JP2024062710A - Information processing device, system, output device, program, storage medium, and information processing method - Google Patents

Abstract

[Problem] To provide an information processing device that allows the state of autonomic nerve imbalance to be grasped along a time axis. [Solution] An information processing device 1 that acquires first data indicating a user's pulse, generates second data including multiple combinations of HF and LF/HF from the first data, calculates a regression line from a plot of the multiple combinations of the second data, calculates a statistic representing a variation based on the regression line, generates third data relating to an acceptable region from the regression line and the statistic, acquires fourth data indicating the user's pulse, generates fifth data including multiple combinations of HF and LF/HF from the fourth data, generates sixth data indicating in time series whether each of the multiple combinations of the fifth data is outside the acceptable region, and enables output based on the sixth data. [Selected Figure] Figure 5

Description

The present invention relates to an information processing device, a system, an output device, a program, a storage medium, and an information processing method.

Attempts are being made to grasp the mental state and utilize it in training for sportsmen, athletes, martial artists, and the like, in various work training, and in self-development. For example, Patent Document 1 discloses a bioinformation presentation system that calculates LF and HF by definite integration of a power spectrum obtained from the subject's beat interval through frequency spectrum conversion, and then plots the beat interval and at least one of LF and HF, which are indicators of parasympathetic nervous system activity, on the respective coordinate axes of a Cartesian coordinate system, and displays the bioinformation two-dimensionally to display the subject's psychological and physiological state.

Patent No. 6825716

The bioinformation presentation system of Patent Document 1 makes it possible to grasp changes in the activity of the autonomic nervous system to a certain extent, but in recent years, there has been a particular demand for making it easier to grasp autonomic nervous system imbalances.

The inventors have filed Patent Application No. 2021-111939 in response to this. This application relates to a method and related technology for grasping the state of autonomic nerve activity on a two-dimensional plane by taking the sympathetic nerve activity index and parasympathetic nerve activity index of the autonomic nerve activity index on the XY axis, respectively. This method makes it easy to determine whether the area of autonomic nerve activity is within the normal area or deviates from the normal area.

However, with this method, to monitor the subject's activity state, it is necessary to keep track of the position plotted on a two-dimensional plane, and it is necessary to make it easier to grasp the time-dependent movement of the autonomic nerve activity index. Furthermore, when analyzing after the fact using this method, it is not possible to directly identify from the display the time periods that deviate from the normal state, so it is necessary to go back and review the original data.

The present invention has been made in consideration of the above circumstances, and its purpose is to provide an information processing device, system, output device, program, storage medium, and information processing method that make it possible to grasp the state of autonomic nervous system imbalance over time.

That is, the present invention may include the following inventions.
[1] A biological data acquisition unit that acquires first data indicating a pulse of a first user during a first time period detected via a biological data acquisition member;
an LF and HF calculation unit that generates second data including a plurality of combinations of HF and LF/HF based on the first data;
and a graph data generating unit configured to, when a plurality of combinations of HF and LF/HF of the second data are plotted to show a relationship between HF and LF/HF, calculate a regression line based on the plurality of plots, calculate a statistic representing a variation based on the regression line for the plurality of plots, and generate third data relating to an allowable region based on the regression line and the statistic,
The biological data acquisition unit further acquires fourth data indicating a pulse of the first user during a second time period detected via the biological data acquisition member;
the LF and HF calculation unit further generates fifth data including a plurality of combinations of HF and LF/HF based on the fourth data;
The information processing device further comprises:
a time-series data generating unit that generates sixth data indicating whether each of a plurality of combinations of HF and LF/HF of the fifth data is outside the allowable range in a time series;
and an output unit that outputs information related to the sixth data to an output device, or uses an output device included in the information processing device to output a time series of whether or not each of multiple combinations of HF and LF/HF of the fifth data based on the sixth data is outside the allowable range.
[2] The information processing device of [1], wherein the sixth data indicates, in a time series, the distance from the regression line for each of multiple combinations of HF and LF/HF in the fifth data when plotted to show the relationship between HF and LF/HF, thereby indicating, in a time series, whether each of the multiple combinations of HF and LF/HF in the fifth data is outside the acceptable range.
[3] The graph data generating unit calculates, as the regression line, a regression line based on a plurality of plots in which a first axis indicates a magnitude relationship of the HF value and a second axis indicates a magnitude relationship of the LF/HF value, and a plurality of combinations of the HF and the LF/HF of the second data are plotted,
The distance from the regression line for each of a plurality of combinations of HF and LF/HF of the fifth data, which is indicated by the sixth data, is a distance from the regression line in the first axial direction, a distance from the regression line in the second axial direction, or a shortest distance from the regression line.
The information processing device according to [2].
[4] The graph data generating unit calculates, as the regression line, a regression line based on a plurality of plots in which a first axis indicates a magnitude relationship of the HF value and a second axis indicates a magnitude relationship of the LF/HF value, and a plurality of combinations of the HF and the LF/HF of the second data are plotted,
the graph data generator calculates a standard deviation as a statistic representing the variation based on a distance from the regression line in the first axis direction for each of a plurality of combinations of HF and LF/HF of the second data;
The allowable region is a first region between a first line obtained by translating the regression line in the positive direction of the first axis by three times the standard deviation and a second line obtained by translating the regression line in the negative direction of the first axis by three times the standard deviation, or a second region between a third line obtained by translating the regression line in the positive direction of the first axis by two times the standard deviation and a fourth line obtained by translating the regression line in the negative direction of the first axis by two times the standard deviation.
The information processing device according to [1].
[5] The graph data generation unit calculates a standard deviation as a statistic representing the variation based on a distance from the regression line in the first axis direction for each of a plurality of combinations of HF and LF/HF of the second data;
the allowable region is a first region between a first line obtained by translating the regression line in the positive direction of the first axis by three times the standard deviation and a second line obtained by translating the regression line in the negative direction of the first axis by three times the standard deviation, or a second region between a third line obtained by translating the regression line in the positive direction of the first axis by two times the standard deviation and a fourth line obtained by translating the regression line in the negative direction of the first axis by two times the standard deviation,
The distance from the regression line for each of a plurality of combinations of HF and LF/HF of the fifth data, which is indicated by the sixth data, is a value obtained by dividing the distance from the regression line in the first axial direction by the standard deviation.
The information processing device according to [3].
[6] The information processing device according to [1], wherein the graph data generation unit calculates a regression line based on a plurality of plots in which a first axis indicates the HF value on a logarithmic scale and a second axis indicates the LF/HF value, where each of a plurality of combinations of HF and LF/HF of the second data is plotted as the regression line.
[7] A notification data generating unit is further configured to generate notification data when three chronologically consecutive combinations of the fifth data, among a plurality of combinations of the fifth data and the HF and the LF/HF, are outside the allowable range, based on the sixth data;
The output unit outputs the notification data to the output device as the output of the information related to the sixth data.
The information processing device according to [1].
[8] An information processing device according to any one of [1] to [7],
and at least one of an output device that performs an output in chronological order based on the sixth data to the first user, and the biometric data acquisition member.
[9] An output device that receives information related to the sixth data from the information processing device according to any one of [1] to [7],
an output device comprising: a receiving unit that receives information related to the sixth data; and an output unit that performs a time-series output of whether or not each of a plurality of combinations of HF and LF/HF of the fifth data is outside the allowable range based on the sixth data.
[10] An output device that receives information related to the sixth data from the information processing device according to any one of [1] to [6],
The output device includes:
A receiving unit that receives the sixth data;
an output device comprising: a notification data generating unit that generates notification data based on the sixth data when three chronologically consecutive combinations among a plurality of combinations of HF and LF/HF in the fifth data are outside the allowable range; and an output unit that performs output based on the notification data using an output device.
[11] A program for causing a computer to execute processing by each unit of the information processing device according to any one of [1] to [7].
[12] A non-transitory computer-readable storage medium that stores a program for causing a computer to execute processing by each unit of the information processing device according to any one of [1] to [7].
[13] An information processing method executed by an apparatus including a computer and a storage medium, comprising:
acquiring first data indicative of a pulse of a first user during a first time period sensed via a biometric data acquisition member;
generating second data including a plurality of combinations of HF and LF/HF based on the first data;
When a plurality of combinations of HF and LF/HF of the second data are plotted to show a relationship between HF and LF/HF, a regression line is calculated based on the plurality of plots, and a statistic representing a variation based on the regression line is calculated for the plurality of plots, and third data relating to an allowable region is generated based on the regression line and the statistic.
acquiring fourth data indicative of a pulse of the first user during a second time period detected via the biological data acquisition member;
generating fifth data including a plurality of combinations of HF and LF/HF based on the fourth data;
generating sixth data indicating whether each of a plurality of combinations of HF and LF/HF of the fifth data is outside the allowable range in a time series;
The information processing method further comprises: outputting information relating to the sixth data to an output device; or using an output device included in the device, outputting a time series of whether or not multiple combinations of HF and LF/HF of the fifth data, based on the sixth data, are outside the allowable range.

According to the present invention, the above configuration makes it possible to obtain an information processing device, system, output device, program, storage medium, and information processing method that enable the state of autonomic nervous system imbalance to be grasped along a time axis.

