Patent Publication Number: US-2023142728-A1

Title: Living body abnormality detection device, living body abnormality detection method, and program

Description:
TECHNICAL FIELD 
     The present invention relates to a living body abnormality detection device, a living body abnormality detection method, and a program. 
     BACKGROUND ART 
     There is a known technique in which biological information such as a heart rate is measured with a wearable device and a notification is made to a user when there is an abnormality in the biological information (see Non-Patent Literature 1, for example). 
     In watching systems, observation equipment such as a nurse call button, a human detection sensor, a Doppler sensor, a heart rate monitor, a breath measurement device, a thermo camera, a sphygmomanometer, a clinical thermometer, an illuminometer, a thermometer, or a hygrometer is first connected to an observed person such as an elderly person. The watching system thus acquires observation information for the observed person. The watching system then determines whether or not an emergency notification condition is met based on the observation information, and makes an emergency notification in the case of an emergency. Watching systems that use such vital sensors are known (see Patent Literature 1, for example). 
     CITATION LIST 
     Non-Patent Literature 
     Non-Patent Literature 1: “Your heart rate. What it means, and where on Apple Watch (R) you&#39;ll find it.”, [online], Jan. 21, 2020, [retrieved on Mar. 2, 2020], Internet &lt;URL: https://support.apple.com/ja-jp/HT204666&gt; 
     Patent Literature 
     Patent Literature 1: Japanese Patent Laid-Open No. 2017-151755 
     SUMMARY OF INVENTION 
     Technical Problem 
     In view of the fact that it is difficult for conventional techniques to accurately detect the abnormality of a living body, it is an object of the present invention to accurately detect the abnormality of a living body. 
     Solution to Problem 
     A living body abnormality detection device is required to comprise: 
     a signal acquirer that acquires a first signal including a frequency component of heartbeat; 
     a filter that attenuates a frequency component higher than the frequency component of heartbeat and a frequency component lower than the frequency component of heartbeat based on the first signal to generate a second signal; 
     a frequency analyzer that indicates an analysis result obtained by analyzing a frequency component of the second signal based on the second signal; 
     an energy proportion calculator that calculates an energy proportion that is a proportion occupied by energy of a frequency component for each frequency band with respect to entire energy in the second signal based on the analysis result; 
     a variance value calculator that calculates an energy variance value of a frequency component for each frequency band based on the analysis result; and 
     a detector that at least detects abnormality or normality of a living body based on either one of the energy proportion and the variance value or both of the energy proportion and the variance value. 
     Advantageous Effect of Invention 
     According to the disclosed technique, it is possible to accurately detect the abnormality of a living body. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    shows an example overall configuration of a first embodiment. 
         FIG.  2    shows an example of a Doppler radar. 
         FIG.  3    shows an example of a living body abnormality detection device. 
         FIG.  4    shows an example overall process of the first embodiment. 
         FIG.  5    shows an example of a first signal. 
         FIG.  6    shows an analysis result in an experiment in which abnormality occurs in a low band. 
         FIG.  7    shows an analysis result in an experiment in which no abnormality occurs in a living body. 
         FIG.  8    shows an analysis result in an experiment in which abnormality occurs in a high band. 
         FIG.  9    shows a result of an experiment of detecting abnormality. 
         FIG.  10    shows an example of a learning process. 
         FIG.  11    shows an example functional configuration. 
         FIG.  12    shows an example of IQ data measured by the Doppler radar. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Optimal and minimal embodiments of the invention will be described below with reference to the drawings. Note that the same reference characters refer to similar components in the drawings, and overlapping descriptions will be omitted. Specific examples shown in the figures are illustrative, and further components other than those shown in the figures may also be included. 
     First Embodiment 
     For example, a living body abnormality detection system  1  is a system with an overall configuration as described below. 
     &lt;Example Overall Configuration&gt; 
       FIG.  1    shows an example overall configuration of a first embodiment. For example, the living body abnormality detection system  1  includes a personal computer (PC, hereinafter referred to as a “PC  10 ”), a Doppler radar  12 , a filter  13  and the like. Note that the living body abnormality detection system  1  desirably includes an amplifier  11  or the like, as shown in the figure. The following description will be made with reference to the overall configuration shown in the figure by way of example. 
     The PC  10  is an information processing device and is an example of a living body abnormality detection device. The PC  10  is connected to peripheral devices such as the amplifier  11  via a network, a cable or the like. Note that the amplifier  11 , the filter  13  and the like may be included in the PC  10 . The amplifier  11 , the filter  13  and the like may not be devices, but may be configured by software or configured by both hardware and software. The following description will be made with reference to the example of the living body abnormality detection system  1  as shown in the figure. 
     The Doppler radar  12  is an example of a measurement device. 
     In this example, the PC  10  is connected to the amplifier  11 . The amplifier  11  is connected to the filter  13 . The filter  13  is connected to the Doppler radar  12 . The PC  10  acquires measurement data from the Doppler radar  12  via the amplifier  11  and the filter  13 . That is, the measurement data is signal data indicating the action of a living body including heartbeat or the like. Next, the PC  10  analyzes the heartbeat or the like of the subject  2  based on the acquired measurement data, and measures the movement of the human body such as a heart rate. 
     The Doppler radar  12  acquires a signal (hereinafter referred to as a “biological signal”) indicating action such as heartbeat based on the following principle, for example. 
     &lt;Example of Doppler Radar&gt; 
       FIG.  2    shows an example of the Doppler radar. For example, the Doppler radar  12  is a device with a configuration as shown in  FIG.  2   . Specifically, the Doppler radar  12  includes a source  12 S, a transmitter  12 Tx, a receiver  12 Rx, and a mixer  12 M. The Doppler radar  12  also includes an adjuster  12 LNA such as a low noise amplifier (LNA) for performing a process such as reducing the noise in data received by the receiver  12 Rx. 
     The source  12 S is a transmission source for generating a transmission wave signal transmitted by the transmitter  12 Tx. 
     The transmitter  12 Tx transmits the transmission wave to the subject  2 . Note that the transmission wave signal can be represented by a function Tx(t) with respect to time “t”, and can be represented as in equation (1) below, for example. 
       [Expression 1] 
         Tx ( t )=cos(ω c   t )   (equation 1)
 
