Biological data processing system, biological data processing method, and computer program

Abstract

The present disclosure provides a biological data processing system, a biological data processing method, and a computer program. The biological data processing system comprises: a classification unit that classifies a plurality of pieces of biological data to be classified, which are arranged in time series, into at least a first state group indicating a first state of mind and body and a second state group indicating a second state of mind and body to acquire a plurality of pieces of first biological data belonging to the first state group and a plurality of pieces of second biological data belonging to the second state group; a correction unit that executes a correction operation on the plurality of pieces of first biological data and the plurality of pieces of second biological data to calculate a plurality of pieces of time-series correction data; and an accumulation unit that accumulates the correction data along a time axis to calculate a plurality of pieces of time-series accumulated data. The correction operation is an operation that adjusts a balance in magnitude between the plurality of pieces of first biological data and the plurality of pieces of second biological data.

Description

Biometric data processing system, biometric data processing method, and computer program

[0001] The present disclosure relates to a biometric data processing system, a biometric data processing method, and a computer program.

[0002] The information processing system described in Patent Document 1 detects signs of drowsiness based on a cumulative pulse rate (cumulative value). Specifically, a calculation unit calculates the cumulative pulse rate by accumulating trend values ​​similar to the RRI value. The cumulative pulse rate can detect an accumulative situation rather than an instantaneous change.

[0003] Japanese Patent Application Laid-Open No. 2021-145851

[0004] However, the information processing system described in Patent Document 1 does not mention the possibility of the cumulative value diverging. In particular, a diverging cumulative value may have difficulty in representing the homeostatic mechanism of a living organism. In other words, a diverging cumulative value may have difficulty in reflecting the homeostasis of a living organism. Homeostasis is the tendency to maintain a constant physical and mental state in response to changes within the living organism and changes in the external environment of the living organism.

[0005] Therefore, the present disclosure has been made in consideration of the above-mentioned problems, and its purpose is to provide a biometric data processing system, a biometric data processing method, and a computer program that can derive cumulative data that reflects the homeostasis of a living organism.

[0006] According to the present disclosure, a biometric data processing system is provided, comprising: a classification unit that classifies multiple biometric data to be classified in chronological order into at least a first state group indicating a first physical state and a second state group indicating a second physical state, thereby acquiring multiple first biometric data belonging to the first state group and multiple second biometric data belonging to the second state group; a correction unit that calculates multiple time-series correction data by performing a correction operation on the multiple first biometric data and the multiple second biometric data; and an accumulation unit that calculates multiple time-series cumulative data by accumulating the correction data along a time axis, wherein the correction operation is an operation that adjusts the balance in magnitude between the multiple first biometric data and the multiple second biometric data.

[0007] Furthermore, according to the present disclosure, there is provided a biometric data processing method including the steps of: classifying a plurality of biometric data to be classified in chronological order into at least a first state group indicating a first physical state and a second state group indicating a second physical state, thereby obtaining a plurality of first biometric data belonging to the first state group and a plurality of second biometric data belonging to the second state group; calculating a plurality of corrected data in time series by performing a correction operation on the plurality of first biometric data and the plurality of second biometric data; and calculating a plurality of cumulative data in time series by accumulating the correction data along a time axis, wherein the correction operation is an operation that adjusts the balance in magnitude between the plurality of first biometric data and the plurality of second biometric data.

[0008] Furthermore, according to the present disclosure, a computer program is provided that causes a computer to perform the steps of: classifying multiple biometric data to be classified in chronological order into at least a first status group indicating a first physical or mental state and a second status group indicating a second physical or mental state, thereby acquiring multiple first biometric data belonging to the first status group and multiple second biometric data belonging to the second status group; calculating multiple time-series correction data by performing a correction operation on the multiple first biometric data and the multiple second biometric data; and calculating multiple time-series cumulative data by accumulating the correction data along a time axis, wherein the correction operation is an operation that adjusts the balance in magnitude between the multiple first biometric data and the multiple second biometric data.

[0009] According to the present disclosure, it is possible to provide a biological data processing system, a biological data processing method, and a computer program capable of deriving cumulative data that reflects the homeostasis of a living organism.

[0010] 1 is a block diagram showing an example of a configuration of a biometric data processing system according to an embodiment of the present invention. FIG. 2 is a flowchart showing an example of a biometric data processing method according to the embodiment. FIG. 3 is a block diagram showing an example of a configuration of a biometric data processing device according to the embodiment. FIG. 4 is a graph showing an example of biometric data calculated by a preprocessing unit according to the embodiment. FIG. 5 is a graph for explaining a classifying unit according to the embodiment. FIG. 6 is a graph showing an example of first biometric data and second biometric data calculated by a classifying unit according to the embodiment. FIG. 7 is a graph showing an example of corrected data calculated by a correcting unit according to the embodiment. FIG. 8 is a graph showing an example of accumulated data calculated by an accumulating unit according to the embodiment. FIG. 9 is a graph showing an example of accumulated data after statistical processing calculated by a statistical processing unit according to the embodiment. FIG. 10 is a flowchart showing an example of accumulation processing of biometric data by the biometric data processing device according to the embodiment. FIG. 11 is a diagram showing an example of a configuration of a biometric data analysis device according to the embodiment. FIG. 12 is a graph showing another example of accumulated data after statistical processing calculated by a statistical processing unit according to the embodiment. FIG. 13 is a graph showing an example of a frequency analysis result by a first analysis unit according to the embodiment. FIG. 14 is a flowchart showing an example of analysis processing of accumulated data by the biometric data analysis device according to the embodiment. FIG. 15 is a graph showing an example of biometric data according to a second modified example of the embodiment. FIG. 16 is a graph showing an example of first biometric data and second biometric data calculated by a classifying unit according to the second modified example of the embodiment. FIG. 17 is a graph showing an example of corrected data calculated by a correcting unit according to the second modified example of the embodiment. 10 is a graph showing an example of cumulative data calculated by an accumulator according to a second modified example of the embodiment. FIG. 11 is a graph showing an example of cumulative data after statistical processing calculated by a statistical processor according to a second modified example of the embodiment. FIG. 12 is a graph showing an example of biometric data according to a fourth modified example of the embodiment. FIG. 13 is a graph showing an example of cumulative data according to the fourth modified example of the embodiment.

[0011] Preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. In this specification and drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant description will be omitted.

[0012] FIG. 1 is a diagram showing an example of the configuration of a biometric data processing system 1 according to an embodiment of the present invention. As shown in FIG. 1, the biometric data processing system 1 receives biometric data from a client system 100. The biometric data processing system 1 then accumulates the biometric data to calculate cumulative data. In this case, the biometric data processing system 1 performs processing on the biometric data before accumulation to suppress divergence after accumulation. As a result, the biometric data processing system 1 can derive cumulative data that reflects the homeostasis of a living organism, which attempts to maintain a constant mental and physical state. This point will be described in more detail below.

[0013] The biometric data processing system 1 is connected to a network NW. The network NW includes, for example, the Internet, a closed network, a public telephone network, a local area network (LAN), and a short-distance wireless network.

[0014] At least one client system 100 is connected to the network NW. The client system 100 includes a cloud server 101, a plurality of first terminals 102, and a plurality of biometric information detection devices 103. The cloud server 101, the first terminals 102, and the biometric information detection devices 103 are connected to the network NW. A plurality of second terminals 200 are also connected to the network NW. In the following description, when it is not necessary to distinguish between the first terminal 102 and the second terminal 200, the first terminal 102 and the second terminal 200 may be collectively referred to as "terminal TM."

[0015] The biometric information detection device 103 detects biometric information from a subject. The biometric information detection device 103 has, for example, a sensor that detects biometric information contactlessly or non-contactly. The biometric information detection device 103 then generates multiple pieces of time-series biometric data indicating the biometric information. The biometric data will be described later. The biometric information detection device 103 is, for example, a wearable device. The wearable device may be, for example, a wristwatch type, a ring type, or a sticker type. The biometric information detection device 103 is synchronized with the first terminal 102 and transmits the biometric data to the first terminal 102. The first terminal 102 is, for example, a mobile terminal such as a smartphone. The biometric information detection device 103 may transmit the biometric data to the cloud server 101 or the biometric data processing system 1 via the network NW.

[0016] The biological information detection device 103 may be a wearable type, a portable type, a tabletop type, or a dedicated detector (measuring device) for detecting biological information. Alternatively, instead of the biological information detection device 103, the first terminal 102 may have the functions of the biological information detection device 103. Alternatively, the first terminal 102 may be a personal computer (PC), for example.

[0017] The first terminal 102 transmits the biometric data to the cloud server 101 via the network NW. The cloud server 101 transmits the biometric data to the biometric data processing system 1 via the network NW. The biometric data processing system 1 processes the biometric data.

[0018] It is also possible to have the first terminal 102 transmit the biometric data to the biometric data processing system 1 via the network NW without providing the cloud server 101 .

[0019] More specifically, the biometric data processing system 1 includes a relay server 2, a first database 3, a biometric data processing device 4, a second database 5, a biometric data analysis device 6, and an information providing server 7. The relay server 2, the first database 3, the biometric data processing device 4, the second database 5, the biometric data analysis device 6, and the information providing server 7 are connected to a network NW. The biometric data processing device 4 and the biometric data analysis device 6 are, for example, servers.

[0020] Each of the relay server 2 and the information providing server 7 includes a control unit, a communication unit, and a memory unit, and may also include an input unit and a display unit. The hardware configurations of the control unit, communication unit, memory unit, input unit, and display unit are similar to the hardware configurations of the control unit 41, communication unit 44, memory unit 45, input unit 42, and display unit 43 of the biometric data processing device 4 shown in FIG. 3 , which will be described later. The first database 3 and the second database 5 include at least a storage device such as a hard disk drive. The first database 3 and the second database 5 may have the same hardware configuration as the relay server 2 or the information providing server 7. The biometric data processing system 1 does not necessarily include all or some of the relay server 2, the first database 3, the second database 5, the biometric data analysis device 6, and the information providing server 7.

[0021] 2 is a flowchart showing an example of a data processing method executed by the biometric data processing system 1. The data processing method includes steps S1 to S7.

[0022] 1 and 2 , first, in step S1, the relay server 2 receives multiple pieces of time-series biometric data for each subject from the cloud server 101. The relay server 2 is equipped with, for example, an API (Application Programming Interface).

[0023] Next, in step S2, the first database 3 stores the plurality of time-series biometric data received by the relay server 2 for each subject.

[0024] Next, in step S3, the biometric data processing device 4 performs classification processing and correction calculations on the multiple time-series biometric data stored in the first database 3 for each subject.The biometric data processing device 4 then accumulates the multiple time-series biometric data that have been classified and corrected, and calculates multiple cumulative time-series data.By performing the classification processing and correction calculations, it is possible to derive cumulative data that reflects the homeostasis of the living body.This point will be described in detail later.