FIG. 1 shows an example of the configuration of a system including an information processing apparatus according to the first embodiment. FIG. 2 is a block diagram showing an example of a hardware configuration of the information processing device according to the first embodiment. FIG. 3 is a block diagram showing an example of a software configuration of a control unit of the information processing device according to the first embodiment. FIG. 4 is a diagram for explaining the configuration related to the graph data generation process, out of the software configuration of the control unit of the information processing device according to the first embodiment shown in FIG. FIG. 5 is a diagram for explaining the configuration related to the time-series data generation process, out of the software configuration of the control unit of the information processing device according to the first embodiment shown in FIG. FIG. 6 shows a flowchart of an example of an operation executed by the information processing device according to the first embodiment. FIG. 7 is an example of a typical electrocardiogram waveform as biological data used in the information processing device according to the first embodiment. FIG. 8 is a diagram illustrating the concept of determining LF and HF from frequency analysis of an electrocardiogram waveform by the information processing device according to the first embodiment. 9 shows an example of a display based on the graph data generated by the information processing device according to the first embodiment. The original data is data collected over about half a day in the afternoon, including before and after the presentation, of a subject who gave a presentation at a presentation event. FIG. 10 shows an example of a display based on time-series data generated in association with the graph data of FIG. 9 by the information processing device according to the first embodiment. 11 shows another example of a display based on the graph data generated by the information processing device according to the first embodiment. The original data is about 28 hours of data from a driver who participated in a solar car race, including practice runs, sleep, and the actual race. FIG. 12 shows an example of a display based on time-series data generated in association with the graph data of FIG. 11 by the information processing device according to the first embodiment. FIG. 13 shows a schematic configuration of an output device used together with the information processing device according to the first embodiment. FIG. 14 shows a schematic configuration of an information processing apparatus according to another embodiment.

Hereinafter, an embodiment of the present invention will be described with reference to the drawings.
Hereinafter, the present invention will be described in more detail based on the following embodiment. However, the present invention is not limited to the following embodiment, and it is of course possible to carry out the present invention with appropriate modifications within the scope of the above and below-mentioned intent, and all of these are included in the technical scope of the present invention.

[First embodiment]
(Configuration example)
1 shows an example of the configuration of a system SYS including an information processing device 1 according to the first embodiment. In addition to the information processing device 1, the system SYS includes, for example, a biometric data acquisition member 2, a biometric data acquisition device 3, and an output device 4.

The biometric data acquisition member 2 is, for example, an electrode for acquiring biometric data. The biometric data acquisition device 3 is, for example, a device that performs analog-to-digital conversion of a voltage signal acquired via the biometric data acquisition member 2 and is capable of transmitting the analog-to-digital converted signal to an external device via a communication network NW. The detailed configurations of the biometric data acquisition member 2 and the biometric data acquisition device 3 will be described later.

The information processing device 1 is, for example, a measuring instrument and a personal computer. The output device 4 is, for example, a smartphone, a mobile terminal (for example, a tablet terminal), a smart watch, a head-mounted display, smart glasses, smart contact lenses, a personal computer, and the like. In FIG. 1, the information processing device 1 and the output device 4 are shown as separate devices connected via a communication network NW, but this embodiment is not limited to this. A combination of the information processing device 1 and the output device 4 may constitute a single device. For example, a combination of the information processing device 1 and the output device 4 may constitute a measuring instrument, a personal computer, a smartphone, a mobile terminal (for example, a tablet terminal), a smart watch, a head-mounted display, and the like. Similarly, two or more of the biometric data acquisition member 2, the biometric data acquisition device 3, the information processing device 1, and the output device 4 may constitute a single device.

The biometric data acquisition device 3 acquires biometric data indicating the heartbeat of a user (hereinafter also referred to as a subject) to whom the biometric data acquisition member 2 is attached, via the biometric data acquisition member 2. The biometric data acquisition device 3 transmits the biometric data to the information processing device 1 via the communication network NW. The information processing device 1 receives the biometric data and generates graph data and/or time series data based on the biometric data.

The generation of graph data by the information processing device 1 will be described. As described below, under the control of the LF and HF calculation unit 112, the information processing device 1 calculates LF and HF by definite integration of the power spectrum obtained from the subject's beat interval via frequency spectrum conversion, and further calculates LF/HF. Under the control of the graph data generation unit 113, the information processing device 1 generates graph data relating to a graph in which, for example, HF is plotted on a first axis and LF/HF is plotted on a second axis intersecting the first axis. The information processing device 1 can transmit the graph data to the output device 4 via the communication network NW.

The output device 4 receives the graph data and, based on the graph data, displays a graph with HF on the first axis and LF/HF on the second axis as described above. This display makes it easier to understand the plot when the subject's autonomic nervous system is in a state of dysfunction. More specifically, by including data relating to an acceptable range, for example, in the graph data, the acceptable range is also displayed on the graph, making it easier to understand the plot when the subject's autonomic nervous system is in a state of dysfunction.

The information processing device 1 further generates time series data indicating whether the plots of the HF and LF/HF combinations are outside the above-mentioned acceptable ranges, under the control of the time series data generating unit 114. The information processing device 1 transmits the time series data to the output device 4 via the communication network NW.

The output device 4 receives the time series data and displays a graph showing whether the plots of HF and LF/HF combinations are outside the acceptable range over time based on the time series data. This display makes it easy to understand the changes in the subject's autonomic nerve activity over time.

The information processing device 1 may generate notification data based on the time series data and transmit the notification data to the output device 4. The output device 4 may receive the notification data and, based on the notification data, perform a time series output of whether the plots of the HF and LF/HF combinations are outside the acceptable range.

Next, we will explain in more detail the biometric data, as well as the biometric data acquisition component 2 and the biometric data acquisition device 3.

The biodata acquisition device 3 acquires biodata indicating the subject's beat interval through the biodata acquisition member 2. In this specification, the case where the biodata is electrocardiogram information will be mainly described as an example. The biodata acquisition member 2 is preferably a biodata acquisition electrode, and more preferably a biocontact electrode. The biodata acquisition member 2 is preferably connected to the biodata acquisition device 3 directly or via wiring. The biodata acquisition device 3 may be capable of transmitting the acquired electrocardiogram information to the information processing device 1, for example, and may be equipped with a thermometer, GPS position information, acceleration sensors for each of the XYZ axes, etc. The biodata acquisition device 3 may convert the electrocardiogram information into beat interval information such as RRI described later, or the information processing device 1 may convert the electrocardiogram information into beat interval information such as RRI. In this specification, unless otherwise specified, both the electrocardiogram information before the conversion and the beat interval information such as RRI after the conversion may be referred to as biodata.

The biodata acquisition member 2 is preferably a plurality of electrodes for acquiring biodata provided on a clothing-type (not shown) bioinformation measuring device. Examples of clothing-type bioinformation measuring devices include sensing wear and wearable smart devices. The biodata acquisition device 3 acquires electrocardiogram information, for example, by having a subject wear the clothing-type bioinformation measuring device and measuring voltage over time through a plurality of biocontact electrodes provided on the bioinformation measuring device. The input impedance in the voltage measuring section of the biodata acquisition device 3 is preferably 100 kΩ or more, more preferably 300 kΩ or more, and even more preferably 1 MΩ or more. There is no particular upper limit. The clothing of the bioinformation measuring device is preferably made of a fabric having a 20% elongation stress of 20 N or less. In addition, it is preferable that the subject wears the clothing so that the clothing pressure is 0.1 kPa or more and 1.5 kPa or less. The clothing pressure is based on the assumption that the subject has a standard Japanese body shape, but may be adjusted according to the body shape of the subject and the size of the clothing. It is also preferable to place the skin-contact electrodes in areas where the clothing pressure is 0.3 kPa or more. This can reduce the discomfort felt by the skin-contact electrodes.

The beat interval indicated by the biological data is, for example, the interval between heartbeats. The unit of the interval is preferably ms. The beat interval can be obtained by reading the interval between R waves from an electrocardiogram, or by measuring the interval between adjacent heartbeats. As shown in Figure 7, the high, steep peaks that appear regularly are R waves. It is said that the beat interval and its fluctuations reflect the mental and neurological state.

The beat interval indicated by the biological data is preferably the R-R interval (hereinafter sometimes referred to as "RRI"), which is the interval between an R wave and the next R wave in an electrocardiogram signal. The RRI has a clear signal peak, making it difficult to misidentify the peak position, and therefore can improve the measurement accuracy of the beat interval. Furthermore, the frequency spectrum conversion performed by the information processing device 1 requires a certain number of waves over a certain period of time, but if the measurement accuracy of the beat interval is high, spectrum conversion can be performed in a short time with a small number of waves, enabling rapid detection close to real time. Note that FIG. 7 is an example of a typical electrocardiogram waveform.

In addition, as the biometric data indicating the beat interval, biometric data indicating the pulse interval based on pulse wave information related to changes in blood flow may be used instead of biometric data indicating the heartbeat interval based on electrocardiogram information. The pulse interval can be acquired by measuring the interval between adjacent pulses. The pulse wave can be acquired from the subject's wrist, fingers, etc. by the biometric data acquisition member 2. Furthermore, a parameter related to blood pressure may be calculated from the difference between the electrocardiogram information and the pulse wave information at a position away from the heart.

The biometric data acquisition member 2 will now be described in more detail.
As described above, a skin contact electrode can be used as the biological data acquisition member 2. An electrode using a conductive fabric can be used as the skin contact electrode. Examples of the conductive fabric include woven fabric, nonwoven fabric, knitted fabric, embroidery thread, sewing thread, etc., made of fibers containing at least conductive thread.

As the skin contact electrode, an electrode using an elastic conductor composition can be used. The elastic conductor layer may be a layer having elasticity and a resistivity of 1×10 ⁰ Ωcm or less. Elasticity means that the layer can be repeatedly stretched by 10% or more while maintaining electrical conductivity. The elastic conductor layer preferably has a breaking elongation of 40% or more, more preferably 50% or more, and even more preferably 80% or more. The breaking elongation can be measured by applying the conductive paste to a predetermined film thickness on a release sheet, peeling it off after drying, and performing a tensile test. The elastic conductor layer preferably has a tensile modulus of elasticity of 10 to 500 MPa. The average thickness of the elastic conductor layer is, for example, preferably 20 μm or more, more preferably 50 μm or more, and also preferably 500 μm or less, more preferably 250 μm or less, and particularly preferably 90 μm or less.

The elastic conductor layer can be formed, for example, using a conductive paste. The conductive paste preferably contains (i) conductive particles such as silver particles or carbon particles, (ii) a flexible resin such as rubber or elastomer, and (iii) a solvent.

In addition, conductive gel can be used for the skin contact electrode. Examples of conductive gel include gel electrode materials used on the surface of skin contact electrodes used in medical devices.

