Patent Publication Number: US-2019183358-A1

Title: Information processing apparatus, information processing method, and program

Description:
TECHNICAL FIELD 
     The present disclosure relates to an information processing apparatus, an information processing method, and a program. 
     BACKGROUND ART 
     Techniques for measuring information regarding blood flow such as a pulse and a blood flow velocity are frequently used in the field of medicine and the like. As an example of an apparatus for measuring a pulse and a blood flow velocity, a blood flowmeter can be mentioned. A blood flowmeter can be installed on a measured person without giving discomfort, pain, or the like to the measured person and easily measure the pulse and the blood flow velocity. For example, an example of a blood flowmeter is disclosed in Patent Literature 1. 
     CITATION LIST 
     Patent Literature 
     Patent Literature 1: JP 2013-146371A 
     DISCLOSURE OF INVENTION 
     Technical Problem 
     In the above-described blood flowmeter, a signal on which a noise component is superimposed is sometimes detected in a case in which a sampling frequency is lowered to suppress power consumption. For example, in a case in which a sampling frequency does not satisfy a predetermined condition for a frequency of a signal obtained from blood flow, noise sometimes occurs due to a folding phenomenon. Then, in a case in which a process is performed on a detection signal on which such a noise component is superimposed, it is difficult to obtain accurate blood flow information such as a pulse due to the noise component. 
     Accordingly, the present disclosure is devised in view of the foregoing circumstances and proposes an information processing apparatus, an information processing method, and a program capable of obtaining accurate blood flow information while suppressing power consumption. 
     Solution to Problem 
     According to the present disclosure, there is provided an information processing apparatus including: an estimation unit configured to estimate another kind of blood flow information associated with one kind of blood flow information from the one kind of blood flow information obtained through blood flow measurement on the basis of relation information indicating a relation between the two different kinds of blood flow information. 
     In addition, according to the present disclosure, there is provided an information processing method including: estimating another kind of blood flow information associated with one kind of blood flow information from the one kind of blood flow information obtained through blood flow measurement on the basis of relation information indicating a relation between the two different kinds of blood flow information. 
     Furthermore, according to the present disclosure, there is provided a program causing a computer to realize: a function of estimating another kind of blood flow information associated with one kind of blood flow information from the one kind of blood flow information obtained through blood flow measurement on the basis of relation information indicating a relation between the two different kinds of blood flow information. 
     Advantageous Effects of Invention 
     According to the present disclosure, as described above, it is possible to obtain accurate blood flow information while suppressing power consumption. 
     Note that the effects described above are not necessarily limitative. With or in the place of the above effects, there may be achieved any one of the effects described in this specification or other effects that may be grasped from this specification. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram illustrating a functional configuration of an information processing system  1  according to an embodiment of the present disclosure. 
         FIG. 2  is an explanatory diagram illustrating an example of an operation pattern of a radiation unit  100  and a detection unit  102  according to the embodiment of the present disclosure. 
         FIG. 3  is a diagram illustrating an example of a form of a measurement module  10  according to an embodiment of the present disclosure. 
         FIG. 4  is an explanatory diagram for describing a form when the measurement module  10  illustrated in  FIG. 3  is installed. 
         FIG. 5  is an explanatory diagram illustrating a blood flow measurement method applied to the embodiment of the present disclosure. 
         FIG. 6  is an explanatory diagram illustrating a first processing method applied to the embodiment of the present disclosure. 
         FIG. 7  is an explanatory diagram illustrating a second processing method applied to the embodiment of the present disclosure. 
         FIG. 8  is an explanatory diagram illustrating a power spectrum  606  on which folding noise components  602  and  604  are superimposed. 
         FIG. 9  is a block diagram illustrating a functional configuration of a processor  300  of an information processing apparatus  30  according to a first embodiment of the present disclosure. 
         FIG. 10  is an explanatory diagram illustrating an information processing method according to the first embodiment of the present disclosure. 
         FIG. 11  is a diagram illustrating of a flowchart of a first operation in the information processing method according to the first embodiment of the present disclosure. 
         FIG. 12  is a diagram illustrating of a flowchart of a second operation in the information processing method according to the first embodiment of the present disclosure. 
         FIG. 13  is a block diagram illustrating a functional configuration of a processor  300   a  of the information processing apparatus  30  according to a modification example of the first embodiment of the present disclosure. 
         FIG. 14  is a block diagram illustrating a functional configuration of a processor  300   b  of the information processing apparatus  30  according to a second embodiment of the present disclosure. 
         FIG. 15  is an explanatory diagram illustrating an information processing method according to a second embodiment of the present disclosure. 
         FIG. 16  is an explanatory diagram illustrating an information processing method according to a modification example of the second embodiment of the present disclosure. 
         FIG. 17  is a block diagram illustrating a functional configuration of a processor  300   c  of the information processing apparatus  30  according to a third embodiment of the present disclosure. 
         FIG. 18  is an explanatory diagram illustrating an information processing method according to the third embodiment of the present disclosure. 
         FIG. 19  is a block diagram illustrating a functional configuration of a processor  300   d  of the information processing apparatus  30  according to a fourth embodiment of the present disclosure. 
         FIG. 20  is an explanatory diagram illustrating an information processing method according to the fourth embodiment of the present disclosure. 
