Patent Publication Number: US-2023148881-A1

Title: Skin condition estimation method, skin condition estimation device, and skin condition estimation system

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This is a continuation application of International Application No. PCT/JP2021/027725, with an international filing date of Jul. 27, 2021, which claims priority of Japanese Patent Application No. 2020-131861 filed on Aug. 3, 2020, the contents of which are incorporated herein by reference. 
    
    
     BACKGROUND ART 
     The present disclosure relates to a skin condition estimation method, a skin condition estimation device, and a skin condition estimation system. 
     JP2020-14710A discloses a skin condition differentiation method for estimating a skin condition using a muscle amount as an index. In the differentiation method described in JP2020-14710A, analysis is performed by deriving a parameter representing a skin condition by applying a muscle amount obtained by measurement to an estimation equation obtained by multivariate analysis. 
     BRIEF SUMMARY 
     In recent years, it has become desirable to estimate a future skin condition. 
     A skin condition estimation method according to an aspect of the present disclosure is a skin condition estimation method executed by a computer and includes: acquiring first information related to hormone balance; and estimating a future skin condition on an estimation date after a first acquisition date on which the first information is acquired based on the first information. 
     A skin condition estimation device according to an aspect of the present disclosure includes: a measurement unit that acquires information related to hormone balance; and an estimator that estimates future skin information on an estimation date after a date on which the information is acquired based on the information acquired by the measurement unit. 
     A skin condition estimation system according to an aspect of the present disclosure includes: a measurement device; and a processing device that communicates with the measurement device, in which the measurement device includes: a measurement unit that acquires information related to hormone balance; and a first communicator that transmits the information, and in which the processing device includes: a second communicator that receives the information; and an estimator that estimates a future skin condition on an estimation date after a date on which the information is acquired based on the information. 
     According to the present disclosure, a future skin condition can be estimated. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a block diagram showing a schematic configuration of an example of a skin condition estimation device according to a first embodiment of the present disclosure; 
         FIG.  2    is a schematic diagram showing an example of measurement timing; 
         FIG.  3    is a graph showing an example of a correlation coefficient between a basal body temperature and a skin condition; 
         FIG.  4    is a flowchart showing an example of a skin condition estimation method according to the first embodiment of the present disclosure; 
         FIG.  5    is a block diagram showing a schematic configuration of a skin condition estimation device according to a first modification of the first embodiment of the present disclosure; 
         FIG.  6 A  is a schematic view showing an example of a display screen of a display unit according to the first modification; 
         FIG.  6 B  is a schematic view showing another example of the display screen of the display unit according to the first modification; 
         FIG.  7    is a block diagram showing a schematic configuration of an example of a skin condition estimation device according to a second embodiment of the present disclosure; 
         FIG.  8    is a schematic diagram showing an example of an acceleration pulse wave; 
         FIG.  9    is a schematic diagram showing an example of measurement timing; 
         FIG.  10    is a flowchart showing an example of a skin condition estimation method according to the second embodiment of the present disclosure; 
         FIG.  11    is a block diagram showing a schematic configuration of an example of a skin condition estimation device according to a third embodiment of the present disclosure; 
         FIG.  12    is a schematic diagram showing an example of measurement timing; 
         FIG.  13    is a flowchart showing an example of a skin condition estimation method according to the third embodiment of the present disclosure; 
         FIG.  14    is a flowchart of a skin condition estimation method according to a second modification of the third embodiment the present disclosure; 
         FIG.  15    is a schematic diagram showing an example of measurement timing of the second modification; 
         FIG.  16    is a block diagram showing a schematic configuration of an example of a skin condition estimation device according to a fourth embodiment of the present disclosure; 
         FIG.  17    is a flowchart of an example of a machine learning method in a skin condition estimation method according to the fourth embodiment the present disclosure; 
         FIG.  18    is a block diagram showing a schematic configuration of an example of a skin condition estimation device according to a fifth embodiment of the present disclosure; 
         FIG.  19    is a block diagram showing a schematic configuration of an example of a skin condition estimation system according to a sixth embodiment of the present disclosure; 
         FIG.  20    is a flowchart showing an example of a skin condition estimation method according to the sixth embodiment of the present disclosure; 
         FIG.  21    is a block diagram showing a schematic configuration of an example of a skin condition estimation system according to a third modification of the sixth embodiment of the present disclosure; 
         FIG.  22    is a block diagram showing a schematic configuration of an example of a skin condition estimation system according to a seventh embodiment of the present disclosure; 
         FIG.  23    is a block diagram showing a schematic configuration of an example of a skin condition estimation system according to an eighth embodiment of the present disclosure; 
         FIG.  24    is a block diagram showing a schematic configuration of an example of a skin condition estimation device according to a ninth embodiment of the present disclosure; 
         FIG.  25    is a flowchart showing an example of a skin condition estimation method according to the ninth embodiment of the present disclosure; 
         FIG.  26    is a flowchart of a skin condition estimation method according to a fourth modification of the ninth embodiment of the present disclosure; 
         FIG.  27    is a graph showing an example of a correlation between an actual measurement value and examples 1 and 2; 
         FIG.  28    is a graph showing an example of a correlation between a comparative example 1 and an actual measurement value; 
         FIG.  29    is a graph showing an example of a correlation between a comparative example 2 and an actual measurement value; 
         FIG.  30    is a graph showing an example of a correlation between a comparative example 3 and an actual measurement value; 
         FIG.  31    is a graph showing an example of a correlation between a comparative example 4 and an actual measurement value; 
         FIG.  32    is a table showing an example of correlation coefficients of examples 1 to 3 and the comparative examples 1 to 4; 
         FIG.  33    is a graph showing an example of a correlation between an actual measurement value and examples 4 and 5; 
         FIG.  34    is a graph showing an example of a correlation between an actual measurement value and examples 6 and 7; 
         FIG.  35    is a graph showing an example of a correlation between an actual measurement value and examples 8 and 9; and 
         FIG.  36    is a table showing an example of correlation coefficients of examples 4 to 9. 
     
    
    
     DETAILED DESCRIPTION 
     Circumstances Leading to Present Disclosure 
     In recent years, it has become desirable to estimate a future skin condition. By knowing the future skin condition, effective care for the skin can be performed, and the skin condition can be kept good. 
     The skin condition differentiation method described in JP2020-14710A estimates the skin condition using the muscle amount as an index. However, in the differentiation method described in JP2020-14710A, although the current skin condition can be estimated, there is a problem that the future skin condition cannot be estimated. 
     As a result of intensive studies, the present inventors have found that there is a correlation between information related to hormone balance and skin condition. Therefore, the present inventors have found a configuration for acquiring information related to hormone balance and estimating a skin condition in the future from the acquisition date of the information based on the information, thereby achieving the present disclosure. 
     A skin condition estimation method according to an aspect of the present disclosure is a skin condition estimation method executed by a computer and includes: acquiring first information related to hormone balance; and estimating a future skin condition on an estimation date after a first acquisition date on which the first information is acquired based on the first information. 
     With such a configuration, the future skin condition can be estimated. 
     The first information may include at least one piece of information of basal body temperature, brain waves, blood, saliva, and urine. 
     With such a configuration, the future skin condition can be easily estimated. 
     The first acquisition date may be 7 days or more and 13 days or less before the estimation date. 
     With such a configuration, the estimation accuracy of the future skin condition can be improved. 
     The acquiring the first information may include acquiring a plurality of pieces of the first information on a plurality of different days, and the estimating may include estimating the future skin condition based on the plurality of pieces of the first information acquired on the plurality of different days. 
     With such a configuration, the estimation accuracy of the future skin condition can be improved. 
     The skin condition estimation method may further include acquiring second information related to a blood vessel condition, and the estimating may include estimating the future skin condition on the estimation date after a second acquisition date on which the second information is acquired based on the first information and the second information. 
     With such a configuration, the estimation accuracy of the future skin condition can be improved. 
     The second information may include at least one piece of information of a pulse wave, a blood pressure, and a form and a function of a blood vessel. 
     With such a configuration, the future skin condition can be easily estimated. 
     The second acquisition date may be different from the first acquisition date. 
     With such a configuration, the estimation accuracy of the future skin condition can be improved. 
     The second acquisition date may be later than the first acquisition date. 
     With such a configuration, the estimation accuracy of the future skin condition can be further improved. 
     The acquiring the second information may include acquiring a plurality of pieces of the second information on a plurality of different days, and the estimating may include estimating the future skin condition based on the plurality of pieces of the second information acquired on the plurality of different days. 
     With such a configuration, the estimation accuracy of the future skin condition can be further improved. 
     The skin condition estimation method may further include acquiring third information related to a blood vessel condition different from the second information, and the estimating may include estimating the future skin condition on the estimation date after a third acquisition date on which the third information is acquired based on the first information and the third information. 
     With such a configuration, the estimation accuracy of the future skin condition can be improved. 
     The third information may be different from the second acquisition date. 
     With such a configuration, the future skin condition can be easily estimated. 
     The third acquisition date may be later than the second acquisition date. 
     With such a configuration, the estimation accuracy of the future skin condition can be improved. 
     The acquiring the third information may include acquiring a plurality of pieces of the third information on a plurality of different days, and the estimating may include estimating the future skin condition based on the plurality of pieces of the third information acquired on the plurality of different days. 
     With such a configuration, the estimation accuracy of the future skin condition can be improved. 
     The estimation date may be current, and the estimating may include estimating a current skin condition based on the first information acquired in the past. 
     With such a configuration, the current skin condition can be estimated. 
     The skin condition estimation method may further include acquiring fourth information having at least one of information related to a current hormone balance and information related to a current blood condition, and the estimating the current skin condition may include estimating the current skin condition based on the first information acquired in the past and the fourth information acquired at present. 
     With such a configuration, the estimation accuracy of the current skin condition can be improved. 
     The skin condition estimation method may further include: acquiring actual measurement information of a skin condition; and creating a regression model in which the first information is input and the future skin condition is output using the first information and information of the skin condition as teacher data. 
     With such a configuration, the estimation accuracy of the future skin condition can be improved. 
     The estimating may include estimating the future skin condition by inputting the first information to the regression model. 
     With such a configuration, the estimation accuracy of the future skin condition can be improved. 
     A skin condition estimation device according to an aspect of the present disclosure includes: a measurement unit that acquires information related to hormone balance; and an estimator that estimates future skin information on an estimation date after a date on which the information is acquired based on the information acquired by the measurement unit. 
     With such a configuration, the future skin condition can be estimated. 
     A skin condition estimation system according to an aspect of the present disclosure includes: a measurement device; and a processing device that communicates with the measurement device, in which the measurement device includes: a measurement unit that acquires information related to hormone balance; and a first communicator that transmits the information, and in which the processing device includes: a second communicator that receives the information; and an estimator that estimates a future skin condition on an estimation date after a date on which the information is acquired based on the information. 
     With such a configuration, the future skin condition can be estimated. 
     Hereinafter, an embodiment of the present disclosure will be described with reference to the accompanying drawings. Note that the following description is merely exemplary in nature and is not intended to limit the present disclosure, an object for application, or a usage. Furthermore, the drawings are schematic, and ratios of dimensions and the like do not necessarily match actual ones. 
     First Embodiment 
     Overall Configuration 
       FIG.  1    is a block diagram showing a schematic configuration of an example of a skin condition estimation device  1 A according to a first embodiment of the disclosure. As shown in  FIG.  1   , the skin condition estimation device  1 A includes a measurement unit  10 , an estimator  20 , and a controller  30 . The skin condition estimation device  1 A estimates a future skin condition based on information measured by the measurement unit  10 . In the first embodiment, the skin condition region estimated by the skin condition estimation device  1 A is the face of a human. 
