JP2018057450A - Drowsiness estimation device and drowsiness estimation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To enable drowsiness to be estimated appropriately by following an activity index that changes with time in consideration of a lapse of time based on human physiology.SOLUTION: A drowsiness estimation device includes RRI acquisition means 21 for acquiring RRI data, index value calculation means 23 for calculating an index value on an activity index concerning an activity of autonomic nerves, drowsiness estimation means 24 for evaluating the index value calculated by the index value calculation means 23 by a threshold and estimating drowsiness, approximate function calculation means 26 for calculating an approximate function indicating relationships of the index value with the RRI data values from a correlation diagram plotting a plurality of index values to RRI data values, and correction index value calculation means 27 for creating a correction index value function based on the proximate function, and calculating a correction index value by the correction index value function. The drowsiness estimation means 24 evaluates the correction index value by a threshold and estimates drowsiness.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a drowsiness estimation device and a drowsiness estimation program.

  The inventors of the present application filed a sleepiness estimation apparatus including an RRI acquisition means, an index value calculation means, and a sleepiness estimation means. The present invention provides RRI acquisition means for acquiring RRI data, which is R-R interval data, from a signal obtained by an RRI sensor that detects a signal corresponding to an R wave of an electrocardiogram signal, a result of statistical processing of the RRI data, and And / or index value calculation means for calculating index values for a plurality of types of activity indexes related to autonomic nerve activity based on the result of spectral analysis of the RRI data, and an estimation function that is evaluated by a threshold value for each activity index. And drowsiness estimation means for estimating the drowsiness by evaluating the index value calculated by the index value calculation means (see Patent Document 1).

  By the way, it is known that the heart rate tends to decrease with time as compared to the start of work. In addition, activity indices related to parasympathetic nerves such as the activity index used in the above invention, such as SDSD (RMSSD), pNN50, etc. tend to increase with time.

  However, the threshold value used in the sleepiness estimation rule of the above invention is the average value, minimum value, or maximum value of the activity index. That is, the threshold value is a constant value regardless of the passage of time. Therefore, while the threshold value is a constant value, the activity index to be evaluated using this threshold value varies with the passage of time. There was a problem that it was impossible to estimate the sleepiness.

  On the other hand, in Patent Document 2, in the dozing determination apparatus, the threshold for blink detection is changed according to the illuminance, which leads to appropriate determination. However, it has not been able to follow fluctuations over time.

  Further, Patent Document 3 discloses a snooze driving prevention device including an alarm output unit that outputs an alarm when the drowsiness level is higher than a predetermined alarm threshold. The invention of Patent Document 3 includes an alarm threshold correction unit that corrects an alarm threshold, but performs correction based on the number of times the sleepiness level exceeds the threshold, the total time that the sleepiness level exceeds the threshold, and the like. It does not take into account the passage of time based on human physiology.

JP 2006-120061 A JP-A-8-153288 Japanese Patent Laying-Open No. 2015-219771

  The present invention has been made in view of the present state of sleepiness estimation technology as described above, and its purpose is to allow time lapse based on human physiology and to appropriately estimate sleepiness by following a time-varying activity index. An object of the present invention is to provide a sleepiness estimation apparatus and a sleepiness estimation program.

  An apparatus for estimating sleepiness according to the present invention includes an RRI acquisition unit that acquires RRI data that is data of an RR interval from a signal obtained by an RRI sensor that detects a signal corresponding to an R wave of an electrocardiogram signal; Index value calculation means for calculating an index value for an activity index related to autonomic nerve activity based on the result of statistical processing and / or spectrum analysis of the RRI data, and the index value calculated by the index value calculation means A sleepiness estimation means for estimating sleepiness by evaluating a threshold value, RRI data obtained by the RRI acquisition means, and a correlation diagram in which a plurality of index values obtained by the index value calculation means are plotted against the values of the RRI data An approximate function calculating means for calculating an approximate function indicating the relationship of the index value to the value of the RRI data, and the approximate function. And a correction index value calculation means for calculating a correction index value based on the correction index value function, and the drowsiness estimation means evaluates the correction index value with a threshold value to detect drowsiness. It is characterized by estimating.

