Biological index calculation device and biological index calculation method

Abstract

To output a summary result on a biological index in a unit period of a subject and efficiently support analysis of a biological index on the subject.SOLUTION: A biological index calculation device includes: an input unit for inputting biological data on a subject measured by a biological sensor; a biological index estimation unit for estimating a biological index indicating a physical and mental condition of the subject by using the input biological data for a first period; a calculation unit for collecting estimation results of a plurality of biological indexes estimated for each first period in a second period longer than the first period, and on the basis of the frequency of appearance of the physical and mental condition indicated by each of the estimation results on the plurality of biological indexes, and calculating ratios of the respective physical and mental conditions; a graph creation unit for creating a graph indicating the ratios of the respective physical and mental conditions; and a display control unit for displaying the created graph in a display device.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a biomarker calculation device and a biomarker calculation method for calculating a biomarker based on sensing data relating to a person's biometric information. [Background technology]

[0002] Patent Document 1 discloses an analysis support device that creates time-series graph data from an index of the emotional state of a subject calculated at a predetermined sampling interval, associates viewpoint coordinate data with a scene image frame by frame, synchronizes the time series based on the time difference, and displays the created graph of the index of the emotional state and the scene image associated with the viewpoint coordinate on a display unit of the same screen. The analysis support device simultaneously displays one or more indexes of the emotional state of the subject on a radar chart. This makes it possible to visually confirm the correlation between the indexes, the quantitative and temporal relationships of fluctuations, etc., and to provide for analysis. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2015-188649 A Summary of the Invention [Problem to be solved by the invention]

[0004] In Patent Document 1, for example, the index of the emotional state is displayed on a graph such as a radar chart by accumulating biomarker measurement data for a certain period of time. However, it may be difficult to summarize the biomarkers of a subject for a unit period by simply displaying the biomarker measurement data accumulated for a certain period of time on a graph. For example, there is a problem that it is not possible to intuitively and visually show the balance of changes in the biomarkers of a certain subject on a certain day, or the transition of how the biomarkers of a certain subject have changed over the course of a month.

[0005] The present disclosure has been devised in consideration of the above-mentioned conventional situation, and aims to provide a biomarker calculation device and a biomarker calculation method that output summary results of a subject's biomarkers over a unit period of time and efficiently support the analysis of the subject's biomarkers. [Means for solving the problem]

[0006] The present disclosure relates to an input unit for inputting biometric data of a subject measured by a biosensor, and a method for determining the biometric data of a subject using the input biometric data for a first period. Spiritual A biomarker estimation unit that estimates a biomarker indicating a state, and a biomarker estimation unit that estimates a plurality of biomarkers for each first period during a second period that is longer than the first period, and Spiritual Based on the number of occurrences of the state, Spiritual A calculation unit for calculating the ratio of each of the above states. Spiritual A biological index calculation device is provided, comprising: a graph creation unit that creates a graph showing a state ratio; and a display control unit that displays the created graph on a display device.

[0007] The present disclosure also provides a biomarker calculation method executed by a biomarker calculation device, the method including the steps of inputting biomarker data of a subject measured by a biosensor, and calculating a biomarker of the subject using the input biomarker data for a first period. Spiritual A step of estimating a biomarker indicating a state, collecting estimation results of a plurality of the biomarkers estimated for each of the first periods during a second period longer than the first period, and Spiritual Based on the number of occurrences of the state, Spiritual calculating the ratio of states; Spiritual A biomarker calculation method is provided, comprising the steps of generating a graph showing the proportions of states, and displaying the generated graph on a display device. Effect of the Invention

[0008] According to the present disclosure, it is possible to output a summary result of a subject's bioindicators for a unit period, thereby efficiently supporting the analysis of the subject's bioindicators. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram showing a configuration example of a biological indicator monitoring system according to a first embodiment. [Diagram 2] An example of RRI data obtained from a subject. [Diagram 3] FIG. 13 is a diagram showing an example of a biomarker estimation result and a ratio calculation result based on sensing result data accumulated during a second period. [Figure 4] An example of a Lorentz plot of RRI data. [Diagram 5] Figure showing an example of calculation of mean and deviation from Lorenz plot of RRI data [Figure 6] An example of a time series plot of the mean and deviation pairs calculated for each second period. [Figure 7] A diagram showing a first calculation example for determining the direction of change (movement vector) for each first period from the time series plot of FIG. 6. [Figure 8] A second calculation example for determining the direction of change (movement vector) for each first period from the time series plot in Figure 6 is shown. [Figure 9] FIG. 9 is an example of an 8-classification map in which the movement vectors of FIG. 7 or FIG. 8 are aligned to a center point O. [Figure 10A] FIG. 1 is a radar chart showing an example of the occurrence frequency of a plurality of biomarkers of a subject in a unit period. [Figure 10B] FIG. 1 is a radar chart showing an example of the occurrence frequency of a plurality of biomarkers of a subject in a unit period. [Figure 10C] FIG. 1 is a pie chart showing an example of the frequency of occurrence of a biomarker in a unit period of a subject; [Figure 10D] FIG. 1 is a bar graph showing an example of the frequency of occurrence of a biomarker in a unit period of a subject. [Figure 10E]FIG. 1 is a line graph showing an example of the frequency of occurrence of a biomarker of a subject in a unit period. [Figure 11] 1 is a flowchart showing an example of an overall operation procedure of a biomarker calculation device according to the first embodiment. [Figure 12] A flowchart showing a first example of the cumulative calculation process of FIG. [Figure 13] A flowchart showing a second example of the cumulative calculation process of FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, with reference to the drawings as appropriate, a detailed description will be given of an embodiment that specifically discloses a biomarker calculation device and a biomarker calculation method according to the present disclosure. However, more detailed description than necessary may be omitted. For example, detailed description of already well-known matters or duplicate description of substantially the same configuration may be omitted. This is to avoid the following description becoming unnecessarily redundant and to facilitate understanding by those skilled in the art. Note that the accompanying drawings and the following description are provided to enable those skilled in the art to fully understand the present disclosure, and are not intended to limit the subject matter described in the claims.

[0011] In the following embodiment, an example of a use case will be described in which an employee working at a desk in an office is used as a subject, the employee's daily mental and physical state is monitored (calculated) using the employee's biometric data, and the calculated ratio of the employee's state over a certain period of time is displayed in a graph. However, the subject is not limited to an office employee, and may be, for example, a store clerk at a cash register.