     In the present specification, the “skin condition” includes at least one of pore, wrinkle, texture, pigmented skin color, moisture, and oil content. The “pore” is an evaluation item indicating the conspicuousness of the outlet of the hair protruding from the skin surface. The “wrinkle” is an evaluation item indicating a fold, a crimp, or a ridge formed on the surface of the skin. The “texture” is an evaluation item indicating the beauty of the skin determined by fine irregularities engraved in the skin surface. The “pigmentation” is an evaluation item indicating, for example, a spot or uneven skin color caused by deposition of pigments such as melanin on the skin. The “skin color” is an evaluation item indicating the color tone and brightness of the skin. The “moisture” means the amount of moisture contained in the skin. The “oil content” means the amount of oil contained in the skin. These evaluation items are numerically displayed, for example. For example, the skin condition is expressed by five-grade evaluation. The five-grade evaluation is represented by a numerical range of 1 to 5, and the larger the numerical value, the better. 
     The skin condition estimation device  1 A will be described in detail. 
     Measurement Unit 
     The measurement unit  10  acquires information related to hormone balance. The measurement unit  10  may be referred to as a first measurement unit  10 . 
     The hormone includes, for example, female hormone and male hormone. The female hormone has, for example, estrogen and progesterone. The male hormone has, for example, steroid hormone such as testosterone. 
     The information related to hormone balance means information correlated with hormone balance. That is, the information related to hormone balance means information capable of estimating a change in hormone balance. For example, the information related to hormone balance is biological information that changes with a change in the amount of hormone secreted. For example, the information related to hormone balance includes at least one piece of information of basal body temperature, brain waves, blood, saliva, and urine. 
     In the present specification, the information related to hormone balance may be referred to as first information. 
     The “basal body temperature” is a body temperature measured in a resting state in which factors such as a body temperature change due to activity are excluded and only minimum energy necessary for life support is consumed. For example, the basal body temperature can be measured by a basal thermometer in a resting state at the time of awakening. The basal body temperature is expressed in 0.01 units (second decimal place). 
     The “brain waves” are brain waves measured by an electroencephalograph. Examples of the brain waves related to hormone balance include brain waves around 20 or more and 22 Hz or less, 11 Hz, 14 Hz, and 8 or more and 10 Hz or less. 
     The “blood” is, for example, information of the amount of hormone (for example, progesterone) contained in the blood. The amount of hormone contained in the blood can be measured, for example, by a blood test. 
     The “saliva” is, for example, information of the amount of hormone contained in the saliva or the amount of saliva secreted. The amount of hormone contained in the saliva and the amount of saliva secreted can be measured by, for example, a saliva test. In addition, the amount of saliva secreted can be measured by, for example, an oral wetting meter. 
     The “urine” is, for example, information of the amount of hormone (for example, estrogen) contained in the urine. The amount of hormone contained in the urine can be measured, for example, by a urinalysis. 
     The measurement unit  10  is a measurement device (e.g., a sensor) capable of measuring at least one piece of information of basal body temperature, brain waves, blood, saliva, and urine as the information related to hormone balance. 
     In the first embodiment, an example in which the information related to hormone balance is a basal body temperature will be described. Therefore, the measurement unit  10  measures the basal body temperature. The measurement unit  10  includes, for example, a basal thermometer. 
     The measurement unit  10  transmits the information related to hormone balance to the estimator  20 . Specifically, the measurement unit  10  transmits information of the basal body temperature to the estimator  20 . Alternatively, the information of the basal body temperature measured by the measurement unit  10  is input to the estimator  20 . 
       FIG.  2    is a schematic diagram showing an example of measurement timing. In  FIG.  2   , T 0  indicates an estimation date on which the skin condition is estimated. T 1  indicates a measurement date of the basal body temperature by the measurement unit  10 , that is, an acquisition date of the basal body temperature. As shown in  FIG.  2   , the measurement unit  10  measures the basal body temperature on the acquisition date T 1  before the estimation date T 0 . 
       FIG.  3    is a graph showing an example of a correlation coefficient between a basal body temperature and a skin condition. In  FIG.  3   , the horizontal axis represents the number of days of deviation of the acquisition date T 1  of the basal body temperature from the estimation date T 0 , and the vertical axis represents the correlation coefficient between the basal body temperature and the skin condition. The correlation coefficient indicates the degree of correlation between the basal body temperature and the skin condition. A higher correlation coefficient indicates a higher correlation between the basal body temperature and the skin condition. The correlation coefficient is calculated based on an estimation score of the skin condition estimated based on the basal body temperature and an actual measurement score obtained by actually measuring the skin condition. For example, the correlation coefficient is calculated by dividing the covariance by the standard deviation of the respective variables. The calculation of the estimation score will be described later. The measured score was measured using a measurement device capable of scoring the skin condition. As the measurement device, for example, a skin analysis system “Beauty Explorer (registered trademark)” manufactured by Sony Corporation can be used. 
     It can be said that the correlation coefficient has a correlation of 0.5 or more. That is, when the correlation coefficient is 0.5 or more, the skin condition can be estimated with high accuracy based on the information of the basal body temperature. As shown in  FIG.  3   , the acquisition date T 1  of the basal body temperature by the measurement unit  10  with the correlation coefficient of 0.5 or more is 7 days or more and 13 days or less before the estimation date T 0 . The acquisition date T 1  of the basal body temperature with the correlation coefficient of 0.6 or more is 8 days or more and 12 days or less before the estimation date T 0 . In addition, the acquisition date T 1  at which the correlation coefficient becomes the highest is a day 10 days before the estimation date T 0 . 
     Therefore, the acquisition date T 1  by the measurement unit  10  is 7 days or more and 13 days or less before the estimation date T 0 . Preferably, the acquisition date T 1  is 8 days or more and 12 days or less before the estimation date T 0 . More preferably, the acquisition date T 1  is a date 10 days before the estimation date T 0 . As a result, the estimation accuracy of the skin condition by the estimator  20  can be improved. 
     Estimator 
     The estimator  20  estimates a future skin condition based on the information acquired by the measurement unit  10 . Specifically, the estimator  20  estimates the future skin condition on the estimation date T 0  after the acquisition date T 1  of the information acquired by the measurement unit  10  based on the information acquired by the measurement unit  10 . 
     In the first embodiment, the estimator  20  estimates the future skin condition on the estimation date T 0  after the acquisition date T 1  when the basal body temperature is acquired based on the basal body temperature acquired by the measurement unit  10 . 
     The estimator  20  estimates a future skin condition within 7 days or more and 13 days or less from the acquisition date T 1  of the basal body temperature by the measurement unit  10 . Preferably, the estimator  20  estimates a future skin condition within 8 days or more and 12 days or less from the acquisition date T 1  of the basal body temperature by the measurement unit  10 . More preferably, the estimator  20  estimates a future skin condition 10 days after the acquisition date T 1  of the basal body temperature by the measurement unit  10 . 
     In the first embodiment, the estimator  20  estimates the future skin condition on the estimation date T 0  on the date of the acquisition date T 1  when the measurement unit  10  acquires the information. That is, the acquisition date T 1  is current, and the estimation date T 0  is later than the acquisition date T 1 . 
     The estimator  20  receives information of the basal body temperature from the measurement unit  10 . The estimator  20  executes regression analysis using information of the basal body temperature. Specifically, the estimator  20  includes a regression model subjected to machine learning in advance. The regression model is stored in a storage of the estimator  20 . In the first embodiment, the regression model is a model in which the information of the basal body temperature is input and the information of the future skin condition is output. The output information of the future skin condition is, for example, information obtained by quantifying the evaluation of the skin condition. 
     The estimator  20  inputs the information of the basal body temperature measured by the measurement unit  10  to the regression model. The estimator  20  estimates the future skin condition on the estimation date T 0  after the acquisition date T 1  based on the basal body temperature using the regression model. That is, the estimator  20  outputs the information of the future skin condition by inputting the basal body temperature to the regression model. 
     The estimator  20  can be implemented by, for example, a semiconductor element or the like. For example, the estimator  20  can include a microcomputer, a central processing unit (CPU), a micro processing unit (MPU), a graphics processing unit (GPU), a digital signal processor (DSP), a field programmable gate array (FPGA), or an application specific integrated circuit (ASIC). 
     Controller 
     The controller  30  integrally controls the components of the skin condition estimation device  1 A. The controller  30  includes, for example, a memory that stores a program, and a processing circuit (not shown) corresponding to a processor such as a central processing unit (CPU). In the controller  30 , the processor executes the program stored in the memory. The controller  30  can be implemented by, for example, a semiconductor element or the like. For example, the controller  30  may include a microcomputer, a CPU, an MPU, a GPU, a DSP, an FPGA, or an ASIC. The functions of the controller  30  may be configured only by hardware, or may be realized by combining hardware and software. In the first embodiment, the controller  30  controls the measurement unit  10  and the estimator  20 . 
     The skin condition estimation device  1 A can be realized by, for example, an information processing device such as a computer. For example, the measurement unit  10 , the estimator  20 , and the controller  30  may be realized as components of a computer. 
     Operation 
     An example of the operation (skin condition estimation method) of the skin condition estimation device  1 A will be described with reference to  FIG.  4   .  FIG.  4    is a flowchart showing an example of the skin condition estimation method according to the first embodiment of the present disclosure. The skin condition estimation method shown in  FIG.  4    is executed by the skin condition estimation device  1 A. 
     As shown in  FIG.  4   , in step ST 1 , information related to hormone balance is acquired. In step ST 1 , the measurement unit  10  acquires the information related to hormone balance. Specifically, the measurement unit  10  acquires at least one piece of information of basal body temperature, brain waves, blood, saliva, and urine as the information related to hormone balance. In the first embodiment, the measurement unit  10  measures a basal body temperature. 
     In step ST 2 , a future skin condition is estimated based on the information acquired by the measurement unit  10 . In step ST 2 , the estimator  20  estimates the skin condition in the future from the acquisition date T 1  of the information by the measurement unit  10  based on the information related to hormone balance acquired by the measurement unit  10 . In the first embodiment, the estimator  20  estimates the skin condition in the future from the acquisition date T 1  based on the basal body temperature measured by the measurement unit  10 . 
     The estimator  20  executes regression analysis using the regression model based on the information of the basal body temperature. Specifically, the estimator  20  inputs, to the regression model subjected to machine learning in advance, information of the basal body temperature on the acquisition date T 1  that is 7 days or more and 13 days or less before the estimation date T 0 . Preferably, the estimator  20  inputs, to the regression model, information of the basal body temperature on the acquisition date T 1  that is 8 days or more and 12 days or less before the estimation date T 0 . More preferably, the estimator  20  inputs, to the regression model, information of the basal body temperature on the acquisition date T 1  that is 10 days before the estimation date T 0 . As a result, the estimator  20  estimates the skin condition in the future from the acquisition date T 1  by the regression model. 
     Effects 
     According to the skin condition estimation method of the first embodiment, the following effects can be obtained. 
     The skin condition estimation method includes step ST 1  of acquiring information related to hormone balance, and step ST 2  of estimating a future skin condition on the estimation date T 0  after the acquisition date T 1  on which the information is acquired based on the acquired information. With such a configuration, the future skin condition can be easily estimated based on the information related to hormone balance. 
     The information related to hormone balance includes at least one piece of information of basal body temperature, brain waves, blood, saliva, and urine. With such a configuration, the information related to hormone balance can be easily acquired. Furthermore, the future skin condition can be estimated based on the information acquired from the region other than the face. Furthermore, information such as basal body temperature, brain waves, saliva, and urine can be acquired without invading the user. 
     The acquisition date T 1  is 7 days or more and 13 days or less before the estimation date T 0 . With such a configuration, information having a high correlation between hormone balance and skin condition can be acquired. As a result, the estimation accuracy of the future skin condition can be improved. 
