JP4822146B2 - State evaluation apparatus and state evaluation program - Google Patents

Description

  The present invention relates to a state evaluation apparatus and a state evaluation program for evaluating a state of a subject based on biological information, for example.

There is a technique for estimating a driver's arousal level, fatigue, feelings, and the like while driving by measuring biological information such as heartbeat and sweating.
For example, Patent Document 1 proposes a technique for detecting a driver's body condition based on the average value of the driver's heart rate, heart rate cycle, respiratory rate, and respiratory cycle.
JP 7-59757 A

  There is also a technique for measuring a subject's state transition based on a moving average value and a moving variance value of a heartbeat interval using a moving average method which is one of time series analysis methods.

Conventionally, including the device described in Patent Document 1, it is determined whether the subject is in an irritated state (tensed state), sleepy state (relaxed state), or normal state based on an average value or a variance value of heartbeat intervals. It was.
However, the average value and the variance value of the heartbeat interval greatly differ depending on the body shape and constitution of the subject. For this reason, it has been difficult to make a highly accurate state determination using a conventional determination method based on an average value or a variance value of heartbeat intervals.
Moreover, although the state change of the subject can be detected from the moving average value and the moving variance value of the heartbeat interval, the state of the subject after the state change cannot be determined. For example, although it can be detected that the state of the subject has changed to a more tense state, it has not been possible to determine whether the subject has changed from a normal state to a tense state or from a tense state to a deeper tense state.

  Therefore, an object of the present invention is to perform a state evaluation of a subject with higher accuracy.

(1) In order to achieve the above object, according to the first aspect of the present invention, RR interval acquisition means for acquiring N RR interval data indicating an R wave interval of a heartbeat, and the acquired N RR interval data First variation coefficient calculating means for calculating a first variation coefficient of RR interval data distributed from a minimum value to a first predetermined value, and RR interval data distributed from the first predetermined value to a second predetermined value The second variation coefficient calculating means for calculating the second variation coefficient, and the state evaluation means for evaluating the condition of the subject based on the comparison result between the calculated first variation coefficient and the second variation coefficient. A state evaluation apparatus characterized by the above is provided.
(2) The invention according to claim 2, further comprising reference characteristic data acquisition means for acquiring reference characteristic data created based on RR interval data of a subject in a normal state, wherein the state evaluation means is the first The state evaluation according to claim 1, wherein the state of the subject and its degree are evaluated based on a comparison result between the coefficient of variation and the second coefficient of variation and a reference coefficient of variation obtained from the reference characteristic data. Providing equipment.
(3) In the invention according to claim 3, the reference characteristic data is created based on a sample composed of n pieces of RR interval data of a subject in a normal state, and is based on a distribution range of RR interval data in the sample. A range element, an RR interval data element based on a range from a minimum value of the RR interval data in the sample to a third predetermined value, and a distribution in a range from the minimum value of the RR interval data in the sample to a fourth predetermined value The state evaluation apparatus according to claim 2, wherein a relationship between the RR interval data and the reference variation coefficient is indicated.
In the third aspect, for example, the RR interval data element is preferably an average value of RR interval data distributed in a range from a minimum value of the RR interval data to a third predetermined value in the sample.
In the third aspect, for example, it is preferable that the range from the minimum value to the third predetermined value and the range from the minimum value to the fourth predetermined value coincide.
(4) In the invention according to claim 4, the calculated first variation coefficient and the second variation coefficient are compared, and a variation coefficient variation indicating the difference between the first variation coefficient and the second variation coefficient is calculated. A variation coefficient change amount calculating means, and an RR interval data element based on a range from the minimum value to a first predetermined value, and a range element based on a distribution range of the acquired RR interval data in the reference characteristic data A first reference coefficient of variation specified when fitting, an RR interval data element based on a range from the first predetermined value to a second predetermined value, and a range based on the distribution range of the acquired RR interval data A normal variation coefficient variation calculation unit that calculates a normal variation coefficient variation amount indicating a difference from the second reference variation coefficient specified when the element is applied, and the state evaluation unit calculates the calculated Coefficient of variation Based on the result of comparison between the reduction amount and the normal-time variation coefficient change amount, providing status evaluation device according to claim 3, characterized in that to assess the status and extent of the subject.
(5) In the invention according to claim 5, the state evaluation means evaluates the degree of the state of the subject from a value obtained by subtracting the normal variation coefficient variation from the variation coefficient variation. A state evaluation apparatus according to claim 4 is provided.
(6) In the invention according to claim 6, the reference characteristic data acquisition means acquires reference characteristic data expressed by an approximation function, and the normal time variation coefficient variation calculation means calculates the acquired reference characteristic data. The state evaluation apparatus according to claim 4 or 5, wherein the change amount of the normal variation coefficient is calculated based on the approximate function.
(7) The invention according to claim 7 comprises a sorting means for sorting the range from the minimum value to the maximum value of the N pieces of acquired RR interval data into the first to nth sections in order from the minimum value. The first variation coefficient calculating means calculates a first variation coefficient of RR interval data distributed in the divided first section, and the second variation coefficient calculating means is distributed in the divided second section. The state evaluation device according to any one of claims 1 to 6, wherein the second variation coefficient of the RR interval data is calculated.
(8) In the invention according to claim 8, the sorting means sets the range from the minimum value to the maximum value of the acquired N pieces of RR interval data in the first to fifth sections or the first to first sections. 8. The state evaluation apparatus according to claim 7, wherein the state evaluation apparatus is divided into 6 equal parts.
(9) In the invention according to claim 9, when the value obtained by subtracting the second variation coefficient from the first variation coefficient is within a range from the first threshold value to the second threshold value, the state evaluation means If the value obtained by subtracting the second coefficient of variation from the first coefficient of variation is smaller than the first threshold value, the test subject evaluates that the subject is in tension, and the first coefficient of variation The state according to any one of claims 1 to 8, wherein when the value obtained by subtracting the second variation coefficient is larger than the second threshold value, the subject is evaluated to be in a relaxed state. An evaluation device is provided.
(10) In the invention according to claim 10, the first variation coefficient calculating unit and the second variation coefficient calculating unit calculate the first variation coefficient and the second variation coefficient from a standard deviation based on maximum likelihood variance. The state evaluation apparatus according to any one of claims 1 to 9 is provided.
(11) In the invention according to claim 11, the RR interval acquisition unit includes a RR interval acquisition unit, a first variation coefficient calculation unit, a second variation coefficient calculation unit, and a state evaluation unit. An RR interval acquisition function for acquiring N pieces of RR interval data indicating an R wave interval of a heartbeat; and a first variation coefficient calculating unit configured to obtain a first predetermined value from a minimum value of the acquired N pieces of RR interval data. A first variation coefficient calculating function for calculating a first variation coefficient of RR interval data distributed up to a value, and an RR interval in which the second variation coefficient calculating means distributes from the first predetermined value to a second predetermined value. A second variation coefficient calculation function for calculating a second variation coefficient of the data, and a state evaluation in which the state evaluation means evaluates the state of the subject based on a comparison result between the calculated first variation coefficient and the second variation coefficient. Functions and Providing computer readable state evaluation program.