[0012] 1 is a block diagram showing a configuration example of a biomarker monitoring system 100 according to a first embodiment. The biomarker monitoring system 100 includes biosensors S1, S2, ..., a biomarker calculation device 1, and a display DP1. The biosensors S1, S2, ... and the biomarker calculation device 1, and furthermore, the biomarker calculation device 1 and the display DP1 are connected by wires such as a wired LAN (Local Area Network). Note that the biosensors S1, S2, ... and the biomarker calculation device 1, and furthermore, the biomarker calculation device 1 and the display DP1 may be connected wirelessly instead of by wires.

[0013] The biosensors S1, S2, ... are sensors (measuring instruments) that measure and acquire biodata of the subject, such as a heart rate monitor, an electroencephalograph, a skin potential monitor, and an electroencephalogram Korotkoff sound monitor. The heart rate monitor wraps electrodes around the subject's wrist or neck and measures the subject's heart rate fluctuation (RRI) data as sensed biodata SD1. The electroencephalograph measures the subject's electroencephalogram data as sensed biodata SD1 based on signals obtained from electrodes that contact the subject's head (specifically, the area that produces reactions related to emotions or feelings, such as the left anterior frontal lobe). The skin potential monitor measures skin potential data on the skin of the subject's forearm, etc., as sensed biodata SD1. The electroencephalogram Korotkoff sound monitor, like a blood pressure monitor, measures the electroencephalogram and Korotkoff sound data as sensed biodata SD1 with one cuff (arm band) wrapped around the subject's upper arm near the elbow joint. The biosensors S1, S2, ... transmit the subject's RRI data, electroencephalogram data, skin potential data, electroencephalogram and Korotkoff sound data, etc., as sensed biodata to the bioindex calculation device 1. The biosensors S1, S2, ... may include a surveillance camera capable of capturing color images of the subject. As disclosed in Japanese Patent No. 6358506 or Japanese Patent No. 6323809, for example, this surveillance camera can measure the subject's heart rate sound fluctuation (RRI) data in a non-contact manner based on the color captured image of the subject, and transmit the RRI data to the bioindex calculation device 1 as sensed biodata.

[0014] The bioindicator calculation device 1 inputs sensing biodata of the subject measured by the biosensors S1, S2, ..., and estimates a bioindicator indicating the subject's mental and physical state using the input sensing biodata for a first period (see below). The bioindicator calculation device 1 collects estimation results of a plurality of bioindicators estimated for each first period in a second period (see below) longer than the first period, and calculates the ratio of each mental and physical state based on the occurrence frequency of the mental and physical state indicated by each of the estimation results of the plurality of bioindicators. The bioindicator calculation device 1 creates a graph indicating the ratio of each mental and physical state, and displays it on the display DP1. The bioindicator calculation device 1 includes a processor PRC1, a memory M1, and a communication IF circuit 6. Although the bioindicator calculation device 1 is illustrated as being separate from the display DP1 in FIG. 1, it may be configured to have the display DP1 built in.

[0015] The biomarker calculation device 1 inputs and holds sensed biodata SD1 of a subject measured by biosensors S1, S2, .... In Fig. 1, the sensed biodata SD1 is input to a processor PRC1 as an example of an input unit, but may be stored in a memory M1.

[0016] The processor PRC1 is configured using, for example, a CPU (Central Processing Unit), a DSP (Digital Signal Processor) or an FPGA (Field Programmable Gate Array). The processor PRC1 has a first index estimation unit 11, a second index estimation unit 12, ..., an Nth index estimation unit 1N, an accumulation monitoring unit 2, a ratio calculation unit 3, a graph creation unit 4, and a display control unit 5. The first index estimation unit 11, the second index estimation unit 12, ..., the Nth index estimation unit 1N, the accumulation monitoring unit 2, the ratio calculation unit 3, the graph creation unit 4, and the display control unit 5 are functionally constructed within the processor PRC1 by the processor PRC1 reading and executing programs and data stored in a ROM (see below) of the memory M1. The first index estimation unit 11, the second index estimation unit 12, ..., the Nth index estimation unit 1N are configured as a total of N (N: an integer equal to or greater than 2) index estimation units. When N=2, the second index estimation unit 12 and the Nth index estimation unit 1N have the same configuration.

[0017] The first index estimation unit 11, the second index estimation unit 12, ..., the Nth index estimation unit 1N as an example of a biomarker estimation unit input sensed biodata SD1 of the same or different subjects, and estimate a biomarker indicating the subject's mental and physical state (for example, "relaxed" or "tense" described later) using the sensed biomarker data SD1. For example, the first index estimation unit 11 inputs RRI data (see above) as needed, the second index estimation unit 12 inputs RRI data (see above) and brainwave data as needed, and similarly, the Nth index estimation unit 1N inputs other combinations of sensed biomarker data SD1 different from the combinations of sensed biomarker data SD1 input by each of the first index estimation unit 11 to the (N-1)th index estimation unit (not shown) as needed. The first index estimation unit 11, the second index estimation unit 12, ..., the Nth index estimation unit 1N use different methods for estimating biomarkers.

[0018] The first index estimation unit 11 uses RRI data (see Figure 2) for a first period (e.g., 5 minutes, 15 minutes, 30 minutes, 1 hour, etc.) from the RRI data input at any time, to estimate bioindicators indicating the subject's physical and mental state for the first period, and sends the estimation results to the cumulative monitoring unit 2.

[0019] FIG. 2 is a diagram showing an example of RRI data obtained for a certain subject. The RRI data in FIG. 2 has a data string having, for example, the date and time when the heartbeat sound was measured and the RRI data. In FIG. 2, for example, the RRI of the subject at "10:00:01:00 on November 13, 2019" is "1.01", the RRI of the subject at "10:00:01:98 on November 13, 2019" is "0.98", the RRI of the subject at "10:01:02:92 on November 13, 2019" is "0.94", ...

[0020] The mental and physical state (i.e., biomarker) is the physical or mental state of the subject, and may be, for example, "relaxed," "high performance," "tension," "concentration," or "performance decline," but is not limited to these. The first period is set in advance by an administrator of the biomarker monitoring system 100, and is not limited to the above-mentioned time, but is a collection period of sensed biodata SD1 required for the first index estimation unit 11 to estimate the subject's biomarker. In other words, the first period corresponds to the shortest estimation period of the estimation process performed by the first index estimation unit 11. The estimation result of the biomarker is output, for example, as binary data (i.e., data format of "0" and "1") for each biomarker. For example, when a biomarker estimated using sensed biodata SD1 for a certain first period indicates "relaxed," "relaxed" becomes "1," and each of "high performance," "tension," "concentration," and "performance decline" becomes "0." In addition, there are cases where the patterns of heart rate fluctuations partially overlap, such as "tension" and "concentration," and in such cases, when the estimated results of the biomarkers are handled in the form of binary data of "0" and "1," "tension" = "1" and "concentration" = "1." As will be described later, in the case of multi-value data that is not binary data, "tension" = "0.5," "concentration" = "0.5," "0.6," or "0.4" may be output.

