JP2022049584A - Information processing device and program - Google Patents

Abstract

To provide an information processing device and program, which enable more appropriate evaluation of a subjective degree of stress felt by a subject compared with an arrangement for estimating a subjective degree of stress using cumulative feature values.SOLUTION: An information processing device 6 provided herein comprises a processor 60a configured to use end values of biological items associated with subjective stress at the end of a period of interest in order to output an evaluation value indicative of a subjective degree of stress felt by a subject at the end of the period.SELECTED DRAWING: Figure 2

Description

The present invention relates to an information processing apparatus and a program.

In recent years, a stress determination device for estimating chronic stress related to a subject has been proposed (see, for example, Patent Document 1).

The stress determination device described in Patent Document 1 has a value of a first feature amount representing a feature of the measurement information calculated according to a predetermined first feature amount calculation procedure from the measurement information measured with respect to the measurement target, and the measurement. Psychological burden information storage means that stores a psychological burden estimation model that expresses the relationship with the degree of psychological burden related to the object, the cumulative value of the degree of psychological burden over the first period, and the first. It was measured for the chronic stress information storage means that stores the second chronic stress estimation model that represents the relationship with the stress information that represents the degree of stress when the psychological burden is received during the period, and the first measurement target. From the measurement information, the value of the first feature amount is calculated according to the predetermined first feature amount calculation procedure, and the psychological burden estimation model stored in the psychological burden information storage means is added to the calculated value. By applying, the psychological burden estimation means for calculating the degree of the psychological burden on the first measurement target and the psychology calculated based on the measurement information measured in the second period for the first measurement target. By applying the second chronic stress estimation model to the psychological burden accumulation means for calculating the cumulative burden information representing the cumulative numerical value of the degree of the target burden and the cumulative burden information, the said second period. It is provided with a second chronic stress estimation means for estimating the degree of stress received by the first measurement target.

Japanese Unexamined Patent Publication No. 2018-11720

The subject of the present invention is information processing capable of more appropriately evaluating the degree of subjective stress felt by a subject as compared with a configuration in which the degree of subjective stress is estimated using the cumulative value of the feature amount. To provide equipment and programs.

[1] Outputs an evaluation value indicating the degree of the subjective stress that the subject has at the end of the period according to the end value of the biological item associated with the subjective stress at the end of the period of interest. An information processing device equipped with a processor.
[2] The processor outputs the evaluation value according to the sum of the end value and the maximum value of the biological item in the period of interest.
The information processing apparatus according to the above [1].
[3] The processor predicts the end value from the current value of the biological item, and outputs the evaluation value according to the predicted end value.
The information processing apparatus according to the above [1] or [2].
[4] The processor predicts the end value by adding a first weight to the current value.
The information processing apparatus according to the above [3].
[5] The processor outputs the evaluation value by further adding a second weighting to the maximum value.
The information processing apparatus according to the above [4].
[6] The first weighting and the second weighting vary depending on the time within the period of interest.
The information processing apparatus according to the above [5].
[7] The second weighting is the same function as the first weighting.
The information processing apparatus according to the above [5] or [6].
[8] The processor obtains the biological item from the biological information of the subject.
The information processing apparatus according to any one of the above [1] to [7].
[9] The biological information includes information indicating acceleration indicating the behavior of the subject and information indicating the pulse of the subject.
The information processing apparatus according to the above [8].
[10] The processor outputs the evaluation value further according to the feature amount of subjective stress accumulated before the period of interest.
The information processing apparatus according to any one of the above [1] to [8].
[11] The subject has at the end of the period according to a feature amount that is associated with subjective stress and has a coefficient indicating a correlation with the subjective stress having a value equal to or higher than a predetermined value. An information processing device including a processor that outputs an evaluation value indicating the degree of subjective stress.
[12] An evaluation value indicating the degree of the subjective stress that the subject has at the end of the period according to the end value of the biometric item associated with the processor at the end of the period of interest. A program to control the output.

According to the inventions according to claims 1, 2, 11 and 12, the degree of subjective stress felt by the subject is determined as compared with the configuration in which the degree of subjective stress is estimated using the cumulative value of the feature amount. It can be evaluated more appropriately.
According to the invention of claim 3, it is possible to output a subjective stress evaluation value using the current value without waiting for the end of the period.
According to the invention of claim 4, the accuracy of predicting the end value of the period of interest can be improved.
According to the invention of claim 5, it is possible to output a subjective stress evaluation value with higher accuracy than in the case where only one of a plurality of feature quantities is weighted.
According to the invention of claim 6, it is possible to output the subjective stress evaluation value with higher accuracy than when the weighting is set to a constant value.
According to the invention of claim 7, it is possible to output a subjective stress evaluation value with a smaller amount of calculation than when different weighting is used.
According to the inventions according to claims 8 and 9, subjective stress evaluation values can be output with higher accuracy than when biometric information is not used.

FIG. 1 is a diagram showing a configuration example of an information processing system according to an embodiment of the present invention. FIG. 2 is a block diagram showing an example of a control system of an information processing apparatus. FIG. 3 is a diagram showing an example of a user table. FIG. 4 is a diagram showing an example of a biological information table. FIG. 5 is a diagram for explaining a pulse wave. FIG. 6 is a diagram showing an example of time-dependent changes in the values of biological items. FIG. 7 is a diagram showing an example of time-dependent changes in the values of biological items. FIG. 8 is a diagram showing an example of time-dependent changes in the values of biological items. FIG. 9 is a flowchart showing an example of the operation of the information processing apparatus when creating the first stress estimation model. FIG. 10 is a flowchart showing an example of the operation of the information processing apparatus when calculating the evaluation value of the first stress. FIG. 11 is a flowchart showing an example of the operation of the information processing apparatus when creating the second stress estimation model. FIG. 12 is a flowchart showing an example of the operation of the information processing apparatus when calculating the evaluation value of the second stress. 13 (a) to 13 (d) are diagrams schematically showing how to obtain the stress estimation results up to the previous day.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. In each figure, components having substantially the same function are designated by the same reference numerals and duplicated description thereof will be omitted.

[Summary of Embodiment]
The information processing unit according to the present embodiment has the subjective stress that the subject has at the end of the period, depending on the end value of the biological item associated with the subjective stress at the end of the period of interest. It is equipped with a processor that outputs an evaluation value indicating the degree.