         FIG. 21  is a diagram illustrating of a flowchart of the information processing method according to the fourth embodiment of the present disclosure. 
         FIG. 22  is a block diagram illustrating a configuration of the information processing apparatus  30  according to an embodiment of the present disclosure. 
     
    
    
     MODE(S) FOR CARRYING OUT THE INVENTION 
     Hereinafter, (a) preferred embodiment(s) of the present disclosure will be described in detail with reference to the appended drawings. Note that, in this specification and the appended drawings, structural elements that have substantially the same function and structure are denoted with the same reference numerals, and repeated explanation of these structural elements is omitted. 
     The description will be made in the following order.
     1. Configuration of information processing system  1  according to embodiment of present disclosure   1.1 Configuration of measurement module  10     1.2 Configuration of information processing apparatus  30     2. Blood flow measurement method and processing method according to embodiment of present disclosure   2.1 Blood flow information measurement method   2.2 Processing methods   2.2.1 First processing method   2.2.2 Second processing method   3. Background of embodiment of present disclosure   4. First embodiment   4.1 Configuration of processor  300  according to first embodiment   4.2 Information processing method according to first embodiment   4.2.1 First operation   4.2.2 Second operation   4.3 Modification example of first embodiment   5. Second embodiment   5.1 Configuration of processor  300   b  according to second embodiment   5.2 Information processing method according to second embodiment   5.3 Modification example of second embodiment   6. Third embodiment   6.1 Configuration of processor  300   c  according to third embodiment   6.2 Information processing method according to third embodiment   7. Fourth embodiment   7.1 Configuration of processor  300   d  according to fourth embodiment   7.2 Information processing method according to fourth embodiment   8. Hardware configuration   9. Supplement   

     1. Configuration of Information Processing System  1  According to Embodiment of Present Disclosure 
     First, an information processing system  1  according to an embodiment of the present disclosure will be described with reference to  FIGS. 1 to 4 .  FIG. 1  is a block diagram illustrating a functional configuration of an information processing system  1  according to an embodiment of the present disclosure.  FIG. 2  is an explanatory diagram illustrating an example of an operation pattern of a radiation unit  100  and a detection unit  102  according to the embodiment of the present disclosure.  FIG. 3  is a diagram illustrating an example of a form of a measurement module (measurement unit)  10  according to an embodiment of the present disclosure. Further,  FIG. 4  is an explanatory diagram for describing a form when the measurement module  10  illustrated in  FIG. 3  is installed. 
     According to the embodiment of the present disclosure, blood flow measurement is performed to acquire blood flow information regarding blood flow of a measured person. Specifically, the blood flow information refers to information regarding blood flow, such as a pulse rate, an average blood flow velocity, a blood flow amount, a velocity distribution of particles in a blood vessel, or the like. Further, in the following description, the blood flow information also includes blood flow signal information of a power spectrum or the like used to calculate the above-described information such as a pulse obtained by processing a detection signal obtained through blood flow measurement. In addition, the pulse rate refers to the number of pulsations of an artery per unit time appearing on a body surface or the like due to occurrence of a change in pressure on an inner wall of the artery when muscles of a heart contract at a regular rhythm (pulsation, and further the number of pulsations in the heart per unit time is referred to as a pulse rate) to send blood to the whole body through the artery. Further, the blood flow velocity refers to a velocity of blood (blood component) flowing in one blood vessel or a plurality of blood vessels in a measurement region which is a measured person and a blood flow amount refers to a blood amount passing per unit time through one blood vessel or a plurality of blood vessels in the measurement region. The velocity distribution of particles in blood vessels refers to a velocity distribution of density of particles staying or flowing in blood vessels such as red blood cells in one blood vessel or a plurality of blood vessels in a measurement region. In addition, blood can be considered to be a mixture of substances with a plurality of flow velocities and a feature of blood flow can be indicated by a motion of blood cells which are particles in a blood vessel, that is, a flow velocity of the blood cells. The flow velocity of the blood cells can be used as a main index indicating the feature of the blood flow. In the present disclosure, an average moving velocity of blood cells (particles) in blood is called an average blood flow velocity. 
     In addition, in the embodiment of the present disclosure, to acquire the above-described blood flow information, light is radiated to a part (measurement region) of a measured person such as a hand, an arm, a neck, or a leg and light scattered in substances moving in blood vessels or a stationary biological tissue of the measured person is detected. Here, in the embodiment, blood flow information is acquired by processing detected light (in particular, a detection signal). Note that, in the embodiment to be described below, a case in which a pulse rate is acquired as a result of blood flow measurement will be described as an example. However, in the embodiment of the present disclosure, other blood flow information may be acquired as a result of the blood flow measurement without being limited to a case in which the pulse rate is acquired as a result of the blood flow measurement. 
     The information processing system  1  according to the embodiment mainly includes the measurement module  10  and an information processing apparatus  30 , as illustrated in  FIG. 1 . Further, the information processing system  1  according to the embodiment may include an information presentation apparatus that notices a measurement result or the like to a user (the user may be a measured person who is a target of blood flow measurement or a person or the like using the information processing system  1  according to the embodiment other than the measured person) and is not illustrated in  FIG. 1 . Hereinafter, configurations of the measurement module  10  and the information processing apparatus  30  included in the information processing system  1  will be described sequentially. 