[0021] "Relaxed" is a state in which the heart rate is lowered and the heart rate fluctuation is increased during the first period, specifically indicating a state in which the subject is relaxed. "High performance" is a state in which the heart rate is higher and the heart rate fluctuation is increased during the first period, specifically indicating a state in which the subject can perform well in what he or she has to do, such as work. "Tension" is a state in which the heart rate is higher and the heart rate fluctuation is reduced during the first period, specifically indicating a state in which the subject is somewhat tense. "Concentration" is a state in which the heart rate does not fluctuate much during the first period, specifically indicating a state in which the subject is concentrating. "Performance degradation" is a state in which the heart rate is lowered and the heart rate fluctuation is reduced during the first period, specifically indicating a state in which the subject is unmotivated or sleepy in what he or she has to do, such as work. Details of the method of estimating the biomarkers by the first index estimation unit 11 will be described later with reference to Figs. 4 to 9.

[0022] When the second index estimation unit 12 to the Nth index estimation unit 1N collect a combination of sensing biological data SD1 different from that of the first index estimation unit 11 for a first period, the second index estimation unit 12 to the Nth index estimation unit 1N estimate a biological index indicating the subject's mental and physical state by a method different from that of the first index estimation unit 11, and send the estimation result for the first period to the cumulative monitoring unit 2. The estimation method performed by each of the second index estimation unit 12 to the Nth index estimation unit 1N may be the same as that of the first index estimation unit 11, or may be a different method (for example, a method already known). For example, a stress index such as LF / HF may be calculated using RRI data for the first period, and a biological index indicating the subject's mental and physical state may be estimated.

[0023] The cumulative monitoring unit 2, which is an example of a calculation unit, judges whether or not the estimation results of the subject's bioindices estimated by each of the first index estimation unit 11 to the Nth index estimation unit 1N for each first period have been accumulated and collected for a second period (for example, one hour, one day, one week, one month, etc.). The second period is set in advance by an operation by an administrator of the bioindicator monitoring system 100 so as to be longer than the first period, and is not limited to the above-mentioned time, but is an example of a unit period displayed in a graph created by the graph creation unit 4 described later. The cumulative monitoring unit 2 may temporarily store the estimation results of the subject's bioindices estimated by each of the first index estimation unit 11 to the Nth index estimation unit 1N for each first period in the memory M1. For example, when the first period is one hour and the second period is one day, the cumulative monitoring unit 2 judges whether or not the estimation results of the bioindices estimated by each of the first index estimation unit 11 to the Nth index estimation unit 1N for each hour have been collected for a total of 24 times (i.e., one day). When the cumulative monitoring unit 2 determines that it has collected the estimation results of the bioindices for the second period from each of the first index estimation unit 11 to the Nth index estimation unit 1N, it sends the estimation results of the bioindices for the second period from each of the first index estimation unit 11 to the Nth index estimation unit 1N to the ratio calculation unit 3.

[0024] The ratio calculation unit 3, which is an example of a calculation unit, counts the number of occurrences of each of the plurality of bioindicators in the second period using the estimation results of the bioindicators for the second period from each of the first index estimation unit 11 to the Nth index estimation unit 1N. The ratio calculation unit 3 calculates the ratio of each bioindicator (i.e., mental and physical state) based on the counter values ​​of the number of occurrences of each of the plurality of bioindicators in the second period (see FIG. 3).

[0025] FIG. 3 is a diagram showing an example of the estimation result and the ratio calculation result of the bioindicator based on the sensing result data accumulated in the second period. In the example of FIG. 3, the first period is one hour, and the second period is one day. From 10:00:00 to 10:59:59 on November 13, 2019, the subject's bioindicators were "1" for "tension" and "0" for "relaxation." From 11:00:00 to 11:59:59 on November 13, 2019, the subject's bioindicators were "1" for "tension" and "0" for "relaxation." Similarly, from 12:00:00 to 12:59:59 on November 13, 2019, the subject's bioindicators were "1" for "tension" and "0" for "relaxation."

[0026] The ratio calculation unit 3 calculates the total number of occurrences of the bioindicators "tension" and "relaxation" in the second period (i.e., "November 13, 2019" corresponding to "1 day") as "30" and "45". Note that in FIG. 3, only "tension" and "relaxation" are shown as bioindicators for the sake of simplicity of explanation, but the total number of occurrences of the remaining three bioindicators "high performance", "concentration", and "poor performance" may be included. The ratio calculation unit 3 uses the total number of occurrences of the bioindicators "tension" and "relaxation", "30", and "45", to calculate the ratio of each of the bioindicators "tension" and "relaxation". Specifically, the ratio calculation unit 3 calculates the ratio of the bioindicator "tension" to 40% (=30 / (30+45)), and calculates the ratio of the bioindicator "relaxation" to 60% (=45 / (30+45)). The ratio calculation unit 3 sends the calculation results of the ratios of each bioindicator to the graph creation unit 4.

[0027] The estimation results of the bioindices that the ratio calculation unit 3 acquires from each of the first index estimation unit 11 to the Nth index estimation unit 1N via the accumulation monitoring unit 2 may be output in the form of binary data. However, when the range of values ​​of the estimation results of the bioindices acquired from each of the first index estimation unit 11 to the Nth index estimation unit 1N is the same range (for example, values ​​of "0" to "1"), the estimation results of the bioindices do not need to have the form of binary data. In this case, the ratio calculation unit 3 may convert the values ​​of the estimation results from each of the first index estimation unit 11 to the Nth index estimation unit 1N into the form of binary data (i.e., value "1" or value "0") by a known method. As a result, the data format of the estimation results of the bioindices acquired from each of the first index estimation unit 11 to the Nth index estimation unit 1N is common, and the efficiency of the calculation process when calculating the ratio in the ratio calculation unit 3 is appropriately improved.