  In the drowsiness estimation apparatus according to the present invention, the correction index value calculation means obtains the correction index value function from the index value calculated by the index value calculation means and the approximate function.

  In the drowsiness estimation apparatus according to the present invention, the correction index value calculation means obtains an average value of index values for RRI data values from the correlation diagram, and applies the average value to the correction index value function to perform the correction. An index value function is created.

  In the sleepiness estimation apparatus according to the present invention, the correction index value calculation means calculates at least a first correction index value function and a correction index value by greatly varying the index value by the first correction index value function. It is possible to calculate by setting any one of the second correction index value functions.

  An apparatus for estimating sleepiness according to the present invention includes an RRI acquisition unit that acquires RRI data that is data of an RR interval from a signal obtained by an RRI sensor that detects a signal corresponding to an R wave of an electrocardiogram signal; Index value calculation means for calculating an index value for an activity index related to autonomic nerve activity based on the result of statistical processing and / or spectrum analysis of the RRI data, and the index value calculated by the index value calculation means A sleepiness estimation means for estimating sleepiness by evaluating a threshold value, RRI data obtained by the RRI acquisition means, and a correlation diagram in which a plurality of index values obtained by the index value calculation means are plotted against the values of the RRI data An approximate function calculating means for calculating an approximate function indicating the relationship of the index value to the value of the RRI data, and the approximate function. And a correction threshold value calculation means for calculating a correction threshold value based on the correction finger threshold value function, and the drowsiness estimation means estimates the drowsiness by evaluating the index value with the correction threshold value. It is characterized by.

  In the drowsiness estimation apparatus according to the present invention, the correction threshold value calculation means obtains the correction index value function from the threshold value and the approximation function.

  In the drowsiness estimation apparatus according to the present invention, the correction threshold value calculation means obtains an average value of index values with respect to the value of RRI data from the correlation diagram, and applies the average value to the correction threshold value function to thereby correct the correction threshold value function. It is characterized by creating.

  In the drowsiness estimation apparatus according to the present invention, the correction threshold value calculation means calculates a correction index value by at least changing the index value by at least the first correction index value function and the first correction index value function. It can be calculated by setting any one of the second correction index value functions.

  The drowsiness estimation program according to the present invention is an RRI acquisition unit that acquires RRI data, which is R-R interval data, from a signal obtained by an RRI sensor that detects a signal corresponding to an R wave of an electrocardiogram signal. Index value calculation means for calculating an index value for an activity index related to autonomic nerve activity based on a result of statistical processing of data and / or a result of spectrum analysis of the RRI data, an index calculated by the index value calculation means A sleepiness estimation means for evaluating a value based on a threshold value to estimate sleepiness, a correlation diagram in which a plurality of RRI data obtained by the RRI acquisition means and a plurality of index values obtained by the index value calculation means are plotted against the values of the RRI data Approximate function calculation method for calculating an approximate function indicating the relationship of the index value to the RRI data value A correction index value function is created based on the approximate function, and a correction index value calculation unit is configured to calculate a correction index value by the correction index value function, and the drowsiness estimation unit sets the correction index value as a threshold value. It is made to function so that it may evaluate and estimate sleepiness.

  In the drowsiness estimation program according to the present invention, the correction index value calculation means functions so as to obtain the correction index value function from the index value calculated by the index value calculation means and the approximate function. .

  In the sleepiness estimation program according to the present invention, the correction index value calculation means obtains an average value of index values with respect to values of RRI data from the correlation diagram, and applies the average value to the correction index value function to perform the correction. It is made to function so that an index value function may be created.

  In the drowsiness estimation program according to the present invention, the correction index value calculation means calculates at least a first correction index value function and a correction index value by greatly varying the index value by the first correction index value function. One of the second correction index value functions to be set is set so as to function so that calculation is possible.