[0025] Next, in step S4, the second database 5 stores, for each subject, a plurality of time-series cumulative data calculated by the biometric data processing device 4.

[0026] Next, in step S5, the biological data analysis device 6 analyzes the plurality of time-series cumulative data stored in the second database 5 for each subject.

[0027] Next, in step S6, the second database 5 stores, for each subject, information indicating the analysis results of the plurality of time-series cumulative data by the biological data analysis device 6.

[0028] Next, in step S7, the information providing server 7 transmits information indicating the cumulative data and / or analysis results stored in the second database 5 to the second terminal 200 or the first terminal 102 via the network NW. The second terminal 200 is, for example, a terminal of a medical institution, a health management institution, or a research institution. The first terminal 102 is, for example, a terminal of the subject. In the example of FIG. 1 , the information indicating the cumulative data and / or analysis results is transmitted to the first terminal 102 via the cloud server 101, but may also be transmitted directly to the first terminal 102. The information providing server 7 corresponds to an example of an "output control unit" in the present disclosure.

[0029] Fig. 3 is a block diagram showing an example of the configuration of the biometric data processing device 4 of Fig. 1. As shown in Fig. 3, the biometric data processing device 4 includes a control unit 41, an input unit 42, a display unit 43, a communication unit 44, and a storage unit 45.

[0030] The input unit 42 is an input device for inputting various information to the control unit 41. For example, the input unit 42 is a keyboard and pointing device, or a touch panel.

[0031] The display unit 43 displays various types of information and is, for example, a liquid crystal display or an organic electroluminescence display.

[0032] The communication unit 44 is connected to the network NW. The communication unit 44 communicates with external devices connected to the network NW. The communication unit 44 is a communication device and includes, for example, a network interface controller.

[0033] The storage unit 45 includes a storage device and stores data and computer programs. The storage unit 45 includes a main storage device such as a semiconductor memory and an auxiliary storage device such as a semiconductor memory and a hard disk drive. The storage unit 45 may also include removable media such as an optical disk. The storage unit 45 may also be, for example, a non-transitory computer-readable storage medium.

[0034] The control unit 41 executes various calculations and includes processors such as a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit).

[0035] Specifically, the control unit 41 includes a classification unit 411, a correction unit 412, and an accumulation unit 413. The control unit 41 may include at least one of a pre-processing unit 410 and a statistical processing unit 414. For example, the control unit 41 functions as the pre-processing unit 410, the classification unit 411, the correction unit 412, the accumulation unit 413, and the statistical processing unit 414 by executing a computer program stored in the storage unit 45.

[0036] Next, the biometric data processing device 4 will be described in detail with reference to Figures 1 to 10. Hereinafter, the biometric data that the biometric data processing system 1 receives from the client system 100 will be referred to as "original biometric data D0," and the biometric data that the classification unit 411 processes will be referred to as "biometric data D1." The "original biometric data D0" is data output by the biometric information detection device 103 in Figure 1.

[0037] The original biometric data D0 and the biometric data D1 are vital information indicating the vital signs of a living organism or behavioral information indicating the behavior of the living organism. The living organism is typically a human. However, the living organism may also be an animal other than a human (e.g., another mammal).

[0038] The vital information is, for example, heart rate information, blood pressure information, respiratory information, body temperature information, blood information, or brain wave information.

[0039] The heartbeat information may be, for example, a heart rate, an R-R interval, a time domain index related to the heartbeat, a frequency domain index related to the heartbeat, a nonlinear index related to the heartbeat, electrocardiogram waveform information, or pulse wave information. The time domain index may be, for example, a standard deviation of NN intervals (SDNN), a root mean square of successive differences (RMSSD), a coefficient of variation of RR intervals (CVRR), a standard deviation of RR intervals (SDRR), a standard deviation of the average NN intervals for each 5-minute segment of a 24-hour HRV recording (SDANN), a number of pairs of successive NN intervals that differ by more than 50 ms (NN50), a proportion of NN50 divided by the total number of NN intervals (pNN50), an HRV triangular index (HTI), or a triangular interpolation of the NN interval histogram (TINN). The frequency domain index may be, for example, LF (power or peak of low frequency components), HF (power or peak of high frequency components), LF / HF, ULF (power of extremely low frequency components), or VLF (power of very low frequency components) of heart rate variability.Examples of nonlinear indices include entropy, SD1 (standard deviation in the direction perpendicular to y=x in a Poincaré plot), SD2 (standard deviation in the direction along y=x in a Poincaré plot), SD1 / SD2, ApEn (approximate entropy), SampEn (sample entropy), DFA α1 (detrended flux analysis, which describes short-term fluctuations), DFA α2 (detrended flux analysis, which describes long-term fluctuations), (DFA α1) / (DFA α2), CVI (cardiac vagal index), or CSI (cardiac sympathetic index). HRV stands for heart rate variability.

[0040] The respiratory information may be, for example, a respiratory rate, a respiratory rate, or a respiratory volume. The blood information may be, for example, an arterial blood oxygen saturation (e.g., SpO ２ ) or blood glucose level. The electroencephalogram information is, for example, an electroencephalogram or electroencephalogram attribute information. The electroencephalogram attribute information indicates attribute values ​​that can be acquired from an electroencephalogram. The electroencephalogram attribute information is, for example, sleep duration or sleep depth.

[0041] The behavior information is, for example, the number of steps, sleep information, exercise time, exercise intensity, energy consumption, body movement information, or body physical quantity information. The sleep information is, for example, sleep depth or sleep time. The body movement information is, for example, the movement amount and movement frequency of a specific body movement (e.g., turning over in sleep). The body physical quantity information is a physical quantity resulting from body movement. The physical quantity is, for example, acceleration or speed.

[0042] The original biometric data D0 and the biometric data D1 may be data obtained by subjecting vital information or behavioral information to primary processing. Examples of the primary processing include logarithmic transformation, sign reversal, removal of outliers, or missing data completion. Furthermore, the original biometric data D0 and the biometric data D1 may be data obtained by subjecting vital information or behavioral information to predetermined processing. The predetermined processing refers to a process of performing statistical processing on multiple pieces of vital information or multiple pieces of behavioral information for each of multiple time intervals arranged on a time axis, and calculating a statistical index for each time interval. The statistical index may be, for example, a mean value, a variance, a standard deviation, or a mean squared continuous error. The time interval is not particularly limited and may be, for example, in units of seconds, minutes, days, weeks, or months. Furthermore, the original biometric data D0 and the biometric data D1 may be data obtained by subjecting vital information or behavioral information to primary processing followed by predetermined processing, or data obtained by subjecting the vital information or behavioral information to predetermined processing followed by primary processing.

[0043] Furthermore, for information X of the vital information and behavioral information that cannot be directly obtained as time-series information, information X is calculated from multiple pieces of original data Y in each of multiple time intervals (similar to the time intervals described above) arranged on the time axis. As a result, multiple pieces of information X arranged in time series are obtained. Information X is, for example, a time domain index related to the heartbeat, a frequency domain index related to the heartbeat, or a nonlinear index related to the heartbeat. Original data Y is, for example, an R-R interval.

[0044] In the following description, unless otherwise specified, the original biometric data D0 is electrocardiogram waveform data of a subject, and the biometric data D1 is data indicating an R-R interval. The R-R interval is the time interval from one QRS wave to the next QRS wave in an electrocardiogram waveform.

[0045] First, the preprocessing unit 410 will be described with reference to FIGS. 1, 3, and 4. As shown in FIGS. 1 and 3, the preprocessing unit 410 acquires multiple pieces of original biometric data D0 arranged in chronological order from the first database 3. The preprocessing unit 410 then performs preprocessing operations on the multiple pieces of original biometric data D0 to calculate multiple pieces of biometric data D1 from the multiple pieces of original biometric data D0. The biometric data D1 is stored in the first database 3 or the second database 5. The preprocessing operations are operations that generate, from the multiple pieces of original biometric data D0, time-series biometric data D1 to be classified by the classification unit 411. Note that if the original biometric data D0 is the very data to be classified by the classification unit 411, the preprocessing operations are not performed by the preprocessing unit 410. In this case, the classification unit 411 processes the original biometric data D0 as biometric data D1.

[0046] As an example, the preprocessing calculation is a calculation for generating data indicating the R-R interval from the electrocardiogram waveform data. Fig. 4 is a graph showing a plurality of biological data D1 arranged in time series. The horizontal axis represents time t (minutes), and the vertical axis represents the R-R interval (milliseconds).

[0047] Next, the classification unit 411 will be described with reference to Fig. 3, Fig. 5, and Fig. 6. Fig. 5 is a graph for explaining the classification unit 411. The horizontal axis represents time t (minutes), and the vertical axis represents the R-R interval (milliseconds).

[0048] 3 and 5, the classification unit 411 classifies multiple pieces of chronologically arranged biometric data D1 into a first state group G1 indicating a first mental and physical state and a second state group G2 indicating a second mental and physical state. The first state is different from the second state. In FIGS. 5 to 7, black circles indicate the first state and white diamonds indicate the second state.

[0049] The first state is different from the second state. Specifically, the first state indicates a mental or physical state that is incompatible with the second state. For example, the first state may indicate a mental or physical state that is opposite to the second state. For example, the first state indicates either an active or inactive state of the mind or body. The active state includes, for example, a "stress" state, a "fatigue" state, an "exercise" state, an "active" state, or a "sympathetic dominant" state. The inactive state includes, for example, a "relaxation" state, a "recovery" state, a "rest" state, a "quiet" state, or a "parasympathetic dominant" state.

[0050] For example, the first state indicates either a stable state or an unstable state of the mind and body. For example, the first state indicates either an appropriate state or an inappropriate state of the mind and body. The appropriate state includes, for example, a "sufficient" state, an "appropriate load" state, or a "stable" state. The inappropriate state includes, for example, an "excessive load" state or a "deficient" state.

[0051] Below, examples of combinations of the first state and the second state are shown for each type of biometric data D1. When the biometric data D1 is a heart rate or an R-R interval, the first state is a "stress" state or a "fatigue" state, and the second state is a "relaxed" state or a "recovery" state. When the biometric data D1 is the number of steps, the first state is an "exercise" state, and the second state is a "rest" state. When the biometric data D1 is electrocardiogram waveform information, pulse wave information, or acceleration, the first state is an "active" state, and the second state is a "rest" state.