     &lt;1.1 Configuration of Measurement Module  10 &gt; 
     The measurement module  10  according to the embodiment is a module that is mounted on a part of the body such as skin of the measured person to perform blood flow measurement on the measured person. As illustrated in  FIG. 1 , the measurement module  10  mainly includes a radiation unit  100 , the detection unit  102 , and a controller  104 . Hereinafter, each functional unit included in the measurement module  10  will be described. 
     (Radiation Unit  100 ) 
     The radiation unit  100  radiates radiation light with a predetermined wavelength to a measurement region (a part of the body) of the measured person. The wavelength of the radiation light radiated by the radiation unit  100  can be appropriately selected and, for example, light with a wavelength around 850 nm is radiated. As the radiation unit  100 , a small laser or the like can be used to radiate coherent light. Then, the controller  104  to be described below can control a timing, a radiation time, a radiation interval, a strength, and the like at which the radiation light of the radiation unit  100  is radiated. 
     (Detection Unit  102 ) 
     The detection unit  102  detects light scattered from the measurement region of the measured person. The detection unit  102  includes, for example, a photodiode (photo detector: PD), converts the strength of the received light into an electric signal, and outputs the electric signal to the information processing apparatus  30  to be described below. Note that a charge coupled device (CCD) type sensor, a complementary metal oxide semiconductor (CMOS) type sensor, or the like can be used as the detection unit  102 . In addition, the single photodiode, sensor, or the like or the plurality of photodiodes, sensors, or the like described above are provided in the measurement module  10 . Further, a timing or the like at which the detection unit  102  outputs (reads) a detection signal is controlled by the controller  104  to be described below. 
     (Controller  104 ) 
     The controller  104  controls general measurement in the measurement module  10  by controlling a radiation pattern (a radiation timing, a radiation time, and a radiation interval) of the radiation unit  100 , controlling a reading (sampling) timing of the detection unit  102 , or the like on the basis of a predetermined synchronization signal or the like. For example, the controller  104  controls a radiation frequency of the radiation unit  100  or a sampling frequency of the detection unit  102  synchronized with the radiation frequency in accordance with an operation of the information processing system  1 . In addition, the controller  104  may further include a storage unit (not illustrated) and the storage unit may store various programs, parameters, or the like for controlling the radiation unit  100  or the like. Further, the controller  104  may contain a clock mechanism (not illustrated) that ascertains an accurate time to output a detection signal to the information processing apparatus  30  in association with a time. Specifically, the controller  104  is realized by, for example, a central processing unit (CPU), a read-only memory (ROM), a random access memory (RAM), and the like. Note that some or all of the functions performed by the controller  104  may be performed by the information processing apparatus  30  to be described below. 
     Here, the details of control on the radiation unit  100  and the detection unit  102  by the above-described controller  104  will be described with reference to  FIG. 2 .  FIG. 2  is an explanatory diagram illustrating an example of an operation pattern of the radiation unit  100  and the detection unit  102  according to the embodiment of the present disclosure. In particular, the upper stage of  FIG. 2  schematically illustrates a radiation pattern of the radiation unit  100 , the middle stage of  FIG. 2  illustrates an electric signal detected by the detection unit  102 , and the lower stage of  FIG. 2  illustrates a detection signal obtained through sampling of the detection unit  102 . Note that in the following description, sampling refers to digitizing and reading (outputting) an electric signal generated in the detection unit  102  due to detection of light using, for example, an analog-digital converter or the like. 
     As illustrated in the upper stage of  FIG. 2 , the radiation pattern of the radiation unit  100  is a square wave. In particular, the radiation unit  100  radiates light during a period of a flat portion (ON) of the upper side (a radiation period). On the other hand, the radiation unit  100  pauses the radiation during a period of a flat portion (OFF) of the lower side (a pause period). In addition, as illustrated in the middle stage of  FIG. 2 , a sampling timing of the detection unit  102  is synchronized with the radiation period of the radiation unit  100 . In particular, during the radiation period, the detection unit  102  changes the electric signal in accordance with an amount of light received in the detection unit  102  and samples a change in the electric signal generated in the detection unit  102  at a timing at which the radiation unit  100  pauses the radiation. That is, in the right end portion of a peak of a reading pattern in the middle stage of  FIG. 2 , a change in the electric signal generated in accordance with the amount of light received in the detection unit  102  can be read and the detection signal illustrated in the lower stage of  FIG. 2  can be obtained. Note that, in the following description, the number of samplings per unit time is called a sampling frequency. Note that, in the following description, an example in which the sampling is performed at an equal time interval is used to describe the present disclosure in brief, but the sampling can also be performed at an unequal time interval by using a principle of compressed sensing or the like. The sampling frequency of this case can be considered to be an average value of the number of samplings in a certain time section. 
     Note that radiation and sampling patterns in a radiation and measurement method according to the embodiment of the present disclosure are not limited to the patterns illustrated in  FIG. 2 . For example, in the radiation pattern of the radiation unit  100 , a radiation section in which the radiation unit  100  repeats the radiation a predetermined number of times regularly at a first interval may be repeated at a second interval longer than the first interval. Note that, even in this case, the sampling timing of the detection unit  102  is synchronized with the radiation of the radiation unit  100 . That is, in the embodiment, various radiation and sampling patterns can be selected in accordance with desired blood flow information, measurement accuracy, and the like. 