  According to the present invention, the state of the subject can be determined with higher accuracy by performing the state evaluation of the subject based on the comparison result between the first coefficient of variation and the second coefficient of variation.

It is a figure showing the structure of the state evaluation apparatus and peripheral device in 1st Embodiment. It is the flowchart which showed the operation | movement procedure of the test subject's state evaluation process in a state evaluation apparatus. It is the figure which showed an example of 50 RR space | interval data which are analysis object. It is the figure which showed an example of the map for analysis. It is the figure which showed the example of the calculation result of the variation coefficient in each division. It is the figure which showed an example of 50 RR interval data measured when the test subject is drowsy. It is the figure which showed the calculation result of the variation coefficient of each division calculated based on 50 pieces of RR interval data shown in FIG. It is the figure which showed an example of the tendency model of the variation coefficient of RR interval data. It is the figure which showed the structure of the state evaluation apparatus and peripheral device in 2nd Embodiment. It is the figure which showed an example of the state determination table. It is the figure which showed a part of reference | standard characteristic data. It is the flowchart which showed the operation | movement procedure of the test subject's state evaluation process in 2nd Embodiment. It is the figure which showed the example of the variation coefficient in each division | segmentation of the test subject in a tension state, and a normal variation coefficient. It is the figure which showed the example of the variation coefficient in each division | segmentation of the test subject in a relaxation | loosening state, and a normal variation coefficient.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 State evaluation apparatus 2 Heart rate sensor 11 CPU
12 ROM
13 RAM
14 Input device 15 Output device 16 Storage device 161 Heartbeat data storage area 162 Determination result storage area 163 State evaluation program 164 State determination threshold data 165 Reference characteristic data

Hereinafter, a preferred embodiment of the state evaluation apparatus of the present invention will be described in detail with reference to FIGS.
(1) Outline of Embodiment In the state evaluation apparatus according to the present embodiment, the subject is in any of “normal state”, “tensioned state”, and “relaxed state” based on continuous N (for example, 50) RR interval data. It is determined whether it is in the state.
First, the CPU of the state evaluation device acquires 50 continuous RR interval data (RR1 to RR50) measured by the heart rate sensor, and extracts the maximum value and the minimum value of the acquired RR1 to RR50.

Next, the CPU sets an area range in which the minimum value and the maximum value of the extracted RR interval data are both poles (both ends) in the analysis map, and n (for example, 6) set area ranges are set. Divide into areas. Specifically, in the analysis map, the area range of the set RR interval is divided into areas of the first to sixth sections in order from the minimum value side.
Subsequently, the CPU collates the analysis map in which the region range and section of the RR interval are set with the 50 RR interval data (RR1 to RR50) acquired previously, and distributes them to each section of the analysis map. The coefficient of variation of the RR interval data is calculated.
In the present embodiment, the coefficient of variation of the RR interval data in each section is calculated from the standard deviation based on the maximum likelihood variance.
The CPU compares the variation coefficient of the RR interval data distributed in the first section of the analysis map with the variation coefficient of the RR interval data distributed in the second section.

When the variation coefficient of the second category is larger than the variation coefficient of the first category and the difference between the variation coefficients exceeds the predetermined range set by the first threshold, the CPU Judge that there is.
The CPU determines that the subject is in the “relaxed state” when the variation coefficient of the second classification is smaller than the variation coefficient of the first classification and the difference between the variation coefficients exceeds a predetermined range set by the second threshold. judge.
When the difference between the variation coefficient of the second category and the variation coefficient of the first category is within a predetermined range set by the first threshold value and the second threshold value, the CPU is in the “normal state”. Is determined.
Thus, in the present embodiment, the state of the subject is determined based on the comparison result between the variation coefficient of the first division and the variation coefficient of the second division in the set analysis map.

In order to determine a more detailed state of the subject, the CPU uses the calculated variation coefficient change amount S based on the calculated first variation coefficient and second variation coefficient as the normal variation coefficient variation amount. The state index Z is obtained by comparing with Y.
The normal variation coefficient change amount Y is based on the reference characteristic data indicating the relationship between the average value of the RR interval data and the variation coefficient, which is created based on the sample data of the RR interval data collected from a normal person. Desired.
Specifically, the normal variation coefficient change amount Y is expressed by the first normal variation coefficient C _std 1 corresponding to the RR interval data distributed in the first section of the RR interval data of the subject and the RR interval data distributed in the second section. It is obtained from the corresponding second normal variation coefficient C _std 2.
Then, the CPU refers to the determination table in which the degree of the state of the subject is defined for each range of the state index Z, and specifies the degree of the state of the subject corresponding to the obtained state index Z.

(2) Details of Embodiment (A) First Embodiment FIG. 1 is a diagram illustrating the configuration of a state evaluation device 1 and peripheral devices in the first embodiment.
The state evaluation device 1 includes a CPU 11, a ROM 12, a RAM 13, an input device 14, an output device 15, and a storage device 16.
The CPU 11 is a central processing unit that controls the state evaluation device 1 in accordance with various programs and data stored in the ROM 12 and the storage device 16, and performs various types of arithmetic processing relating to the state evaluation of the subject.
The ROM 12 is a read-only memory in which basic programs and parameters for causing the state evaluation apparatus 1 to function are stored.
The RAM 13 is a storage area that functions as a work area for the CPU 11 and temporarily stores data being processed.
The input device 14 is a device that inputs commands and data to the state evaluation device 1, and is configured by, for example, a keyboard and a mouse that are used when manually inputting subject information.
The output device 15 is a device that outputs the result processed by the state evaluation device 1 to the outside.