[0028] Furthermore, when the range of values ​​of the estimation results of the bioindices obtained from each of the first index estimation unit 11 to the Nth index estimation unit 1N is the same range (for example, values ​​of "0" to "1"), even if the estimation results of the bioindices obtained from each of the first index estimation unit 11 to the Nth index estimation unit 1N do not have the format of binary data, the ratio calculation unit 3 may directly accumulate the values ​​of the estimation results from each of the first index estimation unit 11 to the Nth index estimation unit 1N. This allows the ratio calculation unit 3 to easily calculate the ratio by directly using the estimation results of the bioindices obtained from each of the first index estimation unit 11 to the Nth index estimation unit 1N.

[0029] Furthermore, when the ranges of values ​​of the estimation results of the bioindices obtained from each of the first index estimation unit 11 to the Nth index estimation unit 1N are not the same range (for example, values ​​of "0" to "1"), the ratio calculation unit 3 may normalize the estimation results of the bioindices obtained from each of the first index estimation unit 11 to the Nth index estimation unit 1N so that the values ​​are within the same range described above. After that, the ratio calculation unit 3 may calculate the ratio using the normalized values ​​of the estimation results of the bioindices. This allows the ratio calculation unit 3 to calculate the estimation results of the subject's bioindices with high accuracy, regardless of the type of estimation method of the bioindices of each of the first index estimation unit 11 to the Nth index estimation unit 1N.

[0030] The graph creation unit 4 uses the calculation results of the ratios of the multiple bioindicators of the subject in the second period by the ratio calculation unit 3 to create a bioindicator graph (see Figs. 10A, 10B, 10C, 10D, and 10E) that allows the user to visually grasp the ratios of the multiple bioindicators of the subject in at least the second period, and sends the graph to the display control unit 5. Details of specific examples of the bioindicator graph will be described later with reference to Figs. 10A to 10E.

[0031] The display control unit 5 displays the data of the bioindicator graph created by the graph creation unit 4 on the display DP1 via the communication IF circuit 6.

[0032] The communication IF circuit 6 constitutes an interface circuit that controls communication between the bioindicator calculation device 1 and other devices (e.g., biosensors S1, S2, ..., display DP1) that communicate with the bioindicator calculation device 1. When the communication IF circuit 6 receives bioindicator graph data sent from the display control unit 5, it sends the data to the display DP1. The communication IF circuit 6 also receives sensed biodata SD1 from the biosensors S1, S2, ... and sends it to the processor PRC1. Although not shown in FIG. 1, the sensed biodata SD1 from the biosensors S1, S2, ... is input into the bioindicator calculation device 1 via the communication IF circuit 6.

[0033] The memory M1 is configured using a RAM (Random Access Memory) and a ROM (Read Only Memory), and temporarily stores programs required for executing the operation of the biological index calculation device 1, as well as data or information generated during operation. The RAM is, for example, a work memory used when the processor PRC1 is operating. The ROM stores in advance, for example, programs and data for controlling the processor PRC1.

[0034] The display DP1, as an example of a display device, is a device capable of displaying a display screen of a bioindicator graph created by the bioindicator calculation device 1, and may be, for example, an LCD (Liquid Crystal Display), an organic EL (Electroluminescence) device, or other display device.

[0035] Next, with reference to Figs. 4 to 9, the details of the estimation method of the subject's bioindicator performed by the first index estimation unit 11 will be described. The estimation method described below may be similarly executed by each of the second index estimation unit 12 to the Nth index estimation unit 1N, which are different from the sensed biodata SD1 input by the first index estimation unit 11. Fig. 4 is a diagram showing an example of a Lorentz plot of RRI data. Fig. 5 is a diagram showing an example of calculation of the average and deviation from the Lorentz plot of RRI data. Fig. 6 is a diagram showing an example of a time series plot of a pair of the average and deviation calculated for each second period. Fig. 7 is a diagram showing a first calculation example of obtaining a change direction (movement vector) for each first period from the time series plot of Fig. 6. Fig. 8 is a diagram showing a second calculation example of obtaining a change direction (movement vector) for each first period from the time series plot of Fig. 6. Fig. 9 is a diagram showing an example of an 8-classification map in which the movement vector of Fig. 7 or Fig. 8 is aligned to the center point O.

[0036] In the following description of Fig. 4 to Fig. 9, the second period has, for example, six times (i.e., six times) the first period. In other words, the second period is six consecutive first periods. Note that six times is merely an example, and it goes without saying that the number of times is not limited to six.

[0037] As shown in FIG. 4, the first index estimation unit 11 inputs the sensed biological data SD1 collected for a first period (for example, one hour). Here, as shown in FIG. 4, the sensed biological data SD1 input to the first index estimation unit 11 constitutes a data set in which records RC1 each consisting of a date and time (Date time), "RRI" (for example, current RRI data), and "Next RRI" (for example, RRI data after the next sampling interval (i.e., one heart beat time) have been accumulated for the first period. Note that "Next RRI" may be RRI data after the next sampling interval has elapsed with the current RRI data as a reference, or may be RRI data at the previous sampling interval. The first index estimation unit 11 plots "RRI" and "Next RRI" constituting each record on a two-dimensional graph GRP0 using the sensed biological data SD1 collected for the first period. Plotting RRI and Next RRI when one sampling interval is before and after on a two-dimensional graph in this way is called a Lorentz plot.

[0038] In the graph GRP0 in Fig. 4, the horizontal axis indicates "RRI" and the vertical axis indicates "Next RRI". Specifically, a point A1 having coordinates of ("RRI", "Next RRI") = (0.5748517, 0.6106460) of the record RC1 is Lorentz plotted on the graph GRP0. Similarly, for each record of the sensed biological data SD1 for the first period, each value of ("RRI", "Next RRI") is repeatedly Lorentz plotted at the corresponding coordinate position on the graph GRP0, and this process is executed by the first index estimation unit 11.

[0039] One of the graphs, GRP1, in Figure 5 shows the results of a Lorentz plot of the coordinates of "RRI" and "Next RRI" corresponding to all records that make up the sensed biometric data SD1 for the first period. The Lorentz plot allows us to see not only the variance of "RRI" and "Next RRI" but also the average. In this graph, GRP1, the horizontal axis is "RRI" t” (i.e., RRI data at time t elapsed since the start time of the first period), and the vertical axis is “RRI (t-1) " (i.e., RRI t In this graph, GRP1, for example, the RRI t " is in the range of "0.5 to 1.0," "RRI (t-1) The Lorentzian plot of PTY1 is shown in the range of 0.5 to 1.0. In other words, the RRI t " and "RRI (t-1) Many of the coordinate points of "RRI" and "Next RRI" will be plotted near the straight line XAS1 indicating the 45-degree direction of the graph GRP1.