  The drowsiness estimation program according to the present invention includes an RRI acquisition unit that acquires RRI data that is data of an RR interval from a signal obtained by an RRI sensor that detects a signal corresponding to an R wave of an electrocardiogram signal, and the RRI data Index value calculation means for calculating an index value for an activity index related to autonomic nerve activity based on the result of statistical processing of the RRI data and / or the result of spectrum analysis of the RRI data, and the index value calculated by the index value calculation means A drowsiness estimation means for estimating drowsiness by evaluating a threshold value, RRI data obtained by the RRI acquisition means, and a correlation diagram in which a plurality of index values obtained by the index value calculation means are plotted against the values of the RRI data Approximate function calculating means for calculating an approximate function indicating the relationship of the index value to the RRI data value A correction threshold function is created based on the approximate function and functions as correction threshold calculation means for calculating a correction threshold using the correction finger threshold function. Further, the drowsiness estimation means evaluates the index value based on the correction threshold and sleepiness It is made to function so that it may estimate.

  The drowsiness estimation program according to the present invention is characterized in that the correction threshold value calculation means functions so as to obtain the correction index value function from the threshold value and the approximate function.

  In the drowsiness estimation program according to the present invention, the correction threshold value calculation means obtains an average value of index values with respect to values of RRI data from the correlation diagram, and applies the average value to the correction threshold value function to thereby correct the correction threshold value function. It is made to function so that it may create.

  In the drowsiness estimation program according to the present invention, the correction threshold value calculation means calculates a correction index value by at least changing the index value by at least the first correction index value function and the first correction index value function. One of the second correction index value functions is set to function so that calculation is possible.

  According to the present invention, the RRI data obtained by the RRI acquisition means and the correlation value obtained by plotting a plurality of index values obtained by the index value calculation means with respect to the values of the RRI data, the index values for the RRI data values are obtained. And an approximate function calculating means for calculating an approximate function indicating the relationship of the above and a corrected index value function for creating a corrected index value function based on the approximate function and calculating a corrected index value using the corrected index value function. Taking into account the passage of time based on the physiology of the child, it is possible to appropriately estimate sleepiness following the time-varying activity index.

  According to the present invention, the RRI data obtained by the RRI acquisition means and the correlation value obtained by plotting a plurality of index values obtained by the index value calculation means with respect to the values of the RRI data, the index values for the RRI data values are obtained. The approximate function calculating means for calculating the approximate function indicating the relationship of the above and the corrected threshold value calculating means for creating the corrected threshold function based on the approximate function and calculating the corrected threshold value using the corrected finger threshold function are provided. Considering the passage of time based on the above, it is possible to appropriately estimate sleepiness following the time-varying activity index.

The block diagram which shows 1st Embodiment of the drowsiness estimation apparatus which concerns on this invention. The flowchart which shows the operation mode of embodiment of the sleepiness estimation apparatus which concerns on this invention. The flowchart which shows operation | movement of the function generation mode of 1st Embodiment of the drowsiness estimation apparatus which concerns on this invention. The figure which shows the correlation diagram creation operation | movement in embodiment of the sleepiness estimation apparatus which concerns on this invention. The figure which shows approximation function creation operation | movement from the correlation diagram in embodiment of the sleepiness estimation apparatus which concerns on this invention. The figure which shows approximation function creation operation | movement from the correlation diagram using pNN50 as an activity index in 1st Embodiment of the sleepiness estimation apparatus which concerns on this invention. The figure which shows the average value calculation of RRI from the correlation diagram in embodiment of the sleepiness estimation apparatus which concerns on this invention. The flowchart which shows operation | movement of the practical mode of 1st Embodiment of the drowsiness estimation apparatus which concerns on this invention. The figure which shows the RRI average value calculation operation | movement in embodiment of the sleepiness estimation apparatus which concerns on this invention. The figure which shows the example of evaluation at the time of using the correction parameter | index value function of 1st Embodiment of the drowsiness estimation apparatus which concerns on this invention, and the example of evaluation when not using a correction parameter | index value function. The block diagram which shows 2nd Embodiment of the sleepiness estimation apparatus which concerns on this invention. The flowchart which shows operation | movement of the function generation mode of 2nd Embodiment of the sleepiness estimation apparatus which concerns on this invention. The figure which shows approximation function creation operation | movement from the correlation diagram which used HF as an activity parameter | index in 2nd Embodiment of the sleepiness estimation apparatus which concerns on this invention. The flowchart which shows operation | movement of the practical mode of 1st Embodiment of the drowsiness estimation apparatus which concerns on this invention. The figure which shows the example of evaluation at the time of using the correction threshold value function of 2nd Embodiment of the sleepiness estimation apparatus which concerns on this invention, and the example of evaluation when not using a correction threshold value function.