The storage device 16 includes a heartbeat data storage area 161 for storing the heartbeat data of the subject and a determination result storage area 162 for storing a state determination result (state evaluation result) in the state evaluation apparatus 1.
In addition, the storage device 16 stores various programs related to the state evaluation processing such as the state evaluation program 163, and various data referred to during the state evaluation processing such as the state determination threshold data 164, for example.
In the present embodiment, the heart rate data measured (detected) by the heart rate sensor 2 attached to the subject is stored in the heart rate data storage area 161 in real time.
The heart rate sensor 2 measures the interval between R waves generated when blood is sent from the left ventricle to the aorta, that is, the time interval between the R wave and the next R wave (hereinafter referred to as RR interval), and the measurement result is measured time information. It is a measuring device which outputs together.

In the present embodiment, the heart rate sensor 2 is configured to output the measurement result of the RR interval together with the measurement time information to the heart rate data storage area 161 as RR interval data.
The output destination of the heart rate sensor 2 is not limited to the heart rate data storage area 161. For example, the RR interval data is output to an external storage device such as a memory stick that can be attached to the heart rate sensor 2, and this external storage is performed. The RR interval data may be taken into the heartbeat data storage area 161 via the device. Further, the heart rate sensor 2 may be provided with a wireless communication function, and the RR interval data may be transmitted to the state evaluation device 1 by wireless communication.

Next, a method for evaluating the state of the subject in the state evaluation apparatus 1 configured as described above, specifically, a method for determining whether the subject is in a normal state, a tension state, or a relaxed state will be described. .
FIG. 2 is a flowchart showing an operation procedure of the state evaluation process of the subject in the state evaluation apparatus 1.
First, the CPU 11 acquires N pieces of RR interval data of the subject from the heartbeat data storage area 161 in the storage device 16 (step 11). In this embodiment, 50 RR interval data are acquired as an example.
FIG. 3 is a diagram showing an example of 50 RR interval data to be analyzed.
The CPU 11 reads 50 RR interval data (RR1 to RR50) continuous in time series as shown in FIG. 3 from the heartbeat data storage area 161 and stores them in the RAM 13.

Next, the CPU 11 extracts the maximum value and the minimum value from the acquired 50 RR interval data (step 12).
For example, when acquiring the 50 RR interval data shown in FIG. 3, the CPU 11 extracts 650 [msec] of RR22 as the maximum value of RR interval data, and 350 [msec] of RR5 is the minimum value of RR interval data. Extract as Then, the CPU 11 stores the extracted maximum value and minimum value data in the RAM 13.
Subsequently, the CPU 11 performs a specific area range and section setting process using the extracted maximum value and minimum value data (step 13).
Here, an analysis map used in data analysis in the present embodiment is created by setting a specific region range and section.

FIG. 4 is a diagram showing an example of the analysis map.
The CPU 11 determines the region range of the RR interval having both the minimum value (350 [msec]) and the maximum value (650 [msec]) of the extracted RR interval data as poles, in detail, the horizontal axis of the analysis map shown in FIG. Set.
Then, the CPU 11 divides the set RR interval range (horizontal axis) into n regions. In the present embodiment, as an example, the region range of the RR interval is divided into six regions.
Specifically, as shown in FIG. 4, the CPU 11 divides the set RR interval range into the first, second, third, fourth, fifth, and sixth sections in order from the minimum value side. (Section setting).

In this embodiment, equal division is performed so that the range widths (interval regions) of the respective sections are all equal. For example, in the analysis map shown in FIG. 4, a value (300 [msec]) obtained by subtracting the minimum value (350 [msec]) from the maximum value (650 [msec]) divided by the number of sections (6) ( 50 [msec]) is defined as one section width.
Note that the setting method of the range width of each section is not limited to the equal division setting, and for example, the setting width of each section may be an arbitrary value.

Next, the CPU 11 compares the set analysis map (FIG. 4) with the 50 previously acquired RR interval data (RR1 to RR50), and each of the first to sixth sections of the analysis map. The coefficient of variation of the RR interval data distributed in each section is calculated (step 14).
The CPU 11 stores the calculated variation coefficient of the RR interval data in each section in the RAM 13.

Here, a method for calculating the variation coefficient of the RR interval data distributed in each section will be described.
The present embodiment is intended to evaluate (determine) the state of a subject at a certain point in time. Therefore, a method is used in which the coefficient of variation is calculated from the standard deviation based on the maximum likelihood variance, which is a sample variance that can more appropriately evaluate the condition of the subject in a short time, that is, with a small amount of data.
When n pieces of RR interval data are x ₁ , x ₂ , x ₃ ... x _n and the sample average is represented by [x], the average value of the squares of ([x] −x _i ) is n The variance value s ² is based on the sample average of the data, and the variance value s ² is expressed by the following equation.
s ² = {Σ ([x] −x _i ) ² } / n (1)
However, the addition range by Σ in the above equation (1) is i = 1 to n.
The variation coefficient C is obtained by dividing the standard deviation s by the sample average [x]. Further, the standard deviation s is calculated as the square root of the variance s ^2.
Therefore, the coefficient of variation P is obtained by dividing the square root of the variance value s ² by the sample average [x].

In the present embodiment, a method of calculating the variation coefficient from the standard deviation based on the maximum likelihood variance is used, but the method of calculating the variation coefficient is not limited to this.
For example, the coefficient of variation may be calculated from a standard deviation based on universal dispersion whose dispersion value is expressed by the following equation.
s ² = {Σ ([x] −x _i ) ² } / (n−1) (2)
However, the addition range by Σ in the above equation (2) is i = 1 to n.

Next, the CPU 11 calculates, from the calculated variation coefficient of the RR interval data in the first section (hereinafter referred to as the first variation coefficient), the variation coefficient of the RR interval data in the second section (hereinafter referred to as the second variation coefficient). Is subtracted (step 15). The CPU 11 stores the subtraction result in the RAM 13.
The CPU 11 determines whether or not the subtraction result is smaller than a predetermined first threshold, specifically “−0.001” (step 16).
When the subtraction result is smaller than the predetermined first threshold “−0.001” (step 16; Y), the CPU 11 determines the state of the subject during the measurement period of the 50 RR interval data (RR1 to RR50) as “tensile state”. ] (Step 17). Then, the CPU 11 stores the evaluation result in the determination result storage area 162 of the storage device 16, and then ends the state evaluation process.
“Tension state” refers to a state in which the mind is tense and a state in which the nerve is high and frustrated.