"Stress" is a general term for the burden and load on a person, and "subjective stress" refers to "stress" that is particularly related to an internal state or a psychological state. In the present embodiment, the period of interest is one day as an example, and the period of interest is also referred to as the day in particular. For example, one day as a period of interest may be a day on which the stress evaluation value is output, or may be a day past the day on which the stress evaluation value is output. The "end value" refers to the value of the biological item just before the end of the period of interest. As an example, when the period of interest is a working day, the end value may be, for example, a value of a biological item (for example, an average value) from a certain time (for example, 1 hour) before the end of work to the end of work. ) Applies.

[Embodiment]
FIG. 1 is a diagram showing a configuration example of an information processing system according to an embodiment of the present invention. The information processing system 1 is a measuring device 2 that is attached to the user and measures the user's biological information, a charging stand 3 of the measuring device 2, and a manager (for example, a person who is in a position to manage another user). A user terminal 4 operated by a user including.), And an information processing device 6 such as a server to which the measurement device 2 and the user terminal 4 are connected via the network 5 are provided. Although a plurality of measuring devices 2 and user terminals 4 are shown in FIG. 1 and FIG. 2 described later, each may be one. The user is an example of a target person.

The information processing system 1 is applied to, for example, an office (including a rental office and a shared office), a workplace such as a factory, and an activity area such as a learning place such as a school or a classroom. FIG. 1 shows a case where the information processing system 1 is applied to an office. The measuring device 2 measures, for example, biometric information at the time of the user's activity in the activity area. Biological information refers to information about a living body emitted by the body.

When the measuring device 2 is connected, the charging stand 3 charges the power supply unit 26 described later of the measuring device 2.

As the user terminal 4, for example, a portable information processing device such as a personal computer or a multifunctional mobile phone (smartphone) can be used. An IP address is assigned to the user terminal 4.

The network 5 is, for example, a communication network such as a wireless LAN (Local Area Network) or the Internet.

FIG. 2 is a block diagram showing an example of a control system of the information processing system 1.

(Measurement device configuration)
The measuring device 2 includes a control unit 20 that controls each part of the measuring device 2, a storage unit 21 that stores various information, a first biometric information measuring unit 22 that measures the first biometric information, and a second. It includes a second biometric information measuring unit 23 for measuring biometric information, a measuring button 24 for instructing the start or end of measurement, a wireless communication unit 25, and a power supply unit 26 for supplying power to each unit of the measuring device 2. ..

The control unit 20 is composed of a processor such as a CPU (Central Processing Unit), an interface, and the like. The function of the control unit 20 will be described later.

The storage unit 21 is composed of a ROM (Read Only Memory), a RAM (Random Access Memory), and the like, and stores a processor program 210, user information 211, and the like. Further, the storage unit 21 is provided with a biometric information storage area 212 in which biometric information for one day is stored. The user information 211 includes a user ID that identifies the user, a measurement device ID that identifies the measurement device 2, and the like.

The first biometric information measuring unit 22 uses, for example, an acceleration sensor. As the acceleration sensor, a 3-axis acceleration sensor may be used. Hereinafter, the time-series data of the acceleration detection signal output by the acceleration sensor is also referred to as acceleration data. Acceleration data is an example of first biometric information.

The first biometric information measuring unit 22 may acquire the behavior pattern of the person to be measured based on the detection signal by the acceleration sensor. In this case, the detection signal that serves as a reference for the acceleration sensor is stored in the storage unit 21 for each behavior pattern, and the first biometric information measurement unit 22 stores the behavior pattern corresponding to the detection signal by the acceleration sensor in the storage unit 21. Obtain by referring to the stored contents. Behavioral patterns include, for example, sitting, walking, running, and the like.

The second biometric information measuring unit 23 uses, for example, a pulse wave sensor. As the pulse wave sensor, an optical pulse wave sensor may be used. An electrocardiographic sensor may be used instead of the pulse wave sensor. Hereinafter, the time series data of the pulse wave signal measured by the pulse wave sensor is also referred to as pulse wave data. The pulse wave data is an example of the second biometric information. From the pulse wave data, for example, the pulse interval, the pulse wave amplitude, and the like are acquired on the information processing apparatus 6 side. When the electrocardiographic sensor is used, for example, the heartbeat interval, the electrocardiographic amplitude, and the like are acquired from the electrocardiographic data on the information processing apparatus 6 side.

When the measurement button 24 is first operated after the power is turned on, it outputs a start signal indicating the start of measurement to the control unit 20, and thereafter, every time the operation is performed, the end signal and the start signal indicating the end of measurement are alternately output to the control unit 20. Output to.

When the start signal is output from the measurement button 24, the control unit 20 controls the first biometric information measuring unit 22 and the second biometric information measuring unit 23 to obtain the first biometric information and the second biometric information. Start the measurement. When the end signal is output from the measurement button 24, the control unit 20 controls the first biometric information measuring unit 22 and the second biometric information measuring unit 23 to obtain the first biometric information and the second biometric information. End the measurement.

Further, the control unit 20 stores the first biological information and the second biological information measured between the start signal and the end signal in the biological information storage area 212 of the storage unit 21. Further, at a predetermined time (for example, 21:00), the control unit 20 stores the first biological information and the second biological information stored in the biological information storage area 212 in the storage unit 21. The user information 211 is transmitted to the information processing apparatus 6 via the network 5 by the wireless communication unit 25.

The control unit 20 is, for example, from the first time of the day on the working day or the day of attendance (for example, at the time of going to work, at the time of attendance, at the start of work, at the start of attendance, etc.) to the second time of the day (for example, at the start of attendance). , At the time of leaving work, at the time of leaving, at the end of work, at the end of attendance, etc.), the first biometric information and the second biometric information are passed through the second time (for example, 18:00) or the second time. The information processing device 6 may be transmitted at a predetermined time (for example, 21:00). Further, the control unit 20 may transmit these data to the information processing apparatus 6 in real time. Here, "real time" means continuous or sequential at predetermined time intervals (which may be equal or non-equal intervals).

The wireless communication unit 25 transmits / receives information to / from the information processing device 6 via the network 5 using wireless communication such as Bluetooth (registered trademark) and Wi-Fi (registered trademark).