     The skin condition estimation device  1 A includes the measurement unit  10  that acquires information related to hormone balance, and the estimator  20  that estimates future skin information on the estimation date T 0  after the acquisition date T 1  when the information is acquired based on the information acquired by the measurement unit  10 . With such a configuration, the future skin condition can be easily estimated based on the information related to hormone balance. 
     In the first embodiment, an example in which the measurement unit  10  measures the basal body temperature has been described, but the present disclosure may not be limited thereto. The measurement unit  10  only needs to be able to acquire information related to hormone balance. Furthermore, the information related to hormone balance may be subjected to arbitrary processing before being input to the regression model of the estimator  20 . 
     In the first embodiment, an example in which the skin condition estimation device  1 A includes one measurement unit  10  has been described, but the present disclosure may not be limited thereto. The skin condition estimation device  1 A may include one or a plurality of measurement units  10 . For example, since the skin condition estimation device  1 A includes the plurality of measurement units  10 , estimation accuracy of the skin condition can be improved. In addition, the plurality of measurement units  10  may acquire different information. 
     In the skin condition estimation device  1 A, the measurement unit  10  is not an essential component. That is, the skin condition estimation device  1 A may not include the measurement unit  10 . When the skin condition estimation device  1 A does not include the measurement unit  10 , the information related to hormone balance may be acquired by a separate measurement device that is not included in the skin condition estimation device  1 A. The skin condition estimation device  1 A may include an input unit that inputs information related to hormone balance instead of the measurement unit  10 . The estimator  20  of the skin condition estimation device  1 A may estimate the future skin condition based on the information related to hormone balance input to the input unit. 
     In the first embodiment, an example in which the estimator  20  estimates the skin condition in the future from the acquisition date of the basal body temperature based on the basal body temperature has been described, but the present disclosure may not be limited thereto. The information used for estimating the skin condition may be information related to hormone balance. The estimator  20  may estimate the future skin condition based on information other than the basal body temperature. 
     An example in which the estimator  20  estimates the future skin condition on the estimation date T 0  on the acquisition date T 1  when the information is acquired by the measurement unit  10  has been described, but the present disclosure may not be limited thereto. That is, the timing at which the skin condition is estimated by the estimator  20  may not be limited to the acquisition date T 1 . The timing at which the skin condition is estimated by the estimator  20  may be other than the acquisition date T 1 . The timing at which the skin condition is estimated by the estimator  20  may be between the acquisition date T 1  and the estimation date T 0 . 
     In the first embodiment, an example in which the estimator  20  estimates the future skin condition by the regression analysis using the regression model has been described, but the present disclosure may not be limited thereto. The estimator  20  only needs to be able to estimate the future skin condition based on information related to hormone balance. Furthermore, the estimator  20  may estimate the future skin condition using a model other than the regression model. 
     In the first embodiment, an example in which the measurement unit  10 , the estimator  20 , and the controller  30  are formed separately has been described, but the present disclosure may not be limited thereto. For example, at least two of the measurement unit  10 , the estimator  20 , and the controller  30  may be integrated. 
     In the first embodiment, an example in which the acquisition date T 1  is the present, the estimation date T 0  is the future, and the estimator  20  estimates the future skin condition on the estimation date T 0  on the acquisition date T 1  has been described, but the present disclosure may not be limited thereto. For example, the acquisition date T 1  may be the past, and the estimation date T 0  may be current. In this case, the estimator  20  may estimate the current skin condition based on the first information acquired in the past. With such a configuration, the current skin condition can be estimated. 
     In the first embodiment, an example in which the skin condition estimation method includes steps ST 1  and ST 2  has been described, but the skin condition estimation method may not be limited thereto. In the skin condition estimation method, other steps may be added, some steps may be reduced, or a plurality of steps may be performed in one step. 
     In the first embodiment, the skin condition estimation device and the skin condition estimation method have been described as an example, but the present disclosure is also applicable to a program and a computer-readable recording medium. For example, the program may cause a computer to execute the skin condition estimation method described above. The computer-readable recording medium may store a program for causing a computer to execute the skin condition estimation method described above. The computer-readable recording medium may be, for example, a RAM, a ROM, an EEPROM, a flash memory, or other memory technologies, a CD-ROM, a DVD, or other optical disk storages, or a magnetic cassette, a magnetic tape, a magnetic disk storage, or other magnetic storage device. 
     First Modification 
       FIG.  5    is a block diagram showing a schematic configuration of a skin condition estimation device  1 AA according to a first modification of the first embodiment of the present disclosure. As shown in  FIG.  5   , the skin condition estimation device  1 AA according to the first modification further includes a display unit  31 . The display unit  31  displays the estimation result of the skin condition estimated by the estimator  20 . The display unit  31  is, for example, a display. The display unit  31  is controlled by the controller  30 . 
       FIG.  6 A  is a schematic view showing an example of a display screen of the display unit  31  of the first modification. As shown in  FIG.  6 A , the display unit  31  displays the information of the future skin condition estimated by the estimator  20 . The display screen displayed by the display unit  31  includes, for example, the total skin score in the skin condition after XX days. The total score is a numerical value of the comprehensive evaluation of the skin condition, and is indicated by a numerical value in a range of 1 to 5, for example. In addition, an evaluation icon is displayed on the display unit  31  of  FIG.  6 A  in order to visually recognize the total skin score. The evaluation icon is an image in which the total skin score can be visually recognized. The evaluation icons include, for example, a plurality of evaluation icons including colored heart marks and non-colored heart marks. In a case where there are many colored heart marks among the plurality of evaluation icons, it can be recognized that the total skin score is good. Furthermore, in a case where there are many non-colored heart marks among the plurality of evaluation icons, it can be recognized that the total skin score is not good. 
       FIG.  6 B  is a schematic diagram showing another example of the display screen of the display unit  31  of the first modification. In  FIG.  6 B , “A” indicates good, “B” indicates normal, and “C” indicates not good. As shown in  FIG.  6 B , the display unit  31  may display a skin condition forecast. For example, a skin condition forecast for 10 days may be displayed every day. Specifically, the skin condition forecast may be displayed for each day of the week. 
     Note that the display screen of the display unit  31  of the first modification may not be limited to the example shown in  FIGS.  6 A and  6 B . In addition to the information shown in  FIGS.  6 A and  6 B , other information may be additionally displayed on the display screen of the display unit  31 . Alternatively, the information shown in  FIGS.  6 A and  6 B  may be corrected and displayed. 
     In the modification 1, an example in which the display unit  31  is a display has been described, but the present disclosure may not be limited thereto. For example, the display unit  31  may include one or a plurality of LEDs. The display unit  31  may notify the user whether the future skin condition is good by turning on the LED. 
     In the modification 1, an example in which the display unit  31  is included in the skin condition estimation device  1 AA has been described, but the present disclosure may not be limited thereto. The display unit  31  may not be included in the skin condition estimation device  1 AA. The display unit  31  may be a separate body from the skin condition estimation device  1 AA. For example, the display unit  31  may be a display screen of an information processing terminal such as a smartphone. The skin condition estimation device  1 AA may transmit the estimation result to the display unit  31  of the information processing terminal by network communication or the like. As a result, it is possible to display the estimation result of the future skin condition on the display unit  31  such as an information processing terminal other than the skin condition estimation device  1 AA, and thus, it is possible to improve usability. 
     Second Embodiment 
     A skin condition estimation device and a skin condition estimation method according to a second embodiment of the present disclosure will be described. In the second embodiment, points different from the first embodiment will be mainly described. In the second embodiment, the same or equivalent configurations as those of the first embodiment will be described with the same reference numerals. In the second embodiment, the description overlapping with the first embodiment is omitted. 
     An example of the skin condition estimation device according to the second embodiment will be described with reference to  FIG.  7   .  FIG.  7    is a block diagram showing a schematic configuration of an example of a skin condition estimation device  1 B according to the second embodiment of the present disclosure. 
     The second embodiment is different from the first embodiment in that two measurement units  10  and  11  are provided and the skin condition is estimated based on the information acquired by the two measurement units  10  and  11 . 
     As shown in  FIG.  7   , the skin condition estimation device  1 B further includes the measurement unit  11  that acquires information related to a blood vessel condition. In the second embodiment, the measurement unit  10  is referred to as a first measurement unit  10 , and the measurement unit  11  is referred to as a second measurement unit  11 . In addition, information related to hormone balance is referred to as first information, and information related to a blood vessel condition is referred to as second information. 
     Second Measurement Unit 
     The second measurement unit  11  acquires the second information related to the blood vessel condition. The second information means information correlated with the blood vessel condition. That is, the second information means information capable of estimating a change in the blood vessel condition. Specifically, the second information includes at least one piece of information of a pulse wave, a blood pressure, and a form and a function of a blood vessel. 
     The “pulse wave” means a change in volume of a blood vessel that occurs as the heart pumps blood. The pulse wave is measured by, for example, a photoelectric pulse wave sensor. The photoelectric pulse wave sensor is attached to a user&#39;s finger to measure a pulse wave, for example. 
     The “blood pressure” means a pressure of blood applied to a blood vessel wall. The blood pressure is measured by, for example, a sphygmomanometer. Preferably, the sphygmomanometer can measure the maximum blood pressure value and the minimum blood pressure value. 
     The “form and the function of the blood vessel” is, for example, a thickness of the blood vessel, arteriosclerosis, blood flow, clogging of the blood vessel, and the like. The form and the function of the blood vessels can be measured, for example, by echography. 
     In the second embodiment, an example in which a pulse wave is used as the second information will be described. The second measurement unit  11  measures a pulse wave. The second measurement unit  11  includes, for example, a photoelectric pulse wave sensor. Specifically, the second information is information of an acceleration pulse wave calculated based on the pulse wave measured by the photoelectric pulse wave sensor. The acceleration pulse wave means a pulse wave differentiated twice on the time axis. 
       FIG.  8    is a schematic diagram showing an example of an acceleration pulse wave. As shown in  FIG.  8   , the acceleration pulse wave has an “a wave”, a “b wave”, a “c wave”, a “d wave”, and an “e wave” that peak in chronological order. The peak means a portion where the amplitude increases. In the second embodiment, the ratio between the amplitude of the “a wave” and the amplitude of the “c wave” in the acceleration pulse wave is used as the second information. Specifically, the second information is a value “c/a” obtained by dividing the amplitude value of the “c wave” in the acceleration pulse wave by the amplitude value of the “a wave”. 
     The second measurement unit  11  transmits the second information to the estimator  20 . Alternatively, the second information measured by the second measurement unit  11  is input to the estimator  20 . The second measurement unit  11  is controlled by the controller  30 . 
     In the second embodiment, an example in which the acquisition date of the first information and the acquisition date of the second information are different will be described.  FIG.  9    is a schematic diagram showing an example of measurement timing. As shown in  FIG.  9   , the second measurement unit  11  acquires the second information at a timing different from that of the first measurement unit  10 . The second measurement unit  11  acquires the second information on a second acquisition date T 2  that is a date before the estimation date T 0  and is different from the first acquisition date T 1  on which the first information is acquired by the first measurement unit  10 . 
     The second acquisition date T 2  is a date before the estimation date T 0  and after the first acquisition date T 1 . In the second embodiment, the first acquisition date T 1  is a date 10 days before the estimation date T 0 , and the second acquisition date T 2  is 2 days after the first acquisition date T 1 . In other words, the first acquisition date T 1  is a date 10 days before the estimation date, and the second acquisition date T 2  is a date 8 days before the estimation date T 0 . 
     Similarly to the hormone balance, the blood vessel condition is correlated with the skin condition. In addition, the correlation between the second information related to the blood vessel condition and the skin condition tends to be high on a day before the estimation date T 0  and after the first acquisition date T 1 . Therefore, the second acquisition date T 2  on which the second information is acquired is set to a date before the estimation date T 0  and after the first acquisition date T 1 . 