[0052] When the biometric data D1 is "LF / HF of heart rate variability," the first state is a "sympathetic dominant" state, and the second state is a "parasympathetic dominant" state. When the biometric data D1 is "a nonlinear index of heart rate variability," the first state is an "unstable (disorder)" state, and the second state is a "stable" state. When the biometric data D1 is "sleep time," the first state is a "sufficiency" state, and the second state is a "deficiency (debt)" state. When the biometric data D1 is "arterial blood oxygen saturation," the first state is a "stable" state, and the second state is a "deficiency" state.

[0053] Furthermore, if the biometric data D1 is "heart rate" during exercise, the first state is an "excessive load" state and the second state is an "appropriate load" state. For diseases in which excessive load on the body can cause attacks, inflammation, etc., and insufficient exercise can cause reduced cardiopulmonary function and a limited range of joint motion, the "heart rate" is limited to "during exercise" in order to evaluate the degree of accumulation of daily exercise load. If the biometric data D1 is "arterial blood oxygen saturation" during a period in which the number of steps is less than a certain number, the first state is a "stable" state and the second state is a "deficient" state. In addition to oxygen consumption during exercise, the "arterial blood oxygen saturation" is limited to "a period in which the number of steps is less than a certain number" in order to evaluate the state of adaptation to a low-oxygen environment, such as heat stroke, respiratory attacks, and sleep apnea.

[0054] In the example of FIG. 5, the first state indicates an active state of mind and body (for example, stress or fatigue), and the second state indicates an inactive state of mind and body (for example, relaxation or recovery).

[0055] The first state and the second state may be states that indicate different levels of the biological state. For example, the "REM sleep" state and the "non-REM sleep" state indicate different levels of the mental and physical state. In this way, the first state indicates a mental and physical state that is incompatible with the second state.

[0056] Next, the classification unit 411 will be described in detail. The classification unit 411 classifies multiple pieces of biometric data D1 into a first status group G1 and a second status group G2, thereby acquiring (calculating) multiple pieces of first biometric data B1 belonging to the first status group G1 and multiple pieces of second biometric data B2 belonging to the second status group G2. The first biometric data B1 has one of positive and negative signs. The second biometric data B2 has the other of positive and negative signs. FIG. 6 is a graph showing the first biometric data B1 and the second biometric data B2 arranged in chronological order. The horizontal axis represents time t (minutes), and the vertical axis represents the relative value of the R-R interval (= D1 - C1). In the example of FIG. 6, the first biometric data B1 is a positive value with a positive sign. The second biometric data B2 is a negative value with a negative sign.

[0057] 5 , the classification unit 411 calculates the first classification standard information C1 based on the plurality of biometric data D1 during a specific period T1. The specific period T1 is a period for calculating the first classification standard information C1 and a ratio R (described later). The first classification standard information C1 is, for example, the average value, median, mode, or percentile value of the plurality of biometric data D1 during the specific period T1. In the example of FIG. 5 , the first classification standard information C1 indicates the median.

[0058] The classification unit 411 compares the first classification standard information C1 with the biometric data D1 to be classified, and classifies the biometric data D1 based on the comparison result. The biometric data D1 to be classified is the biometric data D1 during an observation period T2. In other words, the observation period T2 of the biometric data D1 is a period during which a plurality of cumulative data D3, which will be described later, is calculated. In the present embodiment, as an example, the observation period T2 of the biometric data D1 is the same as the specific period T1 for calculating the first classification standard information C1.

[0059] As an example, the classification unit 411 subtracts the first classification standard information C1 from each piece of biometric data D1. Then, as shown in Fig. 6, the classification unit 411 classifies each piece of biometric data D1 into a first status group G1 or a second status group G2 depending on whether each subtraction result (= D1 - C1) indicates a positive or negative value. In Fig. 6, the classification unit 411 sets the subtraction result indicating a positive value to the first biometric data B1, and sets the subtraction result indicating a negative value to the second biometric data B2.

[0060] The classification unit 411 may subtract each piece of biometric data D1 from the first classification standard information C1.

[0061] Here, the classification unit 411 may include a trained model. The trained model is a computer program. The trained model is constructed by performing machine learning using a plurality of pieces of biometric data D1 arranged in chronological order, a plurality of pieces of first biometric data B1, and a plurality of pieces of second biometric data B2 as training data. The trained model includes, for example, a neural network. The trained model inputs a plurality of pieces of biometric data D1 and outputs a plurality of pieces of first biometric data B1 and a plurality of pieces of second biometric data B2. In other words, the classification unit 411 inputs a plurality of pieces of biometric data D1 to the trained model and acquires a plurality of pieces of first biometric data B1 and a plurality of pieces of second biometric data B2 from the trained model.

[0062] Next, the correction unit 412 will be described with reference to FIGS. 3, 6, and 7. As shown in FIGS. 3 and 6, the correction unit 412 calculates a plurality of time-series corrected data D2 by performing a correction calculation on a plurality of first biometric data B1 and a plurality of second biometric data B2 to be corrected. In this embodiment, the first biometric data B1 and second biometric data B2 to be corrected are the first biometric data B1 and second biometric data B2 during the observation period T2. FIG. 7 is a graph showing the time-series corrected data D2. The horizontal axis represents time t (minutes), and the vertical axis represents the corrected relative value of the R-R interval.

[0063] 6 , the correction calculation is a calculation for adjusting the balance in magnitude between the plurality of first biometric data B1 and the plurality of second biometric data B2. In other words, the correction calculation is a calculation for adjusting the ratio R (=FS2 / FS1) of the sum FS2 of the absolute values ​​of the plurality of second biometric data B2 to the sum FS1 of the absolute values ​​of the plurality of first biometric data B1. Preferably, the correction calculation is a calculation for setting the ratio R (=FS2 / FS1) of the sum FS2 of the absolute values ​​of the plurality of second biometric data B2 to the sum FS1 of the absolute values ​​of the plurality of first biometric data B1 to essentially "1" during the observation period T2.

[0064] In this preferred example, the correction calculation includes a process of calculating the ratio R and a process of correcting the first biometric data B1 or the second biometric data B2 based on the ratio R. That is, the correction unit 412 calculates the ratio R for the observation period T2 (specific period T1). Then, for example, if the sum FS1 is greater than the sum FS2, the correction unit 412 corrects each piece of second biometric data B2 for the observation period T2 by multiplying the reciprocal of the ratio R (1 / R) by the second biometric data B2. Thus, the correction unit 412 obtains multiple corrected pieces of second biometric data B2# as the multiplication result. As a result, after the correction calculation is performed, the ratio R becomes "1." As shown in FIG. 7 , the multiple pieces of first biometric data B1 and the corrected multiple pieces of second biometric data B2# constitute multiple pieces of corrected data D2.

[0065] For example, when the sum FS1 is greater than the sum FS2, the correction unit 412 may multiply each of the first biometric data B1 in the observation period T2 by the ratio R to obtain a plurality of corrected first biometric data B1# (not shown) as a multiplication result. In this case, the corrected plurality of first biometric data B1# and the plurality of second biometric data B2 constitute a plurality of corrected data D2.

[0066] Here, since it is sufficient that the ratio R after the correction calculation is substantially "1," the correction calculation may include a process of calculating a first correction parameter and a second correction parameter based on the ratio (FS1:FS2) between the sum FS1 and the sum FS2, and a process of correcting the first biometric data B1 and the second biometric data B2 based on the first correction parameter and the second correction parameter. In this case, the first biometric data B1 is corrected by multiplying it by the first correction parameter, and the second biometric data B2 is corrected by multiplying it by the second correction parameter. In this case, the corrected plurality of first biometric data B1# and the corrected plurality of second biometric data B2# constitute the plurality of corrected data D2. For example, when FS1:FS2 = 1:2, the first correction parameter is "4" and the second correction parameter is "2."

[0067] The correction unit 412 may also calculate a first approximation function that approximates the plurality of first biometric data B1 and a second approximation function that approximates the plurality of second biometric data B2. The correction unit 412 then analytically calculates a first area based on the first approximation function and analytically calculates a second area based on the second approximation function. Furthermore, the correction unit 412 calculates a ratio R of the absolute value of the second area (which essentially corresponds to the sum of the absolute values ​​of the plurality of second biometric data B2) to the absolute value of the first area (which essentially corresponds to the sum of the absolute values ​​of the plurality of first biometric data B1). The correction unit 412 then corrects the first biometric data B1 or the second biometric data B2 based on the ratio R, as described above. Note that, as described above, the correction calculation may include a process of calculating a first correction parameter and a second correction parameter based on the ratio between the absolute value of the first area and the absolute value of the second area, and a process of correcting the first biometric data B1 and the second biometric data B2 based on the first correction parameter and the second correction parameter.

[0068] The correction unit 412 may divide the observation period T2 into multiple time intervals TG, calculate a representative value of two or more correction data D2 for each time interval TG, and set the representative value as new correction data D2. The representative value may be, for example, an average value, a median value, or a mode value. The time interval TG is not particularly limited and may be, for example, in units of seconds, minutes, days, weeks, or months.

[0069] Next, the accumulator 413 will be described with reference to FIGS. 3, 7, and 8. As shown in FIGS. 3 and 7, the accumulator 413 calculates a plurality of time-series accumulated data D3 by accumulating the time-series corrected data D2 along the time axis. The accumulated data D3 may be referred to as a corrected accumulated index. FIG. 8 is a graph showing the time-series accumulated data D3. The horizontal axis indicates time t (minutes), and the vertical axis indicates the accumulated value of the corrected relative value of the R-R interval. In this embodiment, the corrected data D2 to be accumulated is the corrected data D2 for the observation period T2. Therefore, the classification unit 411 calculates the accumulated data D3 at each time t during the observation period T2. In other words, the accumulated data D3 at each time t indicates the accumulated value of the corrected data D2 from the start of accumulation to that time t.

[0070] As an example, the accumulator 413 calculates a cumulative sum D3j as the cumulative data D3 using equation (1). D3j represents the cumulative sum at time t=j in the observation period T2. D2i represents the corrected data D2 at time t=i. ts represents the start time of the accumulation. The accumulator 413 calculates multiple cumulative sums D3j in time series during the observation period T2 while updating j.

[0071]

[0072] The accumulator 413 may calculate the accumulated data D3 by performing numerical integration on the corrected data D2.

[0073] As described above with reference to FIGS. 3 to 8 , according to this embodiment, the correction calculation by the correction unit 412 is a calculation that adjusts the balance in magnitude between the first biometric data B1 and the second biometric data B2, which represent different states. Therefore, when the accumulator 413 accumulates the corrected data D2 by the correction unit 412, divergence of the accumulated data D3 can be suppressed. Correction that suppresses divergence, i.e., correction that adjusts the balance, can give the accumulated data D3 characteristics similar to the biological mechanism that ensures homeostasis. In other words, it is possible to derive accumulated data D3 that reflects the biological homeostasis. Biological homeostasis refers to the biological tendency to maintain a constant mental and physical state in response to changes within the organism and changes in the external environment.