The power supply unit 26 uses, for example, a secondary battery such as a lithium ion secondary battery. A primary battery, a solar cell, or the like may be used.

(Configuration of information processing device)
The information processing device 6 includes a control unit 60 that controls each unit of the information processing device 6, a storage unit 61 that stores various information, and a wireless communication unit 62.

The control unit 60 is composed of a processor 60a such as a CPU (Central Processing Unit), an interface, and the like. The processor 60a executes the program 610 stored in the storage unit 61 to execute the reception means 600, the biological data calculation means 601 and the first model creation means 602, the first evaluation value calculation means 603, and the second model. It functions as a production means 604, a second evaluation value calculation means 605, a prescription means 606, and the like. Details of each means 600 to 606 will be described later.

The storage unit 61 is composed of a ROM (Read Only Memory), a RAM, (Random Access Memory), a hard disk, and the like, and includes a program 610, a user table 611 (see FIG. 3), a biometric information table 612 (see FIG. 4), and stress subjectivity. Various information such as evaluation data 613, first stress estimation model 614, and second stress estimation model 615 are stored. In the biometric information table 612, biometric information of a plurality of users is measured and accumulated daily.

The stress subjective evaluation data 613 is a questionnaire result (hereinafter, also referred to as “stress subjective evaluation”) in which the user responds to the questionnaire regarding stress as a subjective evaluation, and is stored in the storage unit 61 for each user ID. The stress questionnaire selects, for example, the degree of stress felt in five stages for a plurality of questions.

The first stress estimation model 614 estimates the degree of subjective stress (hereinafter, also referred to as “first stress”) accumulated before the period of interest (in the present embodiment, the day before). This is the model used to do this. On the other hand, the second stress estimation model 615 is a subjective stress at the end of the period of interest (in this embodiment, that is, the working day or the attending day) (hereinafter, "second stress"). It is also called a model used to estimate the degree of). The end of work is an example of "the end of the period of interest". Details of the first stress estimation model 614 and the second stress estimation model 615 will be described later.

FIG. 3 is a diagram showing an example of the user table 611. The user table 611 has a plurality of items such as a user ID, a password, a measuring device ID, and an IP address. The user ID is an ID that identifies a user. The measurement device ID is an ID that identifies the measurement device 2. The IP address is an IP address assigned to the user terminal 4.

FIG. 4 is a diagram showing an example of the biological information table 612. The biological information table 612 is stored in the storage unit 61 for each user ID. The figure shows the case where the user ID is “u001”. The biological information table 612 has a plurality of items such as a biological information ID, a measurement date, a measurement time, a first biological information, a second biological information, TP, PP, PI, LF, HF, and ACC. The user ID is an ID that identifies a user. The biometric information ID is an ID that identifies biometric information. The measurement date is the date on which the first biometric information and the second biometric information transmitted from the measurement device 2 are received. The measurement time is the time when the first biometric information and the second biometric information transmitted from the measurement device 2 are received. Acceleration data is recorded in the first biometric information item. Pulse wave data is recorded in the second biometric information item.

In FIG. 4, TP is an abbreviation for Total Power, which is an overall calculated value obtained by adding the values for each frequency (hereinafter referred to as power spectrum) when the frequency analysis of the time-series data of the pulse wave interval is performed, and is an autonomic nerve. Shows the power of the whole activity. PP is an abbreviation for Pulse Pressure and indicates the pulse wave amplitude reflected by the pulse pressure. PI is a translation of Pulse interval and indicates a pulse wave interval. LF is an abbreviation for Low Frequency, which is a calculated value obtained by adding the values of power spectra of relatively low frequencies, and indicates the power of activity of the sympathetic nerve and the parasympathetic nerve. HF is an abbreviation for high frequency (Hi Frequency), which is a calculated value obtained by adding the values of relatively high frequency power spectra, and indicates the power of parasympathetic nerve activity. ACC is an abbreviation for Acceleration and indicates acceleration. VLF is an abbreviation for (Very Low Frequency) and indicates the overall activity of a very slow mechanism of sympathetic function.

In the TP, a value obtained by summing the VLF value, the LF value, and the HF value (hereinafter, also referred to as “TP value”), which will be described later, is recorded. The pulse wave amplitude (hereinafter, also referred to as “PP value”) (see FIG. 5) is recorded in the PP. The pulse wave interval (hereinafter, also referred to as “PI value”) (see FIG. 5) is recorded in the PI. In the LF, a value (hereinafter, also referred to as “LF value”) obtained by totaling the power spectra of the low frequency component region (hereinafter, also referred to as “LF component region”) is recorded. In the HF, a value obtained by totaling the power spectra of the high frequency component region (hereinafter, also referred to as “HF component region”) (hereinafter, also referred to as “HF value”) is recorded. The peak value of acceleration (hereinafter, also referred to as "ACC value") is recorded in the ACC.

The TP value, PP value, PI value, LF value, HF value and ACC value are calculated by the biometric data calculation means 601 based on the acceleration data and the pulse wave data. TP, PP, PI, LF, HF and ACC are examples of "biological items". The TP value, PP value, PI value, LF value, HF value and ACC value are examples of biometric data. The biometric data calculation means 601 may calculate other biometric data such as LF / HF values.

FIG. 5 is a diagram for explaining a pulse wave. PI indicates the pulse interval, and PP indicates the pulse wave amplitude reflected by the pulse pressure. When stressed, PI becomes shorter and PP becomes larger. The degree of stress can be estimated to some extent from PI, PP, etc.

A power spectrum can be obtained by spectrally analyzing the time-series data of the pulse interval. The LF component region reflects the activity of the sympathetic and parasympathetic nerves. The HF component region reflects the activity of the parasympathetic nerve. The LF / HF value indicates the activity of the sympathetic nerve and is an index of stress. The VLF value + LF value + HF value indicates the total power of the entire autonomic nerve.

Next, each means 600 to 606 of the control unit 60 will be described.

Upon receiving the first biometric information (for example, acceleration data), the second biometric information (for example, pulse data) and the user information 211 from the measuring device 2, the receiving means 600 generates a biometric information ID and the user information 211. The biometric information ID is recorded in the biometric information ID item of the biometric information table 612 corresponding to the user ID included in, and the date when the first biometric information and the second biometric information are received is recorded in the measurement date item. Acceleration data is recorded in the first biometric information item, and pulse data is recorded in the second biometric information item.