  Embodiments of a drowsiness estimation device and a drowsiness estimation program according to the present invention will be described below with reference to the accompanying drawings. In the drawings, the same components are denoted by the same reference numerals and redundant description is omitted. FIG. 1 shows a block diagram of a first embodiment of a drowsiness estimation apparatus according to the present invention. In the present embodiment, a heart rate sensor can be used as the RRI sensor 10 that detects a signal corresponding to the R wave of the electrocardiogram signal.

  In addition to the heart rate sensor, the RRI sensor 10 may use a configuration of a part for taking out an electrocardiogram signal of an electrocardiograph or a pulse wave sensor. The RRI sensor 10 is provided in a living body, detects an electrocardiogram signal wirelessly or by wire, and outputs an RRI (before shaping) 1001.

  As shown in FIG. 1, in the sleepiness estimation apparatus of this embodiment, the terminal 20 includes an RRI acquisition unit 21, a data shaping unit 22, an index value calculation unit 23, a sleepiness estimation unit 24, and an output unit 25. The functions and operations of these means are described in detail in Patent Document 1. The sleepiness estimation apparatus according to the present embodiment further includes an approximate function calculation unit 26 and a correction index value calculation unit 27.

040
The RRI acquisition unit 21 acquires RRI data transmitted by the RRI sensor 10. The RRI sensor 10 may output an electrocardiogram signal. In this case, the RRI acquisition unit 21 creates RRI data based on the electrocardiogram signal. The data shaping means 22 has a configuration for performing at least one of trend removal, abnormal value removal, data interpolation, and filter processing, which is removal of a predetermined frequency component. Details of these configurations are as described in Patent Document 1.

  The index value calculation means 23 calculates an index value of the autonomic nerve based on the RRI data. For example, the index value calculation means 23 can calculate a plurality of values based on the result of statistical processing of the RRI data and / or the result of spectrum analysis of the RRI data. It can be configured to calculate the index value of the autonomic nerve activity index as a type. The index value calculation means 23 calculates an index value for unit time using the RRI data for each first time, collects the index values for unit time for each type of activity index, and vectorizes the index values. Create an index value vector time series in a series.

  The sleepiness estimation means 24 estimates sleepiness by evaluating the activity index calculated by the index value calculation means 23 on the basis of a sleepiness estimation rule constituted by an estimation function that is evaluated based on a threshold value related to an activity index and / or a fluctuation state. To do.

  The output unit 25 outputs the estimation result obtained by the sleepiness estimation unit 24 and is used for generating an alarm, stopping the operation of the device, warning, or the like. The terminal 20 can be configured by a smartphone, a tablet terminal, a mobile terminal, or the like.

  A cloud storage 30 is connected to the terminal 20. The cloud storage 30 includes sensor characteristic information 1101 and sleepiness estimation rules 1102 in advance. The sensor characteristic information 1101 is used when the data shaping unit 22 executes data shaping. The sleepiness estimation rule 1102 is used when the sleepiness estimation means 24 estimates sleepiness. The cloud storage 30 indicates that the acquired RRI data acquired in the terminal 20 may be stored as the acquired RRI 1012X of history data. This acquired RRI 1012X is used for updating the sleepiness estimation rule 1102 You may use for.

  The terminal 20 performs processing under the control of the CPU, and each means is realized by the CPU executing a program. The approximate function calculation means 26 calculates the value of the RRI data from the correlation diagram in which a plurality of index values obtained by the index value calculation means 23 are plotted against the RRI data obtained by the RRI acquisition means 21 and the value of the RRI data. An approximate function indicating the relationship of the index value with respect to is calculated. The correction index value calculation means 27 creates a correction index value function based on the approximate function and calculates a correction index value using this correction index value function. When the correction index value is obtained in this way, the sleepiness estimation means 24 estimates the sleepiness by evaluating the correction index value with a threshold value.