[0040] For example, if the graph GRP1 is rotated 45 degrees clockwise, it becomes the other graph GRP2 in Fig. 5. That is, in the graph GRP2 with the line XAS1 as the horizontal axis and the line YAX1 perpendicular to the line XAS1 as the vertical axis, the horizontal axis is the average μ x and deviation σ x The vertical axis shows the average μ y and deviation σ y Therefore, the first index estimation unit 11 calculates the average μ x and deviation σ x and the mean μ y and deviation σ y Similarly, the first index estimation unit 11 calculates the average μ x and deviation σ x and the mean μ y and deviation σ y Calculate.

[0041] FIG. 6 shows the average μ of the RRI data in each of the first period PRD1 of the first round, the first period PRD2 of the second round, the first period PRD3 of the third round, the first period PRD4 of the fourth round, the first period PRD5 of the fifth round, and the first period PRD6 of the sixth round. x and deviation σ x The first index estimation unit 11 estimates the average μ x and deviation σ x The calculation results are plotted on a graph GRP3 with the horizontal axis being the average μ and the vertical axis being the deviation σ. The six coordinate points (specifically, (average μ x1 , deviation σ x1 ), (average μ x2 , deviation σ x2 ), (average μ x3 , deviation σ x3 ), (average μ x4 , deviation σ x4 ), (average μ x5 , deviation σ x5 ), (average μ x6 , deviation σ x6 )) are plotted on the graph GRP3. For example, when sensing is performed by the biosensors S1, S2, ..., the subject moves (for example, the subject's head moves momentarily), which may disturb part of the sensed biodata SD1, causing disturbances in the RRI data for each beat, but by finding the average and deviation of the RRI data over the first period (for example, one hour), averaging the RRI data provides stability (reliability) of the data when estimating the bioindicators, and is expected to improve the estimation accuracy.

[0042] As shown in Fig. 6 and Fig. 7, the first index estimation unit 11 calculates movement vectors V1, V2, V3, V4, and V5 from a coordinate point to another coordinate point in the order of plotting on the graph GRP3. x1 , deviation σ x1 ) to the coordinate point (average μ x2 , deviation σ x2 ) The movement vector V2 is the movement vector to the coordinate point (average μ x2 , deviation σx2 ) to the coordinate point (average μ x3 , deviation σ x3 ) The movement vector V3 is the movement vector to the coordinate point (average μ x3 , deviation σ x3 ) to the coordinate point (average μ x4 , deviation σ x4 ) The movement vector V4 is the movement vector to the coordinate point (average μ x4 , deviation σ x4 ) to the coordinate point (average μ x5 , deviation σ x5 ) The movement vector V5 is the movement vector to the coordinate point (average μ x5 , deviation σ x5 ) to the coordinate point (average μ x6 , deviation σ x6 ) is the motion vector to

[0043] As an example of extraction and mapping of the first movement vector, the first index estimation unit 11 extracts each of the movement vectors V1-V5 from the graph GRP3, aligns the starting points of each of the movement vectors V1-V5, and maps the movement vectors V1-V5 when the starting points are aligned to the 8-classification map GRP5 (see FIG. 9). The first index estimation unit 11 outputs an estimation result of the subject's bioindicators for the second period based on this 8-classification map GRP5.

[0044] As an example of extraction and mapping of the second movement vector, the first index estimation unit 11 sets a predetermined reference point P as shown in the graph GRP4 of FIG. 8, and calculates the distance between each coordinate point (average μ x1 , deviation σ x1 ), coordinate points (average μ x2 , deviation σ x2 ), coordinate points (average μ x3 , deviation σ x3 ), coordinate points (average μ x4 , deviation σ x4 ), coordinate points (average μ x5 , deviation σ x5 ), coordinate points (average μ x6 , deviation σ x6) may be extracted. The reference point P may be, for example, a coordinate point (average μ x1 , deviation σ x1 ), coordinate points (average μ x2 , deviation σ x2 ), coordinate points (average μ x3 , deviation σ x3 ), coordinate points (average μ x4 , deviation σ x4 ), coordinate points (average μ x5 , deviation σ x5 ), coordinate points (average μ x6 , deviation σ x6 ) is a coordinate point corresponding to the average value of the heart rate variability data of the subject at rest. The reference point P may be determined by measuring the average and deviation of the heart rate variability data of the subject at rest in advance. The reference point P is not limited to the average value of the above-mentioned coordinate points or the coordinate point determined by the average and deviation of the heart rate variability data at rest. The first index estimation unit 11 may map the movement vectors U1 to U6 to the 8-classification map GRP5. An example of mapping the movement vectors U1 to U6 to the 8-classification map is omitted.

[0045] The 8-classification map GRP5 in Fig. 9 is a circular map having 8 axes (corresponding to diameters) intersecting at a center point O, and classifies and indicates bioindicators indicating the subject's mental and physical state. In the 8-classification map GRP5, at least some of the intersections C1, C2, C3, C4, C5, C6, C7, and C8 between the 8 axes and the circumference are set with counter values ​​of the number of occurrences of the bioindicators. Specifically, "Relaxation" is set at the intersection C1, "High Performance" at the intersection C3, "Tension" at the intersection C5, "Concentration" at the intersection C6, and "Downgraded Performance" at the intersection C7.

[0046] The first index estimation unit 11 maps the movement vectors V1 to V5 (see FIG. 7, for example) described with reference to FIG. 7 or FIG. 8 onto the 8-classification map GRP5 so that the start points of each of them coincide with the center point O. The first index estimation unit 11 reads out a counter value corresponding to an axis in the center direction of the sector portion AR1 (for example, an axis in the direction from the center point O toward the intersection point C8 in FIG. 9) with an interior angle of 90 degrees centered on the center point O, while scanning the sector portion AR1 clockwise or counterclockwise by 45 degrees. For example, when the sector portion AR1 is specified by the intersection point C1-center point O-intersection point C7 (see FIG. 9), the first index estimation unit 11 reads out the number of movement vectors V2 and V5 mapped within the sector portion AR1 (here, the value 2) as the counter value corresponding to the intersection point C8. Furthermore, when sector portion AR1 is specified by intersection C8-center point O-intersection C6, first index estimation unit 11 reads out the number of movement vectors V5, V2, V4 mapped within sector portion AR1 (here, a value of 3) as a counter value corresponding to intersection C7. Thereafter, first index estimation unit 11 similarly reads out the number of movement vectors V5 mapped within sector portion AR1 (here, a value of 1) as a counter value corresponding to intersection C1.