     In equation (1) above, the letter “ω c ” represents the angular frequency of the transmission wave. 
     It is assumed that the subject  2 , that is, the reflection surface of the transmitted signal has a displacement of x(t) at time “t”. In this example, the reflection surface is the chest wall of the subject  2 . The displacement x(t) can be represented as in equation (2) below, for example. 
       [Expression 2] 
         x ( t )= m ×cos(ω t )   (equation 2)
 
     In equation (2) above, the letter “m” represents a constant indicating the amplitude of the displacement. Also, in equation (2) above, the letter “ω” represents the angular speed, which shifts due to the movement of the subject  2 . Note that the variables similar to those in equation (1) above are the same variables. 
     The receiver  12 Rx receives a reflected wave reflected by the subject  2  after being transmitted by the transmitter  12 Tx. The reflected wave signal can be represented by a function Rx(t) with respect to time t, and can be represented as in equation (3) below, for example. 
     
       
         
           
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     In equation (3) above, the letter “do” represents the distance between the subject  2  and the Doppler radar  12 . The letter “λ” represents the wavelength of the signal. The same notation applies hereinafter. 
     The Doppler radar  12  mixes the function Tx(t) (equation (1) above) indicating the transmission wave signal and the function R(t) (equation (3) above) indicating the reception wave signal to generate a Doppler signal. Note that the Doppler signal can be represented by a function B(t) with respect to time t, as in equation (4) below. 
     
       
         
           
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     Defining the angular frequency of the Doppler signal as “ω d ”, the angular frequency ω d  of the Doppler signal can be represented as in equation (5) below. 
     
       
         
           
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     The phase “θ” in equation (4) above and equation (5) above can be represented as in equation (6) below. 
     
       
         