The biometric data calculation means 601 calculates and calculates biometric data such as TP value, PP value, PI value, LF value, HF value and ACC value based on the acceleration data and pulse wave data recorded in the biometric information table 612. The results are recorded in the corresponding items in the biometric information table 612.

The first model creating means 602 sends a questionnaire about stress to the user terminals 4 of a plurality of subjects via the network 5, receives the questionnaire results answered to the questionnaire transmitted from each user terminal 4, and receives the questionnaire. The result is stored in the storage unit 61 as stress subjective evaluation data 613. The first model creating means 602 creates the first stress estimation model 614 by performing multiple regression analysis with the stress subjective evaluation included in the stress subjective evaluation data 613 as the objective variable and the feature amount as the explanatory variable. The first model creating means 602 stores the created first stress estimation model 614 in the storage unit 61.

When creating the first stress estimation model 614, the first model creating means 602 specifies, for example, the following features. The first stress estimation model 614 extracts the features correlated with the stress subjective evaluation from about 400 features, and performs multiple regression analysis to obtain nine features effective for the first stress estimation. Identify. The nine identified features will be described below. The features used for stress estimation are not limited to the following nine features.

(I) Diurnal difference of TP value on the previous day The diurnal difference of TP value means the difference between the maximum value and the minimum value of TP value in one day. The TP value represents the vitality of the autonomic nervous system. When the stress received is high, the autonomic nervous system is exhausted and the TP value does not increase. The exhaustion of the autonomic nervous system on the previous day cannot be fully recovered and remains on the next day. The day before is one day before the day.
(Ii) Ratio of PP value exceeding threshold value on the previous day The ratio of PP value exceeding threshold value means the ratio of the number of times the PP value exceeds the threshold value in one day. When stressed, the PP value rises like a spike.
(Iii) Daily average crossing rate of TP value on the previous day The daily average crossing rate of TP value means the rate at which the TP value intersects with the average value of TP value on one day. When the stress received is high, the index shows a high value.
(Iv) Daily average crossing rate of TP value on the day The daily average crossing rate of TP value has the same meaning as the daily average crossing rate of TP value on the previous day.
(V) Diurnal coefficient of variation of the TP value of the day The coefficient of variation of the TP value of the day is an index expressed by the standard deviation / average of the TP value. The diurnal coefficient of variation of the TP value increases when the stress is low.
(Vi) Diurnal difference in TP value on the current day The diurnal difference in TP value has the same meaning as the diurnal difference in TP value on the previous day.
(Vii) The minimum value of the LF / HF value on the day The LF / HF value is a value obtained by dividing the LF value by the HF value, and when stress is applied, the sympathetic nervous system is activated and the LF / HF value becomes high.
(Viii) Diurnal difference of ACC value The diurnal difference of ACC value means the difference between the maximum value and the minimum value of the peak value of acceleration in one day. When the stress is high, sudden movements occur and the diurnal difference in the ACC value becomes large.
(Ix) Daily average crossing rate of HF value The daily average crossing rate of HF value means the rate at which the HF value intersects with the average value of HF value in one day. The HF value represents the activity of the parasympathetic nervous system, and on days when stress is high, the activity / inactivity of the parasympathetic nervous system is frequently switched.

Here, (i), (ii) and (iii) are examples of feature quantities related to stress on the previous day. (Iv), (v), (vi), (vii) and (viii) are examples of features related to stress on the day. (Ix) is an example of the feature quantity related to the recovery of stress on the day.

The first evaluation value calculation means 603 substitutes the calculated value of the feature amount specified by the first model creation means 602 into the first stress estimation model 614 created by the first model creation means 602, and a specific user. The evaluation value indicating the degree of the first stress of is calculated. That is, the first evaluation value calculation means 603 expresses the evaluation value Estress_1 indicating the degree of subjective stress (that is, the first stress) accumulated on the day by the following equation (1). Calculated using the stress estimation model 614 of 1.
Estress_1 = (features of stress on the previous day) + (features of stress on the day)-(features of stress recovered on the day) + constant (w ₀ )
= (W ₁ x ₁ + w ₂ x ₂ + w ₃ x ₃ ) + (w ₄ x ₄ + w ₅ x ₅ + w ₆ x ₆ + w ₇ x ₇ + w ₈ x ₈ )-(w ₉ x ₉ ) + (w ₀ ) ... (1)

The characteristics of stress on the previous day are, for example, the diurnal difference of the TP value of the previous day, the average crossover rate of the TP value of the previous day, and the ratio of the PP value of the previous _day exceeding the threshold value. _3. Let the coefficients be w ₁ to w ₃ . In addition, in order to improve the correlation coefficient with the stress subjective evaluation, it is preferable that the coefficients w1 to w3 _are not ₀ .

The characteristics of stress on the day are, for example, the daily average crossover rate of the TP value, the daily coefficient of variation of the TP value, the daily difference of the TP value, the minimum value of the LF / HF value, and the daily difference of the ACC value. Let the quantities be _x4 to _x8 and the coefficients be _w4 to _w8 .

The feature amount for which the stress on the day is recovered is, for example, the daily average crossover rate of the HF value, the feature amount is _x9 , and the coefficient is _w9 .

The second model creating means 604 creates the second stress estimation model 615 by performing multiple regression analysis with the stress subjective evaluation included in the stress subjective evaluation data 613 as the objective variable and the feature amount as the explanatory variable. Further, the second model creating means 604 obtains the forgetting coefficient k (t) (details of the forgetting coefficient will be described later) constituting the second stress estimation model 615. The second model creating means 604 stores the created second stress estimation model 615 in the storage unit 61.

The second model creating means 604 specifies, for example, the following features when creating the second stress estimation model 615. The second stress estimation model 615 extracts the features associated with the stress subjective evaluation from about 3,000 features, and performs multiple regression analysis to identify the features effective for stress estimation. Specifically, the second model creating means 604 specifies a feature amount in which the coefficient showing the correlation with the stress subjective evaluation is equal to or more than a predetermined value. The following are examples of some feature quantities, but the feature quantities are not limited to those listed below.