(2) Hardware Configuration of Information Processing Apparatus FIG. 2 is a block diagram showing an example of a hardware configuration of the information processing apparatus 1 according to the first embodiment.

The information processing device 1 includes a control unit 11, a program storage unit 12, a data storage unit 13, an input/output interface (input/output I/F) 14, and a bus BUS. Each of the program storage unit 12, the data storage unit 13, and the input/output interface 14 is connected to the control unit 11 via the bus BUS.

The control unit 11 has a hardware processor such as a central processing unit (CPU).

The program storage unit 12 is a storage medium that combines a non-volatile memory that can be written to and read from at any time, such as a hard disk drive (HDD) or a solid state drive (SSD), with a non-volatile memory such as a read only memory (ROM). The program storage unit 12 stores middleware such as an operating system (OS), as well as programs used to execute various control processes according to this embodiment. The program storage unit 12 may be realized, for example, by an auxiliary storage device such as a flash memory, a CD-ROM, a DVD disk, or a portable recording medium such as a USB memory.

The data storage unit 13 is a storage medium that combines, for example, a non-volatile memory such as an HDD or SSD that can be written to and read from at any time, and a volatile memory such as a RAM (Random Access Memory). The data storage unit 13 is used as a working area for the hardware processor of the control unit 11, temporarily stores data, and functions as a buffer and cache. The data storage unit 13 may be realized, for example, by an auxiliary storage device such as a flash memory, a CD-ROM, a DVD disk, or a portable recording medium such as a USB memory.

Under the control of the control unit 1, the input/output interface 14 transmits and receives data to and from external devices using a communication protocol defined by the communication network NW. The input/output interface 14 is configured, for example, by an interface compatible with a wired LAN or wireless LAN.

(3) Software configuration of the information processing device Fig. 3 is a block diagram showing an example of the software configuration of the control unit 11 of the information processing device 1 according to the first embodiment. The various functional units shown in the control unit 11 are merely examples, and do not need to be distinguished as shown in Fig. 3. A certain functional unit shown as a single block may be realized by being divided into a plurality of units, or functional units shown as different blocks may be realized as a certain integrated unit.

The control unit 11 includes, for example, a biometric data acquisition unit 111, an LF and HF calculation unit 112, a graph data generation unit 113, a time series data generation unit 114, a data output unit 115, and a notification data generation unit 116. The graph data generation unit 113 includes, for example, a regression analysis data generation unit 1131 and an acceptable range calculation unit 1132. The processing functions of each functional unit included in the control unit 11 are realized by the control unit 11 causing a hardware processor of the control unit 11 to execute a program stored in the program storage unit 12. Although the case where a program stored in the program storage unit 12 is used has been described, the program used may be one provided through the communication network NW.

The input/output interface 14 receives biometric data transmitted from the biometric data acquisition device 3 via the communication network NW, and inputs the biometric data to the control unit 11. The input/output interface 14 also receives data output from the control unit 11, and transmits the data to the output device 4 via the communication network NW according to instructions from the control unit 11.

The data storage unit 13 includes, for example, a biometric data storage unit 131, an LF and HF storage unit 132, a regression analysis data storage unit 133, an acceptable range storage unit 134, a time series data storage unit 135, and a notification data storage unit 136. These various storage units are described as being included in the information processing device 1, but one or more of these storage units may be provided, for example, on cloud computing.

The biometric data storage unit 131 stores biometric data. The LF and HF storage unit 132 stores data on a combination of LF and HF, and/or data on a combination of HF and LF/HF. The regression analysis data storage unit 133 stores data generated by processing by the regression analysis data generation unit 1131. The acceptable range storage unit 134 stores data generated by processing by the acceptable range calculation unit 1132. The time series data storage unit 135 stores time series data generated by processing by the time series data generation unit 114. The notification data storage unit 136 stores notification data.

The biometric data acquisition unit 111 executes a process of acquiring biometric data transmitted from the biometric data acquisition member 2 via the input/output interface 14. The LF and HF calculation unit 112 executes a process of generating data of a combination of LF and HF based on the biometric data. The LF and HF calculation unit 112 further executes a process of calculating LF/HF and generating data of a combination of HF and LF/HF. The graph data generation unit 113 executes a process of generating graph data related to plotting a plurality of combinations of HF and LF/HF on a graph based on data including a plurality of combinations of HF and LF/HF. The regression analysis data generation unit 1131 executes a process of calculating a regression line based on each plot of a plurality of combinations of HF and LF/HF on a graph and generating regression line data indicating the regression line. The acceptable range calculation unit 1132 executes a process of generating data related to an acceptable range based on the regression line. The regression line data and the data related to the acceptable range may be included in the above-mentioned graph data. The time series data generation unit 114 executes a process of generating time series data that indicates, in a time series, whether or not the plots of HF and the LF/HF combination are outside the allowable region. The data output unit 115 executes a process of outputting the graph data and/or the time series data to the output device 4 via the input/output interface 14. The notification data generation unit 116 generates notification data, for example, based on the time series data. The data output unit 115 executes a process of outputting the notification data to the output device 4 as well.

Figure 4 is a diagram for explaining the configuration related to the graph data generation process of the information processing device 1 according to the first embodiment.

First, we will describe the processing related to the data indicative of the subject's heartbeat during the first time period. In Figure 4, the data flow related to this processing is indicated by dashed lines.

The biometric data acquisition unit 111 acquires, via the input/output interface 14, biometric data (which may also be referred to as first data) indicating the heartbeat of the subject (which may also be referred to as the first user) during a first time period, detected via the biometric data acquisition member 2 and the biometric data acquisition device 3, and executes a process of storing the first data in the biometric data storage unit 131.

The LF and HF calculation unit 112 may execute a process of reading out the first data from the biological data storage unit 131, calculating a power spectrum through frequency spectrum conversion from time series information of fluctuations in beat intervals indicated by the first data for each of a plurality of time periods included in the first time period based on the first data, and calculating LF and HF based on the power spectrum to generate data including a plurality of combinations of LF and HF, and storing the data in the LF and HF storage unit 132. The LF and HF calculation unit 112 may further execute a process of calculating LF/HF, generating data including a plurality of combinations of HF and LF/HF (hereinafter, also referred to as second data), and storing the second data in the LF and HF storage unit 132.

The graph data generation unit 113 executes a process of reading the second data from the LF and HF storage unit 132.

The regression analysis data generating unit 1131 executes a process of calculating a regression line based on multiple plots when multiple combinations of HF and LF/HF of the second data are plotted to show the relationship between HF and LF/HF, generating regression line data showing the regression line, and storing the regression line data in the regression analysis data storage unit 133. This specification mainly describes the case where such regression line data is generated, but instead of the regression line data, the regression analysis data generating unit 1131 may generate, for example, data of a curve function in which the value of HF is determined using LF/HF as a variable, by regression analysis.

The acceptable range calculation unit 1132 executes a process of reading out the regression line data from the regression analysis data storage unit 133. Next, the acceptable range calculation unit 1132 executes a process of calculating a statistical amount representing a variation based on the regression line for the above-mentioned multiple plots related to the second data. Next, the acceptable range calculation unit 1132 executes a process of generating data (which may be referred to as third data hereinafter) related to an acceptable range based on the regression line and the statistical amount, and storing the third data in the acceptable range storage unit 134. The third data may be, for example, data indicating one or more straight lines of the boundary of the acceptable range calculated from the regression line and the statistical amount.

The data output unit 115 executes a process of reading the third data from the allowable range storage unit 134 and outputting the third data to the output device 4 via the input/output interface 14. This output enables the output device 4 to display based on the third data. In this display, for example, the allowable range indicated by the third data is displayed. The data output unit 115 may further execute a process of reading the second data from the LF and HF storage unit 132 and outputting the second data to the output device 4 in the same manner. The data output unit 115 may further execute a process of reading the regression line data from the regression analysis data storage unit 133 and outputting the regression line data to the output device 4 in the same manner. Such output of the second data and/or regression line data also enables the output device 4 to display based on the second data and/or regression line data. The order in which the data output unit 115 executes the output process of the third data, the output process of the second data, and the output process of the regression line data is not limited to that described here, and at least two pieces of data may be output in parallel.

Next, a process related to data showing the subject's heartbeat during the second time period will be described. In FIG. 4, the flow of data related to this process is indicated by a dashed line. Note that the second time period is, for example, a time period after the first time period, but this embodiment is not limited to this. The second time period may be a time period before the first time period, or the second time period and the first time period may partially overlap.

The biometric data acquisition unit 111 acquires biometric data (hereinafter, also referred to as fourth data) indicating the heartbeat of the first user during the second time period detected via the biometric data acquisition member 2 and the biometric data acquisition device 3 via the input/output interface 14, and executes a process of storing the fourth data in the biometric data storage unit 131.

The LF and HF calculation unit 112 reads the fourth data from the biological data storage unit 131, and calculates LF, HF, and LF/HF for each of the multiple time periods included in the second time period based on the fourth data in the same manner as described for the first data, thereby generating data including multiple combinations of HF and LF/HF (hereinafter, may also be referred to as fifth data), and executes a process of storing the fifth data in the LF and HF storage unit 132. In the storage process, for example, timing information corresponding to each combination of HF and LF/HF is also stored.

The data output unit 115 executes a process of reading out the fifth data from the LF and HF storage unit 132 and outputting the fifth data to the output device 4 via the input/output interface 14. This output enables the output device 4 to display based on the fifth data. In this display, for example, a graph is displayed in which multiple combinations of HF and LF/HF of the fifth data are plotted to show the relationship between HF and LF/HF. This display is performed, for example, together with the display based on the third data described above.

Figure 5 is a diagram for explaining the configuration related to the time series data generation process of the information processing device 1 according to the first embodiment.