           
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     In equation (6) above, the letter “θ 0 ” represents the phase shift at the chest wall of the subject  2 , that is, at the reflection surface. 
     Next, the Doppler radar  12  outputs the position, speed or the like of the subject  2  based on the result of comparing the transmitted transmission wave signal and the received reception wave signal, that is, the result of calculation in the equations above. 
     For example, I-data (in-phase data) and Q-data (quadrature-phase data) can be generated from the reception wave. Then, the distance by which the chest wall of the subject  2  moves can be detected by using the I-data and Q-data. It is also possible to detect whether the chest wall of the subject  2  moves frontward or backward based on the phase indicated by the I-data and Q-data. Therefore, the movement of the chest wall due to heartbeat can detect an indicator of the heartbeat or the like by using changes in the frequencies of the transmission wave and reception wave. 
     &lt;Example Hardware Configuration of Living Body Abnormality Detection Device&gt; 
       FIG.  3    shows an example of the living body abnormality detection device. For example, the PC  10  includes a central processing unit (CPU, hereinafter referred to as a “CPU  10 H 1 ”), a memory  10 H 2 , an input device  10 H 3 , an output device  10 H 4 , and an input interface (I/F) (hereinafter referred to as an “input I/F  10 H 5 ”). Note that the hardware components included in the PC  10  are connected by a bus (hereinafter referred to as a “bus  10 H 6 ”), and data or the like is transmitted and received between the hardware components via the bus  10 H 6 . 
     The CPU  10 H 1  is a control device for controlling the hardware components of the PC  10  and a computing device for performing computation for realizing various processing operations. 
     The memory  10 H 2  is a primary memory, an auxiliary memory and the like, for example. Specifically, the primary memory is a memory or the like, for example. The auxiliary memory is a hard disk or the like, for example. The memory  10 H 2  stores data including intermediate data used by the PC  10 , programs used for various processing and control operations, and the like. 
     The input device  10 H 3  is a device for inputting parameters and instructions required for calculation to the PC  10  in response to an operation of the user. Specifically, the input device  10 H 3  is a keyboard, a mouse, a driver and the like, for example. 
     The output device  10 H 4  is a device for outputting various processing results and calculation results obtained by the PC  10  to the user or the like. Specifically, the output device  10 H 4  is a display or the like, for example. 
     The input I/F  10 H 5  is an interface connected to an external device such as a measurement device for transmitting and receiving data or the like. For example, the input I/F  10 H 5  is a connector, an antenna or the like. That is, the input I/F  10 H 5  transmits and receives data to/from the external device via a network, a wireless connection, a cable or the like. 
     Note that the hardware configuration is not limited to the configuration shown in the figure. For example, the PC  10  may further include a computing device, a memory or the like for performing processing in a parallel, distributed or redundant manner. The PC  10  may also be an information processing system connected to another device via a network or a cable for performing computation, control and storage in a parallel, distributed or redundant manner. That is, the present invention may be realized by an information processing system including one or more information processing devices. 
     The PC  10  thus acquires a biological signal indicating the action of the living body by using a measurement device such as the Doppler radar  12 . Note that the biological signal may be acquired when necessary in real time, or may be collectively acquired by the PC  10  after a device such as the Doppler radar stores the biological signal for a certain period. A recording medium or the like may be used for the acquisition. The PC  10  may include a measurement device such as the Doppler radar  12 , and the PC  10  may acquire the biological signal by performing measurement using the measurement device such as the Doppler radar  12  and generating the biological signal. 
     &lt;Example Overall Process&gt; 
       FIG.  4    shows an example overall process. For example, the overall process described below is performed every time window (preset to 60 seconds, for example). 
     (Example of Acquiring First Signal) 
     In step S 101 , the PC  10  acquires a first signal. For example, the first signal is a signal as shown below. 
       FIG.  5    shows an example of the first signal. In the figure, the horizontal axis indicates time, showing time points at which measurement is performed. The vertical axis indicates electric power estimated based on measurement results of the Doppler radar. 
     Hereinafter, a biological signal including a frequency component of heartbeat as shown in the figure is referred to as a “first signal”. 
     (Example of Band-Pass Filtering) 
     In step S 102 , the PC  10  performs band-pass filtering on the first signal to attenuate frequency components higher than the frequency component of heartbeat and frequency components lower than the frequency component of heartbeat. That is, the PC  10  attenuates frequency components of frequency bands other than the frequency component of heartbeat on the first signal. For example, the PC  10  performs filtering using a digital filter or the like with a cut-off frequency other than the frequency component of heartbeat. 
     For example, since the heart rate of an adult male is about 50 to 180 beats per minute, the frequency component of heartbeat mainly contains frequency components of about 0.8 Hz to 3 Hz. Therefore, to provide a margin such that the frequency component of heartbeat is not attenuated, the PC  10  desirably performs band-pass filtering to attenuate frequency components higher than 4.0 Hz and frequency components lower than 0.4 Hz. With such configuration, the PC  10  can attenuate frequency components that would be noise without attenuating the frequency component indicating heartbeat through the band-pass filtering. 