     Further, although not illustrated in  FIG. 1 , the measurement module  10  has a power source supplying power to the radiation unit  100  or the like. Further, the measurement module  10  may include a communication unit (not illustrated) or the like communicating with the information processing apparatus  30  or the like to be described below in addition to the radiation unit  100 , the detection unit  102 , and the controller  104  described above. In addition, the measurement module  10  may include a sensor (not illustrated) such as a pressure sensor detecting that the measurement module  10  is mounted on a part of the body of the measured person. 
     In addition, the measurement module  10  can have, for example, the form of a wearable apparatus mounted on the body of the measured person for use. For example, the measurement module  10  may be a device that has the shape of a wrist-watch, a ring, a wristband, an anklet, a necklace, an earphone, or the like and can be mounted on a part of the measured person such as a wrist, an arm, a neck, a leg, or an ear. In addition, the measurement module  10  may be a device that has a pad shape such as a sticking plaster and can be pasted to a part of the measured person such as a hand, an arm, a neck, or a leg. Further, the measurement module  10  may have an implant shape embedded in a part of the body of the measured person. 
     Hereinafter, an example of a specific form of the measurement module  10  according to the embodiment will be described with reference to  FIGS. 3 and 4 . For example, as illustrated in  FIG. 3 , the measurement module  10  can have the form of a belt shape. As illustrated in  FIG. 3 , the measurement module  10  includes a band unit  110 , a control unit  112 , and a measurement unit  114  in a belt shape. The control unit  112  is a portion in which the above-described controller  104  is provided. Note that in a case in which the measurement module  10  and the information processing apparatus  30  to be described below are an integrated apparatus, each functional unit to be described below in the information processing apparatus  30  may be provided in the control unit  112 . In addition, the measurement unit  114  is a portion in which the radiation unit  100  and the detection unit  102  described above are provided and comes into contact with or faces the body of the measured person when the measurement module  10  is mounted on a part of the body. 
     The band unit  110  is, for example, a component that fixes the measurement module  10  to be wrapped around a wrist of the measured person and is formed of a material such as a soft silicone gel to form a ring shape that conforms to the shape of the wrist. That is, since the band unit  110  can be formed in a ring shape that conforms to the shape of the wrist, as illustrated in  FIG. 4 , the measurement module  10  is wrapped to be fixed around the wrist of the measured person. In addition, when the measurement module  10  is moved during blood flow measurement, accurate measurement may not be performed. Therefore, the measurement module  10  is preferably fixed on a measurement region of the measured person. Accordingly, an adhesive layer  116  which can be adhered to skin of the measured person may be provided in a portion of the band unit  110  coming into contact with the skin of the measured person. Further, it is preferable to freely adjust a circumferential length of a ring when the measurement module  10  is formed in the ring shape to correspond to thicknesses of various wrists. Accordingly, the fixing unit  118  is provided at an end of the band unit  110  and the fixing unit  118  can be superimposed on any portion on the band unit  110  to be fixed at various positions on the band unit  110 . In this way, the measurement module  10  can be mounted in accordance with the thickness of the wrist of the measured person to be fixed. 
     &lt;1.2 Configuration of Information Processing Apparatus  30 &gt; 
     Referring back to  FIG. 1 , a configuration of the information processing apparatus  30  of the information processing system  1  according to the embodiment will be described. The information processing apparatus  30  is an apparatus that acquires blood flow information such as a pulse using a detection signal obtained by the measurement module  10 . As illustrated in  FIG. 1 , the information processing apparatus  30  mainly includes a processor  300  and a storage unit  302 . Hereinafter, each functional unit included in the information processing apparatus  30  will be described. 
     (Processor  300 ) 
     The processor  300  acquires blood flow information by processing the detection signal obtained by the measurement module  10 . The acquired blood flow information can be output to the storage unit  302  to be described below or another apparatus. Note that the details of the processor  300  will be described later. 
     (Storage Unit  302 ) 
     The storage unit  302  stores a program or various kinds of data to be used in a process in the above-described processor  300  and stores the blood flow information or the like acquired by the processor  300 . In addition, in addition to the data or the like, the storage unit  302  may appropriately store various parameters, ongoing progress of a process, and the like necessarily stored when any process is performed. Then, the processor  300  or the like can freely access the storage unit  302  to write or read data. 
     Note that the information processing apparatus  30  may also include a communication unit (not illustrated) communicating with the measurement module  10  or the like in addition to the above-described processor  300  and storage unit  302 . Further, the information processing apparatus  30  may include an input unit (not illustrated) or the like receiving a manipulation from a user using the information processing system  1  according to the embodiment. 
     In addition, the information processing apparatus  30  may be an apparatus integrated with the above-described measurement module  10  or may be an apparatus separate from the above-described measurement module  10 . In the latter case, for example, the information processing apparatus  30  may be an information processing apparatus such as a smartphone, a tablet, or a personal computer (PC) or may be an information processing apparatus connected to another apparatus (for example, a medical apparatus or the like). Further, the information processing apparatus  30  may be an information processing apparatus such as a server installed in a location away from the measured person or the like. 