The time-series data generating unit 114 executes a process of reading the third data from the acceptable range storage unit 134 and reading the fifth data from the LF and HF storage unit 132. In the process of reading the fifth data, for example, timing information for each of the multiple combinations of HF and LF/HF in the fifth data is also read. The time-series data generating unit 114 executes a process of generating time-series data (which may also be referred to as sixth data hereinafter) that indicates, in a time series, whether each of the multiple combinations of HF and LF/HF in the fifth data is outside the above-mentioned acceptable range, based on the third data and the fifth data, and storing the sixth data in the time-series data storage unit 135. The above-mentioned timing information may also be used in the process of generating the time-series data.

The sixth data, for example, indicates the distance from the regression line for each of the multiple combinations of HF and LF/HF in the fifth data in a time series when the multiple combinations are plotted to show the relationship between HF and LF/HF. More specifically, for example, in a display based on the sixth data, for each of the multiple combinations of HF and LF/HF in the fifth data, one axis indicates timing information of the combination, and the other axis indicates the distance between the plot of the combination and the regression line. As the distance, the distance from the regression line in the first axial direction, the distance from the regression line in the second axial direction, or the shortest distance from the regression line is used. When the distance from the regression line in the first axial direction is used as the distance, a positive value may be used for a combination of HF and LF/HF that is larger than the regression line in the first axial direction, and a negative value may be used for a combination of HF and LF/HF that is smaller than the regression line in the first axial direction. The same is true when the distance in the second axis direction from the regression line is used as the distance. In this way, in the display based on the sixth data, whether or not each of the multiple combinations of HF and LF/HF in the fifth data is outside the allowable range is displayed in a time series based on such distance. This is because the allowable range is also determined based on the distance from the regression line, as described below. In this specification, the sixth data mainly shows the distance from the regression line in a time series, but this embodiment is not limited to this. As the sixth data, for example, a binary value indicating whether or not the combination of HF and LF/HF is outside the allowable range in a time series may be used, such as a low (L) level when the combination is not outside the allowable range and a high (H) level when the combination is outside the allowable range.

The data output unit 115 executes a process of reading the sixth data from the time-series data storage unit 135 and outputting the sixth data to the output device 4 via the input/output interface 14. This output enables the output device 4 to display based on the sixth data. For example, a visual display along a time series of whether or not each of multiple combinations of HF and LF/HF in the fifth data is outside the allowable range is possible.

The notification data generating unit 116 reads out the sixth data from the time-series data storage unit 135, and generates notification data based on the sixth data when three chronologically consecutive combinations of the multiple combinations of HF and LF/HF in the fifth data fall outside the allowable range, and executes a process of storing the notification data in the notification data storage unit 136. The notification data may be a simple notification signal for a real-time warning to the user based on the notification data, but when a warning is to be displayed after the fact, such as when used together with a display based on the sixth data, the notification data may include, for example, timing information for displaying the warning at an appropriate location on the display.

In this specification, notification data is mainly described as being generated when three chronologically consecutive combinations of the multiple combinations of HF and LF/HF are outside the acceptable range, but this embodiment is not limited to this. For example, notification data may be generated when a single combination of the multiple combinations of HF and LF/HF is outside the acceptable range. Alternatively, notification data may be generated when a preset number of chronologically consecutive combinations of the multiple combinations of HF and LF/HF are outside the acceptable range. Any value can be set as the number.

The data output unit 115 executes a process of reading out the notification data from the notification data storage unit 136 and outputting the notification data to the output device 4 via the input/output interface 14. This output enables the output device 4 to output based on the notification data. For example, a warning, as described below, can be issued. For example, a warning can be displayed along the timeline on a graph that displays the timeline, and/or other real-time warnings can be output along the timeline as to whether or not the multiple combinations of HF and LF/HF in the fifth data are outside the allowable range.

In this way, the data output unit 115 executes a process of outputting information related to the sixth data, that is, the sixth data and/or the notification data, to the output device 4. This enables the output device 4 to output, based on the sixth data as described above, a chronological order of whether or not each of the multiple combinations of HF and LF/HF of the fifth data is outside the allowable range.

(Example of operation)
An example of the operation of the information processing device 1 configured as above will be described.
(1) Overall Operation Flow Fig. 6 is a diagram showing a flowchart of an example of an operation executed by the information processing device 1. The operation described below is merely an example, and the operation according to the present embodiment is not limited thereto.

Prior to this operation, the biometric data acquisition device 3 acquires biometric data (first data) indicating the heartbeat of the subject (first user) during a first time period via the biometric data acquisition member 2. Under the control of an output unit (not shown), the biometric data acquisition device 3 transmits the biometric data to the information processing device 1 via the communication network NW. In response to this transmission, the operation shown in the flowchart of FIG. 6 is started.

The control unit 11 of the information processing device 1 acquires biometric data (first data) indicating the heartbeat of the subject (first user) during the first time period under the control of the biometric data acquisition unit 111 (ST01).

Next, under the control of the LF and HF calculation unit 112, the control unit 11 calculates a power spectrum through frequency spectrum conversion from time series information of fluctuations in beat intervals indicated by the first data for each of a plurality of time periods included in the first time period based on the first data, calculates LF and HF based on the power spectrum, and further calculates LF/HF to generate data (second data) including a plurality of combinations of HF and LF/HF (ST02). Note that the operation of ST02 may be performed at least partially in parallel with the operation of ST01.

Next, under the control of the regression analysis data generation unit 1131, the control unit 11 calculates a regression line based on multiple plots when multiple combinations of HF and LF/HF of the second data are plotted to show the relationship between HF and LF/HF (ST03).

Next, under the control of the allowable range calculation unit 1132, the control unit 11 calculates statistics representing the variation based on the regression line for the above-mentioned multiple plots related to the above-mentioned second data (ST04).

Next, under the control of the acceptable range calculation unit 1132, the control unit 11 generates data (third data) relating to the acceptable range based on the regression line and the statistics (ST05).

Next, under the control of the biometric data acquisition unit 111, the control unit 11 acquires, via the input/output interface 14, biometric data (fourth data) indicating the heartbeat of the first user during the second time period detected via the biometric data acquisition member 2 and the biometric data acquisition device 3 (ST06).

Then, under the control of the LF and HF calculation unit 112, the control unit 11 calculates LF and HF for each of the multiple time periods included in the second time period based on the fourth data in the same manner as described for the first data, and further calculates LF/HF to generate data (fifth data) including multiple combinations of HF and LF/HF (ST07).

Next, under the control of the time series data generating unit 114, the control unit 11 generates time series data (sixth data) based on the third data and the fifth data, which indicates in a time series manner whether each of the multiple combinations of HF and LF/HF in the fifth data is outside the above-mentioned acceptable range (ST08).

Then, under the control of the data output unit 115, the control unit 11 outputs information related to the sixth data to the output device 4 via the input/output interface 14 (ST09). The information may be the sixth data itself, or may be notification data, which will be described later. When the notification data is output, the control unit 11 executes an operation of generating notification data in advance. In the notification data generation process, under the control of the notification data generation unit 116, the control unit 11 executes a process of generating notification data based on the sixth data when, for example, three chronologically consecutive combinations out of the multiple combinations of HF and LF/HF in the fifth data fall outside the above-mentioned allowable range.

Although the above description assumes that the operations ST06 and ST07 are executed after the operation ST05, this embodiment is not limited to this. For example, the operations ST06 and ST07 may each be executed in parallel with at least one of the operations ST02, ST03, ST04, and ST05. Furthermore, the operations ST07, ST08, and ST09 may be executed at least in part in parallel with the operation ST06. Furthermore, at least a part of the operation ST06 may be executed before the operation ST01.

The following provides a detailed explanation of the operations described in relation to the flow shown in Figure 6.

(2) LF and HF Calculation Process The LF and HF calculation process executed by the control unit 11 under the control of the LF and HF calculation unit 112 in the operations of ST02 and ST07 will be described in more detail.

Figure 7 shows an example of a typical electrocardiogram waveform as biological data showing beat intervals. An example of a beat interval is the RRI, which is the interval between an R wave and the next R wave.

LF can be calculated by, for example, integrating the power spectrum obtained from the beat interval through frequency spectrum conversion from frequencies _Lf1 to _Lf2 in a definite manner. HF can be calculated by integrating the power spectrum from frequencies _Hf1 to _Hf2 in a definite manner. _Lf1 , _Lf2 , _Hf1 , and _Hf2 satisfy the relationships _Hf1 > _Lf1 and _Hf2 > _Lf2 .

More specifically, LF may be obtained by integrating, in definite form, from frequency _Lf1 to frequency Lf2, a power spectrum _F2 (first power spectrum) obtained by squaring a frequency spectrum F (frequency spectrum F) obtained by subjecting a beat interval, which is a time signal _f , to frequency spectrum conversion. HF may be obtained by integrating in definite form from _{frequency Hf1} (> _Lf1 ) to _{frequency Hf2} (> _Lf2 ).

The units of LF and HF calculated using the first power spectrum _F2 include ^ms2 . As a method of frequency spectrum transformation, for example, fast Fourier transform (FFT), wavelet analysis, maximum entropy method, etc. can be used. Note that, although the case where FFT is used is described as an example in this specification, other methods can also be used.

For example, the discrete Fourier transform G _k of a beat interval RRI _k obtained by performing spline interpolation on the beat interval and resampling at a sampling interval Δt is expressed by the following formula (I), and the power spectrum F ₂ (first power spectrum) (unit: ms ² /Hz) is expressed by the following formula (II): Here, k represents a time series, N represents the number of data, and S is an arbitrary scale, and generally S=1 in the power spectrum.

On the other hand, the values of LF and HF may be obtained by integrating the power spectrum F (second power spectrum) (unit: ms) obtained from the value obtained by frequency spectrum conversion of the beat interval over a predetermined interval. In this way, by using the value obtained by frequency spectrum conversion of the beat interval as the power spectrum, the values of LF and HF can be calculated more easily. It is preferable that the units of LF and HF calculated using the second power spectrum F are dimensionless quantities. The power spectrum F (second power spectrum) is expressed by the following formula (III).