  The sleepiness estimation apparatus according to the present embodiment configured as described above operates in the standby operation mode S11, the function generation mode S12, and the practical mode S13, as shown in FIG. The preliminary operation mode S11 is a mode in which the processing shown in Patent Document 1 is performed, and is a premise of the present invention. Since the operation of the standby operation mode S11 is described in detail in Patent Document 1, the description thereof is omitted.

  In the function generation mode S12, as shown in FIG. 3, the index value calculation means 23 calculates an index value for an activity index related to autonomic nerve activity using the acquired RRI data of the acquired RRI 1012X (S21). If this calculation is performed in advance and stored in the cloud storage 30, this process is omitted. Further, when a threshold value is not used for the evaluation of the index value, this process is not performed for the index value.

  Next, using the index value and RRI data, a correlation diagram in which a plurality of index values with respect to the RRI data values are plotted is created as shown in FIG. 4 (S22). Further, based on this correlation diagram, an approximate function is obtained by, for example, regression analysis (S23). An example of obtaining the approximate function f (x) is shown in FIG. The approximate function is generally an n (integer) degree polynomial, a logarithmic function, an exponential function, or the like. In addition, since the RRI data is unique to each individual, the regression coefficient of the approximate function f (x) also differs for each individual.

  FIG. 6 shows an example in which the approximate function f (x) is obtained with the index value as pNN50. Here, the approximate function f (x) is obtained as f (x) = 0.000690373x−0.41594. Next, based on the correlation diagram, an average value m of the index value with respect to the value of the RRI data is obtained as shown in FIG. 7 (S24). Also in FIG. 6, the average value m is obtained, and m = 0.0986823.

Next, a correction index value function is created based on the approximate function obtained above (S25). The correction index value function ν can be expressed by the following equation (1) using the approximate function f (x).

  The correction reference constant C is a value that serves as a reference for correction, and is a value that does not change. In particular, the average value m may be the correction reference constant C. The value of the correction intensity constant K can be normally set to 1. If the characteristic of the approximate function f (x) causes inconvenience such as a negative index value to take a positive value, the value of the correction strength constant K is appropriately set (by the operator) ) Can be set and the correction intensity can be adjusted.

In the present embodiment, the correction index value calculation means 27 greatly varies the index value by at least the correction index value function ν of the above-described equation 1 which is the first correction index value function and the first correction index value function. The second correction index value function for calculating the correction index value can be set and calculated. The second correction index value function is expressed by the following equation 2. C and K are the same as those in Equation 1.

  As described above, the correction index value calculation means 27 includes the first correction index value function and the second correction index value for calculating the correction index value by largely varying the index value by the first correction index value function. Any one of the functions may be used, or the calculation may be performed using a correction index value function other than this not illustrated.

When the correction index value function is obtained as described above, it is possible to operate in the practical mode S13 shown in FIG. This process is performed as shown in FIG. That is, x _t to be applied to the correction index value function of Equation 1 or Equation 2 is obtained (S31). Specifically, as shown in FIG. 9, the current time is t, and the T ₁ time before the time t is the index value calculation section, and the T ₂ (T ₁ <T ₂ ) time before the time t is the RRI average. A value calculation interval. The average value of the index values with respect to the RRI data value in the RRI average value calculation section is defined as x _t .

Further, the index value ν in the correction index value function determined conventionally index value calculating means 23 calculates, to obtain a correction index value ν by the correction index value function with this (S32). The sleepiness estimation means 24 evaluates the correction index value with a threshold value and estimates sleepiness (S33). Assuming that the threshold already used by the sleepiness estimation means 24 is b, this evaluation indicates that there is sleepiness when ν ≧ b. FIG. 10 shows an example of evaluation when the correction index value function is used and an example of evaluation when the correction index value function is not used.