When the subtraction result is larger than the predetermined first threshold value “−0.001” (step 16; N), the CPU 11 determines whether or not the subtraction result is larger than the predetermined second threshold value, specifically “+0.002”. Is determined (step 18).
When the subtraction result is larger than the predetermined second threshold “+0.002” (step 18; Y), the CPU 11 determines (evaluates) the state of the subject in the measurement period as “relaxed state” (step 19). After the evaluation result is stored in the determination result storage area 162, the state evaluation process is terminated.
Note that the “relaxed state” indicates a state where the spirit is indistinct or sleepy.

On the other hand, when the subtraction result is smaller than the predetermined second threshold “+0.002” (step 18; N), that is, when the subtraction result is equal to or greater than the predetermined first threshold “−0.001”, the predetermined second threshold If it is within the range of the threshold value “+0.002” or less, the CPU 11 determines (evaluates) the state of the subject in the measurement period as “normal state” (step 20), and stores the evaluation result in the determination result storage area 162. After storing, the state evaluation process is terminated.
The CPU 11 performs processing by reading the first threshold value and the second threshold value from the state determination threshold value data 164 stored in the storage device 16 when collating the subtraction results. The threshold data of the state determination threshold data 164 can be arbitrarily set based on the measurement environment of the RR interval data, the characteristics of the subject, the state determination accuracy, and the like.

FIGS. 5A to 5C are diagrams illustrating examples of calculation results of variation coefficients in each section.
In the present embodiment, as shown in FIG. 5A, the second variation coefficient is larger than the first variation coefficient, and the difference between the variation coefficients exceeds a predetermined range set by the first threshold value. The subject is determined to be “tensioned”.
In addition, as shown in FIG. 5B, when the second coefficient of variation is smaller than the first coefficient of variation and the difference of the coefficient of variation exceeds a predetermined range set by the second threshold, Judged to be “relaxed”.
Then, as shown in FIG. 5C, when the difference between the second variation coefficient and the first variation coefficient is within a predetermined range set by the first threshold value and the second threshold value, the subject Is determined to be in a “normal state”.
Thus, in this embodiment, it is comprised so that a test subject's state may be determined based on the comparison result of a 1st variation coefficient and a 2nd variation coefficient.

In the above-described embodiment, the case where the region range of the RR interval is divided into six regions when generating the analysis map has been described. However, the number of divisions of the region range of the RR interval is not limited to this. .
Next, an example in which the number of sections in the area range of the RR interval at the time of generating the analysis map is changed will be shown.
FIG. 6 is a diagram showing an example of 50 RR interval data measured when the subject is drowsy.
FIGS. 7A to 7E are diagrams showing calculation results of the variation coefficients of the respective sections calculated based on the 50 pieces of RR interval data shown in FIG.
FIG. 7A shows the calculation results of the coefficient of variation of each section when the range (horizontal axis) of the RR interval in the analysis map is divided into three areas, and FIGS. 7B to 7E. These show the calculation results of the coefficient of variation for each segment when the range (horizontal axis) of the RR interval in the analysis map is segmented into four, five, six, and seven regions.

As can be seen from FIGS. 7A to 7E, in any case, the first coefficient of variation is larger than the second coefficient of variation. That is, in any case, the subject is determined to be in the “relaxed state” based on the comparison result between the first variation coefficient and the second variation coefficient.
In other words, whether the subject is in a “relaxed state”, whether the number of sections in the RR interval range (horizontal axis) in the analysis map is reduced to 3 or 7 Can do.
In the subject state evaluation process based on the RR interval data according to the present embodiment, in consideration of the number of samples of the RR interval data, the measurement time of the RR interval data, the processing speed in the CPU 11, the analysis accuracy of the heartbeat data, etc. It is preferable to adopt a method of dividing the range of the RR interval (horizontal axis) in the analysis map into five or six regions.

Moreover, the setting method of the range width of each division is not limited to the equal division setting, and the setting width of each division may be an arbitrary value.
Here, consider a case where the region range of the RR interval is divided into three parts of a first part to a third part. Set each range width of the 1st division and the 2nd division to be smaller than the range width of 1 division when the entire range is divided into 3 parts, and subtract the range width of the 1st division and the 2nd division from the entire range The remaining area may be set to the range width of the third section.
The ratio of the range width in each section may be set to be, for example, first section: second section: third section = 1: 1: 4.
However, in order to obtain an appropriate comparison result of variation coefficients, it is preferable that the first section, the second section, and the range width are set equal.

As described above, according to the present embodiment, the state of the subject can be easily evaluated (determined) based on N (for example, 50) RR interval data.
FIG. 8 is a diagram illustrating an example of a trend model of a variation coefficient of RR interval data.
The average value and variance value of the RR interval vary depending on the body shape and constitution of the subject. However, a specific tendency (relationship) as shown in FIG. 8 exists between the size of the RR interval (average value) and the coefficient of variation of the RR interval. That is, there is a specific tendency (relationship) between the state of the subject and the coefficient of variation, regardless of whether the subject has a small average RR interval or a subject with a large tendency.
Therefore, the state evaluation device 1 of the present embodiment is configured to evaluate the state of the subject based on such tendency characteristics.

In the state evaluation method based on the variance value of the conventional RR interval data, when the measurement data is small, that is, when the measurement time of the data is short, the subject is in any state of “normal state”, “tensioned state”, and “relaxed state” It was difficult to properly determine whether or not
However, in the present embodiment, it is possible to appropriately determine whether the subject is in a “normal state”, “tensed state”, or “relaxed state” in a short measurement time, that is, with a small amount of data. As described in the present embodiment, for example, when 50 pieces of RR interval data are used, the state of the subject can be determined based on data measured (collected) in only several tens of seconds.

When determining the state of the subject in a shorter time, for example, about 10 seconds, for example, the number of RR interval data to be analyzed is set to about 10 to 20, and the number of sections is set to about 3. Process.
On the other hand, when there is a margin in the time until the state determination is output, for example, when determining the state of the subject in about 2 minutes, the number of RR interval data to be analyzed is, for example, about 100 to 150 Set to.
As described above, the value of the number of RR interval data (N) to be analyzed can be arbitrarily changed.

  In the present embodiment, the determination (evaluation) result is configured to output whether the subject is in a “normal state”, “tensioned state”, or “relaxed state”. It is not limited to. For example, whether or not the subject is only in a “normal state” or not, whether or not the subject is only in a “tension” state, It may be determined only whether or not.