     2. Blood Flow Measurement Method and Processing Method According to Embodiment of Present Disclosure 
     As described above, in the embodiment of the present disclosure, the blood flow measurement is performed to acquire the blood flow information regarding blood flow of the measured person. As described above, in the embodiment, to acquire the above-described blood flow information, light is radiated to a part of a measured person such as a hand, an arm, a neck, or a leg, light scattered in substances moving in blood vessels or a stationary biological tissue of the measured person is detected, and the detected light (in particular, a detection signal) is processed. Hereinafter, a blood flow measurement method, a processing method, and the like according to the embodiment of the present disclosure will be described in detail. 
     &lt;2.1 Blood Flow Information Measurement Method&gt; 
     An analysis technology for a velocity distribution using a laser Doppler blood flow measurement technology or a dynamic light scattering (DLS) technique can be exemplified as an example of the blood flow measurement method according to the embodiment of the present disclosure. First, an interference phenomenon of coherent light for blood flow common to both the technologies will be described with reference to  FIG. 5 .  FIG. 5  is an explanatory diagram illustrating a blood flow measurement method applied to the embodiment of the present disclosure. In particular,  FIG. 5  schematically illustrates an interference phenomenon of coherent light by blood flow. Reference numeral  502  in  FIG. 5  denotes an example of a waveform of a detection signal obtained through the measurement. 
     The blood flow information measurement method according to the embodiment of the present disclosure is a method of using the phenomenon that light scattered by the scattering substances (mainly, red blood cells) moving in blood vessels of the measured person produces interference light by the Doppler effect and location movement of scattering substances when light from the radiation unit  100  is radiated to a measurement region (a part of the body) of the measured person. The interference light is received by the detection unit  102  such as a photodiode and blood flow information is calculated from a distribution of a Doppler shift frequency in the received interference light. 
     In particular, as illustrated in  FIG. 5 , in a case in which light with a frequency f radiated to a measurement region of the measured person by the radiation unit  100  is scattered by a situated stationary tissue  70  such as skin or a subcutaneous tissue of the measured person, the scattered light maintains the frequency f. On the other hand, in a case in which the light with the frequency f radiated to the measurement region of the measured person is scattered by scattering substances (for example, red blood cells can be exemplified and the red blood cells are a substance with a diameter of 8 to 10 μm)  72  moving in blood vessels of the measured person (for example, moving particles causing Doppler shift in the scattered light), the frequency of the scattered light is shifted by the Doppler effect and location movements of the scattering substances, and thus the scattered light has a frequency f+Δf. Then, the scattered light with the frequency f scattered by the stationary tissue  70  interferes with the scattered light with the frequency f+Δf scattered by the moving scattering substances  72 , and thus the detection unit  102  can detect the interference light with optical beats. Note that the shift frequency Δf is considerably smaller than the frequency f of the radiated light. 
     Then, by processing the interference light (detection signal) detected by the detection unit  102  and denoted by reference numeral  502  in  FIG. 5 , it is possible to obtain blood flow information. Note that, since the detection signal  502  is a signal in which optical beats with a plurality of different frequencies by the scattered light from the particles performing a plurality of different movements in the blood vessels are superimposed, as illustrated in  FIG. 5 , the detection signal  502  is seen as an irregular signal such as white noise. However, the detection signal  502  is a signal in which the interference beats with the plurality of frequencies are superimposed, as described above. Therefore, by performing a frequency analyzing process to be described below on the detection signal, it is possible to acquire velocity distribution information of particle movements causing Doppler shift. In the present disclosure, since an observation target is blood flow, a velocity distribution of particles such as the red blood cells in blood vessels can be ascertained. In addition, since blood flow information in a biological tissue within a range in which the radiated light arrives can be acquired, blood flow information in a region including blood vessels in a deep part located to a certain depth from skin of the body of the measured person as well as blood vessels of the surface of the skin of the measured person can be acquired. 
     &lt;2.2 Processing Methods&gt; 
     Further, in the embodiment of the present disclosure, blood flow information is acquired by processing a detection signal detected by the detection unit  102 , as described above. In the embodiment of the present disclosure, for example, any of two methods to be described below can be used as a method of processing a detection signal to acquire blood flow information. In particular, as the processing method according to the embodiment, a first processing method in which a frequency analyzing process (Fourier transform) used generally for laser Doppler velocity detection is first performed and a second processing method of calculating an autocorrelation function used in a DLS technique can be exemplified. Hereinafter, the details of the first and second processing methods will be described sequentially. 
     &lt;2.2.1 First Processing Method&gt; 
     First, the first processing method will be described with reference to  FIG. 6 .  FIG. 6  is an explanatory diagram illustrating the first processing method applied to the embodiment. In the first processing method, as illustrated in  FIG. 6 , blood flow information is acquired by first performing, for example, a frequency analyzing process such as a fast Fourier transform (FFT) on the detection signal (I(t) of  FIG. 6 ) obtained by the detection unit  102  for each interval of a plurality of ranges (windows  500  of  FIG. 6 ). 