[0074] In this embodiment, the cumulative data D3 is calculated by accumulating multiple sets of corrected data D2 indicating the first and second mental and physical states on a time axis. Therefore, the cumulative data D3 reflects the mental and physical states (first and second states) accumulated over time. As a result, the cumulative data D3 quantitatively and chronologically represents the mental and physical states (first and second states) accumulated over time.

[0075] For example, living organisms such as humans have a biological mechanism that alternates between a first state, such as stress and fatigue, and a second state, such as relaxation and recovery, thereby ensuring homeostasis, which attempts to maintain a constant mental and physical state. Therefore, if accumulating biological data causes the cumulative data to diverge toward one of the first and second states, the correlation with the biological mechanism is lost. Therefore, in this embodiment, a correction calculation is performed to suppress the divergence of the cumulative data D3 and ensure the correlation between the cumulative data D3 and the biological mechanism. Furthermore, the cumulative data D3 quantitatively and chronologically represents the first state, such as stress and fatigue, and the second state, such as relaxation and recovery, accumulated over time.

[0076] In particular, in this embodiment, the correction calculation refers to a calculation that sets the ratio R of the sum FS2 of the absolute values ​​of the second biometric data B2 to the sum FS1 of the absolute values ​​of the first biometric data B1 to essentially "1." In other words, the correction calculation refers to a calculation that prevents the cumulative data D3 from diverging. In further other words, the correction calculation refers to a calculation that essentially prohibits the cumulative data D3 from diverging. Therefore, it is possible to more effectively prevent the cumulative data D3 from diverging, and to derive cumulative data D3 that better reflects the homeostasis of the living body.

[0077] The ratio R being substantially "1" does not necessarily mean that the ratio R is "1" but may be any ratio R that can reflect constancy in the cumulative data D3. For example, the ratio R may be "0.8 or more and 1.2 or less," or "0.9 or more and 1.1 or less." The closer the ratio R is to "1," the better, but the most preferable case is "1."

[0078] The calculation that substantially prohibits divergence includes not only the case where divergence is prohibited but also the case where divergence is prohibited to the extent that constancy can be reflected in the cumulative data D3. In other words, the calculation that substantially prohibits divergence includes the calculation that suppresses divergence. However, it is most preferable that the correction calculation is the calculation that prohibits divergence.

[0079] In this embodiment, the first state in classification by the classification unit 411 indicates a physical and mental state opposite to the second state. Therefore, for example, the biological mechanism that "a living organism such as a human maintains homeostasis by balancing a first state such as stress and fatigue with a second state such as relaxation and recovery" can be effectively reflected in the correction data D2 and the cumulative data D3.

[0080] In particular, the first state indicates either an active or inactive state of the body or mind, either a stable or unstable state of the body or mind, or either an appropriate or inappropriate state of the body or mind. Furthermore, the second state indicates a state opposite to the first state. Therefore, for example, the biological mechanism that "a living organism such as a human maintains homeostasis by balancing an active state and an inactive state, a stable state and an unstable state, or an appropriate state and an inappropriate state" can be effectively reflected in the correction data D2 and the cumulative data D3.

[0081] Furthermore, in this embodiment, the classification unit 411 compares the first classification standard information C1 with the biometric data D1 and classifies the biometric data D1 based on the comparison result, thereby enabling classification of the biometric data D1 through simple processing.

[0082] Furthermore, in this embodiment, the classification unit 411 compares the biometric data D1 to be classified during the observation period T2, which is the same as the specific period T1 used to calculate the first classification reference information C1, with the first classification reference information C1, and classifies the biometric data D1 based on the comparison result. The correction unit 412 then performs a correction calculation on the multiple first biometric data B1 and multiple second biometric data B2 during the observation period T2 based on the multiple first biometric data B1 and multiple second biometric data B2 during the observation period T2. The accumulation unit 413 then accumulates the correction data D2 to calculate multiple cumulative data D3 in time series at multiple times during the observation period T2. In this manner, in this embodiment, the observation period T2 and the specific period T1 are matched. Therefore, the relative relationship between the first state and the second state within the observation period T2 can be reflected in the cumulative data D3. As a result, for example, periods of high physical and mental stress (first state) can be compared with periods of low physical and mental stress (second state) during the observation period T2.

[0083] Here, the lengths of the specific period T1 and the observation period T2 are not particularly limited, and may be measured in units of seconds, minutes, hours, weeks, months, or years.

[0084] Furthermore, for example, if the observation period T2 changes, the first classification standard information C1 and the ratio R are calculated for the new observation period T2 (i.e., the new specific period T1) even for the same living organism. In this case, for example, multiple consecutive observation periods T2 are set at 30-day intervals, and the calculation of the first classification standard information C1 and the ratio R for 30 days is repeated every 30 days.

[0085] Furthermore, in this embodiment, the specific period T1 and the observation period T2 may be periods during which the mental and physical state is in a specific state. Therefore, the preprocessing unit 410, the classification unit 411, the correction unit 412, and the accumulation unit 413 perform their respective processes during periods during which the mental and physical state is in a specific state. Therefore, the cumulative data D3 is calculated under the specific condition of "periods during which the mental and physical state is in a specific state." As a result, cumulative data D3 that is more specifically tailored to the intended use can be obtained. The specific state is not particularly limited, but may be, for example, a sleeping state, a waking state, a state in which the number of steps is less than a certain number, a walking state, a state in which the exercise intensity is equal to or greater than a certain level, a state in which the exercise is being performed, or a state of rest (non-exercise). The specific state may also be a combination of two or more states, such as a state of resting while awake. Thus, the specific state may be, for example, a state related to physical behavior.

[0086] Next, the statistical processing unit 414 will be described with reference to FIGS. 3, 8, and 9. FIG. 9 is a graph showing cumulative data D4 after statistical processing arranged in chronological order. The horizontal axis represents time t (minutes), and the vertical axis represents the statistically processed value relative to the cumulative value. As shown in FIGS. 3, 8, and 9, the statistical processing unit 414 calculates multiple cumulative data D4 after statistical processing by performing statistical processing on multiple cumulative data D3. Therefore, according to this embodiment, it is easy to grasp the trends of the multiple cumulative data D4. The statistical processing may be, for example, a centering process, a standardization process, or a normalization process. The cumulative data D3 to be statistically processed is the cumulative data D3 during the observation period T2.

[0087] Centering is a process of calculating a centered value Cj of cumulative data D3 shown in equation (2). In this case, the centered value Cj is a statistically processed value and represents cumulative data D4 after statistical processing. In equation (2), Cj represents the centered value at time t = j. D3j represents the value of cumulative data D3 at time t = j. Av represents the average value of multiple cumulative data D3 during observation period T2. In centering, difference values ​​are distributed around the average value Av without changing units, making it easy to intuitively grasp cumulative data D4.

[0088] Cj=D3j−Av…(2)

[0089] The standardization process is a process of calculating a standardized value Sj of the cumulative data D3 shown in formula (3). In this case, the standardized value Sj is a statistically processed value and represents the cumulative data D4 after statistical processing. In formula (3), Sj represents the standardized value at time t = j. σ represents the standard deviation of the multiple cumulative data D3 during the observation period T2. In the example of Figure 9, the statistically processed value on the vertical axis is the standardized value. In the standardization process, the unit "1" becomes "1 standard deviation," making the cumulative data D4 easier to understand statistically.

[0090] Sj=(D3j-Av) / σ...(3)

[0091] The normalization process is a process of calculating a normalized value Nj of the cumulative data D3 shown in equation (4). In this case, the normalized value Nj is a statistically processed value and represents the cumulative data D4 after statistical processing. In equation (4), Dmn is the minimum value of the cumulative data D3 during the observation period T2. Dmx is the maximum value of the cumulative data D3 during the observation period T2. In the normalization process, each cumulative data D4 takes a value between 0 and 1. In this case, for example, it can be expressed as a percentage (%), which makes it easier to understand and interpret.

[0092] Nj=(D3j-Dmn) / (Dmx-Dmn)...(4)

[0093] As described above with reference to FIG. 9 , according to this embodiment, statistical processing is performed on multiple cumulative data D3. In this case, as an example, the statistical processing unit 414 performs centering, standardization, or normalization on the cumulative data D3 of a specific living body based on a statistical index calculated from the cumulative data D3 of the specific living body. The statistical index is, for example, the average value Av, the standard deviation σ, the minimum value Dmn, or the maximum value Dmx. According to this preferred example, statistical processing is performed using a statistical index based on the cumulative data D3 of only the specific living body. Therefore, in the cumulative data D4, it is possible to easily determine when a specific living body, for example, an individual subject, is in the first state and when it is in the second state, regardless of other living bodies of the same type.

[0094] As another example, the statistical processing unit 414 may perform centering, standardization, or normalization on the cumulative data D3 of a specific organism among the multiple organisms of the same type, based on a statistical index calculated from the cumulative data D3 of the multiple organisms of the same type. According to this preferred example, the statistical processing is performed using a statistical index based on the cumulative data D3 of a group (multiple organisms of the same type), so that in the time-series cumulative data D4, it is possible to determine when a specific organism, for example, an individual subject, is in the first state and when it is in the second state, based on the group.

[0095] As described above, statistical processing is performed on the cumulative data D from various viewpoints, but the statistical processing does not necessarily have to be performed on the cumulative data D.

[0096] Next, the flow of processing by the biometric data processing device 4 will be described with reference to FIGS. 3 and 10. FIG. 10 is a flowchart showing the "biometric data accumulation processing" of step S3 in FIG. 2. As shown in FIG. 10, the "biometric data accumulation processing" includes steps S11 to S15. A computer program stored in the storage unit 45 causes the control unit 41 to execute steps S11 to S15. In other words, the computer program product realizes steps S11 to S15 when the computer program is executed by the control unit 41. The control unit 41 corresponds to an example of a "computer" in the present disclosure.

[0097] As shown in FIGS. 3 and 10, first, in step S11, the pre-processing unit 410 generates a plurality of pieces of biometric data D1 (FIG. 4) based on a plurality of pieces of original biometric data D0 acquired from the first database 3.

[0098] Next, in step S12, the classification unit 411 classifies the multiple biometric data D1 into a first status group G1 and a second status group G2, thereby calculating multiple first biometric data B1 belonging to the first status group G1 and multiple second biometric data B2 belonging to the second status group G2 (Figure 6).

[0099] Next, in step S13, the correction unit 412 calculates corrected data D2 by executing a correction calculation on the first biometric data B1 and the second biometric data B2 (FIG. 7).