     The estimator  20  estimates the skin condition in the future from the second acquisition date T 2  based on the first information and the second information. In the second embodiment, the estimator  20  has a regression model in which the first information and the second information are input and the information of the future skin condition is output. The estimator  20  estimates the future skin condition by inputting the first information and the second information to the regression model. 
     In the second embodiment, the estimator  20  estimates the future skin condition on the estimation date T 0  on the date of the acquisition date T 2  when the second measurement unit  11  acquires the information. 
     An example of the operation (skin condition estimation method) of the skin condition estimation device  1 B will be described with reference to  FIG.  10   .  FIG.  10    is a flowchart showing an example of the skin condition estimation method according to the second embodiment of the present disclosure. In  FIG.  10   , step ST 11  is similar to step ST 1  shown in  FIG.  4    of the first embodiment, and thus detailed description thereof will be omitted. 
     As shown in  FIG.  10   , in step ST 11 , the first information related to hormone balance is acquired. In step ST 11 , the first measurement unit  10  acquires the first information. 
     In step ST 12 , the second information related to the blood vessel condition is acquired. In step ST 12 , the second measurement unit  11  acquires the second information on the second acquisition date T 2  different from the first acquisition date T 1  on which the first information is acquired. The second information includes at least one piece of information of a pulse wave, a blood pressure, and a form and a function of a blood vessel. The second acquisition date T 2  is a date before the estimation date T 0  and after the first acquisition date T 1 . 
     In the second embodiment, in step ST 12 , an acceleration pulse wave is calculated based on a pulse wave measured by a photoelectric pulse wave sensor. In addition, in step ST 12 , a value “c/a” obtained by dividing the amplitude value of the “c wave” in the acceleration pulse wave by the amplitude value of the “a wave” is calculated. In step ST 12 , the calculated value “c/a” is acquired as the second information. 
     In step ST 13 , a future skin condition is estimated based on the first information and the second information. In step ST 13 , the estimator  20  estimates the skin condition in the future from the second acquisition date T 2  based on the first information and the second information. 
     The estimator  20  inputs the first information and the second information to the regression model and executes regression analysis. The estimator  20  inputs the first information acquired on the first acquisition date T 1  and the second information acquired on the second acquisition date T 2  to the regression model subjected to machine learning in advance. As a result, the estimator  20  estimates the future skin condition on the estimation date T 0  after the second acquisition date T 2 . 
     Effects 
     According to the skin condition estimation method of the second embodiment, the following effects can be obtained. 
     The skin condition estimation method further includes step ST 12  of acquiring the second information related to the blood vessel condition. In step ST 13  of estimating, the future skin condition on the estimation date T 0  after the second acquisition date T 2  when the second information is acquired is estimated based on the first information and the second information. With such a configuration, the future skin condition can be estimated. 
     The second information includes at least one piece of information of a pulse wave, a blood pressure, and a form and a function of a blood vessel. With such a configuration, the second information related to the blood vessel condition can be easily acquired. Furthermore, the future skin condition can be estimated using the information acquired from the region other than the face. In addition, information such as a pulse wave, a blood pressure, and a form and a function of a blood vessel can be acquired without invading the user. 
     The second acquisition date T 2  on which the second information is acquired is different from the first acquisition date T 1  on which the first information is acquired. With such a configuration, the estimation accuracy can be improved by estimating the future skin condition based on the first information and the second information having different acquisition timings. 
     The second acquisition date T 2  is after the first acquisition date T 1 . With such a configuration, the second information can be acquired at a timing when the correlation between the second information and the skin information becomes high. As a result, the estimation accuracy of the future skin condition can be improved. 
     The skin condition estimation device  1 B includes the first measurement unit  10 , the second measurement unit  11 , and the estimator  20 . The first measurement unit  10  acquires the information related to hormone balance. The second measurement unit  11  acquires the second information related to the blood vessel condition. The estimator  20  estimates the future skin condition on the estimation date T 0  after the second acquisition date T 2  based on the first information and the second information. With such a configuration, the future skin condition can be estimated. 
     In the second embodiment, an example in which the first measurement unit  10  and the second measurement unit  11  are separate bodies has been described, but the present disclosure may not be limited thereto. For example, the first measurement unit  10  and the second measurement unit  11  may be integrated. 
     In the second embodiment, an example in which the second measurement unit  11  measures the second information related to the blood vessel condition has been described, but the present disclosure may not be limited thereto. The second measurement unit  11  only needs to be able to acquire information correlated with the skin condition other than the first information related to hormone balance. The estimator  20  may estimate the future skin condition based on two or more pieces of information correlated with the skin condition. The second information may be subjected to arbitrary processing before being input to the regression model of the estimator  20 . 
     In the second embodiment, an example in which the ratio “c/a” between the “a wave” and the “c wave” calculated based on the acceleration pulse wave is used as the second information has been described, but the present disclosure may not be limited thereto. The second information may be information related to a blood vessel condition. For example, as the second information, a ratio “b/a” between the “a wave” and the “b wave” in the acceleration pulse wave may be used. 
     In the second embodiment, an example in which the second measurement unit  11  acquires the second information at a timing different from that of the first measurement unit  10  has been described, but the present disclosure may not be limited thereto. The first acquisition date T 1  of the first measurement unit  10  and the second acquisition date T 2  of the second measurement unit  11  may be the same day. That is, step ST 12  of acquiring the second information may acquire the second information on the first acquisition date T 1 . The estimator  20  may estimate the future skin condition based on the first information and the second information acquired on the same day. Even in such a configuration, the estimation accuracy can be improved. 
     In the second embodiment, an example in which the first acquisition date T 1  is a date 10 days before the estimation date and the second acquisition date T 2  is a date 8 days before the estimation date T 0  has been described, but the present disclosure may not be limited thereto. The first acquisition date T 1  and the second acquisition date T 2  may be any date before the estimation date T 0 . 
     In the second embodiment, an example in which the skin condition estimation method includes steps ST 11  to ST 13  has been described, but the skin condition estimation method may not be limited thereto. In the skin condition estimation method, other steps may be added, some steps may be reduced, or a plurality of steps may be performed in one step. 
     Third Embodiment 
     A skin condition estimation method according to a third embodiment of the present disclosure will be described. In the third embodiment, points different from the second embodiment will be mainly described. In the third embodiment, the same or equivalent configurations as those of the second embodiment will be described with the same reference numerals. In the third embodiment, the description overlapping with the second embodiment is omitted. 
     An example of the skin condition estimation method of the third embodiment will be described with reference to  FIG.  11   .  FIG.  11    is a block diagram showing a schematic configuration of an example of a skin condition estimation device  1 C according to the third embodiment of the present disclosure. 
     The third embodiment is different from the second embodiment in that third information related to a blood vessel condition different from the second information is acquired, and the skin condition is estimated based on the first information, the second information, and the third information. 
     As shown in  FIG.  11   , the skin condition estimation device  1 C acquires the third information by the second measurement unit  11 . The third information is information related to a blood vessel condition different from that of the second information. In the third embodiment, the third information is information of the acceleration pulse wave calculated based on the pulse wave measured by the second measurement unit  11 , and is a ratio between the amplitude of the “a wave” and the amplitude of the “b wave” in the acceleration pulse wave (see  FIG.  8   ). Specifically, the third information is a value “b/a” obtained by dividing the amplitude value of the “b wave” in the acceleration pulse wave by the amplitude value of the “a wave”. 
     In the third embodiment, the first information related to hormone balance is information of basal body temperature. The second information related to the blood vessel condition is information of a value “c/a” obtained by dividing the amplitude value of the “c wave” in the acceleration pulse wave by the amplitude value of the “a wave”. 
     In the third embodiment, an example in which the acquisition date of the first information, the acquisition date of the second information, and the acquisition date of the third information are different from each other will be described.  FIG.  12    is a schematic diagram showing an example of measurement timing. As shown in  FIG.  12   , the third information is acquired on a third acquisition date different from the first acquisition date T 1  on which the first information is acquired and the second acquisition date T 2  on which the second information is acquired. 
     The third acquisition date T 3  is a date before the estimation date T 0  and after the first acquisition date T 1 . The third acquisition date T 3  is a date after the second acquisition date T 2 . In the third embodiment, the first acquisition date T 1  is a date 10 days before the estimation date T 0 , the second acquisition date T 2  is a date 2 days after the first acquisition date T 1 , and the third acquisition date T 3  is a date 7 days after the second acquisition date T 2 . In other words, the first acquisition date T 1  is a date 10 days before the estimation date, the second acquisition date T 2  is a date 8 days before the estimation date T 0 , and the third acquisition date T 3  is a date one day before the estimation date T 0 . 
     When the third information is a value “b/a” obtained by dividing the amplitude value of the “b wave” in the acceleration pulse wave by the amplitude value of the “a wave”, the correlation between the third information and the skin condition tends to be high on a day before the estimation date T 0  and after the first acquisition date T 1  and the second acquisition date T 2 . Therefore, the third acquisition date T 3  on which the third information is acquired is set to a date before the estimation date T 0  and after the first acquisition date T 1  and the second acquisition date T 2 . 
     The estimator  20  estimates the skin condition in the future from the third acquisition date T 3  based on the first information, the second information, and the third information. 
     In the third embodiment, the estimator  20  has a regression model in which the first information, the second information, and the third information are input and the information of the future skin condition is output. The estimator  20  estimates the future skin condition by inputting the first information, the second information, and the third information to the regression model. 
     In the third embodiment, the estimator  20  estimates the future skin condition on the estimation date T 0  on the third acquisition date T 3  on which the third information is acquired. 
     An example of the operation (skin condition estimation method) of the skin condition estimation device  1 C will be described with reference to  FIG.  13   .  FIG.  13    is a flowchart showing an example of the skin condition estimation method according to the third embodiment of the present disclosure. In  FIG.  13   , steps ST 21  and ST 22  are similar to steps ST 11  and ST 12  shown in  FIG.  10    of the second embodiment, and thus detailed description thereof is omitted. 
     As shown in  FIG.  13   , in step ST 21 , the first information related to hormone balance is acquired. In step ST 21 , the first measurement unit  10  acquires the first information. 
     In step ST 22 , the second information related to the blood vessel condition is acquired. In step ST 22 , the second measurement unit  11  acquires the second information on the second acquisition date T 2  after the first acquisition date T 1  on which the first information is acquired. 
     In step ST 23 , the third information related to the blood vessel condition different from the second information is acquired. In step ST 23 , the second measurement unit  1  acquires the third information on the third acquisition date T 3  different from the first acquisition date T 1  on which the first information is acquired and the second acquisition date T 2  on which the second information is acquired. The third acquisition date T 3  is a date before the estimation date T 0  and after the first acquisition date T 1  and the second acquisition date T 2 . 
     In the third embodiment, in step ST 23 , an acceleration pulse wave is calculated based on the pulse wave measured by the second measurement unit  11 . In addition, in step ST 23 , a value “b/a” obtained by dividing the amplitude value of the “b wave” in the acceleration pulse wave by the amplitude value of the “a wave” is calculated. In step ST 23 , the calculated value “b/a” is acquired as the third information. 
     In step ST 24 , a future skin condition is estimated based on the first information, the second information, and the third information. In step ST 24 , the estimator  20  estimates the skin condition in the future from the third acquisition date T 3  based on the first information, the second information, and the third information. 
     The estimator  20  inputs the first information, the second information, and the third information to the regression model and executes regression analysis. The estimator  20  inputs the first information acquired on the first acquisition date T 1 , the second information acquired on the second acquisition date T 2 , and the third information acquired on the third acquisition date T 3  to the regression model subjected to machine learning in advance. As a result, the estimator  20  estimates the future skin condition on the estimation date T 0  after the third acquisition date T 3 . 