[0047] The first index estimation unit 11 refers to the settings of “Relaxed” for intersection C1, “High performance” for intersection C3, “Tension” for intersection C5, “Concentration” for intersection C6, and “Deterioration in performance” for intersection C7, and estimates that, within the second period, the number of occurrences of “Relaxed” is “1,” the number of occurrences of “High performance” is “2,” the number of occurrences of “Tension” is “0,” the number of occurrences of “Concentration” is “1,” and the number of occurrences of “Deterioration in performance” is “3.”

[0048] Next, the types of graphs created by the graph creation unit 4 of the biomarker calculation device 1 according to the first embodiment will be described with reference to Figs. 10A to 10E. Figs. 10A and 10B are diagrams showing radar charts illustrating an example of the frequency of appearance of a plurality of biomarkers in a unit period of a subject. Fig. 10C is a diagram showing a pie chart illustrating an example of the frequency of appearance of a biomarker in a unit period of a subject. Fig. 10D is a diagram showing a bar graph illustrating an example of the frequency of appearance of a biomarker in a unit period of a subject. Fig. 10E is a diagram showing a line graph illustrating an example of the frequency of appearance of a biomarker in a unit period of a subject. In the description of Figs. 10A to 10E, the same elements are given the same reference numerals to simplify or omit the description.

[0049] FIG. 10A shows a radar chart GRP6 of a characteristic PTY2 of a ratio based on the number of occurrences of each of a plurality of biomarkers in a second period (e.g., one day) of a subject whose identification number UID1 is "FFFFF0000001". The circles shown in the radar chart GRP6 correspond to the 8-classification map GRP5 in FIG. 9. In the radar chart GRP6, the maximum value of the counter value corresponding to the eight axes described with reference to FIG. 9 is 80, and the counter values ​​corresponding to the eight axes are plotted and connected by dashed lines. Therefore, according to the radar chart GRP6, the ratio of the maximum value of 80 that each biomarker appears in a second period (e.g., one day) of a subject whose identification number UID1 is "FFFFF0000001" to the counter value (i.e., how frequently the corresponding biomarker was seen in the second period) can be visually shown.

[0050] FIG. 10B shows a radar chart GRP7 of a characteristic PTY3 of a ratio based on the number of occurrences of each of a plurality of biomarkers in a second period (e.g., one day) of a subject whose identification number UID2 is "FFFFF0000002". The circles shown in the radar chart GRP7 correspond to the 8-classification map GRP5 in FIG. 9. In the radar chart GRP7, the maximum value of the counter value corresponding to the eight axes described with reference to FIG. 9 is 100, and the counter values ​​corresponding to each of the eight axes are plotted and connected by dashed lines. Therefore, according to the radar chart GRP7, the ratio of the occurrence of each biomarker to the maximum counter value of 100 in a second period (e.g., one day) of a subject whose identification number UID2 is "FFFFF0000002" (i.e., how frequently the corresponding biomarker was seen in the second period) can be visually shown. 10A, even if the maximum value of the occurrence frequency of the bioindicator is different between 80 and 100 (in other words, even if the number of first periods during which the bioindicator can be estimated differs for each subject), the occurrence frequency of each bioindicator is shown as a percentage, so that the bioindicators of multiple people in a certain period, such as the second period, can be easily compared. The reason why the first period during which the bioindicator can be estimated differs for each subject is because the number of times the sensing biodata SD1 is acquired by the biosensors S1, S2, ... differs between a person who basically does desk work all day and a person who leaves his / her desk often for meetings or business trips, for example.

[0051] FIG. 10C shows a pie chart GRP8 of the proportions based on the frequency of occurrence of each of a plurality of biomarkers in a second period (e.g., one day) of a subject whose identification number UID3 is "FFFFF000003". In the pie chart GRP8, the "Relaxed" area ZN1, the "High Performance" area ZN2, the "Tension" area ZN3, the "Concentration" area ZN4, and the "Other" area ZN5 are displayed in different colors according to their proportions. Therefore, the pie chart GRP8 makes it possible to visually and easily present the proportions of each biomarker in a second period (e.g., one day) of a subject whose identification number UID3 is "FFFFF000003".

[0052] FIG. 10D shows a bar graph GRP9 in which the proportions based on the frequency of occurrence of each of a plurality of biomarkers for a certain subject for each second period (e.g., one day) are collected for a unit period (e.g., a third period longer than the second period). In FIG. 10D, the third period is, for example, one month, but is not limited to one month as long as it is longer than the second period. Specifically, the bar graph GRP9 is configured by arranging bar graphs created for each second period from a bar graph BR1 corresponding to the first second period of the third period to a bar graph BR2 corresponding to the last second period of the third period. Therefore, the bar graph GRP9 makes it possible to visually and easily present the transition of the biomarkers for each second period (e.g., daily) throughout the entire third period of the subject.

[0053] FIG. 10E shows a line graph GRP10 in which line graphs TRD1, TRD2, TRD3, TRD4, and TRD5 are integrated, each of which is configured by collecting the proportion of a plurality of biomarkers in a second period (e.g., one day) of a certain subject for the second period. In FIG. 10E, the third period is, for example, one month, but is not limited to one month as long as it is longer than the second period. In FIG. 10E, the biomarkers are, for example, "other," "concentration," "tension," "high performance," and "relaxation," and line graphs for each of the five biomarkers are shown as TRD1, TRD2, TRD3, TRD4, and TRD5. In the line graphs TRD1, TRD2, TRD3, TRD4, and TRD5 that constitute the line graph GRP10, the start timing and end timing of the third period are the same (i.e., common). Specifically, the line graphs TRD1 and TRD4 are configured by arranging bar graphs created for each second period from the bar graph BR3 corresponding to the first second period of the third period to the bar graph corresponding to the last second period of the third period. Similarly, the line graphs TRD2 and TRD5 are configured by arranging bar graphs created for each second period from the bar graph BR4 corresponding to the first second period of the third period to the bar graph corresponding to the last second period of the third period. The line graph TRD3 is configured by arranging bar graphs created for each second period from the bar graph BR5 corresponding to the first second period of the third period to the bar graph corresponding to the last second period of the third period. Therefore, according to the line graph GRP10, not only can the transition of the biomarkers for each second period (e.g., daily) be visually and easily presented for each biomarker throughout the entire third period of the subject, but also the trend of the transition of the biomarkers for each second period (i.e., daily) (in other words, emotions) can be presented in an easy-to-understand manner.