[0100] Next, in step S14, the accumulator 413 accumulates the correction data D2 along the time axis to calculate a plurality of time-series accumulated data D3 (FIG. 8).

[0101] Next, in step S15, the statistical processing unit 414 performs statistical processing on the plurality of cumulative data D3 to calculate cumulative data D4 after statistical processing ( FIG. 9 ). Then, the “biometric data cumulative processing” is completed, and the process proceeds to step S4 in FIG. 2.

[0102] Next, details of the biological data analysis device 6 in Fig. 1 will be described with reference to Fig. 11 to Fig. 14. Fig. 11 is a diagram showing an example of the configuration of the biological data analysis device 6 in Fig. 1. As shown in Fig. 11, the biological data analysis device 6 includes a control unit 61, an input unit 62, a display unit 63, a communication unit 64, and a storage unit 65.

[0103] The hardware configurations of the control unit 61, input unit 62, display unit 63, communication unit 64, and memory unit 65 are similar to the hardware configurations of the control unit 41, input unit 42, display unit 43, communication unit 44, and memory unit 45 in Figure 3, respectively.

[0104] The control unit 61 includes an analysis unit 610 and a support unit 620. For example, the control unit 61 functions as the analysis unit 610 and the support unit 620 by executing a computer program stored in the storage unit 65. The analysis unit 610 includes at least one of a first analysis unit 611, a second analysis unit 612, and a third analysis unit 613. The third analysis unit 613 will be described later in a fifth modified example.

[0105] The first analyzer 611 directly or indirectly performs frequency analysis on a plurality of pieces of time-series cumulative data D3 ( FIG. 8 ). "Direct frequency analysis" refers to directly analyzing the frequency of the cumulative data D3. "Indirect frequency analysis" refers to performing a predetermined process on the cumulative data D3 and performing frequency analysis on the data calculated. In this case, the predetermined process is, for example, statistical processing by the statistical processing unit 414. The first analyzer 611 then performs frequency analysis on a plurality of pieces of statistically processed cumulative data D4 ( FIG. 9 ) calculated by the statistical processing unit 414.

[0106] For example, the first analysis unit 611 performs frequency analysis by applying a discrete Fourier transform, a fast Fourier transform, or an autoregressive model to the cumulative data D3 or the cumulative data D4 after statistical processing.

[0107] According to this embodiment, the cumulative data D3 and cumulative data D4, which represent biological homeostasis, can be analyzed in the frequency domain based on the results of frequency analysis by the first analysis unit 611. As an example, a case will be described in which a woman's menstrual cycle, which has a cycle of 28 days, is calculated. In this case, for example, the observation period T2 (specific period T1) is set to four months. The biological data D1 is the average heart rate during daily sleep. The first analysis unit 611 then performs a discrete Fourier transform on the cumulative data D3 based on the biological data D1 to calculate the power spectral density. As a result, it can be observed that the 28-day cycle wave (power spectral density) is the strongest.

[0108] Another example will be described. Fig. 12 is a graph showing cumulative data D4 after statistical processing. The horizontal axis indicates time t (months), and the vertical axis indicates the statistically processed value. In this case, the statistically processed value indicates a value calculated by performing statistical processing on the cumulative value of the corrected relative values ​​of the R-R interval. In the example of Fig. 12, the statistically processed value is a standardized value.

[0109] The first analyzer 611 calculates the intensity of each wavelength component (each frequency component) by performing a discrete Fourier transform on the multiple pieces of accumulated data D4 in Fig. 12. Fig. 13 is a graph showing the results of the frequency analysis by the first analyzer 611. The horizontal axis represents wavelength (unit: days), and the vertical axis represents the intensity of the wavelength component.

[0110] As can be seen from FIGS. 12 and 13, the first analyzer 611 can detect that the 30-day cycle wave (wavelength component) is the strongest in the RR interval over a one-year period.

[0111] The information providing server 7 (FIG. 1) transmits the result of the frequency analysis by the first analysis unit 611 to the terminal TM, thereby causing the terminal TM to output the result of the frequency analysis as an image or sound (step S7 in FIG. 2). As a result, the user of the terminal TM can understand, use, and analyze the result of the frequency analysis.

[0112] The second analysis unit 612 directly or indirectly analyzes multiple pieces of cumulative data D3 in a time series from the past and evaluates the past or present mental and physical state based on the analysis results. And / or the second analysis unit 612 directly or indirectly analyzes multiple pieces of cumulative data D3 in a time series from the past and predicts the future mental and physical state based on the analysis results. "Direct analysis" refers to directly analyzing the cumulative data D3. "Indirect analysis" refers to analyzing data obtained by performing a predetermined process on the cumulative data D3. In this case, the predetermined process is, for example, statistical processing by the statistical processing unit 414.

[0113] The information providing server 7 transmits information indicating the evaluation result and / or prediction result by the second analysis unit 612 to the terminal TM, thereby causing the terminal TM to output the evaluation result and / or prediction result as an image or sound (step S7 in FIG. 2 ). As a result, according to this embodiment, the user of the terminal TM can understand, use, and analyze the evaluation result and / or prediction result.

[0114] The support unit 620 generates support information for supporting the user based on the result of the frequency analysis by the first analysis unit 611 or the evaluation or prediction result by the second analysis unit 612. The support information is, for example, information that supports the user in maintaining or recovering their health. The support information may include, for example, warning information.

[0115] The information providing server 7 transmits the support information generated by the support unit 620 to the terminal TM, thereby causing the terminal TM to output the support information as an image or sound (step S7 in FIG. 2). As a result, according to this embodiment, the user of the terminal TM can take action based on the support information.

[0116] The information providing server 7 transmits the cumulative data D3 generated by the biometric data processing device 4 and / or information indicating the characteristics of the cumulative data D3 to the terminal TM, thereby causing the terminal TM to output the cumulative data D3 and / or the information indicating the characteristics as an image or sound (step S7 in FIG. 2). As a result, according to this embodiment, the user of the terminal TM can understand, use, and analyze the cumulative data D3 and / or the information indicating the characteristics. The information indicating the characteristics of the cumulative data D3 is, for example, cumulative data D4 after statistical processing.

[0117] Next, the flow of processing by the biological data analysis device 6 will be described with reference to FIGS. 3 and 14. FIG. 14 is a flowchart showing an example of the "accumulated data analysis process" of step S5 in FIG. 2. As shown in FIG. 14, the "accumulated data analysis process" includes steps S21 to S24. A computer program stored in the storage unit 65 causes the control unit 61 to execute steps S21 to S24. In other words, the computer program product realizes steps S21 to S24 when the computer program is executed by the control unit 61. The control unit 61 corresponds to an example of a "computer" in the present disclosure.

[0118] As shown in FIGS. 3 and 14, first, in step S21, the first analyzer 611 directly or indirectly performs frequency analysis on the accumulated data D3.

[0119] Next, in step S22, the second analysis unit 612 directly or indirectly analyzes the cumulative data D3, and evaluates the past or present mental and physical state based on the analysis results.

[0120] Next, in step S23, the second analysis unit 612 directly or indirectly analyzes the cumulative data D3, and predicts the future physical and mental state based on the analysis results.

[0121] Next, in step S24, the support unit 620 generates support information for supporting the user based on the cumulative data D3, the cumulative data D4, the result of the frequency analysis by the first analysis unit 611, the evaluation result by the second analysis unit 612, and / or the prediction result by the second analysis unit 612. Then, the "accumulated data analysis process" is completed, and the process proceeds to step S6 in FIG. 2.

[0122] (First Modification) In the above embodiment, the specific period T1 for calculating the first classification standard information C1 and the ratio R is the same as the observation period T2 of the biometric data D1. In contrast, in a first modification of the present embodiment, the specific period T1 and the observation period T2 are different. The following mainly describes the differences with reference to FIG. 3 .

[0123] Specifically, the classification unit 411 compares the biometric data D1 to be classified during an observation period T2, which is different from the specific period T1, with the first classification standard information C1 calculated during the specific period T1, and classifies the biometric data D1 based on the comparison result. Therefore, during the observation period T2, multiple pieces of first biometric data B1 and multiple pieces of second biometric data B2 to be corrected are calculated.

[0124] Then, the correction unit 412 performs a correction calculation on the plurality of first biometric data B1 and the plurality of second biometric data B2 to be corrected during the observation period T2, based on the plurality of first biometric data B1 and the plurality of second biometric data B2 calculated from the plurality of biometric data D1 during the specific period T1. That is, the correction unit 412 corrects the plurality of first biometric data B1 or the plurality of second biometric data B2 to be corrected during the observation period T2, based on the ratio R calculated from the plurality of first biometric data B1 and the plurality of second biometric data B2 during the specific period T1. Therefore, a plurality of correction data D2 is calculated during the observation period T2.

[0125] The accumulator 413 then accumulates the correction data D2 to calculate multiple pieces of time-series accumulated data D3 at multiple times during the observation period T2.

[0126] As described above, according to the first modification, the accumulated data D3 for the observation period T2 can be derived based on the biometric data D1 for the specific period T1. For example, the biometric data processing device 4 sets the specific period T1 to a certain period before a medical procedure, and sets the observation period T2 to a certain period after the medical procedure. The medical procedure may be, for example, treatment or surgery. Furthermore, for example, the biometric data processing device 4 sets the specific period T1 to "last month" and the observation period T2 to "this month."

[0127] For example, if the specific period T1 is set to a period during which the living body is healthy, the ratio R is calculated as a parameter of homeostasis in the healthy state. Therefore, if the observation period T2 is set to a period during which the living body is ill, classification based on the ratio R in the healthy state is performed during the ill period. As a result, the condition of the ill period can be evaluated based on the healthy state. In this way, the homeostasis parameter (ratio R) established during the specific period T1 can be used to evaluate the disturbance or breakdown of the living body's homeostasis (e.g., illness) from the perspective of homeostasis. From this perspective, even in the first modification, the cumulative data D3 can be considered to indirectly reflect the homeostasis of the living body. Homeostasis may be directly reflected in the cumulative data D3, as in the above embodiment, or indirectly reflected in the cumulative data D3, as in the first modification.

[0128] Furthermore, part or all of the specific period T1 may overlap with part of the observation period T2. For example, the biometric data processing device 4 sets the specific period T1 to "one month in April of each year" and sets the observation period T2 to "the fiscal year from April to March of the following year." Note that, for example, part of the specific period T1 may overlap with the entire observation period T2.

[0129] The length of the specific period T1 and the length of the observation period T2 may be the same or different.

[0130] The specific period T1 is a past period. The observation period T2 is a period that follows the specific period T1 on the time axis. In this case, the observation period T2 is, for example, a past period.