     Note that the frequency bands targeted by the band-pass filtering may be set in consideration of the age, sex, state and the like of the living body. For example, in a state of having done a heavy exercise or a state of being agitated, the heart rate has a higher frequency than in a resting state. Therefore, the frequency component of heartbeat is a frequency component higher than in the resting state. On the other hand, in the resting state, the frequency component of heartbeat is a low frequency component. Thus, in the PC  10 , the frequency bands targeted by the band-pass filtering may be dynamically changed or narrowed down, for example, according to the state of the living body or the like. 
     Specifically, in a state in which it is considered that the frequency component of heartbeat is a high frequency component, such as a state of having done a heavy exercise, a heart rate of about 100 to 210 beats per minute (which corresponds to about 1.6 Hz to 3.5 Hz in frequency) is assumed, and the PC  10  performs band-pass filtering to attenuate other frequency components. On the other hand, in a state in which it is considered that the frequency component of heartbeat is a low frequency component, such as a resting state, a heart rate of about 50 to 84 beats per minute (which corresponds to about 0.8 Hz to 1.4 Hz in frequency) is assumed, and the PC  10  performs band-pass filtering to attenuate other frequency components. 
     As described above, a state or the like can be input or a value may be set in consideration of a state or the like to perform the band-pass filtering in accordance with the state. 
     Hereinafter, a signal generated by the band-pass filtering is referred to as a “second signal”. 
     (Example of Frequency Analysis) 
     In step S 103 , the PC  10  performs frequency analysis on the second signal. For example, the frequency analysis is realized by a fast Fourier transform (FFT) or the like. In this manner, the PC  10  calculates a spectrum indicating energy for each frequency band. It is desirable that the PC  10  indicates an analysis result in a normalized form and by a spectrum. Hereinafter, the spectrum is indicated by normalized values. A specific example of the analysis result will be described later. 
     The following description will be made with reference to an example in which a process of calculating an energy proportion (step S 104  and step S 105  in the figure) and a process of calculating an energy variance value (step S 106  in the figure) are performed in parallel. However, these processes may not be parallel, but either one may be performed earlier. 
     (Example of Calculating Energy of Entire Frequency Band, Normal Frequency Band, and Abnormal Frequency Band) 
     In step S 104 , the PC  10  calculates energy of an entire frequency band, a normal frequency band, and an abnormal frequency band. 
     (Example of Calculating Energy Proportions of Normal Frequency Band and Abnormal Frequency Band) 
     In step S 105 , the PC  10  calculates energy proportions of the normal frequency band and the abnormal frequency band. 
     Note that the details of the energy and energy proportion of each frequency band calculated in step S 104  and step S 105  will be described later. 
     (Example of Calculating Energy Variance Values of Normal Frequency Band and Abnormal Frequency Band) 
     In step S 106 , the PC  10  calculates energy variance values of the normal frequency band and the abnormal frequency band. 
     The details of the energy variance values calculated in step S 106  above will be described later. 
     (Example of Determining Whether or not Living Body is Abnormal Based on Either One of Energy Proportion and Variance Value or Both of Energy Proportion and Variance Value) 
     In step S 107 , the PC  10  determines whether or not the living body is abnormal based on either one of the energy proportion and the variance value or both of the energy proportion and the variance value. 
     Next, if it is determined that there is abnormality in the living body (YES in step S 107 ), the PC  10  proceeds to step S 108 . On the other hand, if it is determined that there is no abnormality in the living body (NO in step S 107 ), the PC  10  ends the overall process. 
     (Example of Detecting Abnormality of Living Body) 
     In step S 108 , the PC  10  detects abnormality of the living body. 
     If abnormality of the living body is detected in step S 107  or step S 108  shown above, the PC  10  desirably provides an alert as described below. 
     (Example of Providing Alert) 
     In step S 109 , the PC  10  provides an alert. 
     For example, the alert is a message or the like informing the user or a predetermined recipient that abnormality occurs in the living body. Therefore, the alert may be in any form as long as it can inform the user or the recipient of the abnormality. For example, the alert may be provided by light, sound, a notification of the heart rate, a message with predetermined text, or a combination thereof. Providing an alert in this manner can quickly inform that abnormality occurs in the living body. 
     &lt;Experimental Result&gt; 
     For example, the following analysis result is obtained as the analysis result of the frequency analysis, that is, step S 103  by experiments. 
     &lt;Example of Analysis Result of Frequency Analysis&gt; 
     Hereinafter, a spectrum indicating frequency components on the horizontal axis and energy for each frequency component on the vertical axis is indicated by normalized values. 
     In the following analysis result, the entire frequency band considered, R 1 , corresponds to 30 bpm (beats per minute, a unit indicating the heart rate per minute) to 180 bpm. Therefore, when converted into frequency, the entire frequency band R 1  is a frequency band of “30 bpm÷60 sec=0.5 Hz” to “180 bpm÷60 sec=3.0 Hz”. Thus, the entire frequency band R 1  may be set to a certain limited range such as “0.5 Hz” to “3.0 Hz” as long as it is within the range of a frequency band obtained from the living body such as 0.5 Hz to 3.5 Hz. 
     In this experiment, a normal frequency band R 2  corresponds to 50 bpm to 120 bpm. Thus, it is desirable that the frequency band of “normality” is configurable. Therefore, when converted into frequency, the normal frequency band R 2  is a frequency band of “50 bpm÷60 sec=0.83 . . . Hz ≈0.83 Hz” to “120 bpm÷60 sec=2.0 Hz”. 
     A frequency band other than the normal frequency band R 2  in the entire frequency band R 1  is defined as an abnormal frequency band. Hereinafter, an abnormal frequency band in a frequency band lower than the normal frequency band R 2  is simply referred to as a “low band R 3 ”. An abnormal frequency band in a frequency band higher than the normal frequency band R 2  is simply referred to as a “high band R 4 ”. 