     Specifically, in the first processing method, an FFT is performed on the detection signal at each interval of a predetermined time range to acquire a plurality of power spectra (P(f) of  FIG. 6 ) which are a function of a frequency. Further, by taking a product of a beat frequency having a proportional relation with a velocity for each frequency in each of the acquired power spectra (fP(f) of  FIG. 6 ) and performing integration in the entire power spectra and normalization, it is possible to obtain an average blood flow velocity. Then, by acquiring a plurality of blood flow velocities on the basis of the plurality of power spectra obtained from the plurality of windows with different time ranges, it is possible to acquire a waveform indicating a change in the average blood flow velocity over time. Further, by performing an interpolation process or the like on the acquired waveform, a pulse waveform indicating a change in the blood flow velocity by pulse can be acquired and a pulse rate or the like can be calculated from the pulse waveform. Note that, in  FIG. 6 , the plurality of superimposed windows  500  deciding the ranges for generating the power spectra are not expressed. However, in an actual process, the windows  500  can be caused to be mutually superimposed. Thus, by processing the windows  500  caused to be superimposed, it is possible to more densely generate the plurality of power spectrum lines that are formed chronologically. 
     &lt;2.2.2 Second Processing Method&gt; 
     Next, the second processing method will be described with reference to  FIG. 7 .  FIG. 7  is an explanatory diagram illustrating the second processing method applied to the embodiment. In the second processing method, as illustrated in  FIG. 7 , blood flow information is acquired by first calculating an autocorrelation function from the detection signal (I(t) of  FIG. 7 ) and processing the calculated autocorrelation function (G(τ) of  FIG. 7 ). 
     In particular, according to the Wiener-Khichin theorem, the power spectra of I(t) are acquired by performing Fourier transform on the autocorrelation function after the autocorrelation function is obtained. Note that according to the second processing method in which the autocorrelation function is used, the accurate power spectra can be obtained even in a case in which a detected detection signal is a signal that does not have periodicity as in the present disclosure. Here, by processing the acquired power spectra, it is possible to acquire desired blood flow information. 
     More specifically, in the second processing method, calculation of the autocorrelation function is performed on the detection signal for each predetermined time range to acquire the plurality of autocorrelation functions. Further, in the second processing method, according to the Wiener-Khichin theorem, the FFT is performed on each of the calculated autocorrelation function to acquire a plurality of power spectra (P(f) of  FIG. 7 ) which are a function of a frequency. The power spectra are proportional to existence density of particles moving at velocities corresponding to the frequencies of the power spectra. Therefore, by performing an integration process on the acquired power spectra in a predetermined frequency range, it is possible to obtain relative densities of the particles in the blood vessels within a predetermined velocity range. Further, by acquiring the relative densities of the plurality of particles from the plurality of power spectra with different time ranges, it is possible to acquire a waveform indicating a change in the relative densities of the particles over time. Then, by performing an interpolation process or the like on the obtained waveform, it is possible to acquire a pulse waveform indicating a change in the relative densities of the particles by pulse and calculate a pulse rate or the like from the pulse waveform. Note that in the above-described first and second processing methods, different methods are used in the way of obtaining the power spectra and the way of obtaining the blood flow information subsequently, but any combination can be used in the present disclosure. That is, in the present disclosure, after the power spectra are obtained in accordance with the method described in the first processing method, the relative densities of the particles within the predetermined velocity range indicated by the second processing method can also be obtained. In addition, in the present disclosure, after the power spectra are obtained in accordance with the method described in the second processing method, the average blood flow velocity indicated in the first processing method can also be obtained. 
     3. Background of Embodiment of Present Disclosure 
     Incidentally, since the measurement module  10  according to the embodiment of the present disclosure is a wearable apparatus mounted on the measured person, the measurement module  10  is preferably compact. Therefore, it is preferable to reduce a power source volume of the measurement module  10 . Accordingly, in order to reduce the power source volume, power consumption in the measurement module  10  is required to be suppressed as small as possible. Further, considering that the measurement module  10  is mounted on the measured person for a long time to be able to perform blood flow measurement for a long time as much as possible, power consumption in the measurement module  10  is required to be suppressed as small as possible. 
     In the measurement module  10 , the power consumption is considerably consumed by the radiation unit  100 . Accordingly, in order to suppress the power consumption, it is considered that the number of radiations of light is reduced and furthermore the number of samplings (sampling frequency) synchronized with the radiation is reduced. In other words, by lowering the sampling frequency, it is possible to reduce a radiation period (the number of radiations or a time) of the radiation unit  100  synchronized with the sampling, and thus it is possible to suppress the power consumption in the radiation unit  100 . 
     However, a signal with a frequency equal to or greater than ½ of a sampling frequency and included in an original signal is mixed as a folding signal with a discretely sampled signal according to the Nyquist theorem. When the sampling frequency is lowered, the folding signal increases and shortly reaches the magnitude which may not be negligible. Thus, as a noise signal, the folding signal has an adverse influence on extraction of information (blood flow information in the present disclosure). Specifically, in the method described in the embodiment of the present disclosure, a folding noise component is superimposed on the power spectrum, the noise component has an adverse influence on a process at the rear stage, and thus it is difficult to obtain accurate flood flow information. First, a power spectrum  606  on which folding noise components  602  and  604  are superimposed will be described with reference to  FIG. 8 .  FIG. 8  is an explanatory diagram illustrating the power spectrum  606  on which the folding noise components  602  and  604  are superimposed. 