[0131] Furthermore, according to the first modification, the first classification standard information C1 and the ratio R are determined in advance. Therefore, the preprocessing unit 410, the classification unit 411, the correction unit 412, and the accumulating unit 413 can perform their respective processes even during the observation period T2. As a result, the accumulated data D3 can be calculated sequentially even during the observation period T2. In this case, the observation period T2 is a period from the past to the future.

[0132] In particular, during the observation period T2, the preprocessing unit 410, the classification unit 411, the correction unit 412, and the accumulation unit 413 perform real-time processing. As a result, according to the first modification, the accumulated data D3 can be calculated in real time. Real-time processing means that processing is performed immediately in response to reception of the original biometric data D0.

[0133] Specifically, the preprocessing unit 410 calculates biometric data D1 by processing the original biometric data D0 in real time. The classification unit 411 calculates first biometric data B1 and second biometric data B2 by processing the biometric data D1 in real time. The correction unit 412 calculates corrected data D2 by processing the first biometric data B1 and the second biometric data B2 in real time. The accumulation unit 413 calculates accumulated data D3 by processing the corrected data D2 in real time. The statistical processing unit 414 calculates post-statistical-process accumulated data D4 by processing the accumulated data D3 in real time.

[0134] The second analysis unit 612 directly or indirectly analyzes the plurality of time-series cumulative data D3 or D4 in real time and evaluates the current mental and physical state based on the analysis results. And / or the second analysis unit 612 directly or indirectly analyzes the plurality of time-series cumulative data D3 or D4 in real time and predicts the future mental and physical state based on the analysis results. The "direct analysis" and "indirect analysis" are the same as the "direct analysis" and "indirect analysis" in the above embodiment.

[0135] The information providing server 7 (FIG. 1) transmits information indicating the evaluation result and / or prediction result by the second analysis unit 612 to the terminal TM in real time, thereby causing the terminal TM to output the evaluation result and / or prediction result as an image or sound (step S7 in FIG. 2). As a result, according to the first modification, the user of the terminal TM can understand, use, and analyze the evaluation result and / or prediction result in real time.

[0136] The support unit 620 generates support information for supporting the user in real time based on the result of the frequency analysis by the first analysis unit 611 or the evaluation or prediction result by the second analysis unit 612. The support information is, for example, information that supports the user in maintaining or recovering their health. The support information may include, for example, warning information.

[0137] The information providing server 7 transmits the support information generated by the support unit 620 to the terminal TM in real time, thereby causing the terminal TM to output the support information as an image or sound (step S7 in FIG. 2 ). As a result, according to this embodiment, the user of the terminal TM can immediately take action based on the support information (e.g., warning information).

[0138] The observation period T2 may be a period before the specific period T1 on the time axis. In the first modification, the specific period T1 and the observation period T2 are different. However, the first modification also uses the ratio R, which is a correction parameter calculated in the correction calculation, and therefore divergence of the cumulative data D3 for the observation period T2 is suppressed.

[0139] (Second Modification) In the above embodiment, the biometric data D1 is classified based on the first classification standard information C1. In contrast, in a second modification of this embodiment, the biometric data D1 is classified based on predetermined second classification standard information C2. Below, differences will be mainly described with reference to Figures 3 and 15 to 19.

[0140] In the second modified example, for example, the original biometric data D0 is data indicating the number of steps. In this case, for example, preprocessing by the preprocessing unit 410 is not performed. Therefore, the original biometric data D0 is processed as the biometric data D1 to be classified.

[0141] FIG. 15 is a graph showing biometric data D1 according to the second modified example. The vertical axis represents the number of steps. FIG. 16 is a graph showing first biometric data B1 and second biometric data B2 calculated by the classification unit 411 according to the second modified example. The vertical axis represents the number of steps. FIG. 17 is a graph showing corrected data D2 calculated by the correction unit 412 according to the second modified example. The vertical axis represents the number of steps after correction. FIG. 18 is a graph showing cumulative data D3 calculated by the accumulator 413 according to the second modified example. The vertical axis represents the cumulative value of the number of steps after correction. FIG. 19 is a graph showing cumulative data D4 after statistical processing calculated by the statistical processing unit 414 according to the second modified example. The vertical axis represents the statistically processed value for the cumulative value. In the example of FIG. 19, the statistically processed value is a standardized value. In FIGS. 15 to 19, the horizontal axis represents time t (minutes). In addition, in FIGS. 16 and 17, black circles represent the first state, and white diamonds represent the second state. The first state indicates a state in which the living organism is active (active state). The second state indicates a state in which the living organism is inactive (inactive state). Specifically, the first state indicates a state in which the living organism is moving (active state). The second state indicates a state in which the living organism is stationary (inactive state).

[0142] As shown in FIGS. 3 and 15 , the classification unit 411 compares the second classification reference information C2 with the biological data D1 to be classified and classifies the biological data D1 based on the comparison result. The second classification reference information C2 is set to a value of the biological data that substantially indicates a cessation of mental and physical activity. In the example of FIG. 15 , the second classification reference information C2 is set to a value of the number of steps that substantially indicates a cessation of mental and physical activity. In other words, the second classification reference information C2 is set to zero. According to the second modification, the biological data D1 is classified into an active state and an inactive state, and then correction and accumulation are performed. Therefore, the cumulative data D3 reflects the comparison result and balance between the active state and the inactive state. As a result, the cumulative data D3 is more suitable for evaluating the biological mechanism that repeats activity and inactivity.

[0143] 15 and 16, the classification unit 411 classifies, among the plurality of biometric data D1, biometric data D1 that is greater than the second classification standard information C2 into a first status group G1, thereby calculating first biometric data B1 belonging to the first status group G1. Furthermore, the classification unit 411 classifies, among the plurality of biometric data D1, biometric data D1 that is equal to the second classification standard information C2 into a second status group G2, thereby calculating second biometric data B2 belonging to the second status group G2. In this case, the classification unit 411 sets the second biometric data B2 to a predetermined value. In the example of FIG. 16, the predetermined value is "-1."

[0144] The predetermined value is determined experimentally and / or empirically. The classification unit 411 sets the sign of the predetermined value to be opposite to the sign of the first biometric data B1. In the example of FIG. 16, the predetermined value has a negative sign. In the example of FIG. 16, the magnitude of the predetermined value is "1". Note that the correction unit 412 performs a correction calculation such that the ratio R becomes substantially 1, so the magnitude of the predetermined value may be any value.

[0145] 16 and 17 , the correction unit 412 performs a correction operation on the plurality of first biometric data B1 and the plurality of second biometric data B2 to calculate a plurality of time-series corrected data D2. The plurality of corrected data D2 is composed of the plurality of first biometric data B1 and the plurality of corrected second biometric data B2#.

[0146] As shown in FIGS. 17 and 18, the accumulator 413 accumulates the correction data D2 along the time axis to calculate a plurality of pieces of time-series accumulated data D3.

[0147] As shown in FIGS. 18 and 19, the statistical processing unit 414 performs statistical processing on a plurality of pieces of cumulative data D3 to calculate a plurality of pieces of cumulative data D4 after the statistical processing.

[0148] (Third Modification) In the above embodiment, the biometric data D1 is classified according to the first classification standard information C1. In contrast, in a third modification of the present embodiment, the biometric data D1 is classified according to a period. Differences will be mainly described below with reference to FIG. 3 .

[0149] The classification unit 411 classifies the biometric data D1 of a first period into a first status group G1, and the biometric data D1 of a second period different from the first period into a second status group G1. The first period indicates a period during which the physical and mental state is a specific state. That is, the first status in the first status group G1 is the specific state. In other words, the first status indicates the physical and mental state during the first period, and the second status indicates the physical and mental state during the second period. The first biometric data B1 and the second biometric data B2 obtained by such classification are corrected, and cumulative data D3 is calculated from the corrected data D2. Therefore, the cumulative data D3 reflects the comparison result and balance between a specific physical and mental state (first status) and another physical and mental state (second status). As a result, according to the third modification, the cumulative data D3 is more suitable for evaluating the mechanisms of a living body.

[0150] For example, the first period may be a certain period before sleep, a period during sleep, a certain period after waking up, a period during exercise, a period during a seizure seen in a specific disease, a period of injury or illness, or a period during clinical trial participation. For example, the second period may be a period other than the first period. Alternatively, for example, the second period may be a period other than the first period during which the physical and mental state is different from the specific state in the first period.

[0151] Specifically, the classification unit 411 calculates first biometric data B1 of the first status group G1 and second biometric data B2 of the second status group G2 by encoding the biometric data D1 depending on whether the biometric data D1 belongs to the first period or the second period. The encoding is a process of setting the biometric data D1 belonging to the first period to a first specific value (first biometric data B1) having a positive or negative sign, and setting the biometric data D1 belonging to the second period to a second specific value (second biometric data B2) having the other positive or negative sign. For example, the first specific value is "+1" and the second specific value is "-1."

[0152] For example, if the first period is a period during sleep and the second period is a period during wakefulness, the classification unit 411 encodes the biometric data D1 of the first period as “+1” and encodes the biometric data D1 of the second period as “−1.” The cumulative data D3 based on such classification can be used to evaluate, for example, the period during which the subject had a sleep debt.

[0153] (Fourth Modification) In the above embodiment, the cumulative sum D3j is expressed by equation (1). In contrast, in a fourth modification of this embodiment, a limit value (a threshold value Th described below) is set for the cumulative sum D3j. Below, differences will be mainly described with reference to FIGS. 3, 20, and 21.

[0154] In the fourth modified example, for example, the original biometric data D0 is data indicating sleep duration. In this case, for example, preprocessing by the preprocessing unit 410 is not performed. Therefore, the original biometric data D0 is processed as the biometric data D1 to be classified. FIG. 20 is a graph showing the biometric data D1 according to the fourth modified example. The vertical axis indicates the daily sleep duration, and the horizontal axis indicates the time t (month). The classification unit 411 performs processing on the biometric data D1 in FIG. 20 to calculate first biometric data B1 and second biometric data B2. In this case, for example, the first state is "sufficient sleep" and the second state is "insufficient sleep." Then, the correction unit 412 performs a correction calculation on the first biometric data B1 and the second biometric data B2 to generate corrected data D2. The accumulator 413 then accumulates the corrected data D2 to calculate multiple cumulative time-series data D3.

[0155] 21 is a graph showing an example of accumulated data D3 according to Modification 4. The vertical axis represents the corrected accumulated value of sleeping time, and the horizontal axis represents time t (months).