     Effects 
     According to the skin condition estimation method of the third embodiment, the following effects can be obtained. 
     The skin condition estimation method further includes step ST 23  of acquiring the third information related to the blood vessel condition different from the second information. In step ST 24  of estimating, the future skin condition on the estimation date T 0  after the third acquisition date T 3  is estimated based on the first information, the second information, and the third information. With such a configuration, the future skin condition can be estimated. 
     The third acquisition date T 3  is different from the second acquisition date T 2 . With such a configuration, the estimation accuracy can be further improved by estimating the future skin condition based on the first information, the second information, and the third information having different acquisition timings. 
     The third acquisition date T 3  is later than the first acquisition date Ti and the second acquisition date T 2 . With such a configuration, the third information can be acquired at a timing when the correlation between the third information and the skin information becomes high. As a result, the estimation accuracy of the future skin condition can be improved. 
     In the third embodiment, an example in which the second measurement unit  11  acquires the second information and the third information has been described, but the present disclosure may not be limited thereto. For example, the third information may be acquired by a device different from the second measurement unit  11 . 
     In the third embodiment, an example in which the ratio “b/a” between the “a wave” and the “b wave” calculated based on the acceleration pulse wave is used as the third information has been described, but the present disclosure may not be limited thereto. The third information may be information related to a blood vessel condition. For example, the third information may include at least one piece of information of a pulse wave, a blood pressure, and a form and a function of a blood vessel. 
     In the third embodiment, an example in which the first acquisition date T 1 , the second acquisition date T 2 , and the third acquisition date T 3  are different has been described, but the present disclosure may not be limited thereto. For example, at least two of the first acquisition date T 1 , the second acquisition date T 2 , and the third acquisition date T 3  may be the same date. Even in such a configuration, the estimation accuracy can be improved. 
     In the third embodiment, an example in which the first acquisition date T 1  is a date 10 days before the estimation date, the second acquisition date T 2  is a date 8 days before the estimation date T 0 , and the third acquisition date T 3  is a date one day before the estimation date T 0  has been described, but the present disclosure may not be limited thereto. For example, the third acquisition date T 3  may be a date before the estimation date T 0  and before the second acquisition date T 2 . 
     In the third embodiment, an example in which the estimator  20  estimates the future skin condition on the estimation date T 0  on the third acquisition date T 3  has been described, but the present disclosure may not be limited thereto. The estimator  20  may estimate the current skin condition based on the first information, the second information, and the third information acquired in the past. 
     In the third embodiment, an example in which the skin condition estimation method includes steps ST 21  to ST 24  has been described, but the skin condition estimation method may not be limited thereto. In the skin condition estimation method, other steps may be added, some steps may be reduced, or a plurality of steps may be performed in one step. 
     Second Modification 
       FIG.  14    is a flowchart showing a skin condition estimation method according to a second modification of the third embodiment of the present disclosure.  FIG.  15    is a schematic diagram showing an example of measurement timing of the second modification. As shown in  FIGS.  14  and  15   , the skin condition estimation method of the second modification further includes step ST 24  of acquiring fourth information related to a current blood vessel condition, and step ST 26  of estimating the current skin condition based on the first information, the second information, the third information, and the fourth information. 
     In step ST 25 , the fourth information related to the current blood vessel condition is acquired. The fourth information includes at least one of a pulse wave, a blood pressure, and a form and a function of a blood vessel. In step ST 25 , the second measurement unit  11  acquires the fourth information. The fourth information is, for example, a ratio “b/a” between the “a wave” and the “b wave” calculated based on the acceleration pulse wave. 
     In the second modification, the estimation date T 0  is current. In step ST 25 , the second measurement unit  11  acquires the fourth information related to the current blood vessel condition on the estimation date T 0 . 
     In step ST 26 , the current skin condition is estimated based on the first information, the second information, the third information, and the fourth information. In step ST 26 , the estimator  20  estimates the current skin condition based on the first information, the second information, and the third information acquired in the past and the fourth information acquired at present. 
     With such a configuration, the current skin condition can be accurately estimated. 
     In the second modification, an example in which the fourth information is information related to the blood vessel condition has been described, but the present disclosure may not be limited thereto. For example, the fourth information may have at least one of information related to the current hormone balance and information related to the current blood condition. 
     In the modification  2 , an example in which the estimator  20  estimates the current skin condition based on the first information, the second information, the third information, and the fourth information has been described, but the present disclosure may not be limited thereto. For example, the estimator  20  may estimate the current skin condition based on the first information acquired in the past and the fourth information acquired at present. Alternatively, the estimator  20  may estimate the current skin condition based on at least one of the first information, the second information, and the third information acquired in the past and the fourth information acquired currently. Even in such a configuration, the current skin condition can be estimated. 
     Fourth Embodiment 
     A skin condition estimation device and a skin condition estimation method according to a fourth embodiment of the present disclosure will be described. In the fourth embodiment, points different from the third embodiment will be mainly described. In the fourth embodiment, the same or equivalent configurations as those of the third embodiment will be described with the same reference numerals. In the fourth embodiment, the description overlapping with the third embodiment is omitted. 
     An example of the skin condition estimation device according to the fourth embodiment will be described with reference to  FIG.  16   .  FIG.  16    is a block diagram showing a schematic configuration of an example of a skin condition estimation device  1 D according to the fourth embodiment of the present disclosure. 
     The fourth embodiment is different from the third embodiment in that a third measurement unit  12  that acquires actual measurement information of the skin condition is included and an estimator  20 A includes a learning unit  21  (e.g., as part of a machine learning system). 
     As shown in  FIG.  15   , the skin condition estimation device  1 D includes the third measurement unit  12  that acquires actual measurement information of the skin condition. Furthermore, the estimator  20 A includes the learning unit  21 . 
     Third Measurement Unit 
     The third measurement unit  12  is a skin measuring instrument that acquires actual measurement information of the skin condition. The third measurement unit  12  acquires, for example, actual measurement information of the skin condition of the face region of the human. The actual measurement information of the skin condition acquired by the third measurement unit  12  is used as teacher data of the learning unit  21  (e.g., training data) described later. The third measurement unit  12  is a device that can quantify the evaluation of the skin condition of the face region of the human. As the third measurement unit  12 , for example, a skin analysis system “Beauty Explorer (registered trademark)” manufactured by Sony Corporation can be used. Note that the third measurement unit  12  may not be limited to the skin analysis system “Beauty Explorer (registered trademark)” manufactured by Sony Corporation. 
     The actual measurement information of the skin condition acquired by the third measurement unit  12  is transmitted to the learning unit  21  of the estimator  20 A. 
     Learning Unit 
     The learning unit  21  creates a regression model by machine learning using the first information, the second information, the third information, and the actual measurement information of the skin condition acquired by the third measurement unit  12  as teacher data. Specifically, the learning unit  21  creates a regression model in which the first information, the second information, and the third information are input and the information of the future skin condition is output. 
     Examples of the machine learning method performed by the learning unit  21  include the k-nearest neighbor algorithm. The k-nearest neighbor algorithm is a method of learning using teacher data, and is a simple method of determining a class label of a sample whose class is unknown by majority decision using neighboring k samples. For example, KNeighborsRegressor registered in the scikit-learn library of Python can be cited. 
     As the learning result, it is possible to obtain a model with high prediction accuracy by machine learning by selecting the most accurate parameter by the cross test. As a commercially available tool for easily performing the cross test, for example, DataRobot manufactured by DataRobot, Inc. can be used. 
     Note that the k-nearest neighbor algorithm has been described as the machine learning method performed by the learning unit  21 , but the machine learning method may not be limited thereto. For example, the machine learning method performed by the learning unit  21  may be a method using a decision tree or the like. 
       FIG.  17    is a flowchart of an example of a machine learning method in the skin condition estimation method according to the fourth embodiment of the present disclosure. As shown in  FIG.  17   , in step ST 31  of the machine learning method, the first information related to hormone balance is acquired. In step ST 31 , the first measurement unit  10  acquires the first information. The acquired first information is transmitted to the learning unit  21  of the estimator  20 A. 
     In step ST 32 , the second information related to the blood vessel condition is acquired. In step ST 32 , the second measurement unit  11  acquires the second information. The acquired second information is transmitted to the learning unit  21  of the estimator  20 A. 
     In step ST 33 , the third information related to the blood vessel condition different from the second information is acquired. In step ST 33 , the second measurement unit  11  acquires the third information. The acquired third information is transmitted to the learning unit  21  of the estimator  20 A. 
     In step ST 34 , the actual measurement information of the skin condition is acquired. In step ST 34 , the third measurement unit  12  acquires the actual measurement information of the skin condition. The acquired actual measurement information of the skin condition is transmitted to the learning unit  21  of the estimator  20 A. 
     In step ST 35 , a regression model is created using the first information, the second information, the third information, and the actual measurement information of the skin condition. In step ST 35 , the learning unit  21  receives the first information, the second information, the third information, and the actual measurement information of the skin condition. The learning unit  21  creates a regression model using the received first information, second information, third information, and actual measurement information of the skin condition as teacher data. Specifically, the learning unit  21  creates a regression model in which the first information, the second information, and the third information are input and the future skin information is output. 
     In addition, the learning unit  21  may use the acquisition dates of the first information, the second information, and the third information as teacher data. As a result, the learning unit  21  can calculate the correlation between the acquisition dates of the first information, the second information, and the third information and the skin condition. The learning unit  21  can use the first information, the second information, and the third information acquired on the acquisition date on which the correlation with the skin condition becomes high as inputs to the regression model. 
     Effects 
     According to the skin condition estimation method and the skin condition estimation device according to the fourth embodiment, the following effects can be obtained. 
     The skin information estimation method includes a machine learning method. The machine learning method includes step ST 31  of acquiring the first information, step ST 32  of acquiring the second information, step ST 33  of acquiring the third information, step ST 34  of acquiring the actual measurement information of the skin condition, and step ST 35  of creating the regression model. The first information is information related to hormone balance. The second information is information related to a blood vessel condition. The third information is information related to a blood vessel condition different from that of the second information. In step ST 35 , using the first information, the second information, the third information, and the actual measurement information of the skin condition, a regression model in which the first information, the second information, and the third information are input and a future skin condition is output is created. With such a configuration, it is possible to create a regression model with improved estimation accuracy. 
     In step ST 35 , a regression model in which the acquisition dates of the first information, the second information, and the third information are input is further created. With such a configuration, it is possible to estimate the acquisition date on which the correlation with the skin condition becomes high. As a result, it is possible to create a regression model in which the estimation accuracy of the future skin condition is further improved. 
     The skin condition estimation device  1 D includes the third measurement unit  12  and the learning unit  21 . The third measurement unit  12  acquires actual measurement information of the skin condition. The learning unit  21  creates a regression model in which the first information, the second information, and the third information are input and a future skin condition is output by machine learning using the first information, the second information, and the third information, and the actual measurement information of the skin condition acquired by the third measurement unit  12  as teacher data. With such a configuration, it is possible to create a regression model with improved estimation accuracy. 
     In the fourth embodiment, an example in which the skin condition estimation device  1 D includes the first measurement unit  10 , the second measurement unit  11 , and the third measurement unit  12  has been described, but the present disclosure may not be limited thereto. For example, the skin condition estimation device  1 D may include the first measurement unit  10  and the third measurement unit  12 , and may not include the second measurement unit  11 . 
     Alternatively, in the skin condition estimation device  1 D, the measurement units  10  to  12  are not an essential configuration. That is, the skin condition estimation device  1 A may not include the measurement units  10  to  12 . When the skin condition estimation device  1 D does not include the measurement units  10  to  12 , the first information, the second information, the third information, and the information of the skin condition may be acquired by a separate measurement device that is not included in the skin condition estimation device  1 D. The skin condition estimation device  1 D may include an input unit that inputs the first information, the second information, the third information, and the actual measurement information of the skin condition instead of the measurement units  10  to  12 . The learning unit  21  may create a regression model using the first information, the second information, the third information, and the actual measurement information of the skin condition input to the input unit as teacher data. 