The above-described state evaluation device 1 according to the present embodiment may be incorporated in a driving support device and used as a device for evaluating the state of a vehicle driver.
In this case, the driver's state is determined in real time by attaching the heart rate sensor 2 to the driver. And it comprises so that an appropriate driving assistance process may be performed based on the determination result (evaluation result) of a driver | operator's state.
For example, when it is determined that the driver is in a “tensioned state”, for example, the vibration massage device is operated or the oxygen concentration adjusting device is operated to increase the indoor oxygen concentration.
Further, when it is determined that the driver is in the “relaxed state”, the refresh mode is activated in the driving support device, for example, the warning sound of the alarm (warning) device is sounded at a fast tempo, or the vibration warning device And the cold air injection device are operated.

(B) Second Embodiment In the first embodiment, based on a value obtained by subtracting the second variation coefficient from the first variation coefficient (hereinafter referred to as variation coefficient change amount S), the subject is in a “tension state”, The method for determining whether the state is “relaxed” or “normal” has been described.
In the second embodiment, a more detailed method for determining the condition of the subject by comparing the variation coefficient change amount S with the statistical data of the RR interval data of the subject in the normal state will be described.

FIG. 9 is a diagram illustrating a configuration of the state evaluation device 1 ′ and peripheral devices in the second embodiment.
In addition, in 2nd Embodiment, the same code | symbol is attached | subjected about the structure which overlaps with 1st Embodiment mentioned above, and detailed description is abbreviate | omitted.
In the second embodiment, a state determination table is stored in the storage device 16 ′ as state determination threshold data 164 ′.
FIG. 10 is a diagram illustrating an example of the state determination table.
As shown in FIG. 10, the state determination table is a reference table showing a correspondence relationship between a range (threshold condition) of a state index Z, which will be described later, and the state of the subject.
In the state determination table, seven subject states are associated with each range of the state index Z. Specifically, depending on the range of the state index Z, it is quite sleepy (relaxed state: strong), sleepy (relaxed state: medium), a little sleepy (relaxed state: weak), normal (normal state), a little frustrated (tensed state: weak) , Irritated (tensed state: medium), considerably irritated (tensed state: strong).

Note that in the second embodiment, seven subject states are associated with the state determination table according to the range of the state index Z in the state determination table, but the method for defining the subject state in the state determination table is not limited to this. .
For example, the degree in the relaxed state and the tensioned state may be defined in two stages, or the type of the degree in the relaxed state and the tensioned state may be further defined and defined.

Further, as shown in FIG. 9, reference characteristic data 165 is stored in the storage device 16 ′ of the state evaluation device 1 ′.
The reference characteristic data 165 was created based on statistical results of N pieces of RR interval data (hereinafter referred to as RR interval data samples) collected from subjects in a normal state (hereinafter referred to as normal subjects). It is the data which showed the distribution characteristic of the variation coefficient of RR interval data of a normal test subject.
Specifically, the reference characteristic data 165 is based on a “range element” based on the distribution range of the RR interval data in the RR interval data sample and a range from the minimum value of the RR interval data to the first predetermined value in the RR interval data sample. This is data showing the relationship between the “RR interval data element” and the “variation coefficient” of the RR interval data distributed in the range from the minimum value of the RR interval data to the second predetermined value in the RR interval data sample.
In the reference characteristic data 165, the convergence accuracy of the variation coefficient distribution characteristic can be further improved by matching the first predetermined value and the second predetermined value.

In the second embodiment, the size w of the distribution range of the RR interval data is adopted as the “range element” of the reference characteristic data 165, and the average value of the RR interval data is adopted as the “RR interval data element”. Explained as an example.
In the second embodiment, as an example of the reference characteristic data 165, the relationship between the average value of the RR interval data and the coefficient of variation will be described using data indicated by the size w of the distribution range of the RR interval data.

In the second embodiment, the average value of the RR interval data that is the “RR interval data element” of the reference characteristic data 165 is the minimum value of the region range of the RR interval having the maximum value and the minimum value of the RR interval data sample as both poles. The average value of the RR interval data distributed in the 1st division at the time of dividing into the 1st-nth division in order from the side is shown. In the second embodiment, as in the case of the subject state determination process in the first embodiment, a case where the region range of the RR interval is divided into six regions will be described as an example.
The size w of the distribution range of the RR interval data serving as the “range element” of the reference characteristic data 165 is the RR interval region range having both the maximum value and the minimum value of the RR interval data sample as the poles in order from the minimum value side. The width | variety of the 1st division at the time of dividing | segmenting into 1st-nth division is shown. For example, when the first division is 350 msec to 400 msec, the size w of the distribution range of the RR interval data in the reference characteristic data 165 is 50 msec.
In addition, the “variation coefficient” of the reference characteristic data 165 is the value obtained when the range of the RR interval having the maximum value and the minimum value of the RR interval data sample is divided into the first to nth sections in order from the minimum value side. The coefficient of variation of RR interval data distributed in one section is shown.

FIG. 11 is a diagram showing a part of the reference characteristic data 165.
FIG. 11 shows an extracted characteristic curve showing the relationship between the average value of the RR interval data and the variation coefficient when the size w of the distribution range of the RR interval data is 34 msec, 68 msec, and 113 msec.
As shown in FIG. 11, the variation coefficient of the RR interval data in the normal subject has a tendency to decrease as the average value of the RR interval data increases, which is common to the range w of the distribution range of the RR interval data. appear.

Usually, individual differences exist in the relationship between the average value and the variance value of the RR interval data of normal subjects.
However, the relationship between the average value of the RR interval data of normal subjects and the coefficient of variation has been found from the statistical values of many collected experimental (actually measured) data to converge to a characteristic curve as shown in FIG.
Therefore, in the state evaluation apparatus 1 ′ according to the second embodiment, the reference value (reference fluctuation value) of the coefficient of variation exceeding the individual difference (common to all people) is obtained from the reference characteristic data 165 indicating such a specific tendency. And the degree of the condition of the subject is determined based on this value.
Note that the coefficient of variation of the RR interval data of the normal subject is calculated based on the standard deviation based on the maximum likelihood variance described in the first embodiment or the standard deviation based on universal variance.