     When converted it into frequency, the low band R 3  is a frequency band of “30 bpm÷60 sec=0.5 Hz” to “50 bpm÷60 sec=0.83 . . . Hz≈0.83 Hz”. 
     When converted into frequency, the high band R 4  is a frequency band of “120 bpm÷60 sec=2.0 Hz” to “180 bpm÷60 sec=3.0 Hz”. 
     Thus, it is desirable that abnormality is classified by dividing the abnormal frequency band into the low band R 3  and the high band R 4 . The following description will be made with reference to an example of using classification into three, “normal”, “high band”, and “low band”. However, the normal frequency band may be classified into “high”, “middle”, “low”, and the like. In addition, the classification may be performed by further dividing the frequency bands into smaller frequency bands. Further, the classification may be classification into two, “normal” and “abnormal”. 
     &lt;Experimental Result Obtained When Abnormality Occurs in Low Band&gt; 
       FIG.  6    shows an analysis result in an experiment in which abnormality occurs in the low band. This case is a case where abnormality in which the heart rate of the living body is low at “45.7 bpm” occurs. Thus, energy in the low band R 3  is relatively high, as indicated by a first peak PK 1 . In this experiment, the energy proportion of the normal frequency band R 2 , the energy proportion of the low band R 3 , and the energy proportion of the high band R 4 , that is, calculation results of step S 105  are the following values. 
     The energy proportion of the low band R 3  is “30.7%”. 
     The energy proportion of the normal frequency band R 2  is “49.8%”. 
     The energy proportion of the high band R 4  is “19.5%”. 
     In this experiment, the variance value of the normal frequency band R 2 , the variance value of the low band R 3 , and the variance value of the high band R 4 , that is, calculation results of step S 106  are the following values. 
     The variance value of the low band R 3  is “3556.7×10 −6 ”. 
     The variance value of the normal frequency band R 2  is “918.8×10 −6 ”. 
     The variance value of the high band R 4  is “118.1×10 −6 ”. 
     &lt;Experimental Result Obtained When No Abnormality Occurs in Living Body&gt; 
       FIG.  7    shows an analysis result in an experiment in which no abnormality occurs in the living body. In this case, the heart rate of the living body is normal at “67.7 bpm”, and the frequency component of heart rate is in a “normal” state. Thus, a peak is indistinctive in the result, as compared to when abnormality occurs. 
     The energy proportions, that is, calculation results of step S 105 , calculated in a manner similar to the case of abnormality, are the following values. 
     The energy proportion of the low band R 3  is “28.1%”. 
     The energy proportion of the normal frequency band R 2  is “45.1%”. 
     The energy proportion of the high band R 4  is “26.8%”. 
     The variance values, that is, calculation results of step S 106 , calculated in a manner similar to the case of abnormality, are the following values. 
     The variance value of the low band R 3  is “1820×10 −6 ”. 
     The variance value of the normal frequency band R 2  is “272.2×10 −6 ”. 
     The variance value of the high band R 4  is “114.3×10 −6”.    
     &lt;Experimental Result Obtained when Abnormality Occurs in High Band&gt; 
       FIG.  8    shows an analysis result in an experiment in which abnormality occurs in the high band. This case is a case where abnormality in which the heart rate of the living body is high at “123.5 bpm” occurs. Thus, energy in the high band R 4  is high, as indicated by a second peak PK 2 . 
     The energy proportions, that is, calculation results of step S 105 , calculated in a manner similar to other cases, are the following values. 
     The energy proportion of the low band R 3  is “4.5%”. 
     The energy proportion of the normal frequency band R 2  is “47.9%”. 
     The energy proportion of the high band R 4  is “47.6%”. 
     The variance values, that is, calculation results of step S 106 , calculated in a manner similar to other cases, are the following values. 
     The variance value of the low band R 3  is “59.9×10 −6 ”. 
     The variance value of the normal frequency band R 2  is “765.0×10 −6 ”. 
     The variance value of the high band R 4  is “596.5×10 −6 ”. 
     Energy is calculated by integrating the frequency bands (resulting in the surface area of the frequency bands in the figure). Therefore, the respective energy proportions are calculated by calculating the entire energy and calculating proportions occupied by energy of the respective frequency bands. 
     As described above, when abnormality occurs, the variance value and the energy proportion of the abnormal frequency band are higher values than in the case of “normality”. Therefore, the PC  10  detects that abnormality occurs in the living body when either one of the variance value and the energy proportion is a high value. Thus, abnormality may be detected in a configuration in which it is determined on the whole that there is abnormality when either one of the variance value and the energy proportion is a high value, that is, in an “OR” configuration. 
     However, the PC  10  desirably has a configuration in which abnormality is detected on the whole when abnormality of the living body is detected in both determinations for the variance value and the energy proportion, that is, an “AND” configuration. 
     That is, the PC  10  first determines whether or not the living body is abnormal separately based on the variance value and the energy proportion. Next, the PC  10  detects abnormality of the living body in the case of a detection result that the living body is abnormal as it is determined that the values are high in both determination results (YES in step S 107  and step S 108 ). 
     Thus, the PC  10  is desirably configured to use the “AND” of both determinations for the variance value and the energy proportion. With such an “AND” configuration, the PC  10  can accurately determine abnormality of the living body. 
     &lt;Result of Detection of Abnormality&gt; 
       FIG.  9    shows a result of an experiment of detecting abnormality. The horizontal axis in the figure indicates the serial numbers of experimental results. On the vertical axis, “0” indicates a detection result of “normality”. Also, on the vertical axis, “−1” indicates a detection result of “abnormality of a low heart rate”. Also, on the vertical axis, “1” indicates a detection result of “abnormality of a high heart rate”. Therefore, coincidence on the vertical axis between a true value indicated by “Ground-truth of classification” and a detection result of “Prediction of classification”, which is a detection result of this embodiment, means a result in which abnormality is accurately detected. 