[0156] 21 , in the fourth modification, the accumulator 413 compares the cumulative data D3 to be compared with the threshold value Th, and based on the comparison result, sets the threshold value Th to new cumulative data D3 (solid line) instead of the cumulative data D3 to be compared (dashed line). In the example of FIG. 21 , if the cumulative data D3 (dashed line) exceeds the threshold value Th, the accumulator 413 sets the threshold value Th to new cumulative data D3 (solid line) instead of the cumulative data D3 (dashed line). Therefore, according to the fourth modification, by setting the threshold value Th, it is possible to derive cumulative data D3 that more closely resembles the mechanism of a living body.

[0157] For example, in the case where sleep debt occurs due to lack of sleep and sleep time cannot be accumulated, setting a limit on the accumulated data D3 on the assumption that sleep time cannot be accumulated, rather than uniformly accumulating correction data D2, allows for a more appropriate approximation of the biological mechanism. In the example of Figure 21, by setting a threshold value Th as a limit, the characteristics of sleep time that cannot be accumulated are represented by the accumulated data D3. In other words, the sleep debt is represented by the accumulated data D3.

[0158] As an example, the accumulator 413 calculates the cumulative sum D3j as the cumulative data D3 using equations (5) to (7). D3#j indicates the cumulative sum at time t=j. D2i indicates the corrected data D2 at time t=i. ts indicates the start time of the accumulation. The accumulator 413 calculates multiple cumulative sums D3#j in time series while updating j. This is the same as equation (1).

[0159]

[0160] The accumulator 413 determines whether the cumulative sum D3#j exceeds the threshold value Th. If the accumulator 413 determines that the cumulative sum D3#j does not exceed the threshold value Th, it calculates the cumulative sum D3j using equation (6). On the other hand, if the accumulator 413 determines that the cumulative sum D3#j exceeds the threshold value Th, it calculates the cumulative sum D3j using equation (7).

[0161]

[0162]

[0163] In a fifth modification of this embodiment, the preprocessing unit 410, the classification unit 411, the correction unit 412, the accumulation unit 413, and the statistical processing unit 414 perform processing for each of a plurality of attribute groups having different attributes. Differences from the above embodiment will be mainly described below with reference to FIG. 3 .

[0164] The third analysis unit 613 derives features according to the attributes of the attribute groups by comparing the cumulative data D3 between multiple attribute groups or by comparing information indicating the features of the cumulative data D3 between multiple attribute groups. In this case, the features of the cumulative data D3 are, for example, the results of the frequency analysis by the first analysis unit 611 or the evaluation results or prediction results by the second analysis unit 612.

[0165] The information providing server 7 (FIG. 1) transmits information indicating characteristics according to the attributes of the attribute group to the terminal TM, causing the terminal TM to output the information indicating characteristics according to the attributes of the attribute group as an image or sound (step S7 in FIG. 2). As a result, according to the sixth modification, the user of the terminal TM can grasp, use, and analyze the characteristics of each attribute group. The attributes are, for example, medical history or gender.

[0166] (Sixth Modification) In the above embodiment, the classification unit 411 classified the plurality of biometric data D1 into two status groups. In contrast, in a sixth modification of the present embodiment, the classification unit 411 classifies the plurality of biometric data D1 into three or more status groups. Differences will be mainly described below with reference to FIG. 3 .

[0167] The classification unit 411 classifies the plurality of biometric data D1 into, for example, a first state group G1 indicating a first physical and mental state, a third state group G3 indicating a third physical and mental state, and a second state group G2 indicating a second physical and mental state, thereby calculating first biometric data B1 belonging to the first state group G1, third biometric data B3 belonging to the third state group G3, and second biometric data B2 belonging to the second state group G2. The third state indicates a physical and mental state between the first state and the second state. The third state is, for example, a neutral physical and mental state.

[0168] In the sixth modified example, the first classification standard information C1 includes a first standard value and a second standard value. For example, the first standard value is greater than the second standard value. The classifier 411, for example, subtracts the first standard value from the biometric data D1 and sets the subtraction result, which indicates a positive value, as the first biometric data B1. The classifier 411, for example, subtracts the second standard value from the biometric data D1 and sets the subtraction result, which indicates a negative value, as the second biometric data B2. For example, the classifier 411 sets the biometric data D1 between the first standard value and the second standard value as the third biometric data B3.

[0169] The correction unit 412 calculates corrected data D2 by performing a correction calculation on the first biometric data B1 and the second biometric data B2. The accumulation unit 413 accumulates the corrected data D2 to calculate accumulated data D3. In this way, in the sixth modified example, the third biometric data B3 is excluded from the correction and accumulation targets.

[0170] For example, even when a plurality of biometric data D1 are classified into four or more status groups, the first biometric data B1 and the second biometric data B2 are the objects to be corrected and accumulated.

[0171] Although the preferred embodiments and modifications of the present disclosure have been described in detail above with reference to the accompanying drawings, the technical scope of the present disclosure is not limited to such examples. It is clear that a person skilled in the art of the present disclosure can conceive of various modified or altered examples within the scope of the technical idea described in the claims, and it is understood that these also naturally fall within the technical scope of the present disclosure.

[0172] The device or system described herein may be implemented as a single device, or may be implemented by multiple devices (e.g., cloud servers) partially or entirely connected via a network. For example, some or all of the preprocessing unit 410, classification unit 411, correction unit 412, accumulator 413, statistical processing unit 414, analysis unit 610, and support unit 620 may be implemented by the same computer or server. For example, the preprocessing unit 410, classification unit 411, correction unit 412, accumulator 413, statistical processing unit 414, analysis unit 610, and support unit 620 may each be implemented by separate computers or servers. For example, the relay server 2, the biometric data processing device 4, or the biometric data analysis device 6 may have the functionality of the information providing server 7. For example, the first database 3 and the second database 5 may be implemented by a single computer or server.

[0173] The series of processes performed by the device described herein may be implemented using software, hardware, or a combination of software and hardware. A computer program for implementing each function of the control units 41 and 61 according to this embodiment may be created and installed on a PC or the like. A computer-readable recording medium storing such a computer program may also be provided. Examples of the recording medium include a magnetic disk, an optical disk, a magneto-optical disk, and a flash memory. The computer program may also be distributed, for example, via a network, without using a recording medium.

[0174] Furthermore, the processes described herein using flowchart diagrams do not necessarily have to be performed in the order shown. Some process steps may be performed in parallel. Additional process steps may be employed, and some process steps may be omitted.

[0175] Furthermore, the effects described herein are merely descriptive or exemplary and are not limiting. In other words, the technology according to the present disclosure may achieve other effects that will be apparent to those skilled in the art from the description of this specification, in addition to or in place of the above-described effects.

[0176] The following configurations also fall within the technical scope of the present disclosure.

[0177] (Item 1) A biometric data processing system comprising: a classification unit that classifies multiple biometric data to be classified in chronological order into at least a first status group indicating a first physical or mental status and a second status group indicating a second physical or mental status, thereby acquiring multiple first biometric data belonging to the first status group and multiple second biometric data belonging to the second status group; a correction unit that calculates multiple time-series correction data by performing a correction operation on the multiple first biometric data and the multiple second biometric data; and an accumulation unit that calculates multiple time-series cumulative data by accumulating the correction data along a time axis, wherein the correction operation is an operation that adjusts the balance in magnitude between the multiple first biometric data and the multiple second biometric data.

[0178] (Item 2) The biometric data processing system according to (Item 1), wherein the correction calculation is a calculation for making the ratio of the sum of the absolute values ​​of the plurality of second biometric data to the sum of the absolute values ​​of the plurality of first biometric data substantially 1.

[0179] (Item 3) The biological data processing system according to (Item 1), wherein the correction operation indicates an operation that prevents the cumulative data from substantially diverging.

[0180] (Item 4) The biometric data processing system according to any one of (Item 1) to (Item 3), wherein the first state indicates a mental and physical state that is incompatible with the second state.

[0181] (Item 5) A biometric data processing system according to any one of (Item 1) to (Item 4), wherein the first state indicates either an active state or an inactive state of the mind and body, either a stable state or an unstable state of the mind and body, or either an appropriate state or an inappropriate state for the mind and body.

[0182] (Item 6) The classification unit calculates first classification standard information based on multiple pieces of biometric data collected over a specific period of time, compares the first classification standard information with the biometric data to be classified, and classifies the biometric data based on the comparison results. This is a biometric data processing system according to any one of (Item 1) to (Item 5) of claim 1 or claim 2.

[0183] (Item 7) The classification unit compares the biometric data to be classified during an observation period that is the same as the specific period with the first classification standard information, and classifies the biometric data based on the comparison result; the correction unit performs the correction operation on the plurality of first biometric data and the plurality of second biometric data during the observation period based on the plurality of first biometric data and the plurality of second biometric data during the observation period; and the accumulation unit calculates the plurality of cumulative data in time series at multiple times during the observation period. (Item 6) The biometric data processing system described in

[0184] (Item 8) The classification unit compares the biometric data to be classified during an observation period different from the specific period with the first classification standard information, and classifies the biometric data based on the comparison result; the correction unit performs the correction calculation on the multiple first biometric data and multiple second biometric data to be corrected during the observation period based on multiple first biometric data and multiple second biometric data calculated from the multiple biometric data during the specific period; and the accumulation unit calculates the multiple cumulative data in time series at multiple times during the observation period. (Item 6) The biometric data processing system described in

[0185] (Item 9) The biometric data processing system according to any one of (Item 1) to (Item 5), wherein the classification unit compares the biometric data to be classified with second classification standard information set to a value of the biometric data that essentially indicates a cessation of physical and mental activity, and classifies the biometric data based on the comparison result.

[0186] (Item 10) The classification unit classifies, among the plurality of biometric data to be classified, biometric data of a first period into the first status group, and classifies biometric data of a second period different from the first period into the second status group, and the first period indicates a period when the physical and mental state is in a specific state. The biometric data processing system according to any one of (Item 1) to (Item 5).

[0187] (Item 11) The biometric data processing system according to any one of (Item 1) to (Item 10), wherein the classification unit, the correction unit, and the accumulation unit execute their respective processes during a period when the physical and mental state is in a specific state.

[0188] (Item 12) The biometric data processing system according to any one of (Item 1) to (Item 11), wherein the accumulation unit compares the accumulated data with a threshold value, and sets the threshold value as new accumulated data instead of the accumulated data based on a comparison result.

[0189] (Item 13) The biometric data processing system according to any one of (Item 1) to (Item 12), further comprising: a statistical processing unit that performs centering, standardization, or normalization on the cumulative data; and an output control unit that outputs the processing results by the statistical processing unit or information indicating characteristics of the processing results to a terminal.

[0190] (Item 14) The biometric data processing system according to (Item 13), wherein the statistical processing unit performs the centering process, the standardization process, or the normalization process on the cumulative data of a specific living body based on a statistical index calculated from the cumulative data of the specific living body.