  Next, a second embodiment will be described. The block diagram of the drowsiness estimation apparatus which concerns on 2nd Embodiment is shown in FIG. In this embodiment, there is no difference except that the correction index value calculation means 27 of the first embodiment is replaced with a correction threshold value calculation means 28. As shown in FIG. 2, the sleepiness estimation apparatus according to the present embodiment operates in a standby operation mode S11, a function generation mode S12, and a practical mode S13. The operation in the standby operation mode S11 is the same as that in the first embodiment.

  In the function generation mode S12, an operation is performed as shown in FIG. In this flowchart, steps S21 to S24 are the same as those in FIG.

FIG. 13 shows an example in which the approximate function f (x) is obtained using the index value as HF. Here, the approximate function f (x) is obtained as f (x) = 0.00141895x ² -2.03121x + 823.339. Further, in FIG. 13, the average value m is obtained, and m = 107.621.

Next, a correction threshold function is created based on the approximate function obtained above (S45). The correction threshold function b can be expressed by the following equation (3) using the approximate function f (x).

  The correction reference constant C is a value that serves as a reference for correction, and is a value that does not change. In particular, the average value m may be the correction reference constant C. The value of the correction intensity constant K can be normally set to 1. If the characteristic of the approximate function f (x) causes inconvenience such as a negative index value to take a positive value, the value of the correction strength constant K is appropriately set (by the operator) ) Can be set and the correction intensity can be adjusted.

In the present embodiment, the correction threshold value calculation means 28 calculates the correction threshold value by largely varying the threshold value by at least the correction threshold value function b of the above-described equation 3 that is the first correction threshold value function and the first correction threshold value function. Any one of the second correction threshold functions can be set and calculated. This second correction threshold function is represented by the following equation 4. C and K are the same as those in Equation 3.

  As described above, the correction threshold value calculation means 28 is one of the first correction index value function and the second correction threshold value function that calculates the correction threshold value by largely changing the threshold value by the first correction threshold value function. May be used, or calculation may be performed using a correction threshold function other than the above that is not illustrated.

When the correction threshold function is obtained as described above, it can be operated in the practical mode S13 shown in FIG. This process is performed as shown in FIG. That is, x _t to be applied to the correction threshold function of Equation 3 or Equation 4 is obtained (S61). Specifically, as shown in FIG. 9, the current time is t, and the T ₁ time before the time t is the index value calculation section, and the T ₂ (T ₁ <T ₂ ) time before the time t is the RRI average. A value calculation interval. The average value of the index values with respect to the RRI data value in the RRI average value calculation section is defined as x _t .

Further, the threshold value b in the correction threshold function is provided in the index value calculation means 23 as usual, so the correction threshold value b is obtained by the correction threshold function (S62). The drowsiness estimation unit 24 estimates the drowsiness by evaluating the index value calculated by the index value calculation unit 23 using the correction threshold (S63). If the index value calculated by the index value calculating means 23 is ν, this evaluation is drowsy when ν ≧ b . FIG. 15 shows an evaluation example using the correction threshold function and an evaluation example when the correction threshold function is not used.

  In the above two embodiments, the index value calculation means 23 calculates the index value for the activity index related to the activity of the autonomic nerve based on the result of statistical processing of the RRI data and the result of the spectrum analysis of the RRI data. An index value may be calculated for an activity index related to autonomic nerve activity based on a result of statistical processing of data or a result of spectrum analysis of RRI data.

  In addition, in the above two embodiments, the sleepiness estimation rule 1102 is configured by an estimation function that is evaluated based on a threshold value related to each activity index and / or a variation state, but includes an estimation function that is evaluated based on a threshold value related to each activity index. The sleepiness estimation rule may be used. That is, in Patent Document 1, the drowsiness estimation means is described as follows: “The index value calculated by the index value calculation means is based on a drowsiness estimation rule constituted by an estimation function evaluated based on a threshold value and / or a fluctuation state regarding each activity index. In the present invention, the sleepiness estimation means evaluates the index value calculated by the index value calculation means with a threshold value among the functions of the sleepiness estimation means of Patent Document 1, and uses the threshold value to estimate sleepiness. Is provided with a function (configuration) for estimating.