     As shown in the figure, in the experimental results other than “4”, the detection results for abnormality of a low heart rate, a normal heart rate, and abnormality of a high heart rate coincide. Thus, the experimental results show that the PC  10  can accurately detect abnormality and can classify the types of abnormality. 
     If abnormality is detected when the energy variance value and the energy proportion in the abnormal frequency band are high, as described above, it is possible to accurately detect abnormality of the living body. 
     Note that whether or not the variance value and the energy proportion are high values is determined by comparison to a preset threshold, for example. Note that the threshold is set in consideration of a result of an experiment performed in advance, such as the above-described experiment. The criteria for the energy and the variance value often vary according to the normalization method and the living body. 
     The abnormal frequency band and the threshold may be changed according to the state of the living body. For example, after doing a heavy exercise or the like, there is often no abnormality even if the heart rate is about “100 bpm” or more. On the other hand, if the heart rate is about “100 bpm” or more in the resting state, it may be determined that there is abnormality. Thus, the ranges of “normality” and “abnormality” vary according to conditions such as the state, age, sex, or mental state of the living body, or a combination thereof. Therefore, the abnormal frequency band, the threshold and the like may be changed according to these conditions. 
     Second Embodiment 
     As compared to the first embodiment, a second embodiment has a configuration of using machine learning for the detection of abnormality. Hereinafter, the difference from the first embodiment will be mainly described, and overlapping descriptions will be omitted. 
     In the second embodiment, it is desirable that a learning process as described below is performed before the process shown in  FIG.  4    is performed. 
       FIG.  10    shows an example of the learning process. That is, defining the overall process shown in  FIG.  4    as an “execution process”, the PC  10  learns a learning model and generates a “learned model” through the learning process as shown in the figure before performing the “execution process”. 
     (Example of Acquisition of Analysis Result) 
     In step S 201 , the PC  10  acquires an analysis result of frequency analysis. For example, the PC  10  acquires data indicating an analysis result of frequency analysis obtained by performing processes similar to step S 101  to step S 103  in the first embodiment. 
     (Example of Learning Using Analysis Result as Training Data) 
     In step S 202 , the PC  10  learns a learning model by using the analysis result acquired in step S 201  as training data. Note that the learning is desirably performed repeatedly according to the accuracy of detecting abnormality to an extent that the accuracy is obtained. 
     (Example of Generating Learned Model) 
     In step S 203 , the PC  10  generates a learned model. 
     For example, the learning model is desirably a support vector machine (SVM). That is, it is desirable that SVM learning is performed by using the energy proportion and the variance value as feature values to generate the learned model. 
     As shown in the first embodiment, the PC  10  detects abnormality of the living body by classifying the state of the living body into “abnormality” and “normality”. In addition, for example, even in the case of “abnormality”, it is desirable that the type of “abnormality” can be further classified, such as whether it is abnormality in the “low band” or abnormality in the “high band”. That is, the threshold for classification is learned by machine learning. Thus, by using an SVM learned model, it is possible to accurately classify the state of the living body. 
     The living body abnormality detection device and the living body abnormality detection system may be configured to use other artificial intelligence (AI). For example, the learned model may be a network structure including a network structure such as a convolution neural network (CNN) or a recurrent neural network (RNN). For example, the learning model is subjected to machine learning using image data indicating the analysis result of frequency analysis such as in  FIG.  6    as training data. With such a configuration, the extraction of feature values can be eliminated. 
     Note that the training data may be in the form of a biological signal, image data indicating the analysis result of frequency analysis such as in  FIG.  6   , a numerical value such as the energy proportion, or a combination thereof. 
     The learned model is used as part of software in the AI. Therefore, the learned model is a program. Thus, the learned model may be distributed or executed via a recording medium, a network or the like, for example. In the execution process, the detection of abnormality is performed by using the learned model. 
     Note that the “learning process” and the “execution process” may be performed by different devices. Therefore, a device for performing the “learning process” may have a functional configuration that does not include a configuration for the “execution process”. On the other hand, a device for performing the “execution process” may have a functional configuration that does not include a configuration for the “learning process”. That is, the living body abnormality detection device and the living body abnormality detection system may have a functional configuration including either one of the configurations for the “learning process” and the “execution process”, not both. 
     Third Embodiment 
     As compared to the first embodiment, the third embodiment has a difference in that a temporal difference of signal values indicated by the second signal is calculated. Hereinafter, the difference from the first embodiment and the like will be mainly described, and overlapping descriptions will be omitted. 
     For example, it is assumed that the second signal value is a signal value “X” shown in equation (7) below. 
       [Expression 7] 
         X=[x   1   , x   2   , x   3   , . . . , x   n−2   , x   n&#39;11   , x   n ]  (equation 7)
 