[0054] Next, the operation procedure of the biomarker calculation device 1 according to the first embodiment will be described with reference to Figs. 11 to 13. Fig. 11 is a flowchart showing an example of the overall operation procedure of the biomarker calculation device 1 according to the first embodiment. Fig. 12 is a flowchart showing a first example of the cumulative calculation process of Fig. 11. Fig. 13 is a flowchart showing a second example of the cumulative calculation process of Fig. 11. In the description of Figs. 12 and 13, the same processes are given the same step numbers, and the description is simplified or omitted, and different contents are described. The processes of Figs. 12 and 13 are mainly executed by the first index estimation unit 11, but may be similarly executed by each of the second index estimation unit 12 to the Nth index estimation unit 1N.

[0055] In FIG. 11, the biomarker calculation device 1 resets (initializes) counter values ​​corresponding to the eight axes of the eight-classification map GRP5 described with reference to FIG. 9 (St1). After step St1, the biomarker calculation device 1 executes cumulative calculation processing for each first period using the sensed biological data SD1 input during the first period (St2). The biomarker calculation device 1 judges whether the cumulative calculation processing for each first period has been executed for the second period (St3). For example, when the first period is one hour and the second period is one day, it is judged whether the cumulative calculation processing has been executed 24 times in total. Note that even if the second period is one day, if the working hours of the subject at the company are a total of nine hours from 9:00 a.m. to 6:00 p.m., the second period may be regarded as substantially nine hours.

[0056] If the cumulative calculation process for the first period has not been executed for the second period (St3, NO), the process of step St2 is repeated until the cumulative calculation process for the first period has been executed for the second period.

[0057] On the other hand, when the biomarker calculation device 1 determines that the cumulative calculation process for each first period has been performed for the second period (St3, YES), it counts the number of occurrences of each of the multiple biomarkers in the second period, and calculates the ratio of each biomarker (i.e., mental and physical state) based on this counter value (St4).The biomarker calculation device 1 uses the calculation result of the ratio of each of the multiple biomarkers of the subject in the second period to create a biomarker graph that allows the user to visually grasp at least the ratio of each of the multiple biomarkers of the subject in the second period, and displays it on the display DP1 (St5).

[0058] 12, the biomarker calculation device 1 acquires RRI data indicating the fluctuation of the heartbeat sound for a first period (St2-1). The biomarker calculation device 1 calculates the average μ and deviation σ of the RRI data for the first period acquired in step St2-1 (St2-2, see FIG. 5).

[0059] Here, step St2-2 will be described in detail.

[0060] The biomarker calculation device 1 pairs two chronologically consecutive RRI data from the RRI data for the first period acquired in step St2-1, and creates two-dimensional data of the RRI data for the first period (St2-2-1, see FIG. 5) by Lorentz plotting (see FIG. 4) each pair of RRI data. In FIG. 5, the horizontal axis (x-axis) shows RRI(t) indicating the RRI data at time t, and the vertical axis (y-axis) shows RRI(t-1) indicating the RRI data at time (t-1).

[0061] The biomarker calculation device 1 calculates the average μ and deviation σ of y=x (i.e., 45 degree direction) from the two-dimensional data created in step St2-2-1 (St2-2-2). This is because, as described above, many coordinates of (RRI(t), RRI(t-1)) are distributed near the straight line of the linear function where y=x.

[0062] The biomarker calculation device 1 maps the coordinate points determined by the values ​​of (average μ, deviation σ) for each first period calculated in step St2-2 onto two-dimensional data. The biomarker calculation device 1 calculates a movement vector from the coordinate point determined by the value of (average μ, deviation σ) in the previous first period to the coordinate point determined by the value of (average μ, deviation σ) in the current first period across each of the first periods constituting the second period (St2-3, see FIG. 7).

[0063] The biomarker calculation device 1 maps the multiple movement vectors calculated in step St2-3 to the 8-classification map GRP5 (see FIG. 9). The biomarker calculation device 1 reads out counter values ​​corresponding to the axis in the central direction of the sector AR1 with an interior angle of 90 degrees centered on the center point O of the 8-classification map GRP5 while scanning the sector AR1 clockwise or counterclockwise by 45 degrees each time. The biomarker calculation device 1 counts and outputs the estimation results of the subject's biomarkers in the second period based on the 8-classification map GRP5 after mapping (St2-4).

[0064] In Fig. 13, the biomarker calculation device 1 sets a predetermined reference point P, and calculates movement vectors U1, U2, U3, U4, U5, and U6 from this reference point P to each of the coordinate points determined by the values ​​of (average μ, deviation σ) for each first period calculated in step St2-2 (St2-3A, see Fig. 8). The subsequent processing is the same as in Fig. 12, so the description will be omitted.

[0065] As described above, the bioindicator calculation device 1 according to the first embodiment inputs biodata (e.g., sensed biodata SD1) of the subject measured by the biosensors S1, S2, .... The bioindicator calculation device 1 estimates bioindicators indicating the subject's mental and physical state using the input biodata for a first period (e.g., one hour). The bioindicator calculation device 1 collects estimation results of a plurality of bioindicators estimated for each first period in a second period (e.g., one day) longer than the first period, and calculates the ratio of each mental and physical state based on the occurrence frequency of the mental and physical state indicated by each of the estimation results of the plurality of bioindicators. The bioindicator calculation device 1 creates a graph showing the ratio of each mental and physical state, and displays the created graph on the display DP1.

[0066] As a result, the bioindicator calculation device 1 can display the bioindicators of the subject in a unit period (e.g., a second period) as a percentage, and can output a summary result of the bioindicators in the unit period, such as changes in the bioindicators, thereby efficiently supporting the analysis of the bioindicators of the subject. For example, the bioindicator calculation device 1 can visually display the percentage of the subject's bioindicators by suppressing the variation of the subject's bioindicators in a second period longer than the first period, compared to a case where the bioindicators are simply accumulated and displayed in a graph in a first period. Therefore, it becomes possible to intuitively and visually display the balance of changes in the bioindicators of a certain subject on a certain day, or the transition of how the bioindicators of a certain subject have changed over the course of a month.