Next, a subject determination method using the reference characteristic data 165 will be described.
FIG. 12 is a flowchart showing an operation procedure of the state evaluation process of the subject in the second embodiment.
The processing of steps 21 to 24 shown in FIG. 12 is the same as the processing contents described in steps 11 to 14 of the state evaluation processing in the first embodiment (FIG. 2), and thus description thereof is omitted.
The CPU 11 calculates a variation coefficient change amount S, which is a difference between the first variation coefficient C1 and the second variation coefficient C2 calculated in the process of step 24 (step 25).
Here, the second variation coefficient C2 is subtracted from the first variation coefficient C1, and this subtraction value is used as the variation coefficient change amount S. That is, the variation coefficient change amount S is expressed by the following equation.
Variation coefficient change amount S = first variation coefficient C1−second variation coefficient C2

Next, the CPU 11 performs the first normal variation coefficient C _std 1 corresponding to the average value [x1] of the RR interval data distributed in the first section of the RR interval data of the subject to be determined (evaluated), and the second A normal variation coefficient change amount Y that is a difference from the second normal variation coefficient C _std 2 corresponding to the average value [x2] of the RR interval data distributed in the section is calculated (step 26).
Here, the second normal fluctuation coefficient C _std 2 is subtracted from the first normal fluctuation coefficient C _std 1, and this subtraction value is set as the normal fluctuation coefficient change amount Y. That is, the normal variation coefficient change amount Y is expressed by the following equation.
Normal variation coefficient change amount Y = first normal variation coefficient C _std 1−second normal variation coefficient C _std 2

Here, a method for deriving the first normal variation coefficient C _std 1 and the second normal variation coefficient C _std 2 will be described.
First, CPU11 calculates | requires the width | variety of the 1st division of the test subject's RR interval data used as judgment (evaluation) object. When the ratio of the range width in each section is equal, the width of the first section can be obtained by dividing the value obtained by subtracting the minimum value RRmin from the maximum value RRmax of the acquired 50 RR interval data by the number of sections. it can.
Then, the CPU 11 determines, based on the reference characteristic data 165 (FIG. 11), a characteristic curve of the distribution range size w of the RR interval data corresponding to the width of the first division (the average value of the RR interval data of normal subjects). Curve) showing the relationship with the coefficient of variation.

The CPU 11 determines the value of the coefficient of variation when the average value [x1] of the RR interval data distributed in the first section of the subject's RR interval data is taken as the “average value of the RR interval data” in the specified characteristic curve. Ask for. The value of the variation coefficient is set as a first normal variation coefficient C _std 1.
Similarly, in the specified characteristic curve, the CPU 11 changes the average value [x2] of the RR interval data distributed in the second section of the RR interval data of the subject as the “average value of the RR interval data”. A coefficient value is obtained, and the value of the coefficient of variation is set as a second normal coefficient of variation C _std 2.
In this way, the first normal variation coefficient C _std 1 and the second normal variation coefficient C _std 2 are specified from the reference characteristic data 165.

Subsequently, the CPU 11 calculates a state index Z which is the difference between the variation coefficient change amount S and the normal variation coefficient change amount Y (step 27).
Here, the normal variation coefficient change amount Y is subtracted from the variation coefficient change amount S, and this subtraction value is used as the state index Z. That is, the state index Z is expressed by the following equation.
State index Z = variation coefficient variation S−normal variation coefficient variation Y

Here, the state index Z indicated by the difference between the variation coefficient change amount S and the normal variation coefficient change amount Y will be described.
FIG. 13 is a diagram illustrating an example of a variation coefficient and a normal variation coefficient in each section of a subject in a tension state.
FIG. 14 is a diagram illustrating an example of a variation coefficient and a normal variation coefficient in each section of a subject in a relaxed state.
When the subject is in a tension state, the variation coefficient change amount S is indicated by “− (b + a + c)”, and the normal variation coefficient change amount Y is indicated by “a”. In this case, the state index Z is “− (b + 2a + c)”. Here, “b + 2a + c” indicates the degree of the state of the subject, and the minus sign (−) relating to this indicates that the subject is in a tension state.
Similarly, when the subject is in a relaxed state, the variation coefficient change amount S is indicated by “b + a + c”, and the normal variation coefficient change amount Y is indicated by “a”. In this case, the state index Z is “+ (b + c)”. Here, “b + c” indicates the degree of the state of the subject, and the plus sign (+) relating thereto indicates that the subject is in a relaxed state.
Thus, the state index Z indicates the state and degree of the subject that are relatively obtained based on the reference characteristic data 165.

And CPU11 determines a test subject's state by collating the calculated state index | exponent Z with a state determination table (step 28), and complete | finishes a process.
As described above, according to the second embodiment, by determining the state index Z, the state determination of the subject can be performed at a level finer than the rough state determinations of “tension state”, “relaxed state”, and “normal state”. Can be realized.
In the first embodiment, it can be determined from the variation coefficient change amount S whether the subject is in a “tensioned state” or a “relaxed state”. However, since there is an individual difference in the relationship between the average value and the variance value of the subject's RR interval data, the degree of the subject's state cannot be determined absolutely from the variation coefficient change amount S.
According to the second embodiment, the degree of condition of the subject is absolutely determined by performing arithmetic processing based on the reference value (reference characteristic data 165) of the coefficient of variation with no individual difference on the coefficient of variation S. A state index Z that can be determined can be calculated.

In the above-described second embodiment, the example using the reference characteristic data 165 represented by the two-dimensional graph in which the “RR interval data element” is the x axis and the “variation coefficient” is the y axis has been described. However, the form is not limited to this.
For example, the relationship between the “RR interval data element” and the “variation coefficient” may be stored in the storage device 16 ′ as a relation table (table data) indicating “range element”.

Further, for example, reference characteristic data 165 represented by a three-dimensional graph in which “RR interval data element” is the x axis, “variation coefficient” is the y axis, and “range element” is the z axis may be used.
In this way, when the reference characteristic data 165 is expressed by a three-dimensional graph, a characteristic curved surface in which characteristic curves shown in FIG. 11 are closely arranged is formed in the three-dimensional space.
When the reference characteristic data 165 indicated by such a three-dimensional graph is used, the reference characteristic data 165 (FIG. 11) indicated by the two-dimensional graph in step 26 described above is changed to a process for specifying a characteristic curve. For example, the following processing is performed.
The CPU 11 takes the width of the first section of the RR interval data of the subject on the characteristic curved surface of the reference characteristic data 165 expressed by a three-dimensional graph as “the size w of the distribution range of the RR interval data” (z axis). In this case, a characteristic curve indicating the relationship between “average value of RR interval data” and “variation coefficient” is obtained. Then, the first normal variation coefficient C _std 1 and the second normal variation coefficient C _std 2 are specified based on the obtained characteristic curve.