(I) The feature amount, which is the sum of the peak value and the end value, is, for example, the maximum value within the period of interest (here, a working day) of a specific biological item (hereinafter, also referred to as “peak value”). And the sum of the values (hereinafter, also referred to as "end value") at the end (here, the end of work, for example, 17:00). The end value is an example of the "end value". The end value is not limited to the value at the end of work itself, and may be, for example, an average value from a certain time before the end of work to the end of work. The specific biological item is, for example, TP.
(Ii) End value The feature amount may be the end value of a specific biological item. The biological item is, for example, TP.
(Iii) Peak value The feature amount may be the peak value of a specific biological item. The biological item is, for example, TP.
(Iv) Diurnal difference The feature amount may be the diurnal difference of a specific biological item. Here, the diurnal difference means the difference between the maximum value and the minimum value in a day. The biological item may be TP or an average heart rate.

Below, Table 1 summarizes an example of the correlation coefficient showing the correlation between the above-mentioned feature amount and the stress subjective evaluation. As for the correlation coefficient, the larger the absolute value, the higher the correlation between the feature amount and the stress subjective evaluation.

The second evaluation value calculation means 605 substitutes the feature amount specified by the second model creation means 604 into the second stress estimation model 615 created by the second model creation means 604, and at the end of work of a specific user. An evaluation value indicating the degree of subjective stress (that is, the second stress) of is calculated. That is, in the second evaluation value calculation means 605, the evaluation value Estress_2 indicating the degree of the second stress is represented by the first equation (see (2) to (7)) described below. Calculated using any of the stress estimation models 614. Hereinafter, details will be described with reference to FIGS. 6 to 8.

(I) Example 1: Example of determining stress at the end of work by a peak value and a current value FIG. 6 is a diagram showing an example of a time change of a value of a biological item. The horizontal axis shows the working hours on one working day as the period of interest, and the vertical axis shows the values of biological items. The solid line graph shows the time change of biological items. Note that FIG. 6 shows an example in which TP is used as a biological item. Hereinafter, the same description will be omitted.

As shown in FIG. 6, the second evaluation value calculation means 605 obtains the peak value F _peak during working hours (see the circle frame). Next, the second evaluation value calculation means 605 obtains the _end value Fend. In addition, when the second evaluation value calculation means 605 obtains the _end value Fend, it is between a certain time (for example, 1 hour) before (for example, 16:00) and the end of work (17:00) from the end of work (for example, 1 hour). The average value of the values of the biological items in the shaded area in the figure) may be obtained as the _end value Fend. Next, the second evaluation value calculation means 605 calculates the evaluation value Estress_2 of the subjective stress at the end of the working day by using the second stress estimation model 615 represented by the following equation (2).
Estress_2 = F _peak + _End ... (2)
In other words, the second evaluation value calculation means 605 outputs an evaluation value indicating the degree of subjective stress at the end of work according to the sum of the peak value of TP and the end. Here, when the present is at the end of work, the end value _Fend may be the current value (hereinafter, also referred to as “current value”) F _now . That is,
Estress_2 = F _peak + F _now ... (3)
May be.

(Ii) Example 2: Example of predicting an end value using the current value F _now FIG. 7 is a diagram showing an example of a time change of a value of a biological item. The second evaluation value calculation means 605 predicts the _end value Fend from the current value F _now . Specifically, as shown in FIG. 7, the second evaluation value calculation means 605 ends the value obtained by multiplying the current value F _now by the forgetting coefficient k (t) specified by the second model creation means 604. Calculated as the value _Fend . That is, the second evaluation value calculation means 605 calculates Stress_2 using the second stress estimation model 615 represented by the following equation (4).
Estress_2 = F _peak + k (Δt) × F _now ... (4)

Here, the forgetting coefficient k (t) is a variable that fluctuates depending on time. The forgetting coefficient k (t) is an example of “weighting”. Also, _Δt is the time from the current tend to the _end of work. The broken line is a predicted value calculated by k (Δt) × F _now . In other words, the second evaluation value calculation means 605 predicts the _end value Fend by adding the forgetting coefficient k (t) as an example of weighting to the current value F _now , and also sets the peak value F _peak . , The evaluation value Estress_2 of the subjective stress at the end of work is output according to the sum with the _end value Fend predicted from the current value F _now .

(Iii) Example 3: Example of considering forgetting of peak value F _peak FIG. 8 is a diagram showing an example of time change of a value of a biological item. The second evaluation value calculation means 605 may calculate Stress_2 using the second stress estimation model 615 represented by the following equation (5).
Estress_2 = k (Δt ₁ ) × F _peak + k (Δt ₂ ) × F _now ... (5)

That is, the forgetting coefficient k (Δt) may be added not only to the current value F _now but also to the peak value F _peak . This is because it is considered that the peak value F _peak is also forgotten with time in the same manner as the current value F _now . Here, Δt ₁ is the time from the time when the value of the biological item reaches the peak value (hereinafter, also referred to as “ _peak time”) to the end at the _end of work, and _{Δt 2} is the time from the present to end of work. The time to the hour _tend . The dashed line in FIG. 8 indicates the forgetting coefficient k (t), and the alternate long and short dash line indicates the value of k (t) × F (t).

The forgetting coefficient as the weighting attached to the peak value F _peak does not necessarily have to be the same function as the forgetting coefficient as the weighting attached to the current value F _now , and may be different functions from each other. In this case, the second evaluation value calculation means 605 may calculate Stress_2 using the second stress estimation model 615 represented by the following equation (6).
Estress_2 = k ₁ (Δt ₁ ) × F _peak + k ₂ (Δt ₂ ) × F _now ... (6)
k ₁ (t) is an example of "second weighting", and k ₂ (t) is an example of "first weighting".

(Iv) Example 4: Example I using other features
The feature amount may be, for example, a feature amount in consideration of oblivion in the area of all working hours (for example, 8:00 to 17:00) on the working day. Specifically, the stress_2 may be calculated using the second stress estimation model 615 represented by the following equation (7).
Estress_2 = k (Δt) × F (t) ・ ・ ・ (7)
Here, F (t) is a value of a biological item at time t.