     As indicated by equation (7) above, the signal value “X” is a value indicated by the second signal value at a certain time point. Also, “n” in equation (7) above is a value indicating the sequence number at which the signal value is acquired. 
     For example, the temporal difference is the difference between a signal value (hereinafter referred to as a “first signal value”) at a time point of “n” (hereinafter referred to as a “first time point”) and a signal value (hereinafter referred to as a “second signal value”) at a time point of “n−1” (hereinafter referred to as a “second time point”). Specifically, as indicated by equation (8) below, the temporal difference, “D”, is a result obtained by calculating the difference between the first signal value and the second signal value acquired at the second time point, which is the next previous time point to the first time point (indicated as “X n ”-“X n−1 ” in equation (8) below). 
       [Expression 8] 
         D=[X   n   −X   n−1   ]=[x   2   −x   1   , x   3   −x   2   , . . . , x   n−2   −x   n−1   , x   n−1   −x   n ]  (equation 8)
 
     As in equation (8) above, a temporal difference of signal values indicated by the second signal, that is, a signal obtained by performing band-pass filtering (step S 102 ) on a biological signal is calculated. Note that, although a difference is calculated in equation (8) above for execution by a computer or the like, differentiation may be used for continuity. 
     In the frequency analysis in step S 103 , the PC  10  performs the analysis on the calculation result of the temporal difference, that is, the calculation result of equation (8) above. 
     As described above, the PC  10  is desirably configured to calculate the temporal difference. With such a configuration, the PC  10  can accurately detect abnormality. 
     &lt;Example Functional Configuration&gt; 
       FIG.  11    shows an example functional configuration. For example, the living body abnormality detection device has a functional configuration including a signal acquirer  10 F 1 , a filter  10 F 2 , a frequency analyzer  10 F 4 , an energy proportion calculator  10 F 5 , a variance value calculator  10 F 6 , and a detector  10 F 7 . In addition, the living body abnormality detection device desirably has a functional configuration further including a temporal difference calculator  10 F 3 , a learner  10 F 8 , and an alarm  10 F 9  as shown in the figure. The following description will be made with reference to the functional configuration as shown in the figure by way of example. 
     The signal acquirer  10 F 1  performs a signal acquisition procedure of acquiring a biological signal such as the first signal. For example, the signal acquirer  10 F 1  is realized by the Doppler radar  12 , the input I/F  10 H 5  or the like. 
     The filter  10 F 2  performs a filter procedure of filtering a certain frequency band in the biological signal such as the first signal. For example, the filter  10 F 2  is realized by the CPU  10 H 1 , the filter  13  or the like. 
     The temporal difference calculator  10 F 3  performs a temporal difference calculation procedure of calculating a temporal difference based on the second signal. For example, the temporal difference calculator  10 F 3  is realized by the CPU  10 H 1  or the like. 
     The frequency analyzer  10 F 4  performs a frequency analysis procedure of performing frequency analysis on the second signal or the like or the temporal difference. For example, the frequency analyzer  10 F 4  is realized by the CPU  10 H 1  or the like. 
     The energy proportion calculator  10 F 5  performs an energy proportion calculation procedure of calculating an energy proportion based on the result of analysis by the frequency analyzer  10 F 4 . For example, the energy proportion calculator  10 F 5  is realized by the CPU  10 H 1  or the like. 
     The variance value calculator  10 F 6  performs a variance value calculation procedure of calculating a variance value based on the result of analysis by the frequency analyzer  10 F 4 . For example, the variance value calculator  10 F 6  is realized by the CPU  10 H 1  or the like. 
     The detector  10 F 7  performs a detection procedure of detecting abnormality of the living body based on either one of the energy proportion and the variance value or both of the energy proportion and the variance value. For example, the detector  10 F 7  is realized by the CPU  10 H 1  or the like. 
     The learner  10 F 8  performs learning procedure of learning a learning model MDL by using data or the like indicating the result of analysis by the frequency analyzer  10 F 4  as training data to generate a learned model. For example, the learner  10 F 8  is realized by the CPU  10 H 1  or the like. 