Further, when the reference characteristic data 165 is expressed by a three-dimensional graph, the first normal variation coefficient C _std 1 and the second normal variation coefficient C _std 2 can be specified as follows.
In the characteristic curved surface of the reference characteristic data 165 represented by a three-dimensional graph, the average value [x1] of the RR interval data distributed in the first section of the subject's RR interval data is expressed as “average value of RR interval data” (x axis ), A characteristic curve indicating the relationship between the “variation coefficient” and “the size w of the distribution range of the RR interval data” is obtained. Then, in the obtained characteristic curve, the value of the variation coefficient when the width of the first section of the RR interval data of the subject is “the size w of the distribution range of the RR interval data” is expressed as the first normal variation coefficient C _std. Set to 1.
Similarly, on the characteristic curved surface of the reference characteristic data 165 represented by a three-dimensional graph, the average value [x2] of the RR interval data distributed in the second section of the RR interval data of the subject is expressed as “average value of RR interval data”. A characteristic curve showing the relationship between the “variation coefficient” and “the size w of the distribution range of the RR interval data” when taken on the (x-axis) is obtained. Then, in the obtained characteristic curve, the value of the coefficient of variation when the width of the second section of the RR interval data of the subject is “the size w of the distribution range of the RR interval data” is expressed as the second normal variation coefficient C _std. 2.

In the second embodiment described above, the first normal variation coefficient C _std 1 and the second normal variation coefficient C _std 2 are obtained with reference to the reference characteristic data 165, and the normal variation coefficient change amount Y is obtained from these values. Is calculated. However, the method of calculating the normal variation coefficient change amount Y is not limited to this.
For example, the reference characteristic data 165 expressed by a two-dimensional graph (characteristic curve) or a three-dimensional graph (characteristic curved surface) as described above is converted into an approximate function using various functions such as an exponential function and a logarithmic function, and the coefficients and Find a constant. These coefficients and constants are expressed by executing multiple regression analysis and path analysis using the distribution range size w of RR interval data, the average value of RR interval data, and the variance value of RR interval data. The normal variation coefficient change amount Y is calculated.
That is, the normal variation coefficient change amount Y may be calculated using an approximate function of the reference characteristic data 165.

For example, the normal variation coefficient change amount Y can be expressed by the following equation using an approximate function of the reference characteristic data 165.
Normal variation coefficient change amount Y = H × A ^{(-1.951766667)}
Here, H is 0.0000002 × W ² + 0.000006 × W, and A is a value obtained by dividing the boundary value [msec] between the first section and the second section of the subject's RR interval data by 1000. Yes, W is the width [msec] of the first section of the RR interval data of the subject.
However, this calculation example is limited to the case where the region range of the RR interval data having the maximum value and the minimum value of the RR interval data sample as both poles is divided into equal widths.
Note that the method of calculating the normal variation coefficient change amount Y using the approximate function of the reference characteristic data 165 is not limited to this. You may make it calculate using another approximate expression.

In the second embodiment described above, the normal variation coefficient change amount Y is subtracted from the variation coefficient change amount S, and this subtraction value is defined as the state index Z. However, the method of defining the state index Z is not limited to this. Absent.
For example, a value obtained by subtracting the normal variation coefficient change amount Y from the variation coefficient change amount S and multiplying the subtraction value by α may be defined as the state index Z. In this case, however, a threshold value for setting the range of the state index Z in the state determination table is also set to a value multiplied by α.
Α is a value for setting a scale factor (magnification), and an arbitrary value can be given.

In the second embodiment, as the “range element” in the reference characteristic data 165, the distribution range of the RR interval data sample is divided into the first to nth segments in order from the minimum value side (RR interval). The case of using the size w) of the data distribution range has been described.
However, the value that can be adopted as the “range element” is not limited to this. For example, the value may be a value based on the distribution range of the RR interval data in the RR interval data sample, such as the size of the entire distribution range of the RR interval data sample or a value obtained by dividing the entire distribution range by the predetermined value β.

In the second embodiment, as the “RR interval data element” in the reference characteristic data 165, the distribution range of the RR interval data sample is divided into the first to nth segments in order from the minimum value side. The case where the average value of the distributed RR interval data is used has been described.
The value that can be adopted as the “RR interval data element” is not limited to this. For example, when the RR interval data sample is divided into the first to nth segments, the intermediate value (center value) of the first segment, the start value and the end value of the first segment, and the like of the RR interval data sample in the RR interval data sample Any value based on the range from the minimum value to the first predetermined value may be used.
However, when specifying the first normal variation coefficient C _std 1 and the second normal variation coefficient C _std 2, values used as “range element” and “RR interval data element” of the reference characteristic data 165 A value corresponding to is obtained from the RR interval data of the subject to be determined (evaluated) and applied.

In the second embodiment, as an example, the reference characteristic data 165 indicating the distribution characteristic of the variation coefficient of the RR interval data distributed in the first section when the RR interval data sample is divided into the first to nth sections is used. .
When the RR interval data that is the target for calculating the coefficient of variation of the reference characteristic data 165 is distributed in the vicinity of the minimum value of the distribution range of the RR interval data sample, a distribution characteristic having a tendency as shown in FIG. 11 is obtained. be able to. And the convergence accuracy of the variation coefficient in the reference characteristic data 165 becomes higher as the RR interval data for which the variation coefficient is calculated is closer to the minimum value.
For this reason, it is preferable that the second predetermined value for setting the distribution range of the RR interval data to be calculated for the coefficient of variation is in the vicinity of the minimum value of the distribution range of the RR interval data sample.
Further, when the second predetermined value is set based on the first section when the RR interval data sample is divided into the first to nth sections, the number of sections of the RR interval data sample and the first variation coefficient It is preferable that C1 and the number of sections of the RR interval data of the subject, which are sectioned when calculating the second variation coefficient C2, are matched.

  The subject state determination process shown in the second embodiment may be performed in parallel with the subject state determination process shown in the first embodiment. In this case, it is possible to acquire both the determination result by the subject state determination process according to the first embodiment and the determination result by the subject state determination process according to the second embodiment. And the reliability of a determination result can be improved by comparing the two determination results by the state determination process of 1st Embodiment, and the state determination process of 2nd Embodiment.

