertigo degree calculation method based on a GSR peak 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 vertigo degree calculation method based on a GSR peak, including the steps of:

step S100, a client acquires a GSR signal sent by a GSR sensor, and preprocesses the GSR signal to acquire a GSR peak value;

step S200, the client acquires a preset evaluation time period, and acquires the GSR peak value number in the evaluation time period and the user score input to the client;

step S300, the client calculates the GSR peak value incidence rate in the evaluation time period, wherein the peak value incidence rate is the number of times of peak value occurrence in unit time;

in step S400, the client calculates the correlation between the GSR peak occurrence rate and the user score, and sets the correlation value and the user score as vertigo reference values.

It should be noted that the GSR sensor in this embodiment is any type in the prior art, and only needs to be able to acquire skin electrical signals, which is not described herein again. It can be understood that the evaluation time period may be a preset value, or may be a value obtained by user input after the GSR signal is obtained. It is understood that the evaluation time period may be any value, for example, the total test time period is 10 minutes, 5 groups of data need to be acquired, the evaluation time period is set to 5 minutes, and the specific time is adjusted according to the actual test requirement. It should be noted that the user score may be obtained in any manner, for example, after the subject is collected by sound pickup equipment such as a microphone that is common in the prior art, the user score may be obtained by determining through a voice recognition technology, and any type of voice recognition technology in the prior art may be adopted, which is not described herein again. It is understood that the present embodiment preferably scores the user as a number score, for example, 1 to 5 scores, 1 score indicating the least vertigo degree, 5 score indicating the most vertigo degree, and the number recognition does not need to involve semantic recognition, so that the efficiency of the speech recognition can be improved. In this embodiment, the GSR peak occurrence rate is the number of GSR peaks occurring in a unit time, and the GSR peak is an expression of a strong skin electrical signal, that is, an instant when the emotion of the subject is more excited, and can be used to represent a case where the vertigo degree is large, so that the number of GSR peaks occurring in a unit time can represent the emotional fluctuation of the subject in the evaluation period. It will be appreciated that the GSR peak occurrence rate is calculated as a simple proportion of the total data per unit time of the number of GSR peaks, e.g. 10 phase data per unit time, with a GSR peak occurrence rate of 20% for a GSR peak number of 2.

Referring to fig. 2, further, in another embodiment of the present invention, the preprocessing the GSR signal specifically includes the following steps:

step S110, filtering the GSR signal by the client to acquire phase data;

step S120, the client acquires a peak value starting position and a peak value ending position from the phase data;

in step S130, the client selects a corresponding GSR curve from the initial GSR signal according to the peak start position and the peak end position, and sets the maximum value of the GSR curve as the GSR peak value.

It should be noted that filtering may adopt any filtering method, and in this embodiment, median filtering is preferred to filter the GSR signal, so that signals unrelated to "peak value" such as arousal or high amplitude generated by stimulus can be eliminated, and the GSR signal can more accurately represent vertigo. It should be noted that the peak start position and the peak end position can be determined in any manner, and this embodiment preferably uses, according to the time length, a point where the corresponding time slot in the data is greater than 0.01 microseconds as the peak start position, and a point less than 0 microseconds as the peak end position. It can be understood that, since the phase data can only be used to express the degree of change of the data, and is not the initial data, after the peak start position and the peak end position are obtained, data acquisition needs to be performed in the original GSR signal.

Referring to fig. 3, further, in another embodiment of the present invention, the filtering, by the client, the GSR signal specifically includes the following steps:

step S111, the client samples the GSR signal to obtain a sample set;

step S112, the client selects any sample in the sample set as a sample center, and calculates the median number of the surrounding samples at a fixed time interval;

in step S113, the client sets the difference between the center of the sample and the median as the phase data.

It should be noted that the sampling may adopt any method for sampling data in the existing method, and details are not described herein. It should be noted that the time interval may be any value, and in this embodiment, it is preferable that the time interval is plus or minus 4 seconds, and other samples included in plus or minus 4 seconds are selected as surrounding samples with the center of the sample as the origin of the time axis, so as to calculate the median number from the acquired data.