[0067] The biometric data includes a plurality of types of biometric data. The biometric calculation device 1 is configured with a plurality of biometric estimation units that use different combinations of biometric data from the plurality of types of biometric data to estimate the biometrics. This allows the biometric calculation device 1 to estimate the biometrics of the subject in a diverse and highly accurate manner by varying the types of sensing biometric data SD1 used to estimate the biometrics.

[0068] The biodata is data indicating the heart rate variability of the subject measured at predetermined time intervals by the biosensors S1, S2, .... This allows the bioindicator calculation device 1 to use the RRI data of the subject, and therefore allows the bioindicator (i.e., emotion) of the subject in the second period to be estimated with high accuracy.

[0069] The mental and physical state includes at least two of relaxation, concentration, and tension. This allows the biomarker calculation device 1 to visually indicate the occurrence frequency ratio of at least two of relaxation, concentration, and tension, which are likely to be habitually measured as the mental and physical state of the subject.

[0070] Furthermore, the bioindicator calculation device 1 outputs the estimation results of the bioindicators in the first period in the form of binary data. As a result, the bioindicator calculation device 1 can efficiently calculate the ratio of the bioindicators of the subject, since the estimation results of the bioindicators are standardized as binary data.

[0071] Moreover, the bioindex calculation device 1 normalizes the estimation results of the bioindexes by each of the plurality of bioindex estimation units (the first index estimation unit 11 to the Nth index estimation unit 1N) for each first period and collects them for the second period. As a result, the bioindex calculation device 1 can standardize the range of values ​​that the estimation results of the bioindexes can take, regardless of the type of the estimation method of the bioindexes by each of the first index estimation unit 11 to the Nth index estimation unit 1N, and can calculate the estimation results of the bioindexes of the subject with high accuracy.

[0072] Furthermore, the biomarker calculation device 1 creates a graph in which the calculation results of the ratio of the mental and physical state for each second period are arranged for a third period (e.g., one month) that is longer than the second period. This allows the biomarker calculation device 1 to visually and easily present the changes in the biomarkers for each second period (e.g., daily) throughout the entire third period of the subject.

[0073] Furthermore, the bioindicator calculation device 1 estimates the bioindicator based on a Lorenz plot of a first statistical parameter (e.g., mean μ and deviation σ) based on the biodata in the immediately preceding first period and a second statistical parameter (e.g., mean μ and deviation σ) based on the biodata in the first period to be estimated. This allows the bioindicator calculation device 1 to reduce the variability of the input sensed biodata SD1 of the subject and calculate the bioindicator of the subject with high accuracy.

[0074] Furthermore, when the bioindex calculation device 1 is unable to estimate the bioindex, it outputs the bioindex value of 0. This allows the bioindex calculation device 1 to eliminate the influence of the second period during which the bioindex cannot be estimated and to appropriately calculate the subject's bioindex using only the values ​​that can be estimated.

[0075] Although the embodiments have been described above with reference to the accompanying drawings, the present disclosure is not limited to such examples. It is clear that a person skilled in the art can conceive of various modifications, corrections, substitutions, additions, deletions, and equivalents within the scope of the claims, and it is understood that these also belong to the technical scope of the present disclosure. In addition, the components in the above-mentioned embodiments may be arbitrarily combined within the scope of the invention. [Industrial Applicability]

[0076] INDUSTRIAL APPLICABILITY The present disclosure is useful as a biomarker calculation device and a biomarker calculation method that output summary results of a subject's biomarkers in a unit period and efficiently support the analysis of the subject's biomarkers. [Explanation of symbols]

[0077] 1. Biometric indicator calculation device 2 Cumulative monitoring section 3 Ratio calculation section 4. Graph Creation Section 5. Display control section 6 Communication IF circuit 11 First index estimation section 12 Second index estimation part 1N Nth index estimation section 100 Biometric Monitoring System DP1 Display M1 Memory PRC1 Processor S1, S2 biosensor SD1 Sensing biometric data

Claims

1. an input unit for inputting biometric data of the subject measured by a biosensor; a biomarker estimation unit that estimates a biomarker indicating a mental state of the subject by using the input biomarker data for a first period; a calculation unit that collects estimation results of a plurality of the biomarkers estimated for each of the first periods during a second period that is longer than the first period, and calculates a ratio of each of the mental conditions based on the occurrence frequency of the mental conditions indicated by each of the estimation results of the plurality of the biomarkers; a graph creation unit that creates a graph showing the ratio of each of the mental states; A display control unit that displays the created graph on a display device. Biometric indicator calculation device.

2. The biometric data includes a plurality of types of biometric data, the biometric estimation unit is configured with a plurality of biometric estimation units each having a different combination of biometric data to be used for estimating the biometrics among the plurality of types of biometric data; The biomarker calculation device according to claim 1 .

3. The biological data is data indicating heart rate variability of the subject measured by the biological sensor at predetermined time intervals. The biomarker calculation device according to claim 1 .

4. The mental state includes at least two of relaxation, concentration, and tension; The biomarker calculation device according to claim 1 .

5. the biomarker estimation unit outputs an estimation result of the biomarker in the first time period in a format of binarized data. The biomarker calculation device according to claim 1 .

6. the calculation unit normalizes the estimation results of the biomarkers by the plurality of biomarker estimation units for the first period and collects the normalized results for the second period; The biomarker calculation device according to claim 2 .

7. the graph creation unit creates a graph in which the calculation results of the ratio of the mental state for each of the second periods are arranged for a third period that is longer than the second period. The biomarker calculation device according to claim 1 .

8. the biomarker estimation unit estimates the biomarker based on a Lorenz plot of a first statistical parameter based on the biodata in the immediately preceding first period and a second statistical parameter based on the biodata in the first period to be estimated; The biomarker calculation device according to claim 1 .

9. the biomarker estimation unit outputs a value of 0 as the biomarker when the biomarker cannot be estimated. The biomarker calculation device according to claim 5 .

10. A biomarker calculation method executed by a biomarker calculation device, comprising: inputting biometric data of the subject measured by a biosensor; estimating a biomarker indicating a mental state of the subject using the input biometric data for a first period; collecting estimation results of a plurality of the biomarkers estimated for each of the first periods during a second period longer than the first period, and calculating a ratio of each of the mental conditions based on the occurrence frequency of the mental conditions indicated by each of the estimation results of the plurality of the biomarkers; generating a graph showing the proportion of each of said mental states; and displaying the created graph on a display device. Biometric index calculation method.

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