In the second embodiment described above, the case where the area range of the RR interval data is divided into six sections of the first to sixth sections has been described, but the state determination shown in the second embodiment is the same as that of the first embodiment. In addition, the present invention can also be applied to the case where the region range of the RR interval is divided into the first to fifth divisions, the first to fourth divisions, and the first to third divisions. However, the reference characteristic data 165 used in the state determination of the second embodiment is based on the RR interval data of normal subjects when the number of divisions is the same as the number of categories of the RR interval data of the subject to be determined (evaluated). Use the one created.
Further, the state evaluation device 1 ′ shown in the second embodiment may also be incorporated in the driving support device as in the first embodiment and used as a device for evaluating the state of the driver of the vehicle.

In the first embodiment and the second embodiment described above, N (for example, 50) RR interval data of the subject are collected, and the state is determined based on the data.
The timing for collecting the N pieces of RR interval data used for determining the state of the subject can be arbitrarily set.
For example, when collecting 50 RR interval data for status determination, after collecting 50 consecutive RR interval data (RR1 to RR50), RR2 to RR51, RR3 to RR52, RR4 ... RR53..., RR interval data may be shifted one by one, and the next set of RR interval data for state determination may be continuously collected and the determination process may be executed. It should be noted that the amount of shift of RR interval data when a plurality of sets of determination RR interval data are collected in this way can be arbitrarily set.
As described above, the state of the subject can be recognized in detail by continuously collecting a plurality of sets of determination RR interval data and performing the state determination.

Claims (11)

RR interval acquisition means for acquiring N pieces of RR interval data indicating the R wave interval of the heartbeat;
First variation coefficient calculating means for calculating a first variation coefficient of RR interval data distributed from a minimum value to a first predetermined value of the obtained N pieces of RR interval data;
Second variation coefficient calculating means for calculating a second variation coefficient of RR interval data distributed from the first predetermined value to the second predetermined value;
State evaluation means for evaluating the state of the subject based on the comparison result between the calculated first variation coefficient and second variation coefficient;
The state evaluation apparatus characterized by comprising. Reference characteristic data acquisition means for acquiring reference characteristic data created based on RR interval data of a subject in a normal state,
The state evaluation means evaluates a subject's state and its degree based on a comparison result between the first variation coefficient and the second variation coefficient and a reference variation coefficient obtained from the reference characteristic data. The state evaluation apparatus according to claim 1.   The reference characteristic data is created based on a sample composed of n pieces of RR interval data of a subject in a normal state, a range element based on a distribution range of the RR interval data in the sample, and a minimum of the RR interval data in the sample The relationship between the RR interval data element based on the range from the value to the third predetermined value and the reference variation coefficient of the RR interval data distributed in the range from the minimum value of the RR interval data to the fourth predetermined value in the sample The state evaluation apparatus according to claim 2, wherein: A variation coefficient change amount calculating means for comparing the calculated first variation coefficient and the second variation coefficient and calculating a variation coefficient variation amount indicating a difference between the first variation coefficient and the second variation coefficient;
In the reference characteristic data, the first specified when the RR interval data element based on the range from the minimum value to the first predetermined value and the range element based on the distribution range of the acquired RR interval data are applied. It is specified when a reference variation coefficient, an RR interval data element based on the range from the first predetermined value to the second predetermined value, and a range element based on the distribution range of the acquired RR interval data are applied. A normal time variation coefficient change amount calculating means for calculating a normal time variation coefficient change amount indicating a difference from the second reference variation coefficient,
4. The state evaluation according to claim 3, wherein the state evaluation unit evaluates the state and the degree of the subject based on a comparison result between the calculated variation coefficient variation and a normal variation coefficient variation. apparatus.   5. The state evaluation apparatus according to claim 4, wherein the state evaluation unit evaluates the degree of the state of the subject from a value obtained by subtracting the normal variation coefficient variation from the variation coefficient variation. The reference characteristic data acquisition means acquires reference characteristic data expressed by an approximate function,
The state according to claim 4 or 5, wherein the normal variation coefficient change amount calculating means calculates the normal variation coefficient variation amount based on an approximate function of the acquired reference characteristic data. Evaluation device. A classifying means for classifying a range from a minimum value to a maximum value of the acquired N pieces of RR interval data into first to nth sections in order from the minimum value;
The first variation coefficient calculating means calculates a first variation coefficient of RR interval data distributed in the divided first section,
The said 2nd variation coefficient calculation means calculates the 2nd variation coefficient of RR interval data distributed in the said divided 2nd division, The claim of any one of Claim 1 to 6 characterized by the above-mentioned. State evaluation device.   The classification means divides a range from a minimum value to a maximum value of the acquired N pieces of RR interval data into first to fifth sections or first to sixth sections. Item 8. The state evaluation apparatus according to Item 7. The state evaluation means includes
When the value obtained by subtracting the second coefficient of variation from the first coefficient of variation is in the range of the first threshold value to the second threshold value, the subject is evaluated as being in a normal state,
If the value obtained by subtracting the second coefficient of variation from the first coefficient of variation is less than the first threshold value, the test subject evaluates that the subject is in tension.
The subject is evaluated as being in a relaxed state when a value obtained by subtracting the second coefficient of variation from the first coefficient of variation is greater than the second threshold value. The state evaluation apparatus of Claim.   10. The first variation coefficient calculation unit and the second variation coefficient calculation unit calculate the first variation coefficient and the second variation coefficient from a standard deviation based on maximum likelihood variance. The state evaluation apparatus according to any one of the claims. In a state evaluation apparatus comprising an RR interval acquisition unit, a first variation coefficient calculation unit, a second variation coefficient calculation unit, and a state evaluation unit,
An RR interval acquisition function, wherein the RR interval acquisition means acquires N pieces of RR interval data indicating an R wave interval of a heartbeat;
A first variation coefficient calculating function for calculating a first variation coefficient of RR interval data distributed from a minimum value of the acquired N pieces of RR interval data to a first predetermined value;
A second variation coefficient calculating function for calculating a second variation coefficient of RR interval data distributed from the first predetermined value to a second predetermined value;
A state evaluation function in which the state evaluation means evaluates the state of the subject based on the comparison result between the calculated first variation coefficient and the second variation coefficient;
A computer-readable state evaluation program for realizing the above.

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