Further, in another embodiment of the present invention, after the client selects a corresponding GSR curve from the initial GSR signal according to the peak start position and the peak end position, the method further includes: the GSR curve is linearly transformed according to a min-max normalization method and the result is mapped to the [0,1] interval.

It should be noted that the min-max normalization method is only preferred in this embodiment, and the method is a common method in the existing normalization methods, has the characteristics of simple calculation and good normalization degree, and may also adopt other normalization methods, which are not described herein again. It can be understood that, because the original GSR signal is relatively discrete and is directly used for relatively complicated calculation, the present embodiment performs low-pass filtering on the curve of the GSR signal, performs smooth curve processing, obtains an extreme value through normalization, and more easily obtains the GSR peak value through comparison. It should be noted that the mapping interval may be any, and selecting [0,1] is only preferable in this embodiment, and can convert the original data into a dimensionless pure value, so that the data is more comparable.

Further, in another embodiment of the present invention, the client calculates the correlation between the GSR peak occurrence rate and the user score as a pearson correlation.

It should be noted that, the correlation may adopt a correlation calculation method that is common in the prior art, and in this embodiment, pearson correlation is preferably adopted, which can calculate the correlation between two data more simply, and the higher the correlation between the GSR peak occurrence rate and the user score is calculated, the higher the confidence level of the user score is, and other correlation calculation methods may also be adopted, which are not described herein again.

Referring to fig. 4, the second embodiment of the present invention further provides an apparatus for performing a vertigo degree calculation method based on a GSR peak, 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 is described by taking the computer as an example.

In the computer 4000 for executing the vertigo degree calculation method based on the GSR peak value, a CPU unit 4100 is included, the CPU unit 4100 being configured to execute the steps of:

the client acquires a GSR signal sent by a GSR sensor, and preprocesses the GSR signal to acquire a GSR peak value;

the method comprises the steps that a client side obtains a preset evaluation time period, and obtains the GSR peak value number in the evaluation time period and user scores input to the client side;

the client calculates the GSR peak value incidence rate in the evaluation time period, wherein the peak value incidence rate is the number of times of peak value occurrence in unit time;

the client calculates the correlation between the GSR peak rate and the user score, and sets the correlation value and the user score as vertigo reference values.

Further, in another embodiment of the present invention, the CPU unit 4100 is further configured to perform the following steps:

the client filters the GSR signal to obtain phase data;

the client acquires a peak value starting position and a peak value ending position from the phase data;

and the client selects a corresponding GSR curve from the initial GSR signal according to the peak value starting position and the peak value ending position, and sets the maximum value of the GSR curve as a GSR peak value.

Further, in another embodiment of the present invention, the CPU unit 4100 is further configured to perform the following steps:

the client samples the GSR signal to obtain a sample set;

the client selects any sample in the sample set as a sample center, and calculates median number of surrounding samples at a fixed time interval;

and the client sets the difference value between the sample center and the median number as phase data.

Further, in another embodiment of the present invention, the CPU unit 4100 is further configured to perform the following steps: the GSR curve is linearly transformed according to a min-max normalization method and the result is mapped to the [0,1] interval.

The computer 4000 and the CPU unit 4100 may be connected by a bus or other means, and the computer 4000 further includes a memory as a non-transitory computer readable storage medium for storing a non-transitory software program, a non-transitory computer executable program, and a module, such as a program instruction/module corresponding to an apparatus for performing the GSR peak value based vertigo degree calculation method according to an embodiment of the present invention. The computer 4000 controls the CPU unit 4100 to execute various functional applications for executing the GSR peak-based vertigo degree calculating method and data processing, i.e., to implement the GSR peak-based vertigo degree calculating method of the above-described method embodiment, 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 stored data area may store data created from the use of the CPU unit 4100, 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 the CPU unit 4100, and such remote memory may be connected to the computer 4000 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 4100, perform the GSR peak-based vertigo degree calculation 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, and the computer-executable instructions are executed by the CPU unit 4100, so as to implement the above-mentioned vertigo degree calculating method based on a GSR peak value.

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 giddiness degree calculation method based on the GSR peak in the present embodiment is based on the same inventive concept as the above-described giddiness degree calculation method based on the GSR peak, 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.