(V) Example 5: Example II using other features
In the above equations (2) to (6), the terms relating to the peak value F _peak and the peak value F _peak are not necessarily provided. That is, any of the following formulas (1A), (2A), (3A), (4A), (5A) and (6A) may be used.
_{Estress_2} = Fend ... (2A)
Estress_2 = F _now ... (3A)
Estress_2 = k (Δt) × F _now ... (4A)
Estress_2 = k (Δt ₂ ) × F _now ... (5A)
Estress_2 = k ₂ (Δt ₂ ) × F _now ... (6A)

The prescription means 606 identifies and outputs a prescription according to Estress_2. As an example, the prescription means 606 selects and outputs a prescription according to the time _Δt from the current ton to the _end of work tend. The prescription includes, for example, the following. The prescription is not limited to those exemplified below.
(A) If it is before noon (12:00), encourage people to take a break, such as recommending lunch. It may also be recommended to consume hot beverages.
(B) If it is past noon, a break such as a nap may be recommended.
(C) If a certain amount of time has passed after noon (for example, around 15:00), it may be recommended to insert a coffee break.
(D) Immediately before the _end of work tend (immediately before the end of work, for example, from a certain time before the _end of work to the _end of work), for example, stress is applied to a specific manager such as a boss. You may notify that it is expensive.

The prescription means 606 may specify the prescription according to Estress_1 and Estress_2 as well as Estress_2. Specifically, the prescription means 606 may specify the prescription according to the sum of Estress_1 and Estress_2. That is, the processor 60a has the feature amount of the subjective stress (that is, the first stress) accumulated before the period of interest and the subjective stress (that is, the second stress) at the end of the period of interest. ), And the evaluation value may be output according to.

(Operation of information processing device)
Next, an example of the operation of the information processing apparatus 6 will be described with reference to FIGS. 9 to 12. Hereinafter, the operation of the information processing apparatus 6 will be described as follows: (1) Creation of the first stress estimation model 614 and operation of evaluation of the first stress using the model, and (2) Creation of the second stress estimation model 615 and this. The second stress evaluation operation using the above will be described separately.

(1) Operation of Creation of First Stress Estimating Model 614 and Evaluation of First Stress Using It FIG. 9 shows an example of the operation of the information processing apparatus 6 when creating the first stress estimation model 614. It is a flowchart which shows. FIG. 10 is a flowchart showing an example of the operation of the information processing apparatus 6 when outputting the evaluation value of the first stress.

(1-1) Creation of First Stress Estimating Model 614 The receiving means 600 receives acceleration data, pulse wave data, and user information 211 from measurement devices 2 mounted on each user as a plurality of (for example, 18) subjects. Is accepted (S1).

Next, the receiving means 600 removes the pulse wave data in the section where the body movement is large based on the acceleration data from the received pulse wave data (S2). For example, the user's behavior pattern may be estimated based on the pulse wave data in the section where the acceleration exceeds the threshold value, or the pulse wave data in the section during walking or running may be excluded.

Next, the receiving means 600 generates a biometric information ID, and records the biometric information ID, the measurement date, the measurement time, the acceleration data, and the pulse wave data in the biometric information table 612 corresponding to the user ID included in the user information 211. .. That is, the biometric information ID is recorded in the biometric information ID item of the biometric information table 612, the day when the biometric information is received is recorded on the measurement day, the acceleration data is recorded in the first biometric information item, and the second Record pulse wave data in the item of biometric information.

The biometric information table 612 records, for example, biometric information of a plurality of subjects measured on a working day over a certain period (for example, one week or two weeks or more). The biometric information may be measured every day including holidays over a certain period of time.

The biological data calculation means 601 calculates the pulse wave amplitude (PP value) and pulse interval (PI value) from the pulse wave data (S3), and acquires the acceleration peak value (ACC value) from the acceleration data (S4). Further, the biological data calculation means 601 calculates a TP value, an LF value, an HF value, and the like in addition to the PP value, the PI value, and the ACC value. The biological data calculation means 601 records a TP value, a PP value, a PI value, an LF value, an HF value, an ACC value, and the like in the corresponding items of the biological information table 612.

The first model creating means 602 refers to the biological information table 612, and from the TP value, PP value, PI value, LF value, HF value and ACC value, the feature amount x ₁ to x ₃ of the previous day and the feature amount of the day Calculate _x4 to _x9 (S5).

The first model creating means 602 acquires the questionnaire result of the subject for the questionnaire regarding stress, and stores it in the storage unit 61 as stress subjective evaluation data 613 (S6).

The first model creating means 602 performs multiple regression analysis with the stress subjective evaluation included in the stress subjective evaluation data 613 as the objective variable and the feature quantities x ₁ to x ₉ and the constant w ₀ as the explanatory variables, and performs the first stress. An estimation model 614 is created and stored in the storage unit 61 (S7).

(1-2) Output of First Stress Evaluation Value The receiving means 600 receives acceleration data, pulse wave data, and user information 211 from the measuring device 2 attached to a specific user (S11).

Next, as described above, the receiving means 600 removes the pulse wave data in the section where the body movement is large based on the acceleration data from the received pulse wave data (S12).

Next, as described above, the reception means 600 generates the biometric information ID, and the biometric information ID, the measurement date, the measurement time, and the biometric information ID, the measurement date, and the measurement time are displayed in the biometric information table 612 corresponding to the user ID of the specific user included in the user information 211. Record acceleration data and pulse wave data.

In the biometric information table 612 of a specific user, for example, biometric information of a subject of the specific user measured on a working day over a certain period (for example, one week or two weeks or more) is recorded. The biometric information may be measured every day including holidays over a certain period of time.

The biological data calculation means 601 calculates the pulse wave amplitude (PP value) and the pulse interval (PI value) from the pulse wave data (S13), and acquires the peak value of the acceleration from the acceleration data (S14). Further, as described above, the biometric data calculation means 601 calculates the TP value, the LF value, the HF value, and the like in addition to the PP value, the PI value, and the ACC value. The biological data calculation means 601 records a TP value, a PP value, a PI value, an LF value, an HF value, an ACC value, and the like in the corresponding items of the biological information table 612.