     First, an original power spectrum  600  of an original signal is illustrated in  FIG. 8 . In addition, when fs is a sampling frequency, the noise component  602  with a waveform in which the waveform of the original signal is folded at a position of ½ fs according to the Nyquist theorem occurs, as indicated as a noise component by an odd number of folding in  FIG. 8 . Further, a noise component in which the folding noise component is further folded at a position corresponding to a multiple of a Nyquist frequency also occurs. For example, the noise component  604  that has a waveform in which the noise component  602  is folded at a position of a frequency corresponding to a zero multiple of the Nyquist frequency (that is, the frequency is zero) occurs to appear as a noise component by folding of an even number in  FIG. 8 . Then, the noise components  602  and  604  are superimposed on the original power spectrum  600  and a power spectrum  606  on which the noise components are superimposed is detected to appear as a combined power spectrum in  FIG. 8 . Further, in a case in which the above-described process is performed on the power spectrum  606  on which such folding noise components  602  and  604  are superimposed, it is difficult to acquire accurate blood flow information since the noise components  602  and  604  are superimposed. Note that the folding of two times has been described herein. However, since the folding continues up to the infinity actually, high-frequency components included in an original signal are all convoluted in a signal with ½ fs or less. 
     Note that the folding noise component  604  occurring at a position of a frequency of an even multiple of the Nyquist frequency has a shape in which the power spectrum  600  on which no noise component is superimposed is translated along the frequency axis (the X axis). Accordingly, it is easy to suppose an influence of the noise component  604  from the detection signal (the power spectrum  606  on which the noise component is superimposed) and the influence on calculation of the blood flow information can be said to be small. In contrast, the folding noise component  602  occurring at a position of a frequency of an odd multiple of the Nyquist frequency has a shape in which power spectrum  600  on which no noise component is superimposed is inverted using the corresponding position of the Nyquist frequency as a mirror plane. Accordingly, it is difficult to suppose an influence of the noise component  602  from the detection signal (the power spectrum  606  on which the noise component is superimposed) and the influence on calculation of the blood flow information can be said to be large. In addition, for the blood flow information corresponding to a frequency range (or a velocity range) considerably influenced by the folding noise components, of course, it is difficult to acquire accurate information due to the folding noise components. 
     That is, in a case in which the sampling frequency is lowered to suppress power consumption in the measurement module  10 , the folding noise components are superimposed on the detection signal, and thus it is difficult to obtain the accurate blood flow information from the detection signal due to the folding noise components. 
     Accordingly, the present inventors have created embodiments of the present disclosure in view of the foregoing circumstances. According to the embodiments of the present disclosure, it is possible to obtain accurate blood flow information even in a case in which a detection signal on which folding noise components are superimposed since a sampling frequency is lowered to suppress power consumption of the measurement module  10  is acquired. In particular, according to the embodiments of the present disclosure, blood flow information such as a power spectrum which is not influenced from the folding noise components is estimated from the detection signal on which the folding noise components are superimposed. Then, according to the embodiments, more desired blood flow information is acquired by processing the estimated blood flow information such as a power spectrum. Accordingly, according to the embodiment of the present disclosure, it is possible to obtain the accurate blood flow information by using the estimated blood flow information such as the power spectrum which is not influenced from the folding noise components even in a case in which the sampling frequency is lowered. Hereinafter, detailed configurations and operations of the embodiments of the present disclosure will be described sequentially in detail. 
     4. First Embodiment 
     In a first embodiment, a power spectrum on which a folding noise component is greatly superimposed since a sampling frequency is lowered is detected, but a power spectrum in a satisfactory state in which the folding noise component is negligible is estimated from the power spectrum. Accordingly, in the embodiment, to perform the above-described estimation, the information processing apparatus  30  performs machine learning of a relation between a power spectrum on which no folding noise component is superimposed and a power spectrum on which a folding noise component corresponding to the power spectrum is superimposed. Then, on the basis of relation information obtained through the machine learning, the information processing apparatus  30  estimates a power spectrum which corresponds to the power spectrum and which is in a satisfactory state in which the folding noise component is negligible, from the power spectrum on which a separately acquired folding noise component is superimposed. Further, in the embodiment, a pulse rate which is desired blood flow information is acquired using the estimated power spectrum. 
     In particular, in the embodiment, to perform the machine learning of the relation information, blood flow measurement is performed at a high sampling frequency (hereinafter referred to as a first sampling frequency) when a power spectrum in a satisfactory state in which a folding noise component is negligible (hereinafter referred to as a first power spectrum) is acquired. By setting the first sampling frequency to be twice or more a frequency at which a noise component can be sufficiently attenuated to the extent that an adverse influence of a folding phenomenon is negligible, it is possible to avoid the adverse influence of the noise component occurring in the folding phenomenon by the Nyquist theorem. Hereinafter, a frequency at which the noise component is sufficiently attenuated to the extent that the adverse influence of the folding phenomenon is negligible is referred to as a maximum frequency. It is possible to acquire a first detection signal (a first blood flow signal) in a state in which the adverse influence of the folding noise component is negligible through the blood flow measurement at the first sampling frequency which is twice or more the maximum frequency and a first power spectrum by further processing the first detection signal. The first sampling frequency is a frequency equal to or greater than 16 kHz and is, for example, a frequency of about 100 kHz. Note that power consumption in the measurement module  10  increases in the blood flow measurement at the first sampling frequency. 