     In the fourth embodiment, an example in which the learning unit  21  uses the first information, the second information, and the third information, and the actual measurement information of the skin condition acquired by the third measurement unit  12  as teacher data has been described, but the present disclosure may not be limited thereto. The second information and the third information may not be used as teacher data. In this case, the learning unit  21  may use the first information and the actual measurement information of the skin condition acquired by the third measurement unit  12  as teacher data. That is, the learning unit  21  may create a regression model in which the first information is input and the future skin condition is output using the first information and the actual measurement information of the skin condition as teacher data. 
     In the fourth embodiment, an example in which the skin condition estimation method includes steps ST 31  to ST 35  has been described, but the skin condition estimation method may not be limited thereto. In the skin condition estimation method, other steps may be added, some steps may be reduced, or a plurality of steps may be performed in one step. 
     In the fourth embodiment, an example in which the skin condition estimation method includes the machine learning method has been described, but the present disclosure may not be limited thereto. The machine learning method may not be included in the skin condition estimation method. 
     In the fourth embodiment, an example in which the estimator  20 A is included in the learning unit  21  has been described, but the present disclosure may not be limited thereto. The learning unit  21  may not be included in the estimator  20 A. For example, the learning unit  21  may be included in a learning device separate from the skin condition estimation device  1 D. 
     Fifth Embodiment 
     A skin condition estimation device according to a fifth embodiment of the present disclosure will be described. In the fifth embodiment, points different from the third embodiment will be mainly described. In the fifth embodiment, the same or equivalent configurations as those of the third embodiment will be described with the same reference numerals. In the fifth embodiment, the description overlapping with the third embodiment is omitted. 
     An example of the skin condition estimation device according to the fifth embodiment will be described with reference to  FIG.  18   .  FIG.  18    is a block diagram showing a schematic configuration of an example of a skin condition estimation device  1 E according to the fifth embodiment of the present disclosure. 
     The fifth embodiment is different from the third embodiment in that an information acquisition unit  13  is included instead of the measurement units  10  and  11 . 
     As shown in  FIG.  18   , the skin condition estimation device  1 E includes the information acquisition unit  13 , the estimator  20 , and the controller  30 . In the third embodiment, the measurement units  10  and  11  are devices separate from the skin condition estimation device  1 E. 
     Information Acquisition Unit 
     The information acquisition unit  13  acquires the first information, the second information, and the third information. The information acquisition unit  13  is, for example, an input unit that can input information. As the input unit, for example, an input interface such as a keyboard, a mouse, or a touch panel can be used. Alternatively, the input unit may be, for example, a microphone for inputting by voice. The information acquisition unit  13  is controlled by the controller  30 . 
     For example, the user acquires the first information, the second information, and the third information using the first measurement unit  10  and the second measurement unit  11  separate from the skin condition estimation device  1 E. For example, the first information, the second information, and the third information are displayed on the display unit of each of the measurement units  10  and  11 . The user inputs the first information, the second information, and the third information to the information acquisition unit  13 . 
     The first information, the second information, and the third information input to the information acquisition unit  13  are transmitted to the estimator  20 . 
     The estimator  20  receives the first information, the second information, and the third information from the information acquisition unit  13 , and the estimator  20  estimates the future skin condition based on the first information, the second information, and the third information. 
     Effects 
     The skin condition estimation device according to the fifth embodiment can achieve the following effects. 
     The skin condition estimation device  1 E includes the information acquisition unit  13  and the estimator  20 . The information acquisition unit  13  acquires the first information, the second information, and the third information. The estimator  20  estimates the future skin condition based on the first information, the second information, and the third information acquired by the information acquisition unit  13 . With such a configuration, it is not necessary to include the measurement unit, so that the cost can be reduced. 
     Note that, in the fifth embodiment, an example in which the information acquisition unit  13  is an input unit capable of inputting information has been described, but the present disclosure may not be limited thereto. For example, the information acquisition unit  13  may include a communicator (e.g., a transmitter and/or receiver) including a circuit that communicates with the measurement units  10 ,  11 , and  12  in conformity with a predetermined communication standard (for example, LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), USB, HDMI (registered trademark), controller area network (CAN), and serial peripheral interface (SPI)). With such a configuration, information can be easily acquired by receiving information from the measurement units  10  and  11 . 
     In the fifth embodiment, an example in which the information acquisition unit  13  acquires the first information, the second information, and the third information has been described, but the present disclosure may not be limited thereto. The information acquisition unit  13  may acquire at least the first information. 
     Sixth Embodiment 
     A skin condition estimation system and a skin condition estimation method according to a sixth embodiment of the present disclosure will be described. In the sixth embodiment, points different from the first embodiment will be mainly described. In the sixth embodiment, the same or equivalent configurations as those of the first embodiment will be described with the same reference numerals. In the sixth embodiment, the description overlapping with the first embodiment is omitted. 
     An example of the skin condition estimation system of the sixth embodiment will be described with reference to  FIG.  19   .  FIG.  19    is a block diagram showing a schematic configuration of an example of a skin condition estimation system  50 A according to a sixth embodiment of the present disclosure. 
     As shown in  FIG.  19   , the skin condition estimation system  50 A includes a measurement device  51  and a processing device  60 . 
     Measurement Device 
     The measurement device  51  is a device that acquires user information. The measurement device  51  includes the measurement unit  10 , a communicator  14 , and a controller  15 . In the sixth embodiment, the communicator  14  is referred to as a first communicator  14 , and the controller  15  is referred to as a first controller  15 . 
     The measurement unit  10  acquires information related to hormone balance. Since the measurement unit  10  is similar to the measurement unit  10  of the first embodiment, detailed description thereof will be omitted. 
     The first communicator  14  transmits the information acquired by the measurement unit  10 . The first communicator  14  includes a circuit that communicates with the processing device  60  in conformity with a predetermined communication standard (for example, LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), USB, HDMI (registered trademark), controller area network (CAN), and serial peripheral interface (SPI)). 
     The first controller  15  integrally controls the components of the measurement device  51 . The first controller  15  includes, for example, a memory that stores a program, and a processing circuit (not shown) corresponding to a processor such as a central processing unit (CPU). In the first controller  15 , the processor executes the program stored in the memory. In the sixth embodiment, the first controller  15  controls the measurement unit  10  and the first communicator  14 . 
     Processing Device 
     The processing device  60  is a device that communicates with the measurement device  51 . The processing device  60  includes the estimator  20 , a communicator  22 , and a controller  23 . In the sixth embodiment, the communicator  22  is referred to as a second communicator  22 , and the controller  23  is referred to as a second controller  23 . For example, the processing device  60  is an information processing terminal such as a server. 
     The estimator  20  estimates the future skin condition on the estimation date after the date on which the information is acquired based on the information related to hormone balance. Since the estimator  20  is similar to the estimator  20  of the first embodiment, detailed description thereof will be omitted. 
     The second communicator  22  receives information transmitted from the measurement device  51 . The second communicator  22  includes a circuit that communicates with the measurement device  51  in conformity with a predetermined communication standard (for example, LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), USB, HDMI (registered trademark), controller area network (CAN), and serial peripheral interface (SPI)). 
     The information received by the second communicator  22  is transmitted to the estimator  20 . 
     The second controller  23  integrally controls the components of the processing device  60 . The second controller  23  includes, for example, a memory that stores a program, and a processing circuit (not shown) corresponding to a processor such as a central processing unit (CPU). In the second controller  23 , the processor executes the program stored in the memory. In the sixth embodiment, the second controller  23  controls the estimator  20  and the second communicator  22 . 
     Operation 
     An example of the operation (skin condition estimation method) of the skin condition estimation system  50 A will be described with reference to  FIG.  20   .  FIG.  20    is a flowchart showing an example of the skin condition estimation method according to the sixth embodiment of the present disclosure. The skin condition estimation method shown in  FIG.  20    is executed by the skin condition estimation system  50 A. 
     As shown in  FIG.  20   , in step ST 41 , information related to hormone balance is acquired. In step ST 41 , the measurement device  51  acquires the information related to hormone balance. 
     In step ST 42 , the acquired information related to hormone balance is transmitted. In step ST 42 , the information acquired by the measurement device  51  is transmitted to the processing device  60 . 
     In step ST 43 , the transmitted information is received. In step ST 43 , the processing device  60  receives the information transmitted from the measurement device  51 . 
     In step ST 44 , a future skin condition is estimated based on the received information. In step ST 44 , the processing device  60  estimates the future skin condition on the estimation date after the date on which the information is acquired by the measurement device  51  based on the received information. Note that the estimation processing of the estimator  20  is similar to that of the first embodiment, and thus description thereof is omitted. 
     Effects 
     The skin condition estimation system according to the sixth embodiment can achieve the following effects. 
     The skin condition estimation system  50 A includes the measurement device  51  and the processing device  60  that communicates with the measurement device  51 . The measurement device  51  includes the measurement unit  10  that acquires information related to hormone balance, and the first communicator  14  that transmits the acquired information. The processing device  60  includes the second communicator  22  that receives information, and the estimator  20  that estimates the future skin condition on the estimation date after the date on which the information is acquired based on the received information. With such a configuration, the future skin condition can be estimated. 
     In the first embodiment, an example in which the skin condition estimation system  50 A includes one measurement device  51  has been described, but the present disclosure may not be limited thereto. The skin condition estimation system  50 A may include one or a plurality of measurement devices  51 . For example, since the skin condition estimation system  50 A includes the plurality of measurement devices  51 , estimation accuracy of the skin condition can be improved. In addition, the plurality of measurement devices  51  may acquire different information. 
     Third Modification 
       FIG.  21    is a block diagram showing a schematic configuration of an example of a skin condition estimation system  50 AA according to a third modification of the sixth embodiment of the present disclosure. As shown in  FIG.  21   , the skin condition estimation system  50 AA includes a plurality of measurement devices  51  and  52 . In the skin condition estimation system  50 AA, the first measurement device  51  acquires the first information related to hormone balance. The second measurement device  52  acquires the second information and the third information related to the blood vessel information. 
     The first measurement device  51  includes the first measurement unit  10 , a first communicator  14 A, and a first controller  15 A. In the first measurement device  51 , the first measurement unit  10  acquires the first information, and the first communicator  14 A transmits the first information to the processing device  60 . The first controller  15 A controls the first measurement unit  10  and the first communicator  14 A. 
     The second measurement device  52  includes the second measurement unit  11 , a second communicator  14 B, and a second controller  15 B. The second measurement device  52  acquires the second information by the second measurement unit  11 , and transmits the second information to the processing device  60  by the second communicator  14 B. The second controller  15 B controls the second measurement unit  11  and the second communicator  14 B. 
     The processing device  60  receives the first information, the second information, and the third information from the first measurement device  51  and the second measurement device  52 . The processing device  60  estimates a future skin condition based on the first information, the second information, and the third information. Note that the estimation processing of the estimator  20  is similar to that of the third embodiment, and thus description thereof is omitted. 
     Note that, in the third modification, an example in which the skin condition estimation system  50 AA includes the two measurement devices  51  and  52  has been described, but the present disclosure may not be limited thereto. The skin condition estimation system  50 AA may include a plurality of measurement devices. 
     Seventh Embodiment 
     A skin condition estimation system according to a seventh embodiment of the present disclosure will be described. In the seventh embodiment, points different from the sixth embodiment will be mainly described. In the seventh embodiment, the same or equivalent configurations as those of the sixth embodiment will be described with the same reference numerals. In the seventh embodiment, the description overlapping with the sixth embodiment is omitted. 