The calculation method of LF and HF will be described with reference to FIG. 8, which is an explanatory diagram of power spectrum integration. In the graph shown in FIG. 8, the vertical axis indicates power spectrum density (unit: ^ms2 /Hz), and the horizontal axis indicates frequency (unit: Hz). LF is a value obtained by definite integration of a power spectrum (e.g., the first power spectrum _F2 ) from 0.04 Hz ( _Lf1 ) to 0.15 Hz ( _Lf2 ), and is the area of the portion hatched with oblique lines in FIG. 8. Here, _Lf1 < _Lf2 . On the other hand, HF is a value obtained by definite integration of a power spectrum (e.g., the first power spectrum _F2 ) from 0.15 Hz ( _Hf1 ) to 0.4 Hz ( _Hf2 ), and is the area of the portion hatched with vertical lines in FIG. 8. Here, _Hf1 < _Hf2 . 8, the integration ranges are set so that _Lf2 and _Hf1 are both equal to 0.15 Hz, but _Lf2 and _Hf1 may be the same or different values as long as the relationships _Lf1 < _Hf1 and _Lf2 < _Hf2 are satisfied. Here, the method of power spectrum integration has been described using the first power spectrum _F2 , but definite integration using the second power spectrum F can also be performed in the same way.

The integral range of LF includes at least 0.1 Hz, and preferably satisfies _Lf1 < 0.1 < _Lf2 . _Lf1 is preferably 0.01 Hz or more, and more preferably 0.04 Hz or more. _Lf2 is more preferably 0.13 Hz or more, and even more preferably 0.14 Hz or more, and is preferably 0.16 Hz or less, and more preferably 0.15 Hz or less.

The integral range of HF includes at least 0.3 Hz, and is preferably H _f1 <0.3<H _f2 . H _f1 is preferably 0.14 Hz or more, more preferably 0.15 Hz or more, and may be 0.17 Hz or less, or may be 0.16 Hz or less. H _f2 is preferably 0.38 Hz or more, more preferably 0.39 Hz or more, and preferably 0.41 Hz or less, and more preferably 0.4 Hz or less.

The biological data, for example electrocardiogram information, used for calculating such LF and HF will be described.
The biometric data acquired through the biometric data acquisition member 2 is stored in the biometric data storage unit 131. In detail, first, a certain amount of time is required for the LF and HF calculation unit 112 to perform frequency spectrum conversion. A heartbeat is a low-frequency signal of approximately 1 Hz, and if the fluctuation of the pulsation interval is treated as up to 0.017 Hz, a minimum of 1/0.017 = approximately 60 seconds of data is required. Therefore, in principle, it is difficult to perform frequency spectrum conversion in real time. On the other hand, if data of an arbitrary time width B is extracted from the data stored in the biometric data storage unit 131 by going back in time at a predetermined time interval A and frequency spectrum conversion is performed in a moving average manner, pseudo real-time frequency spectrum conversion is possible. The predetermined time interval A is preferably 5 seconds or more and 60 seconds or less, more preferably 10 seconds or more and 20 seconds or less. The arbitrary time width B is preferably 1 minute or more and 10 minutes or less, more preferably 1 minute or more and 5 minutes or less, and even more preferably 1 minute or more and 3 minutes or less. This makes it possible to perform frequency spectrum conversion in pseudo real time with a delay of about 10 seconds to 5 minutes.

Each combination of LF and HF ( _HF1 , _LF1 ), ( _HF2 , _LF2 ), ..., ( _HFn , _LFn ) in the population data described later is preferably a set of HF and LF of a predetermined time width derived from electrocardiogram information acquired when the subject is normal. The predetermined time width is preferably 180 seconds or less, more preferably 30 seconds or less, and even more preferably 10 seconds or less.

After acquiring electrocardiogram information related to the population data, the LF and HF values related to newly acquired electrocardiogram information for checking autonomic nerve activity, i.e., related to the sample population data described below, are preferably values calculated as moving averages from the heart rate variability RRI obtained over a specified time span. The specified time span is preferably 300 seconds or less, more preferably 180 seconds or less, and even more preferably 60 seconds or less.

(3) Graph data generation process based on population data Data including multiple combinations of HF and LF/HF generated in the operation of ST02 based on biometric data (first data) indicating the heartbeat of the subject (first user) during the first time period acquired in the operation of ST01 is also referred to as population data below.

The first data is obtained in advance via the biodata acquisition member 2 as electrocardiogram information of a subject in a normal state while the subject is performing normal daily activities, familiar tasks, regular work routines, or resting. Using multiple plots of population data based on the first data, which will be described below, as a reference, it can be determined that the subject has reached an autonomic nervous system imbalance if a plot relating to newly acquired electrocardiogram information is located in an area significantly outside the plots of the population data. Note that a storage unit may be provided in, for example, a clothing-type bioinformation measuring device in which the biodata acquisition member 2 is provided, and/or in the biodata acquisition device 3, and electrocardiogram information relating to the population data, etc., may be stored therein.

(3-1) Plotting HF and LF/HF In relation to the operation of ST03, we will first explain the process of plotting multiple combinations of HF and LF/HF on a graph based on population data, which the control unit 11 can execute under the control of the graph data generation unit 113.

Under the control of the graph data generating unit 113, the control unit 11 generates graph data related to a graph in which HF is plotted on the first axis and LF/HF is plotted on the second axis intersecting the first axis. For example, the graph is preferably a graph in an XY two-dimensional coordinate system in which LF/HF is plotted on the X axis and HF is plotted on the Y axis. On the other hand, the graph may be a graph in which HF is plotted on the X axis and LF/HF is plotted on the Y axis. In addition, in the graph, the first and second axes do not necessarily need to be perpendicular. In addition, the graph may be an XYZ three-dimensional coordinate system in which, in addition to HF and LF/HF, the value of a third component such as acceleration and RRI is plotted on the Z axis.

For example, in a graph with LF/HF on the X-axis and HF on the Y-axis, if the subject's autonomic nervous system is functioning normally, the area where LF/HF is small and HF is large (corresponding to the upper left area of the graph shown in Figure 9) can be interpreted as a relaxed state, and the area where LF/HF is large and HF is small (corresponding to the lower right area of the graph shown in Figure 9) can be interpreted as a state of tension or stress. On the other hand, the lower left and upper right areas can be interpreted as a state in which the autonomic nervous system is not functioning normally, in other words, a state of autonomic nervous system imbalance.

It is preferable to generate graph data in which the first axis shows HF on a logarithmic scale. For this purpose, the control unit 11, for example, under the control of the graph data generation unit 113, calculates the logarithm of HF (log(HF)) based on LF and HF stored in the HF storage unit 132. Since HF values fluctuate greatly even in normal times in healthy people, the graph can be made easier to check by having the first axis show HF on a logarithmic scale. It also makes statistical processing easier. Note that the base of the logarithm is not particularly limited, and may be a natural logarithm or a logarithm with a base of 10 (common logarithm). For convenience, the following explanation uses a common logarithm with a base of 10.

(3-2) Regression Analysis Data Generation Process and Acceptable Range Calculation Process The regression analysis data generation process and the acceptable range calculation process executed by the control unit 11 under the control of the graph data generation unit 113 in the operations of ST03, ST04, and ST05 will now be described in more detail.

The control unit 11, under the control of the regression analysis data generation unit 1131, calculates a regression line from a plurality of plots by the least squares method in a graph in which the logarithm of HF and LF/HF calculated based on the subject's beat interval in the first time period are plotted. The control unit 11, under the control of the acceptable range calculation unit 1132, calculates a statistic representing the variability related to the difference between the logarithm of HF between the plurality of plots and the regression line. It is preferable that the control unit 11 calculates the acceptable range from the regression line and the statistic representing the variability under the control of the acceptable range 1132. This makes it possible to set an acceptable range based on population data. Note that instead of the first axis showing HF on a logarithmic scale, HF and LF/HF may be plotted on a graph, and an acceptable range based on population data may be set on the graph.

Examples of statistics that represent variation include the mean deviation, variance, standard deviation, coefficient of variation, range (the difference between the maximum and minimum values in the Xbar-R control chart), interquartile range, mean absolute deviation, and discrete entropy. It is preferable to use the standard deviation σ as a statistic that represents variation. Using the standard deviation σ improves the reliability of the acceptable range.

The above-mentioned acceptable range is the range between the first line and the second line in the above-mentioned graph, and the first line is preferably a line obtained by translating the regression line in the positive direction of the first axis by 3σ, which is three times the standard deviation σ, and the second line is preferably a line obtained by translating the regression line in the negative direction of the first axis by 3σ. By setting the range to 99.7% acceptable in this manner, the reliability of the acceptable range based on the population data is improved. It is to be noted that the first axis does not have to show HF on a logarithmic scale, and HF and LF/HF may be plotted on a graph to set the 99.7% acceptable range of the population data. Similarly, a 95.4% acceptable range may be set using 2σ, which is twice the standard deviation σ. Examples of graphs in which such regression lines and acceptable ranges are displayed are shown in FIG. 9 and FIG. 11.

The 99.7% acceptable range may be set by determining the range between the straight lines of the following formulas (1) and (2) in the (LF/HF), log(HF) coordinate system.
log(HF)=A×(LF/HF)+B+3×S (1)
log(HF)=A×(LF/HF)+B−3×S (2)
[In formulas (1) and (2), A is the proportionality coefficient of the regression line in the (LF/HF), log(HF) coordinate system of the plots based on the population data, B is the intercept of the regression line, and S is the standard deviation of the distance on the log(HF) axis between each plot based on the population data and the regression line.]

(4) Graph-Related Processing Based on Sample Population Data The data including multiple combinations of HF and LF/HF generated in the operation of ST07 based on the biological data (fourth data) indicating the heartbeat of the first user in the second time period acquired in the operation of ST06 is also referred to as sample population data below. For convenience of explanation, the notification data generation process will be explained after the time-series data generation process, but as described later, it is more preferable that the notification data be generated based on the time-series data.