     The alarm  10 F 9  performs an alert procedure of providing an alert when abnormality occurs in the living body based on the result of detection by the detector  10 F 7 . For example, the alarm  10 F 9  is realized by the output device  10 H 4  or the like. 
     &lt;Example of IQ Data Measured by Doppler Radar&gt; 
       FIG.  12    shows an example of IQ data measured by the Doppler radar. For example, the Doppler radar  12  outputs a signal as shown in the figure. The arctan (Q/I) is then calculated to obtain a biological signal. 
     The Doppler radar  12  can measure the movement of an object based on the Doppler effect, by which the frequency of reflected waves changes when a moving object is irradiated with radio waves. Such a configuration that can measure the movement of a subject in a contactless manner is desirable. 
     &lt;Variation&gt; 
     Note that energy distribution in a region in which heartbeat is present possibly varies temporally. Therefore, the energy, the energy proportion and the like may be dynamically calculated according to the temporal variation of the energy distribution. In particular, under the condition that the time width is beyond an extent that the heart rate changes and a change of energy due to the environment is not large as compared to the change of heartbeat for the time width, it is desirable that the temporal variation is taken into consideration. 
     Note that the living body is not limited to a human but may be an animal or the like. 
     In addition, the biological signal may include breathing. Therefore, the abnormality detection method may also be performed by using the breathing rate, the frequency of breathing and the like. Note that, in the case of using breathing, it often differs in the number of counts per unit time from the heart rate, and therefore the threshold for detection, the range for determining abnormality, the range for determining normality and the like are desirably set separately for the breathing rate. 
     Other Embodiments 
     For example, a transmitter, a receiver, or an information processing device may be a plurality of devices. That is, processing and control may be performed in a virtualized, parallel, distributed or redundant manner. On the other hand, the transmitter, receiver and information processing device may be integrated in hardware or share devices. 
     Note that all or part of each process according to the present invention may be written in a low-level language such as assembler or a high-level language such as an object-oriented language and realized by a program for causing a computer to perform the living body abnormality detection method. That is, the program is a computer program for causing a computer of the information processing device, the living body abnormality detection system or the like to perform each process. 
     Therefore, when each process is performed based on the program, a computing device and a control device included in the computer perform computation and control based on the program in order to perform each process. In order to perform each process, a memory included in the computer stores data used for the process based on the program. 
     The program can be recorded on a computer-readable recording medium and distributed. Note that the recording medium is a medium such as a magnetic tape, a flash memory, an optical disk, a magneto-optical disk or a magnetic disk. The program can be distributed through telecommunication lines. 
     Although preferred embodiments and the like have been described in detail above, there is no limitation to the above-described embodiments and the like, and various modifications and replacements can be made to the above-described embodiments and the like without departing from the scope of the claims. 
     This international application claims priority based on Japanese Patent Application No. 2020-046622, filed on Mar. 17, 2020, the entire contents of which are hereby incorporated by reference into this international application. 
     REFERENCE SIGNS LIST 
     
         
           1 : living body abnormality detection system 
           2 : subject 
           10 : PC 
           10 F 1 : signal acquirer 
           10 F 2 : filter 
           10 F 3 : temporal difference calculator 
           10 F 4 : frequency analyzer 
           10 F 5 : energy proportion calculator 
           10 F 6 : variance value calculator 
           10 F 7 : detector 
           10 F 8 : learner 
           10 F 9 : alarm 
           11 : amplifier 
           12 : Doppler radar 
           13 : filter 
         MDL: learning model 
         PK 1 : first peak 
         PK 2 : second peak 
         R 1 : entire frequency band 
         R 2 : normal frequency band 
         R 3 : low band 
         R 4 : high band