     An example of the skin condition estimation system of the seventh embodiment will be described with reference to  FIG.  22   .  FIG.  22    is a block diagram showing a schematic configuration of an example of a skin condition estimation system  50 B according to the seventh embodiment of the present disclosure. 
     The seventh embodiment is different from the sixth embodiment in that a display device  70  is further provided. 
     As shown in  FIG.  22   , the skin condition estimation system  50 B includes the measurement device  51 , the processing device  60 , and the display device  70 . 
     Display Device 
     The display device  70  is a device that displays the estimation result of the skin condition estimated by the processing device  60 . The display device  70  is, for example, an information processing terminal such as a smartphone or an information processing device having a display. 
     The display device  70  includes the display unit  31 , a communicator  32 , and a controller  33 . 
     The display unit  31  displays the estimation result of the skin condition estimated by the estimator  20 . The display unit  31  is, for example, a display. The display unit  31  is controlled by the controller  33 . 
     The communicator  32  receives the estimation result from the processing device  50 . 
     The controller  33  integrally controls the components of the display device  70 . The controller  33  includes, for example, a memory that stores a program, and a processing circuit (not shown) corresponding to a processor such as a central processing unit (CPU). In the controller  33 , the processor executes the program stored in the memory. In the seventh embodiment, the controller  33  controls the display unit  31  and the communicator  32 . 
     Effects 
     The skin condition estimation system according to the seventh embodiment can achieve the following effects. 
     The skin condition estimation system  50 B further includes the display device  70  that displays the estimation result of the skin condition estimated by the processing device  60 . With such a configuration, it is possible to display the estimation result of the future skin condition. 
     Eighth Embodiment 
     A skin condition estimation system according to an eighth embodiment of the present disclosure will be described. In the eighth embodiment, points different from the sixth embodiment will be mainly described. In the eighth embodiment, the same or equivalent configurations as those of the sixth embodiment will be described with the same reference numerals. In the eighth embodiment, the description overlapping with the sixth embodiment is omitted. 
     An example of the skin condition estimation system of the eighth embodiment will be described with reference to  FIG.  23   .  FIG.  23    is a block diagram showing a schematic configuration of an example of a skin condition estimation system  50 C according to the eighth embodiment of the present disclosure. 
     The eighth embodiment is different from the sixth embodiment in that a control terminal  80  is further provided. 
     As shown in  FIG.  23   , the skin condition estimation system  50 C includes the control terminal  80  and the processing device  60 . 
     Control Terminal 
     The control terminal  80  acquires the first information, the second information, and the third information, and transmits the first information, the second information, and the third information to the processing device  60 . Furthermore, the control terminal  80  receives the estimation result of the skin condition estimated by the processing device  60  from the processing device  60  and displays the estimation result. The control terminal  80  is, for example, an information processing device such as a smartphone or a PC. 
     The control terminal  80  includes an information acquisition unit  41 , a communicator  42 , a display unit  43 , and a controller  44 . 
     The information acquisition unit  41  acquires the first information, the second information, and the third information. The information acquisition unit  41  is, for example, an input unit that can input information. As the input unit, for example, an input interface such as a keyboard, a mouse, or a touch panel can be used. Alternatively, the input unit may be, for example, a microphone for inputting by voice. 
     For example, the user inputs the first information, the second information, and the third information acquired by the measurement device to the information acquisition unit  41 . The information acquisition unit  41  acquires the information input from the user. 
     The communicator  42  communicates with the processing device  60 . The communicator  42  transmits the first information, the second information, and the third information to the processing device  60 . Furthermore, the communicator  42  receives the estimation result of the skin condition from the processing device  60 . 
     The display unit  43  displays the estimation result of the skin condition estimated by the processing device  60 . The display unit  43  is, for example, a display. 
     The controller  44  integrally controls the components of the control terminal  80 . The controller  44  includes, for example, a memory that stores a program, and a processing circuit (not shown) corresponding to a processor such as a central processing unit (CPU). In the controller  44 , the processor executes the program stored in the memory. In the eighth embodiment, the controller  44  controls the information acquisition unit  41 , the communicator  42 , and the display unit  43 . 
     Effects 
     The skin condition estimation system according to the eighth embodiment can achieve the following effects. 
     The skin condition estimation system  50 C includes the control terminal  80  and the processing device  60 . The control terminal  80  acquires the first information, the second information, and the third information, and transmits the first information, the second information, and the third information to the processing device  60 . Furthermore, the control terminal  80  receives the estimation result of the skin condition estimated by the processing device  60  and displays the estimation result. The processing device  60  receives the first information, the second information, and the third information from the control terminal  80 . The processing device  60  estimates a future skin condition based on the received first information, second information, and third information. The processing device  60  transmits the estimation result of the skin condition to the control terminal  80 . 
     With such a configuration, the control terminal  80  can easily acquire information and display the estimation result of the skin condition. In addition, since the skin condition estimation system  50 C does not include the measurement device as an essential component, the cost can be reduced. 
     Note that, in the eighth embodiment, an example in which the information acquisition unit  41  is an input unit capable of inputting information has been described, but the present disclosure may not be limited thereto. For example, the information acquisition unit  41  may include a communicator including a circuit that communicates with the measurement units  10 ,  11 , and  12  in conformity with a predetermined communication standard (for example, LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), USB, HDMI (registered trademark), controller area network (CAN), and serial peripheral interface (SPI)). With such a configuration, information can be easily acquired by receiving information from the measurement units  10 ,  11 , and  12 . 
     In the eighth embodiment, an example in which the information acquisition unit  41  acquires the first information, the second information, and the third information has been described, but the present disclosure may not be limited thereto. The information acquisition unit  41  may acquire at least the first information. 
     In the eighth embodiment, an example in which the control terminal  80  performs both the acquisition of the information and the display of the estimation result of the skin condition has been described, but the present disclosure may not be limited thereto. For example, the control terminal  80  acquires the information, but may not display the estimation result. 
     Ninth Embodiment 
     A skin condition estimation device according to a ninth embodiment of the present disclosure will be described. In the ninth embodiment, points different from the third embodiment will be mainly described. In the ninth embodiment, the same or equivalent configurations as those of the third embodiment will be described with the same reference numerals. In the ninth embodiment, the description overlapping with the third embodiment is omitted. 
     An example of the skin condition estimation device according to the ninth embodiment will be described with reference to  FIG.  24   .  FIG.  24    is a block diagram showing a schematic configuration of an example of the skin condition estimation device  1 F according to the ninth embodiment of the present disclosure. 
     A ninth embodiment is different from the third embodiment in that the first measurement unit  10  and the second measurement unit  11  acquire a plurality of pieces of first information, a plurality of pieces of second information, and a plurality of pieces of third information, and the estimator  20  estimates a future skin condition based on the plurality of pieces of first information, the plurality of pieces of second information, and the plurality of pieces of third information. 
     As shown in  FIG.  24   , in the skin condition estimation device  1 F, the first measurement unit  10  acquires the plurality of pieces of first information. In addition, the second measurement unit  11  acquires the plurality of pieces of second information and the plurality of pieces of third information. 
     The first measurement unit  10  acquires the plurality of pieces of first information on a plurality of different days. In the ninth embodiment, the first measurement unit  10  acquires three pieces of first information of 10 days, 9 days, and 8 days before the estimation date T 0 . 
     The second measurement unit  11  acquires the plurality of pieces of second information and the plurality of pieces of third information on a plurality of different days. In the ninth embodiment, the second measurement unit  11  acquires three pieces of second information of 10 days, 9 days, and 8 days before the estimation date T 0 . In addition, the second measurement unit  11  acquires three pieces of third information of 10 days, 9 days, and 8 days before the estimation date T 0 . 
     The estimator  20  estimates the future skin condition based on the plurality of pieces of first information, the plurality of pieces of second information, and the plurality of pieces of third information. 
       FIG.  25    is a flowchart showing an example of a skin condition estimation method according to a ninth embodiment of the present disclosure. The skin condition estimation method shown in  FIG.  25    is executed by the skin condition estimation device  1 F. 
     As shown in  FIG.  25   , in step ST 51 , the first information related to hormone balance is acquired. Step ST 51  includes step ST 51 A of acquiring the plurality of pieces of first information on a plurality of different days. 
     In step ST 51 A, the first measurement unit  10  acquires the first information a plurality of times on a plurality of different days. For example, the first measurement unit  10  acquires three pieces of first information of 10 days, 9 days, and 8 days before the estimation date T 0 . In the ninth embodiment, the first information is a basal body temperature. 
     In step ST 52 , the second information related to the blood vessel condition is acquired. Step ST 52  includes step ST 52 A of acquiring the plurality of pieces of second information on a plurality of different days. 
     In step ST 52 A, the second measurement unit  11  acquires the second information a plurality of times on a plurality of different days. For example, the second measurement unit  11  acquires three pieces of second information of 10 days, 9 days, and 8 days before the estimation date T 0 . In the ninth embodiment, the second information is a heart beat. 
     In step ST 53 , the third information related to the blood vessel condition different from the second information is acquired. Step ST 53  includes step ST 53 A of acquiring the plurality of pieces of third information on a plurality of different days. 
     In step ST 53 A, the second measurement unit  11  acquires the third information a plurality of times on a plurality of different days. For example, the second measurement unit  11  acquires three pieces of third information of 10 days, 9 days, and 8 days before the estimation date T 0 . In the ninth embodiment, the third information is an acceleration pulse wave. 
     In step ST 53 , a future skin condition is estimated based on the plurality of pieces of first information, the plurality of pieces of second information, and the plurality of pieces of third information. In step ST 53 , the estimator  20  estimates the future skin condition based on the plurality of pieces of first information, the plurality of pieces of second information, and the plurality of pieces of third information. 
     Effects 
     According to the skin condition estimation device and the estimation method according to the ninth embodiment, the following effects can be obtained. 
     In the skin condition estimation device  1 F, the first measurement unit  10  acquires the plurality of pieces of first information on a plurality of different days, and the second measurement unit  11  acquires the plurality of pieces of second information and the plurality of pieces of third information on a plurality of different days. The estimator  20  estimates the future skin condition based on the plurality of pieces of first information, the plurality of pieces of second information, and the plurality of pieces of third information. 
     With such a configuration, the estimation accuracy of the future skin condition can be improved. 
     In the skin information estimating method, step ST 51  of acquiring the first information includes step ST 51 A of acquiring the plurality of pieces of the first information on a plurality of different days. Step ST 52  of acquiring the second information includes step ST 52 A of acquiring the plurality of pieces of second information on a plurality of different days. Step ST 53  of acquiring the third information includes step ST 53 A of acquiring the plurality of pieces of third information on a plurality of different days. In step ST 54  of estimating, the future skin condition is estimated based on the plurality of pieces of first information, the plurality of pieces of second information, and the plurality of pieces of third information. 
     With such a configuration, the estimation accuracy of the future skin condition can be improved. 
     In the ninth embodiment, an example in which the plurality of pieces of first information, the plurality of pieces of second information, and the plurality of pieces of third information are three pieces of information acquired by three times of measurement has been described, but the present disclosure may not be limited thereto. The plurality of pieces of first information, the plurality of pieces of second information, and the plurality of pieces of third information may be two or more pieces of information. 
     In the ninth embodiment, an example in which the plurality of pieces of first information is information measured 10 days before, 9 days before, and 8 days before the estimation date T 0  has been described, but the present disclosure may not be limited thereto. For example, the first measurement unit  10  may acquire the first information on a plurality of different days between 7 days or more and 13 days or less before the estimation date T 0  on which the skin condition is estimated. 
     In the ninth embodiment, an example in which the plurality of pieces of second information is information measured 10 days before, 9 days before, and 8 days before the estimation date T 0  has been described, but the present disclosure may not be limited thereto. For example, the second measurement unit  11  may acquire the second information 2 days after each day when the first measurement unit  10  acquires the plurality of pieces of first information. 