The reception means 600 receives a specific day corresponding to the current day from the user terminal 4 of the administrator or a specific user. The first evaluation value calculation means 603 refers to the biometric information table 612 corresponding to the user ID of a specific user, calculates the feature amount x 1 to x 3 of the previous day from the TP value of the previous day, and calculates the feature amount x ₁ to x ₃ of the previous day, and the TP value of the day, From the LF / HF value, the ACC value, and the HF value, the feature quantities _x4 to x9 of the _day are calculated (S15).

The first evaluation value calculation means 603 substitutes the feature quantities x ₁ to x ₉ and the constant value w ₀ into the first stress estimation model 614, and calculates the evaluation value of the stress accumulated on the day by a specific user. Calculate (S16). The stress evaluation value may be transmitted to the administrator or the user terminal 4 of a specific user.

(2) Operation of Creating a Second Stress Estimating Model 615 and Evaluating a Second Stress Using the Model FIG. 11 shows an example of the operation of the information processing apparatus 6 when creating the second stress estimation model 615. It is a flowchart which shows. FIG. 12 is a flowchart showing an example of the operation of the information processing apparatus 6 when outputting the evaluation value of the second stress.

(2-1) Creation of Second Stress Estimating Model 615 The receiving means 600 receives acceleration data, pulse wave data, and user information 211 from measurement devices 2 mounted on each user as a plurality of (for example, 18) subjects. Is accepted (S21).

Next, the receiving means 600 removes the pulse wave data in the section where the body movement is large based on the acceleration data from the received pulse wave data (S22). For example, the user's behavior pattern may be estimated based on the pulse wave data in the section where the acceleration exceeds the threshold value, or the pulse wave data in the section during walking or running may be excluded.

Next, the receiving means 600 generates a biometric information ID, and records the biometric information ID, the measurement date, the measurement time, the acceleration data, and the pulse wave data in the biometric information table 612 corresponding to the user ID included in the user information 211. .. That is, the biometric information ID is recorded in the biometric information ID item of the biometric information table 612, the day when the biometric information is received is recorded on the measurement day, the time when the biometric information is received is recorded at the measurement time, and the first biological body is recorded. Acceleration data is recorded in the information item, and pulse wave data is recorded in the second biometric information item.

The biological data calculation means 601 calculates the pulse wave amplitude (PP value) and the pulse interval (PI value) from the pulse wave data (S23). Further, the biological data calculation means 601 calculates a TP value, an LF value, an HF value, and the like in addition to the PP value, the PI value, and the ACC value. The biological data calculation means 601 records a TP value, a PP value, a PI value, an LF value, an HF value, an ACC value, and the like in the corresponding items of the biological information table 612.

The second model creating means 604 refers to the biological information table 612, and sets the daily maximum value of each biological item such as TP as the peak value (S24). Further, the second model creating means 604 refers to the biological information table 612, and sets the average of the latest 1-hour data of the biological items as each current value F _now (S25).

The second model creating means 604 calculates each _end value Fend of the _end time tend from the time difference Δt between the current t _now and the _end time tend and the current value F _now (S26). As described above, the second model creating means 604 calculates the feature amount (S27). Next, the stress subjective evaluation included in the stress subjective evaluation data 613 is used as the objective variable, and the feature amount is used as the explanatory variable to perform multiple regression analysis to create a second stress estimation model 615, which is stored in the storage unit 61 (. S28).

(2-2) Output of Second Stress Evaluation Value The receiving means 600 receives acceleration data, pulse wave data, and user information 211 from the measuring device 2 attached to a specific user (S30).

Next, as described above, the receiving means 600 removes the pulse wave data in the section where the body movement is large based on the acceleration data from the received pulse wave data (S31).

Next, as described above, the reception means 600 generates the biometric information ID, and the biometric information ID, the measurement date, the measurement time, and the biometric information ID, the measurement date, and the measurement time are displayed in the biometric information table 612 corresponding to the user ID of the specific user included in the user information 211. Record acceleration data and pulse wave data.

The biological data calculation means 601 calculates the pulse wave amplitude (PP value) and the pulse interval (PI value) from the pulse wave data (S32). Further, as described above, the biometric data calculation means 601 calculates the TP value, the LF value, the HF value, and the like in addition to the PP value, the PI value, and the ACC value. The biological data calculation means 601 records a TP value, a PP value, a PI value, an LF value, an HF value, an ACC value, and the like in the corresponding items of the biological information table 612.

The second evaluation value calculation means 605 refers to the biological information table 612, and sets the daily maximum value of each biological item such as TP as the peak value (S33). Further, the second evaluation value calculation means 605 refers to the biological information table 612, and sets the average of the latest 1-hour data of TP as a biological item as each current value F _now (S34).

The second evaluation value calculation means 605 calculates each _end value Fend of the _end time tend from the time difference Δt between the current t _now and the _end time tend and the current value F _now (S35). As described above, the second evaluation value calculation means 605 calculates the feature amount (S36).

The second evaluation value calculation means 605 substitutes the feature amount into the second stress estimation model 615 and calculates the evaluation value of the subjective stress (that is, the second stress) at the end of work of a specific user (S37). .. The prescription means 606 selects a prescription (S39) according to the evaluation value and Δt (S38), and outputs the selected prescription to the user.

(Modification 1)
In the above embodiment, the stress evaluation value Estress_1 was calculated using the formula (1), but the following formula may also be used.
Estress_1 = (estimated result of stress on the previous day) + (features of stress on the day)-(features of stress recovered on the day) + constant (w ₀ )
... (8)

For the estimation result of the stress on the previous day, for example, the feature amount of the stress on the previous day (w ₁ x ₁ + w ₂ x ₂ + w ₃ x ₃ ) used for calculating the evaluation value of the stress on the day before the previous day is multiplied by a coefficient. It is a stress. When the equation (8) is used, it is possible to omit the arithmetic processing of the individual feature quantities w ₁ x ₁ , w ₂ x ₂ , and w ₃ x ₃ .

(Modification 2)
In the above embodiment, the stress evaluation value Estress_1 was calculated using the formula (1), but the following formula may also be used.
Estress_1 = (features of stress up to the day = features N days ago + features N-1 days ago + ... features of the day before) + (features of stress on the day)-(features of stress recovered on the day) ) + Constant (w ₀ )
... (9)

When the formula (9) is used, an accurate evaluation value can be obtained as compared with the case where the formula (1) is used.