The second time period is a measurement period that is, for example, later than the first time period. The fourth data is, for example, electrocardiogram information acquired via the biodata acquisition member 2 while the subject is performing work or training that is likely to lead to an autonomic nervous system imbalance.

(4-1) Notification Data Generation Process If the combinations of LF and HF related to the sample population data newly acquired after the population data are ( _hf1 , _lf1 ), ( _hf2 , _lf2 ), ..., ( _hfm , _lfm ), the area satisfying the following formula (3) can be regarded as the area in which autonomic nervous activity is normal.
A×( _lft / _hft )+B−3×S≦log( _hft )≦A×( _lft / _hft )+B+3×S (3)

On the other hand, when the subject's log(hf) and (lf/hf) satisfy the following formula (4) or formula (5), or when they remain in a state satisfying the following formula (4) or formula (5) for a predetermined period of time or longer, it can be determined that the autonomic nervous activity is not normal.
A × ( _lft / _hft ) + B - 3 × S > log ( _hft ) ... (4)
log( _hft )>Ax( _lft / _hft )+B+3xS (5)

It is preferable that the information processing device 1 has a notification data generating unit 116 that detects that the logarithm of HF and the plot of LF/HF calculated by the LF and HF calculation unit 112 based on the subject's beat interval measured during the second time period are outside the range of the acceptable region, and generates notification data for recording and/or notifying the outside. For example, the output device 4 displays that the subject has reached autonomic nervous system imbalance based on the notification data generated by the notification data generating unit 116, so that the subject, instructor, doctor, etc. can know that the subject has reached an autonomic nervous system imbalance state. This makes it possible to avoid the risk of the subject continuing work, training, etc. without realizing that he or she has reached an extreme autonomic nervous system imbalance state, which may lead to a worsening condition. In addition, when it is detected that the above plot is outside the range of the acceptable region, it may be simply recorded without notifying the outside. In other words, it is also possible to record it once in the notification data storage unit 136 and check the record later. Note that HF may be used instead of the logarithm of HF.

Furthermore, it is preferable that the LF and HF calculation unit 112 and the graph data generation unit 113 calculate the logarithm of HF and LF/HF based on the electrocardiogram information of the subject acquired during the second time period, and the notification data generation unit 116 determines whether the logarithm of HF is outside the range of the acceptable range, and after determining that it is outside the range, the notification data generation unit 116 generates notification data. The notification data is transmitted to the output device 4 by the data output unit 115, for example. Furthermore, it is preferable that the output device 4 displays a display that can be perceived by the subject, etc., based on the notification data. Note that, since noise is likely to be mixed in when electrocardiogram measurement is performed in situ, it is more preferable that the notification data generation unit 116 is configured to generate notification data when the acceptable range is deviated from for a certain period of time continuously. Specifically, the notification data generation unit 116 may be configured to generate notification data when preferably one or more plots, more preferably two or more plots, even more preferably three or more plots, and even more preferably four or more plots are outside the range of the acceptable range.

Note that when HF or the logarithm of HF is taken on the vertical axis and LF/HF on the horizontal axis, and the difference from the regression line is calculated along the vertical axis, the acceptable range will be the same as the acceptable range calculated by swapping the vertical and horizontal axes.

Such notification data may be used in the following system. The subject is a driver of a vehicle, and the vehicle has a function of receiving notification data from the information processing device 1 regarding detection of deviation of the subject's autonomic nerve activity from a normal state, and switching the vehicle from a normal driving mode to an automatic driving mode, a driving assistance mode, or a remotely controlled mode in response to the notification data. Here, the normal driving mode is, for example, a mode in which the driver performs driving operations himself, and a mode without assistance such as the driving assistance mode. Although the above description has been given using an example of switching from the normal driving mode to an automatic driving mode, a driving assistance mode, or a remotely controlled mode, it is sufficient that the mode is switched in response to the notification data so as to reduce the burden on the driver. For example, switching from the driving assistance mode to the automatic driving mode or the remotely controlled mode may be performed.

(4-2) Time-series Data Generation Process The time-series data generation process executed by the control unit 11 under the control of the time-series data generation unit 114 in the operation of ST08 will now be described in more detail.

In the present invention, the distance of each plotted point from the regression line is calculated and displayed in a time series, thereby displaying the movement of autonomic nerve activity over time. Here, the distance of each plot from the regression line may be calculated on the vertical axis, i.e., the HF axis or the logarithmic axis of HF, or on the horizontal axis, i.e., the LF/HF axis, or the shortest distance from the regression line. All of these methods are equivalent in the end. Furthermore, as the distance of each plot from the regression line, a statistic representing the variation, preferably one normalized by dividing by the standard deviation, may be used. If a value divided by the standard deviation is used, it can be understood that a deviation from the allowable range occurs when this value exceeds 3. Examples of such time series display are shown in Figures 10 and 12.

It is preferable that the output device 4 visualizes and displays a graph. It is also preferable that the output device 4 visualizes and displays a warning that the autonomic nervous system is in a state of imbalance. The display of the warning is performed, for example, based on the notification data described above. In the information processing device 1, the graph may be created on a mathematical virtual plane or virtual space in a machine device such as a computer, and does not necessarily need to be visualized and displayed. In the information processing device 1, the graph may be created by artificial intelligence (AI) or the like. The output device 4 may perform only the display of the warning without displaying the graph. Instead of displaying the warning, a warning may be given in a manner that can be perceived by humans, such as sound, vibration, or heat, not limited to visually. Furthermore, in the case of a system in which the operation mode is switched using notification data as described above, it is not essential to display the graph on the output device 4.

It is also preferable that the time series data is used to generate the notification data described above. This is because, in the time series data, the distance from the regression line of each plot of the sample population data is associated with timing information, so that it is easy to determine whether multiple chronologically consecutive plots are outside the acceptable range. That is, as described above, it is easy to determine the time when the subject's log(hf) and (lf/hf) satisfy the above formula (4) or formula (5), or whether the subject has remained in a state that satisfies the above formula (4) or formula (5) for a predetermined period of time or more. For example, notification data is generated when three chronologically consecutive combinations out of multiple combinations of HF and LF/HF are outside the acceptable range. This makes it possible to exclude noise or sudden autonomic nerve movements and generate notification data when it is clear that the subject is in some abnormal state.

(effect)
For example, as described above, it is possible to easily grasp the autonomic nervous system imbalance state by displaying based on the graph data generated by the information processing device 1. Using the display based on the graph data and/or the notification data based on the graph data, it is possible to monitor the autonomic nervous system activity state of the subject, and further to issue a warning when the autonomic nervous system imbalance state continues for a certain period of time or more.

The autonomic nervous system is literally a nervous activity that works to maintain vital functions regardless of the person's will, and the person cannot directly feel whether the autonomic nervous system is working normally or not. In many cases, when the autonomic nervous system becomes abnormal, that is, falls into an autonomic nervous system imbalance, breathing and heart rate become irregular, and the person indirectly feels the abnormality of the autonomic nervous system in the form of palpitations, shortness of breath, etc. Therefore, by the time the person notices it, the autonomic nervous system imbalance may have already lasted for a long time and may have caused serious damage to the body. For example, when working in a very hot environment, when in a state of extreme tension, when in a state of weakness due to some kind of shock, or when the person is elderly, it is particularly difficult for the person to calmly grasp his or her own condition. By using the display based on the above graph data for risk prevention, it is possible to avoid the worsening of the autonomic nervous system imbalance in such cases. Furthermore, it is possible to prevent unexpected mistakes, accidents, injuries, etc.

The display based on the graph data and/or the notification data based on the graph data may be used to check whether a person, regardless of gender, is undergoing excessive training in various sports training, such as ball games, gymnastics, swimming, shooting, archery, throwing sports, and martial arts. The display based on the graph data and/or the notification data based on the graph data may also be used to monitor the autonomic nerve activity of a driver while driving a car, ship, airplane, heavy construction machinery, etc., to provide skill training in woodworking, ironworking, engraving, sewing, dental technology, medical surgery, cooking, etc., to provide training in performance of wind instruments, string instruments, percussion instruments, vocal music, etc., and to provide artistic training in calligraphy, carving, embroidery, painting, etc.

The output device 4 that receives the graph data and/or the notification data displays any of the above graphs. Examples of such displays include, for example, a sheet-like object, or an image visually displayed by a display. Examples of sheet-like objects include single-layer sheets such as paper and film, or laminates thereof. The image may be a still image or a moving image. As described above, the displayed graph is preferably a graph in an XY two-dimensional coordinate system with LF/HF on the X axis and HF on the Y axis. It may also be a graph with HF on the X axis and LF/HF on the Y axis. In addition, in the graph, the first axis and the second axis do not necessarily have to be perpendicular. In addition, the graph may be an XYZ three-dimensional coordinate system in which the value of the third component such as acceleration or RRI is taken on the Z axis in addition to HF and LF/HF. It is also preferable to take the logarithm of HF as the axis instead of these HFs. It is also preferable that the graph does not display plots of population data information, and only the allowable region between the first line and the second line is displayed. Additionally, the display on the output device 4 may display a warning message to inform the user that the plot is outside the acceptable range.

According to the information processing device 1 of the first embodiment, time series data is generated that indicates whether the plots of the combinations of HF and LF/HF based on the biometric data are outside the acceptable range in a time series. In the display based on the time series data by the output device 4, whether the plots of the combinations of HF and LF/HF based on the biometric data are outside the acceptable range in a time series is displayed. This makes it easy for the subject to grasp the state of autonomic nervous imbalance along the time axis. Furthermore, for such purposes, it is also beneficial to use notification data based on the time series data in addition to or instead of the time series data.

The present invention will be described in more detail below with reference to examples. However, the present invention is not limited to the following examples, and may be modified within the scope of the above and below-mentioned aims, and all such modifications are within the technical scope of the present invention.

As described below, a system was prepared that included a clothing-type biometric information measuring device equipped with a skin-contact electrode as a biometric data acquisition component, an information processing device, and an output device, and each graph was created.