The activity index used in the above two embodiments is an activity index as a result of statistical processing of RRI data.
SDNN: (RRI standard deviation)
RMSSD: (the square root of the root mean square of the difference between adjacent RRIs)
SDSD: (standard deviation of the difference between adjacent RRIs)
pNN50: (Ratio in which the difference between adjacent RRIs exceeds 50 (milliseconds))
At least one of The details of this activity index are described in Patent Document 1, so the description thereof is omitted.

The activity index used in the two embodiments is an activity index as a result of spectrum analysis of the RRI data.
LF: (PSD (power spectral density function) 0.04 to 0.15 [Hz] power)
HF: (PSD 0.15-0.40 [Hz] power)
HF / (LF + HF)
pi (i = 0, 1, 2,..., 9): (PSD 0.15 + i × 0.025 to 0.15+ (i + 1) × 0.025 [Hz] power)
At least one of The details of this activity index are described in Patent Document 1, so the description thereof is omitted.

  The processes in steps S31 and S61 of the above two embodiments are extremely effective in that correction does not take an inappropriate value when the RRI is suddenly displaced. However, in the case where the necessity is scarce, such as when the RRI is unlikely to be suddenly displaced, it is not necessary to perform the essential processing.

  1 is merely an example, and the terminal 20 may include what the cloud storage 30 holds. Further, a server may be provided in place of the cloud storage 30, and the cloud storage 30 may be substituted. Furthermore, a server may be provided in place of the cloud storage 30, and the cloud storage 30 may be substituted, and some of the functions of the terminal 20 may be performed in the server. In addition, the system configuration can be changed as appropriate.

DESCRIPTION OF SYMBOLS 10 Sensor 20 Terminal 21 Acquisition means 22 Data shaping means 23 Index value calculation means 24 Drowsiness estimation means 25 Output means 26 Approximate function calculation means 27 Correction index value calculation means 28 Correction threshold value calculation means 30 Cloud storage

Claims (16)