(Modification 3)
In the above embodiment, the stress evaluation value Estress_1 was calculated using the formula (1), but the following formula may also be used.
Estress_1 = (estimated result of stress up to the day = feature amount N days ago + feature amount N-1 day ago + ... feature amount of the previous day) + (feature amount of stress on the day)-(feature amount of stress recovered on the day) ) + Constant (w ₀ )
... (10)

The stress estimation result up to the previous day is, for example, the corresponding feature amount (feature amount N days ago + feature amount N-1 day ago + ... feature amount of the previous day) with a coefficient corresponding to how many days before the day. It is a product. When the equation (10) is used, it is possible to omit the calculation processing of the individual features N days ago, features N-1 days ago, ..., features of the previous day.

(Modification example 4)
FIG. 13 is a diagram schematically showing how to obtain the stress estimation result up to the previous day. The stress estimation results up to the previous day may be obtained as shown in FIG.

For example, as shown in FIG. 13 (a), the feature amount d on the day before the day (for example, the measurement date is 3 days) (for example, the measurement date is 1 day) and the previous day (for example, the measurement date is 2 days). ) May be used to obtain the stress estimation result S of the previous day from the feature amount d.

Further, as shown in FIG. 13 (b), regarding the estimation result of the stress on the first previous day used in the calculation, the feature amount d on the day before the previous day (for example, the measurement date is one day) and the previous day (for example, the measurement date). The stress estimation result S of the previous day is obtained from the feature amount d of (2 days), and the stress estimation result of the previous day is the stress estimation result S of the day before the previous day (for example, the measurement day is 2 days). , The stress estimation result S of the previous day may be obtained from the feature amount d of the previous day (for example, the measurement day is 3 days).

Further, as shown in FIG. 13 (c), the feature amount d on the day 3 days before the current day (for example, the measurement date is 4 days) (for example, the measurement date is 1 day) and the feature amount d on the day before the previous day (for example, the measurement date). The stress estimation result S of the previous day may be obtained from the feature amount d of (2 days) and the feature amount d of the previous day (for example, the measurement day is 3 days).

Further, as shown in FIG. 13 (d), the stress estimation result of the first previous day used in the calculation is the day 3 days before the current day (for example, the measurement date is 4 days) (for example, the measurement date is 1 day). The stress estimation result S of the previous day is obtained from the feature amount d of the previous day, the feature amount d of the day before the previous day (for example, the measurement day is 2 days), and the feature amount d of the previous day (for example, the measurement day is 3 days). Regarding the estimation result of the stress on the previous day after that, the stress on the previous day is obtained from the stress estimation result S on the day before the previous day (for example, the measurement date is 3 days) and the characteristic amount d on the previous day (for example, the measurement date is 4 days). The estimation result S of may be obtained.

Although the embodiments of the present invention have been described above, the embodiments of the present invention are not limited to the above embodiments, and various modifications and implementations are possible without changing the gist of the present invention. be. For example, in order to reduce the burden on the information processing device 6, all or part of the biometric data or the feature amount calculated on the information processing device 6 side may be calculated on the measuring device 2 side.

Each means of the processor may be configured by a hardware circuit such as a partially or wholly reconfigurable circuit (FPGA: Field Programmable Gate Array) or an application specific integrated circuit (ASIC).

Further, it is possible to omit or change some of the components of the above embodiment without changing the gist of the present invention. Further, within the range that does not change the gist of the present invention, steps can be added, deleted, changed, replaced, etc. in the flow of the above embodiment. Further, the program used in the above embodiment can be recorded and provided on a computer-readable recording medium such as a CD-ROM, stored in an external server such as a cloud server, and used via a network. You can also do it.

1 ... Information processing system, 2 ... Measuring device, 3 ... Charging stand, 4 ... User terminal, 5 ... Network, 6 ... Information processing device, 20 ... Control unit, 21 ... Storage unit, 22 ... First biometric information measurement unit , 23 ... second biometric information measurement unit, 24 ... measurement button, 25 ... wireless communication unit, 26 ... power supply unit, 60 ... control unit, 60a ... processor, 61 ... storage unit, 62 ... wireless communication unit, 210 ... program , 211 ... user information, 212 ... biometric information storage area, 600 ... reception means, 601 ... biometric data calculation means, 602 ... first model creation means, 603 ... first evaluation value calculation means, 604 ... second model. Creation means, 605 ... Second evaluation value calculation means, Prescription means 606, 610 ... Program, 611 ... User table, 612 ... Biological information table, 613 ... Stress subjective evaluation data, 614 ... First stress estimation model, 615 ... Second stress estimation model

Claims (12)

It is equipped with a processor that outputs an evaluation value indicating the degree of the subjective stress that the subject has at the end of the period according to the end value of the biological item associated with the subjective stress at the end of the period of interest. Information processing device. The processor outputs the evaluation value according to the sum of the end value and the maximum value of the biological item in the period of interest.
The information processing apparatus according to claim 1. The processor predicts the end value from the current value of the biological item, and outputs the evaluation value according to the predicted end value.
The information processing apparatus according to claim 1 or 2. The processor predicts the end value by adding a first weight to the current value.
The information processing apparatus according to claim 3. The processor outputs the evaluation value by further adding a second weight to the maximum value.
The information processing apparatus according to claim 4. The first weighting and the second weighting vary depending on the time within the period of interest, respectively.
The information processing apparatus according to claim 5. The second weighting is the same function as the first weighting.
The information processing apparatus according to claim 5 or 6. The processor obtains the biological item from the biological information of the subject.
The information processing apparatus according to any one of claims 1 to 7. The biological information includes information indicating acceleration indicating the behavior of the subject and information indicating the pulse of the subject.
The information processing apparatus according to claim 8. The processor outputs the evaluation value further according to the subjective stress features accumulated up to the period of interest.
The information processing apparatus according to any one of claims 1 to 8. The subjective that the subject has at the end of the period according to the feature amount that is associated with the subjective stress and has a coefficient indicating the correlation with the subjective stress having a value equal to or higher than a predetermined value. An information processing device equipped with a processor that outputs an evaluation value indicating the degree of stress. The processor should output an evaluation value indicating the degree of the subjective stress that the subject has at the end of the period according to the end value of the biological item associated with the subjective stress at the end of the period of interest. A program to control.

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