     In the ninth embodiment, an example in which the plurality of pieces of third information is information measured 10 days before, 9 days before, and 8 days before the estimation date T 0  has been described, but the present disclosure may not be limited thereto. For example, the second measurement unit  11  may acquire the plurality of pieces of third information before the estimation date T 0  and after the date on which the plurality of pieces of first information and the plurality of pieces of second information are acquired. 
     In the ninth embodiment, an example has been described in which the plurality of pieces of first information, the plurality of pieces of second information, and the plurality of pieces of third information are acquired, and the future skin condition is estimated based on the plurality of pieces of first information, the plurality of pieces of second information, and the plurality of pieces of third information, but the present disclosure may not be limited thereto. At least one of the first information, the second information, and the third information may be a plurality of pieces of information. For example, while the first measurement unit  10  performs a plurality of measurements and acquires the plurality of pieces of first information, the second measurement unit  11  may perform one measurement and acquire the second information and the third information. In this case, the estimator  20  may estimate the future skin condition based on the plurality of pieces of first information and the second information and the third information acquired by one measurement. 
     Fourth Modification 
       FIG.  26    is a flowchart of a skin condition estimation method according to a fourth modification of the ninth embodiment the present disclosure. As shown in  FIG.  26   , step ST 51  of acquiring the first information includes step ST 51 A of acquiring the plurality of pieces of first information. On the other hand, step ST 52  of acquiring the second information and step ST 53  of acquiring the third information may not include step ST 52 A of acquiring the plurality of pieces of second information and step ST 53 A of acquiring the plurality of pieces of third information, respectively. 
     In the fourth modification, in step ST 54  of estimating, the future skin condition is estimated based on the plurality of pieces of first information and the second information and the third information acquired by one measurement. Even in such a configuration, the estimation accuracy of the future skin condition can be improved. 
     In the ninth embodiment, an example in which the future skin condition is estimated based on the plurality of pieces of first information, the plurality of pieces of second information, and the plurality of pieces of third information has been described, but the present disclosure may not be limited thereto. For example, the future skin condition may be estimated based on the plurality of pieces of first information without using the second information and the third information, or the future skin condition may be estimated based on the plurality of pieces of first information and the plurality of pieces of second information without using the third information. 
     EXAMPLES 
     Example 1 and Example 2 
     An example 1 and an example 2 will be described. 
     The example 1 is an estimation result of the skin condition obtained by performing the skin condition estimation method of the first embodiment. In the example 1, the basal body temperature was used as the first information related to hormone balance. The basal body temperature was acquired using a household basal thermometer (MC-652LC manufactured by OMRON Corporation) as the measurement unit  10 . In the example 1, the estimator  20  inputs information of the basal body temperature 10 days before the estimation date T 0  to the regression model, and estimates the skin condition on the estimation date T 0 . 
     The example 2 is an estimation result of the skin condition obtained by performing the skin condition estimation method of the third embodiment. In the example 2, the basal body temperature was used as the first information related to hormone balance. In addition, an acceleration pulse wave, which is information extracted from the pulse wave, was used as the second information and the third information related to the blood vessel information. An optical heart rate sensor was used as the measurement unit  11 . The acceleration pulse wave is calculated by secondarily differentiating the measured pulse wave signal. As the second information, a value “c/a” obtained by dividing the amplitude value of the “c wave” in the acceleration pulse wave by the amplitude value of the “a wave” was used. As the third information, a value “b/a” obtained by dividing the amplitude value of the “b wave” in the acceleration pulse wave by the amplitude value of the “a wave” was used. In the example 2, the estimator  20  inputs the first information 10 days before the estimation date T 0 , the second information 8 days before the estimation date T 0 , and the third information one day before the estimation date T 0  to the regression model, and estimates the skin condition on the estimation date T 0 . 
     The measured value is a value acquired by actually measuring the skin condition by a skin measuring instrument. As the skin measuring instrument, a skin analysis system “Beauty Explorer (registered trademark)” manufactured by Sony Corporation was used. 
       FIG.  27    is a graph showing an example of a correlation between an actual measurement value and the examples 1 and 2. As shown in  FIG.  27   , it can be seen that the variation tendency of the skin score in the example 1 and the example 2 correlates with the variation tendency of the skin score of the measured value. 
     The example 1 is a score of the skin condition estimated based on the first information related to hormone balance 10 days before the estimation date T 0 . From the result shown in  FIG.  27   , it can be seen that the skin condition 10 days after the acquisition date T 1  of the first information can be estimated based on the first information. 
     The example 2 is a score of the skin condition estimated based on the first information 10 days before the estimation date T 0 , the second information 8 days before the estimation date T 0 , and the third information one day before the estimation date T 0 . From the result shown in  FIG.  27   , it can be seen that the skin condition 1 day after the acquisition date of the third information can be estimated based on the first to third information. In addition, in the example 2, it can be seen that the future skin condition can be estimated with higher accuracy than in the example 1. 
     Comparative Examples 1 to 4 
     In a comparative example 1, the future skin condition was estimated based on the moisture of the face region. In a comparative example 2, the future skin condition was estimated based on the oil content of the face region. In a comparative example 3, the future skin condition was estimated based on the texture of the face region. In a comparative example 4, the future skin condition was estimated based on the spot on the face region. 
     In the comparative examples 1 to 4, values measured by a skin measuring instrument were used as the information of moisture, oil content, texture, and spot. As the skin measuring instrument, a skin analysis system “Beauty Explorer (registered trademark)” manufactured by Sony Corporation was used. In the comparative examples 1 to 4, the future skin condition was estimated based on the values measured by the skin measuring instrument. 
     In the comparative examples 1 to 4, the correlation between the measured value measured by the skin measuring instrument and the estimation value of the skin condition estimated was examined. 
       FIG.  28    is a graph showing an example of a correlation between the comparative example 1 and an actual measurement value.  FIG.  29    is a graph showing an example of a correlation between the comparative example 2 and an actual measurement value.  FIG.  30    is a graph showing an example of a correlation between the comparative example 3 and an actual measurement value.  FIG.  31    is a graph showing an example of a correlation between the comparative example 4 and an actual measurement value. 
     As shown in  FIGS.  28  to  31   , in the comparative examples 1 to 4, there was no correlation with the measured value. From the results shown in  FIGS.  28  to  31   , it can be seen that it is difficult to estimate the future skin condition based on the information of the moisture, oil content, texture, and spot of the face region. 
     Correlation Coefficients in Examples 1 to 3 and Comparative Examples 1 to 4 
     Examples of correlation coefficients of examples 1 to 3 and the comparative examples 1 to 4 will be described with reference to  FIG.  32   .  FIG.  32    is a table showing an example of correlation coefficients of the examples 1 to 3 and the comparative examples 1 to 4. 
     Note that the example 3 is an estimation result of the skin condition obtained by performing the skin condition estimation method of the second embodiment. 
     In the example 3, the future skin condition was estimated based on the first information related to hormone balance and the second information related to the blood vessel condition. As the first information, information of basal body temperature was used, and as the second information, a value “c/a” obtained by dividing the amplitude value of the “c wave” in the acceleration pulse wave by the amplitude value of the “a wave” was used. In the example 3, the estimator  20  inputs the first information 10 days before the estimation date T 0  and the second information 8 days before the estimation date T 0  to the regression model, and estimates the skin condition on the estimation date T 0 . 
     As shown in  FIG.  32   , in the examples 1 to 3, the correlation coefficient is larger than that in the comparative examples 1 to 4, and it can be seen that the correlation coefficient has a correlation with the measured value. In addition, in the example 2 and the example 3, the correlation coefficient exceeds 0.7, and it can be seen that there is a strong correlation. 
     Examples 4 to 9 
     Examples 4 to 9 will be described. In the examples 4 to 9, the first information is a basal body temperature, the second information is a heart beat, and the third information is an acceleration pulse wave. 
     The example 4 is an estimation result of the skin condition obtained by performing the skin condition estimation method of the first embodiment. In the example 4, the estimator  20  inputs the first information acquired 10 days before the estimation date T 0  to the regression model, and estimates the skin condition on the estimation date T 0 . Note that the example 4 is performed on a day different from that of the example 1. 
     The example 5 is an estimation result of the skin condition obtained by performing the skin information estimation method of the ninth embodiment. In the example 5, the skin condition on the estimation date T 0  was estimated based on the three pieces of first information acquired 10 days, 9 days, and 8 days before the estimation date T 0 . 
     The example 6 is an estimation result of the skin condition obtained by performing the skin information estimation method of the second embodiment. In the example 6, the skin condition on the estimation date T 0  was estimated based on the first information acquired 10 days before the estimation date T 0  and the second information acquired 10 days before the estimation date T 0 . 
     The example 7 is an estimation result of the skin condition obtained by performing the skin information estimation method of the ninth embodiment. In the example 7, the skin condition on the estimation date T 0  was estimated based on the three pieces of first information acquired 10 days, 9 days, and 8 days before the estimation date T 0  and the three pieces of second information acquired 10 days, 9 days, and 8 days before the estimation date T 0 . 
     The example 8 is an estimation result of the skin condition obtained by performing the skin information estimation method of the third embodiment. In the example 8, the skin condition on the estimation date T 0  was estimated based on the first information acquired 10 days before the estimation date T 0 , the second information acquired 10 days before the estimation date T 0 , and the third information acquired 10 days before the estimation date T 0 . 
     The example 9 is an estimation result of the skin condition obtained by performing the skin information estimation method of the ninth embodiment. In the example 8, the skin condition on the estimation date T 0  was estimated based on the three pieces of first information acquired 10 days, 9 days, and 8 days before the estimation date T 0 , the three pieces of second information acquired 10 days, 9 days, and 8 days before the estimation date T 0 , and the three pieces of third information acquired 10 days, 9 days, and 8 days before the estimation date T 0 . 
       FIG.  33    is a graph showing an example of a correlation between an actual measurement value and the examples 4 and 5.  FIG.  34    is a graph showing an example of a correlation between an actual measurement value and the examples 6 and 7.  FIG.  35    is a graph showing an example of a correlation between an actual measurement value and the examples 8 and 9. Note that the measured value is a value obtained by actually measuring the skin condition by a skin measuring instrument. As the skin measuring instrument, a skin analysis system Beauty Explorer (registered trademark) manufactured by Sony Corporation was used. 
     As shown in  FIGS.  33  to  35   , the estimation results of the skin conditions in the examples 4 to 9 are approximate to the actual measurement values. From these results, it is found that the examples 4 to 9 have a correlation with the measured value. 
       FIG.  36    is a table showing an example of correlation coefficients of the examples 4 to 9. As shown in  FIG.  36   , a high correlation coefficient is shown in the examples 4 to 9. Furthermore, it can be seen that the correlation coefficient is higher by estimating the skin condition using a plurality of pieces of information or information acquired on a plurality of different days. That is, the estimation accuracy of the skin condition can be further improved by estimating the skin condition using a plurality of pieces of information or information acquired on a plurality of different days. 
     Note that the regression model used for estimation of the skin condition in the skin condition estimation method according to the present disclosure may be used for cause analysis of a change in the skin condition in a certain period in the past including the present. In a case where the cause of the change in the skin condition in a certain period in the past including the present is analyzed, the change in at least one or more pieces of the already acquired first to third information corresponding to the input of the regression model in the period is independently input to the regression model, and the change value of the skin condition in each information change is obtained. Based on this, the influence of the change in each piece of information of the change in the skin condition can be estimated. 
     Although the present disclosure has been fully described in connection with preferred embodiments with reference to the accompanying drawings, various modifications and corrections will be apparent to those skilled in the art. Such modifications and corrections are to be understood as being included within the scope of the present disclosure as set forth in the appended claims as long as they do not depart therefrom.