[Elastic conductive paste]
Sanyo Chemical Industries Co., Ltd.'s Courtron KYU-1 (glass transition temperature -35 ° C.) was used as the binder, Mitsui Mining & Smelting Co., Ltd.'s micro-sized silver powder SPH02J (average particle diameter 1.2 μm) was used as the silver particles, Lion Specialty Chemicals Co., Ltd.'s Ketjen Black EC600JD was used as the carbon particles, and butyl carbitol acetate was used as the solvent. The elastic conductor paste was prepared by mixing 10 parts by weight of the binder, 70 parts by weight of the silver particles, 1 part by weight of the carbon particles, and 19 parts by weight of the solvent. In detail, the binder resin was dissolved in a solvent in half the amount of a predetermined solvent, and the metal-based particles and carbon-based particles were added to the obtained solution and premixed, and then dispersed with a three-roll mill to form a paste. The obtained paste was then screen printed to a thickness of 25 μm and dried at 100 ° C. for 20 minutes to obtain an elastic conductor layer. The obtained elastic conductor layer had an initial resistivity of 250 μΩ cm and had stretchability that maintained conductivity even after 100 repeated 20% elongation.

[Elastic carbon paste]
A carbon paste for the electrode protective layer was prepared. In detail, 40 parts by mass of nitrile butadiene rubber resin having a glass transition temperature of −19° C., 20 parts by mass of Ketjen Black EC300J manufactured by Lion Specialty Chemicals Co., Ltd., and 50 parts by mass of ethylene glycol monoethyl ether acetate as a solvent were pre-mixed and then dispersed in a three-roll mill to obtain an elastic carbon paste.

[Electrodes, wiring]
A urethane sheet (insulating cover layer) of a specified shape with electrode and connector parts cut out was temporarily attached to a PET release sheet whose surface had been treated with a silicone-based release agent, and an elastic carbon paste was screen-printed on the electrode parts. An elastic conductor paste was then printed in a specified pattern from the electrode parts to the connector position. A double-sided hot melt sheet (insulating base layer) was then laminated to cover the urethane sheet, forming electrodes and wiring on the release sheet.

[First clothing-type vital information measuring device]
The electrodes and wiring formed on the release sheet were placed on the fabric of the underbust part of the sports brassiere, which was formed using a fabric with a 20% elongation stress of 5N, so that the two-sided hot melt sheet side was in contact with each other, and the electrodes and wiring were transferred together with the insulating base layer and the insulating cover layer by heating and pressing with a hot press. Next, a connector was attached, and a biodata acquisition device was attached to produce a first clothing-type bioinformation measurement device capable of measuring electrocardiogram information. As the biodata acquisition device, one capable of transmitting the electrocardiogram information obtained to the information processing device, and equipped with a thermometer, GPS position information, and an acceleration sensor for each of the XYZ axes, was used. In addition, the 20% elongation stress of the electrodes and wiring formed on the release sheet was 0.5N. The obtained first clothing-type bioinformation measurement device was worn by a subject, and the subject was subjected to light exercise to measure the clothing pressure, and the maximum clothing pressure of the underbust part was 0.8 kPa, and the minimum clothing pressure was 0.25 kPa. The subject did not report any discomfort when wearing the first clothing-type bioinformation measurement device.

[Second clothing-type vital information measuring device]
A men's T-shirt was made from a knit fabric made of a 50%/50% cotton/polyester blend, with a hook-and-loop fastener used to adjust the waist circumference from the front to the back. Electrodes and wiring were transferred to the skin side of the chest of the men's T-shirt in the same manner as the first clothing-type vital sign measuring device, snap fasteners were attached as connectors, and a vital sign data acquisition device identical to the first clothing-type vital sign measuring device was attached to produce a second clothing-type vital sign measuring device capable of measuring electrocardiogram information.

Example 1 (Woman giving a presentation at a presentation)
First, a healthy general female was selected as subject A. Subject A was made to wear the first clothing-type vital sign measuring device, and RRI data for about half a day was obtained on the day when a presentation was given in the afternoon at an internal presentation. The sampling rate was 1 kHz. The frequency spectrum of the signal handled was in the range of about 1 Hz or less, so there was no problem with this sampling rate.

The obtained electrocardiogram information was transmitted from a biodata acquisition device attached to the first clothing-type bioinformation measurement device to a computer (analyzer) acting as an information processing device, and the LF and HF calculation section of the analyzer converted the obtained beat interval data into a frequency spectrum, and calculated LF and HF by definite integration of the obtained power spectrum. In detail, in this example, the integral value from 0.01 Hz to 0.15 Hz was treated as LF, and the integral value from 0.15 Hz to 0.40 Hz was treated as HF. The frequency spectrum conversion was performed every minute using the beat intervals obtained in the past three minutes.

From this, for the region excluding the time when the subject entered the presentation venue to the time when he left the presentation venue, the logarithm of HF and LF/HF were calculated as population data in the LF and HF calculation unit 112 and the graph data generation unit 113 of the analyzer, and further, the logarithm of HF was taken on the vertical axis and LF/HF on the horizontal axis, and a regression line was obtained by the least square method. The regression line was as follows. Note that log is the logarithm with base 10.
log(HF) = -0.07288 x (LF/HF) + 2.278
It should be noted that data of combinations of LF and HF where LF/HF exceeds 20 is regarded as an error and is not used in the calculation.

Next, the difference in the vertical axis direction between each plot of the population data and the regression line was calculated, and the standard deviation σ of this difference was determined. A first line was obtained by translating the regression line in the positive direction of the first axis by 3σ, which is three times the standard deviation σ, and a second line was obtained by translating the regression line in the negative direction of the first axis by 3σ. Furthermore, this information was stored in the data memory unit of the analyzer. Figure 9 shows a graph displaying these lines. In Figure 9, the regression line is indicated by the symbol RL, the first line by the symbol L1, and the second line by the symbol L2. The region between the first line L1 and the second line L2 shown in Figure 9 is the 99.7% acceptable region.

Next, on the day that subject A gave a presentation at an in-house presentation, similar calculations were performed from the time he entered the presentation venue to the time he left, and LF/HF and log(HF) were obtained and plotted on the same graph. The results are shown in the graph in Figure 9. Of the various lines connecting the plots, the thin dashed lines represent the period when the subject was waiting in the venue for his turn to speak, the thick solid lines represent the period during the presentation, and the thick dashed lines represent the period when the subject returned to his seat after the presentation. It can be seen from this graph that autonomic nerve activity during the presentation clearly deviated from the acceptable range.

In this way, it is possible to grasp the movement of the autonomic nervous activity index as movement on a two-dimensional plane. However, because the graph does not show the passage of time, it is necessary to express it by coloring the plots or the trajectories between the plots, as in this example. Therefore, when reviewing past data, it is necessary to go back and review the original data in order to determine which points deviated from the acceptable range.

Figure 10 shows the results of plotting the distance of each plot from the regression line over time, as proposed by the present invention. The top graph in Figure 10 is a graph showing LF/HF (sympathetic nerve activity index) over time. The middle graph is a graph showing log(HF) (parasympathetic nerve activity index) over time. These two types of graphs are generally used when discussing heart rate variability. From these two types of graphs, it is possible to read the movements of the sympathetic and parasympathetic nerves, but it is not possible to read whether they are within the acceptable range, i.e., the normal activity range, or whether they are deviating.

The bottom part of Figure 10 is a graph that lists the distance of each plot from the regression line in chronological order, which is the proposal of the present invention. The vertical axis is normalized by dividing by the standard deviation. In the figure, the range bounded by the dashed rectangle represents the time from when the person entered the presentation venue to when the person left the venue. The part surrounded by the dashed oval in the figure represents the time during which the presentation was being held, and it can be clearly seen that the autonomic nerve activity index exceeded 3σ at this time, entering a non-steady state.

Example 2 (A man who participated in a solar car race)
11 and 12 are plots of the results obtained from RRI data for about 28 hours for a male driver who participated in a solar car race, spanning the practice run on the day before the race, sleep, and the day of the race.

Figure 11 is a plot similar to Figure 9 in Example 1. In this example, population data is calculated from the portion excluding the practice run and the period when the driver was in the race car during the actual race. The regression equation is log(HF) = -0.05325 x (LF/HF) + 1.4053
The data during sleep is shown in the upper left of Figure 11, surrounded by a dashed ellipse and labeled "sleep" in a speech bubble. The area surrounded by a dashed ellipse in the lower left (labeled "R2" in a speech bubble), which is clearly outside the allowable range, is the area during which the driver participated in a race at high speeds.

In Fig. 12, like Fig. 10, the upper part is a graph showing LF/HF (sympathetic nerve activity index) in time series, the middle part is a graph showing log(HF) (parasympathetic nerve activity index) in time series, and the lower part is a graph showing the distance of each plot from the regression line in time series, which is the proposal of the present invention. As in Fig. 10, the vertical axis is normalized by dividing by the standard deviation.
"R0" is a plot of a practice run, "sleep" is a plot of a person sleeping, "R1" is a plot of a person participating in a relatively slow racing competition, and "R2" is a plot of a person participating in a relatively fast racing competition.
Overall, the value occasionally exceeds 3σ, but this is sporadic. On the other hand, in the "R2" section, the value clearly exceeds 3σ continuously.

As described above, by using the plot of the present invention, an autonomic nerve activity index can be obtained from the RRI, preferably obtained using a clothing-type vital information measuring device, and rather than simply arranging the activity indexes in a chronological order, it is possible to easily determine over time whether the subject's autonomic nerve activity is in the normal activity range or in the abnormal activity range, and by combining this with other vital indicators, it is possible to accurately grasp conditions such as heat stroke and accumulated fatigue.

[Other embodiments]
In the first embodiment, the description has been given mainly of the configuration and operation of the information processing device 1. The output device 4 used together with the information processing device 1 will also be described.