[0191] (Item 15) The biometric data processing system according to (Item 13), wherein the statistical processing unit performs the centering process, the standardization process, or the normalization process on the cumulative data of a specific organism among the plurality of same-type organisms based on a statistical index calculated from the cumulative data of the plurality of same-type organisms.

[0192] (Item 16) The biometric data processing system according to any one of (Item 1) to (Item 15), wherein the classification unit, the correction unit, and the accumulation unit perform processing for each of a plurality of attribute groups having different attributes, and further includes an analysis unit that derives features according to the attributes of the attribute groups by comparing the cumulative data between the plurality of attribute groups or by comparing information indicating the features of the cumulative data between the plurality of attribute groups, and an output control unit that outputs the information indicating the features according to the attributes of the attribute groups to a terminal.

[0193] (Item 17) The biometric data processing system according to any one of (Item 1) to (Item 16), further comprising: an analysis unit that directly or indirectly performs frequency analysis on the plurality of cumulative data in time series; and an output control unit that outputs the results of the frequency analysis to a terminal.

[0194] (Item 18) The biometric data processing system described in any one of (Item 1) to (Item 17) further comprises an analysis unit that directly or indirectly analyzes the plurality of cumulative data in a time series from the past, and evaluates the past or present mental and physical state or predicts the future mental and physical state based on the analysis results, and an output control unit that outputs the evaluation results or prediction results by the analysis unit to a terminal.

[0195] (Item 19) The biological data processing system according to any one of (Item 1) to (Item 18), wherein the classification unit, the correction unit, and the accumulation unit perform real-time processing, and further include an analysis unit that directly or indirectly analyzes the plurality of time-series accumulated data in real time and evaluates the current mental and physical state or predicts the future mental and physical state based on the analysis results, and an output control unit that outputs the evaluation results or prediction results by the analysis unit to a terminal.

[0196] (Item 20) The biometric data processing system according to (Item 18) or (Item 19), further comprising a support unit that generates support information to support the user based on the evaluation result or prediction result by the analysis unit, and the output control unit causes the support information to be output to the terminal.

[0197] (Item 21) The biometric data processing system according to any one of (Item 1) to (Item 20), further comprising an output control unit that causes a terminal to output the plurality of cumulative data or information indicating characteristics of the plurality of cumulative data.

[0198] (Item 22) A biometric data processing method including the steps of: classifying a plurality of biometric data to be classified in chronological order into at least a first status group indicating a first physical or mental status and a second status group indicating a second physical or mental status, thereby acquiring a plurality of first biometric data belonging to the first status group and a plurality of second biometric data belonging to the second status group; calculating a plurality of corrected data in time series by performing a correction operation on the plurality of first biometric data and the plurality of second biometric data; and calculating a plurality of cumulative data in time series by accumulating the correction data along a time axis, wherein the correction operation is an operation to adjust the balance in magnitude between the plurality of first biometric data and the plurality of second biometric data.

[0199] (Item 23) A computer program that causes a computer to execute the steps of: classifying multiple pieces of biometric data to be classified in chronological order into at least a first status group indicating a first physical or mental status and a second status group indicating a second physical or mental status, thereby acquiring multiple pieces of first biometric data belonging to the first status group and multiple pieces of second biometric data belonging to the second status group; calculating multiple pieces of time-series correction data by performing a correction operation on the multiple pieces of first biometric data and the multiple pieces of second biometric data; and calculating multiple pieces of time-series cumulative data by accumulating the correction data along a time axis, wherein the correction operation is an operation that adjusts the balance in magnitude between the multiple pieces of first biometric data and the multiple pieces of second biometric data.

[0200] The present disclosure provides a biometric data processing system, a biometric data processing method, and a computer program, and has industrial applicability.

[0201] 1 Biometric data processing system, 7 Information providing server (output control unit), 411 Classification unit, 412 Correction unit, 413 Accumulation unit, 414 Statistical processing unit, 610 Analysis unit, 620 Support unit

Claims

1. A biometric data processing system comprising: a classification unit that classifies multiple biometric data to be classified in chronological order into at least a first status group indicating a first physical or mental status and a second status group indicating a second physical or mental status, thereby acquiring multiple first biometric data belonging to the first status group and multiple second biometric data belonging to the second status group; a correction unit that calculates multiple time-series correction data by performing a correction operation on the multiple first biometric data and the multiple second biometric data; and an accumulation unit that calculates multiple time-series cumulative data by accumulating the correction data along a time axis, wherein the correction operation is an operation that adjusts the balance in magnitude between the multiple first biometric data and the multiple second biometric data.

2. The biometric data processing system of claim 1, wherein the correction calculation is an operation that makes the ratio of the sum of the absolute values ​​of the plurality of second biometric data to the sum of the absolute values ​​of the plurality of first biometric data substantially 1.

3. The biological data processing system according to claim 1, wherein the correction operation indicates an operation that prevents the cumulative data from substantially diverging.

4. A biometric data processing system according to claim 1 or claim 2, wherein the first state indicates a mental and physical state that is incompatible with the second state.

5. The biometric data processing system of claim 4, wherein the first state indicates either an active or inactive state of the mind and body, either a stable or unstable state of the mind and body, or either an appropriate or inappropriate state for the mind and body.

6. A biometric data processing system as described in claim 1 or claim 2, wherein the classification unit calculates first classification standard information based on multiple biometric data during a specific period, compares the first classification standard information with the biometric data to be classified, and classifies the biometric data based on the comparison result.

7. The biometric data processing system of claim 6, wherein the classification unit compares the biometric data to be classified during an observation period that is the same as the specific period with the first classification standard information and classifies the biometric data based on the comparison result; the correction unit performs the correction operation on the plurality of first biometric data and the plurality of second biometric data during the observation period based on the plurality of first biometric data and the plurality of second biometric data during the observation period; and the accumulation unit calculates the plurality of accumulated data in time series at multiple times during the observation period.

8. The biometric data processing system of claim 6, wherein the classification unit compares the biometric data to be classified during an observation period different from the specific period with the first classification standard information and classifies the biometric data based on the comparison result; the correction unit performs the correction operation on the multiple first biometric data and multiple second biometric data to be corrected during the observation period based on multiple first biometric data and multiple second biometric data calculated from the multiple biometric data during the specific period; and the accumulation unit calculates the multiple cumulative data in time series at multiple times during the observation period.

9. A biometric data processing system as described in claim 1 or claim 2, wherein the classification unit compares the biometric data to be classified with second classification standard information set to a value of the biometric data that essentially indicates a cessation of mental and physical activity, and classifies the biometric data based on the comparison result.

10. The biometric data processing system of claim 1 or claim 2, wherein the classification unit classifies biometric data of a first period into the first state group, and classifies biometric data of a second period different from the first period into the second state group, and the first period indicates a period when the physical and mental state is in a specific state.

11. A biometric data processing system according to claim 1 or 2, wherein the classification unit, correction unit, and accumulation unit execute their respective processes during a period when the physical and mental state is in a specific state.

12. A biometric data processing system according to claim 1 or 2, wherein the accumulation unit compares the accumulated data with a threshold value, and based on the comparison result, sets the threshold value as new accumulated data instead of the accumulated data.

13. The biometric data processing system according to claim 1 or claim 2, further comprising: a statistical processing unit that performs centering, standardization, or normalization on the cumulative data; and an output control unit that outputs the processing results by the statistical processing unit or information indicating the characteristics of the processing results to a terminal.

14. The biometric data processing system of claim 13, wherein the statistical processing unit performs the centering process, the standardization process, or the normalization process on the cumulative data of the specific biometric entity based on a statistical index calculated from the cumulative data of the specific biometric entity.

15. The biometric data processing system of claim 13, wherein the statistical processing unit performs the centering process, the standardization process, or the normalization process on the cumulative data of a specific organism among the plurality of same-type organisms based on a statistical index calculated from the cumulative data of the plurality of same-type organisms.

16. The biometric data processing system of claim 1 or 2, wherein the classification unit, the correction unit, and the accumulation unit perform processing for each of a plurality of attribute groups having different attributes, and further comprises: an analysis unit that derives features according to the attributes of the attribute groups by comparing the cumulative data between the plurality of attribute groups or by comparing information indicating the features of the cumulative data between the plurality of attribute groups; and an output control unit that outputs the information indicating the features according to the attributes of the attribute groups to a terminal.

17. The biometric data processing system according to claim 1 or claim 2, further comprising: an analysis unit that directly or indirectly performs frequency analysis on the plurality of cumulative data in time series; and an output control unit that outputs the results of the frequency analysis to a terminal.

18. The biometric data processing system of claim 1 further comprises: an analysis unit that directly or indirectly analyzes the multiple cumulative data in a time series from the past, and evaluates the past or present mental and physical state or predicts the future mental and physical state based on the analysis results; and an output control unit that outputs the evaluation results or prediction results by the analysis unit to a terminal.

19. The biometric data processing system of claim 1, further comprising: an analysis unit that performs real-time processing, analyzes the plurality of time-series accumulated data directly or indirectly in real time, and evaluates the current mental and physical state or predicts the future mental and physical state based on the analysis results; and an output control unit that outputs the evaluation results or prediction results by the analysis unit to a terminal.

20. A biometric data processing system as described in claim 18 or claim 19, further comprising a support unit that generates support information to support the user based on the evaluation results or prediction results by the analysis unit, and the output control unit outputs the support information to the terminal.

21. The biometric data processing system according to claim 1 or 2, further comprising an output control unit that causes a terminal to output the plurality of cumulative data or information indicating characteristics of the plurality of cumulative data.

22. A biometric data processing method comprising the steps of: classifying multiple biometric data to be classified in chronological order into at least a first status group indicating a first physical or mental status and a second status group indicating a second physical or mental status, thereby obtaining multiple first biometric data belonging to the first status group and multiple second biometric data belonging to the second status group; calculating multiple corrected data in time series by performing a correction operation on the multiple first biometric data and the multiple second biometric data; and calculating multiple cumulative data in time series by accumulating the correction data along a time axis, wherein the correction operation is an operation to adjust the balance in magnitude between the multiple first biometric data and the multiple second biometric data.

23. A computer program causing a computer to execute the steps of: classifying multiple pieces of biometric data to be classified in chronological order into at least a first state group indicating a first physical state and a second state group indicating a second physical state, thereby obtaining multiple pieces of first biometric data belonging to the first state group and multiple pieces of second biometric data belonging to the second state group; calculating multiple pieces of time-series corrected data by performing a correction operation on the multiple pieces of first biometric data and the multiple pieces of second biometric data; and calculating multiple pieces of time-series accumulated data by accumulating the correction data along a time axis, wherein the correction operation is an operation to adjust the balance in magnitude between the multiple pieces of first biometric data and the multiple pieces of second biometric data.

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