RRI acquisition means for acquiring RRI data, which is R-R interval data, from a signal obtained by an RRI sensor that detects a signal corresponding to an R wave of an electrocardiogram signal;
Index value calculation means for calculating an index value for an activity index related to autonomic nerve activity based on a result of statistical processing of the RRI data and / or a result of spectrum analysis of the RRI data;
Drowsiness estimation means for estimating the drowsiness by evaluating the index value calculated by the index value calculation means using a threshold;
Approximation showing the relationship between the RRI data value and the RRI data obtained by the RRI acquisition means and a correlation diagram in which a plurality of index values obtained by the index value calculation means are plotted against the RRI data values. An approximate function calculating means for calculating a function;
A correction index value function that creates a correction index value function based on the approximate function and calculates a correction index value by the correction index value function;
Comprising
The sleepiness estimation device, wherein the sleepiness estimation means evaluates the correction index value with a threshold and estimates sleepiness.   The sleepiness estimation apparatus according to claim 1, wherein the correction index value calculation unit obtains the correction index value function from the index value calculated by the index value calculation unit and the approximate function.   The correction index value calculation means obtains an average value of index values with respect to RRI data values from the correlation diagram, and applies the average value to the correction index value function to create the correction index value function. The sleepiness estimation apparatus according to claim 1 or 2.   The correction index value calculation means includes at least a first correction index value function and a second correction index value function for calculating a correction index value by largely varying the index value by the first correction index value function. The drowsiness estimation apparatus according to any one of claims 1 to 3, characterized in that calculation is possible by setting any of the following. RRI acquisition means for acquiring RRI data, which is R-R interval data, from a signal obtained by an RRI sensor that detects a signal corresponding to an R wave of an electrocardiogram signal;
Index value calculation means for calculating an index value for an activity index related to autonomic nerve activity based on a result of statistical processing of the RRI data and / or a result of spectrum analysis of the RRI data;
Drowsiness estimation means for estimating the drowsiness by evaluating the index value calculated by the index value calculation means using a threshold;
Approximation showing the relationship between the RRI data value and the RRI data obtained by the RRI acquisition means and a correlation diagram in which a plurality of index values obtained by the index value calculation means are plotted against the RRI data values. An approximate function calculating means for calculating a function;
A correction threshold value calculation means for creating a correction threshold value function based on the approximate function and calculating a correction threshold value by the correction finger threshold value function;
Comprising
The sleepiness estimation device, wherein the sleepiness estimation means evaluates the index value with a correction threshold and estimates sleepiness.   The sleepiness estimation apparatus according to claim 5, wherein the correction threshold value calculation unit obtains the correction index value function from the threshold value and the approximate function.   The correction threshold value calculation means obtains an average value of index values for RRI data values from the correlation diagram, and applies the average value to the correction threshold function to create the correction threshold function. Item 7. The sleepiness estimation apparatus according to Item 5 or 6.   In the correction threshold value calculation means, at least one of a first correction threshold function and a second correction threshold function for calculating the correction threshold value by largely changing the threshold value by the first correction threshold function is set. The sleepiness estimation apparatus according to any one of claims 5 to 7, wherein the sleepiness estimation apparatus is capable of being calculated. Computer
RRI acquisition means for acquiring RRI data, which is R-R interval data, from a signal obtained by an RRI sensor that detects a signal corresponding to an R wave of an electrocardiogram signal;
Index value calculation means for calculating an index value for an activity index related to autonomic nerve activity based on a result of statistical processing of the RRI data and / or a result of spectrum analysis of the RRI data;
Drowsiness estimation means for evaluating the index value calculated by the index value calculation means with a threshold and estimating sleepiness;
Approximation showing the relationship between the RRI data value and the RRI data obtained by the RRI acquisition means and a correlation diagram in which a plurality of index values obtained by the index value calculation means are plotted against the RRI data values. Approximate function calculating means for calculating a function;
A correction index value function that creates a correction index value function based on the approximate function and calculates a correction index value by the correction index value function;
Function as
The sleepiness estimation program further causes the sleepiness estimation means to function so as to estimate the sleepiness by evaluating the correction index value based on a threshold value.   10. The sleepiness estimation program according to claim 9, wherein the correction index value calculation unit functions to obtain the correction index value function from the index value calculated by the index value calculation unit and the approximate function. .   The correction index value calculating means obtains an average value of index values with respect to RRI data values from the correlation diagram, and applies the average value to the correction index value function to create the correction index value function. The sleepiness estimation program according to claim 9 or 10, wherein the sleepiness estimation program is executed.   The correction index value calculation means includes at least a first correction index value function and a second correction index value function that calculates a correction index value by largely varying the index value by the first correction index value function. The sleepiness estimation program according to any one of claims 9 to 11, wherein any one of the functions is set so as to be calculated. RRI acquisition means for acquiring RRI data, which is R-R interval data, from a signal obtained by an RRI sensor for detecting a signal corresponding to an R wave of an electrocardiogram signal;
Index value calculation means for calculating an index value for an activity index related to autonomic nerve activity based on a result of statistical processing of the RRI data and / or a result of spectrum analysis of the RRI data;
Drowsiness estimation means for evaluating the index value calculated by the index value calculation means with a threshold and estimating sleepiness;
Approximation showing the relationship between the RRI data value and the RRI data obtained by the RRI acquisition means and a correlation diagram in which a plurality of index values obtained by the index value calculation means are plotted against the RRI data values. Approximate function calculating means for calculating a function;
A correction threshold value calculating means for creating a correction threshold value function based on the approximate function and calculating the correction threshold value by the correction finger threshold value function;
Function as
The sleepiness estimation program further causes the sleepiness estimation means to function so as to estimate the sleepiness by evaluating the index value with a correction threshold value.   14. The drowsiness estimation program according to claim 13, wherein the correction threshold value calculation unit functions to obtain the correction index value function from the threshold value and the approximate function.   The correction threshold value calculation means is made to function so as to obtain an average value of index values for RRI data values from the correlation diagram and apply the average value to the correction threshold function to create the correction threshold function. 15. The sleepiness estimation program according to claim 13 or 14, characterized in that: The correction threshold value calculation means includes at least one of a first correction index value function and a second correction index value function that calculates a correction index value by largely changing the index value by the first correction index value function. The drowsiness estimation program according to any one of claims 13 to 15, wherein the program is set so that it can be calculated.

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