Figure 13 shows a schematic configuration of the output device 4. The output device 4 includes, as its hardware configuration, a control unit 41, a program storage unit 42, a data storage unit 43, an input/output interface (input/output I/F) 44, and an output device 45. The control unit 41, the program storage unit 42, the data storage unit 43, and the input/output interface 44 are similar in hardware configuration to those described for the control unit 11, the program storage unit 12, the data storage unit 13, and the input/output interface 14 of the information processing device 1 in the first embodiment, and therefore will not be described here. The output device 45 is, for example, a display screen, but is not necessarily limited to this.

The control unit 41 includes, as a software configuration, a receiving unit 411 and an output unit 415. The control unit 41 may include, as a software configuration, a notification data generating unit 416. The data storage unit 43 includes a time series data storage unit 435. The data storage unit 43 may include a notification data storage unit 436.

The receiving unit 411 receives time series data (sixth data) from the information processing device 1 via the input/output interface 44, and executes a process of storing the sixth data in the time series data storage unit 435. The output unit 415 reads the sixth data from the time series data storage unit 435, and executes a process of performing the above-mentioned display on the output device 45 based on the sixth data.

The notification data generation unit 416 executes a process of reading the sixth data from the time-series data storage unit 435, generating notification data, and storing the notification data in the notification data storage unit 436, in the same manner as described above as being executed by the notification data generation unit 116 in the first embodiment. The output unit 415 executes a process of reading the notification data from the notification data storage unit 436, and issuing a warning as described above in the output unit 45 based on the notification data.

If the output device 4 does not include a notification data generation unit 416, the receiving unit 411 receives notification data from the information processing device 1 via the input/output interface, and the output unit 415 executes processing to issue a warning as described above in the output device 45 based on the notification data.

In the first embodiment, the information processing device has been described as transmitting various data to the output device via a communication network. When the information processing device includes, for example, an output device capable of outputting to a subject, the information processing device may use the output device to perform output along the time series described above as being performed by the output device, based on such various data. An example of the configuration of the information processing device in such a case is shown in FIG. 14, in which the information processing device and the output device are respectively the information processing device 1a and the output device 15a. The output device 15a is, for example, a display screen, but is not necessarily limited to this. The output device 15a may perform visual display based on the time series data (sixth data). The output device 15a may perform visual display based on the notification data, but may also perform a warning in a manner perceptible by the subject, such as sound, vibration, heat, etc., in addition to or instead of the visual display. In this way, by using the output device 15a, the information processing device 1a can perform output along the time series regarding whether or not the multiple combinations of HF and LF/HF of the fifth data are outside the allowable range, based on the time series data and/or the notification data.

As described above, the present invention is not limited to the above-described embodiment as it is, and in the implementation stage, the components can be modified and embodied without departing from the gist of the invention. In addition, various inventions can be formed by appropriately combining multiple components disclosed in the above-described embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, components from different embodiments may be appropriately combined.

1, 1a...information processing device, 11...control unit, 111...biometric data acquisition unit, 112...LF and HF calculation unit, 113...graph data generation unit, 1131...regression analysis data generation unit, 1132...acceptable range calculation unit, 114...time series data generation unit, 115...data output unit, 116...notification data generation unit, 12...program storage unit, 13...data storage unit, 131...biometric data storage unit, 132...LF and HF storage unit, 133...regression analysis data storage unit, 134...acceptable range storage unit, 135 ...time series data storage unit, 136...notification data storage unit, 14...input/output interface, 15a...output device, 2...biometric data acquisition member, 3...biometric data acquisition device, 4...output device, 41...control unit, 411...receiving unit, 415...output unit, 416...notification data generation unit, 42...program storage unit, 43...data storage unit, 435...time series data storage unit, 436...notification data storage unit, 44...input/output interface, 45...output device, SYS...system, NW...communications network, BUS...bus.

Claims (13)

a biometric data acquisition unit that acquires first data indicating a pulse of a first user during a first time period detected via a biometric data acquisition member;
an LF and HF calculation unit that generates second data including a plurality of combinations of HF and LF/HF based on the first data;
and a graph data generating unit configured to, when a plurality of combinations of HF and LF/HF of the second data are plotted to show a relationship between HF and LF/HF, calculate a regression line based on the plurality of plots, calculate a statistic representing a variation based on the regression line for the plurality of plots, and generate third data relating to an allowable region based on the regression line and the statistic,
The biological data acquisition unit further acquires fourth data indicating a pulse of the first user during a second time period detected via the biological data acquisition member;
the LF and HF calculation unit further generates fifth data including a plurality of combinations of HF and LF/HF based on the fourth data;
The information processing device further comprises:
a time-series data generating unit that generates sixth data indicating whether each of a plurality of combinations of HF and LF/HF of the fifth data is outside the allowable range in a time series;
and an output unit that outputs information related to the sixth data to an output device, or uses an output device included in the information processing device to output a time series of whether or not each of multiple combinations of HF and LF/HF of the fifth data based on the sixth data is outside the allowable range. The information processing device according to claim 1, wherein the sixth data indicates, in a time series, the distance from the regression line when each of the multiple combinations of HF and LF/HF in the fifth data is plotted to show the relationship between HF and LF/HF, thereby indicating, in a time series, whether each of the multiple combinations of HF and LF/HF in the fifth data is outside the allowable range. the graph data generating unit calculates, as the regression line, a regression line based on a plurality of plots in which a plurality of combinations of HF and LF/HF of the second data are plotted such that a first axis indicates a magnitude relationship of the HF value and a second axis indicates a magnitude relationship of the LF/HF value;
The distance from the regression line for each of a plurality of combinations of HF and LF/HF of the fifth data, which is indicated by the sixth data, is a distance from the regression line in the first axial direction, a distance from the regression line in the second axial direction, or a shortest distance from the regression line.
The information processing device according to claim 2 . the graph data generating unit calculates, as the regression line, a regression line based on a plurality of plots in which a plurality of combinations of HF and LF/HF of the second data are plotted such that a first axis indicates a magnitude relationship of the HF value and a second axis indicates a magnitude relationship of the LF/HF value;
the graph data generator calculates a standard deviation as a statistic representing the variation based on a distance from the regression line in the first axis direction for each of a plurality of combinations of HF and LF/HF of the second data;
The allowable region is a first region between a first line obtained by translating the regression line in the positive direction of the first axis by three times the standard deviation and a second line obtained by translating the regression line in the negative direction of the first axis by three times the standard deviation, or a second region between a third line obtained by translating the regression line in the positive direction of the first axis by two times the standard deviation and a fourth line obtained by translating the regression line in the negative direction of the first axis by two times the standard deviation.
The information processing device according to claim 1 . the graph data generator calculates a standard deviation as a statistic representing the variation based on a distance from the regression line in the first axis direction for each of a plurality of combinations of HF and LF/HF of the second data;
the allowable region is a first region between a first line obtained by translating the regression line in the positive direction of the first axis by three times the standard deviation and a second line obtained by translating the regression line in the negative direction of the first axis by three times the standard deviation, or a second region between a third line obtained by translating the regression line in the positive direction of the first axis by two times the standard deviation and a fourth line obtained by translating the regression line in the negative direction of the first axis by two times the standard deviation,
The distance from the regression line for each of a plurality of combinations of HF and LF/HF of the fifth data, which is indicated by the sixth data, is a value obtained by dividing the distance from the regression line in the first axial direction by the standard deviation.
The information processing device according to claim 3 . The information processing device according to claim 1, wherein the graph data generating unit calculates a regression line based on a plurality of plots of multiple combinations of HF and LF/HF of the second data, with the first axis indicating the HF value on a logarithmic scale and the second axis indicating the LF/HF value, as the regression line. a notification data generating unit configured to generate notification data when three chronologically consecutive combinations of the fifth data, among a plurality of combinations of the fifth data and the HF and the LF/HF, are outside the allowable range, based on the sixth data;
The output unit outputs the notification data to the output device as the output of the information related to the sixth data.
The information processing device according to claim 1 . An information processing device according to any one of claims 1 to 7;
and at least one of an output device that performs an output in chronological order based on the sixth data to the first user, and the biometric data acquisition member. 8. An output device that receives information related to the sixth data from the information processing device according to claim 1,
an output device comprising: a receiving unit that receives information related to the sixth data; and an output unit that performs a time-series output of whether or not each of a plurality of combinations of HF and LF/HF of the fifth data is outside the allowable range based on the sixth data. 7. An output device that receives information related to the sixth data from the information processing device according to claim 1,
The output device is
A receiving unit that receives the sixth data;
an output device comprising: a notification data generating unit that generates notification data based on the sixth data when three chronologically consecutive combinations among a plurality of combinations of HF and LF/HF in the fifth data are outside the allowable range; and an output unit that performs output based on the notification data using an output device. A program for causing a computer to execute processing by each unit of an information processing device according to any one of claims 1 to 7. A non-transitory computer-readable storage medium that stores a program for causing a computer to execute processing by each unit of an information processing device according to any one of claims 1 to 7. An information processing method executed by an apparatus including a computer and a storage medium, comprising:
acquiring first data indicative of a pulse of a first user during a first time period sensed via a biometric data acquisition member;
generating second data including a plurality of combinations of HF and LF/HF based on the first data;
When a plurality of combinations of HF and LF/HF of the second data are plotted to show a relationship between HF and LF/HF, a regression line is calculated based on the plurality of plots, and a statistic representing a variation based on the regression line is calculated for the plurality of plots, and third data relating to an allowable region is generated based on the regression line and the statistic.
acquiring fourth data indicative of a pulse of the first user during a second time period detected via the biological data acquisition member;
generating fifth data including a plurality of combinations of HF and LF/HF based on the fourth data;
generating sixth data indicating whether each of a plurality of combinations of HF and LF/HF of the fifth data is outside the allowable range in a time series;
The information processing method further comprises: outputting information relating to the sixth data to an output device; or using an output device included in the device, outputting a time series of whether or not each of multiple combinations of HF and LF/HF of the fifth data is outside the allowable range based on the sixth data.