     In the embodiment, with regard to the first detection signal in the state in which the adverse influence of the folding noise component is negligible, a second detection signal (second blood flow signal) corresponding to a low sampling frequency (hereinafter referred to as a second sampling frequency) is acquired by performing a process of decimating some of the signals included in the first detection signal in accordance with a predetermined rule (a decimation process). That is, the decimation process is performed to acquire a signal equal to a detection signal acquired in a case in which the process is performed at the second sampling frequency at the time of the blood flow measurement in which the above-described first detection signal is acquired. The second sampling frequency is a frequency selected to suppress the power consumption of the measurement module  10 , is particularly less than twice the maximum frequency, and is a frequency less than the above-described first sampling frequency. Accordingly, since the second sampling frequency is equal to or less than twice the maximum frequency, a folding noise component with a level which has an adverse influence on calculation of the blood flow information is superimposed on the second detection signal. Then, a power spectrum on which a folding noise component is superimposed (hereinafter referred to as a second power spectrum) is acquired by processing the second detection signal on which the folding noise component is superimposed. For example, as the second sampling frequency, a frequency equal to or less than ½ of the above-described first sampling frequency can be selected and, more specifically, a frequency with about several kHz to several 10 kHz can be selected. 
     Then, in the embodiment, a relation between the first power spectrum and the second power spectrum obtained as described above is obtained by machine learning. Specifically, in the machine learning, relation information indicating the relation between the first power spectrum and the second power spectrum is acquired by comparing waveforms in accordance with a predetermined rule or deriving a mathematical correspondent relation. Since a velocity distribution of particles during blood flow has a monotonous phenomenon property smaller as the particles of which velocities are generally faster and a distribution pattern in which restriction is imposed on various conditions caused from the shape of the blood vessels or viscosity of blood is shown, there are many limitations on the distribution shape of the power spectrum. Accordingly, it is possible to acquire the relation information from the above-described machine learning. 
     Further, in the embodiment, a power spectrum on which a folding noise component is superimposed (hereinafter referred to as a third power spectrum) is acquired from a third detection signal on which the folding noise component detected in another blood flow measurement is superimposed. Note that the foregoing another blood flow measurement is performed at the low second sampling frequency to suppress power consumption in the measurement module  10 . Then, on the basis of the relation information obtained through the above-described machine learning, a power spectrum which corresponds to the third power spectrum and is in a satisfactory state in which the folding noise component is negligible (hereinafter referred to as a fourth power spectrum) is estimated from the acquired third power spectrum. As described above, the velocity distribution of the particles during the blood flow has a particular nature and there is limitation on the distribution shape of the power spectrum. Accordingly, on the basis of the relation information obtained through the above-described machine learning, the fourth power spectrum which corresponds to the third power spectrum and is in a satisfactory state in which the folding noise component is negligible can be estimated from the third power spectrum on which the folding noise component is superimposed. Note that the fourth power spectrum corresponds to a power spectrum obtained in a case of the actual measurement at the first sampling frequency in the foregoing other blood flow measurement. 
     Note that in the following description, an operation when the first and second detection signals are acquired through the blood flow measurement and then the relation information is acquired through the machine learning is referred to as a first operation. Further, in the following description, an operation when the third detection signal is acquired through another blood flow measurement, the fourth power spectrum is estimated, and then blood flow information (for example, a pulse rate) is calculated from the estimated fourth power spectrum is referred to as a second operation. In the embodiment, in the first operation, power consumption in the measurement module  10  is high since the measurement is performed at the high first sampling frequency. In the embodiment, however, by performing the first operation only at a predetermined timing rather than in every blood flow measurement, it is possible to suppress the power consumption in the measurement module  10 . Then, in the embodiment, the second operation is an operation at the time of normal blood flow measurement. By performing the blood flow measurement at the low second sampling frequency, it is possible to suppress the power consumption in the measurement module  10 . That is, by performing the second operation in every blood flow measurement, it is possible to suppress the power consumption. Hereinafter, detailed configurations and operations according to the embodiment will be described sequentially in detail. 
     Note that since the configurations of the information processing system  1 , the measurement module  10 , and the information processing apparatus  30  according to the embodiment have been described above, the description thereof will be omitted herein. 
     &lt;4.1 Configuration of Processor  300  According to First Embodiment&gt; 
     Hereinafter, the details of the processor  300  according to the embodiment will be described with reference to  FIGS. 9 and 10 .  FIG. 9  is a block diagram illustrating a functional configuration of the processor  300  of the information processing apparatus  30  according to the embodiment.  FIG. 10  is an explanatory diagram illustrating an information processing method according to the embodiment. As described above, the processor  300  acquires desired blood flow information by processing a detection signal obtained by the measurement module  10 . In particular, as illustrated in  FIG. 9 , the processor  300  mainly includes an inter-signal decimation unit  310 , a broadband spectrum signal generation unit  312 , a narrowband spectrum signal generation unit  314 , a learning unit  316 , a spectrum signal estimation unit  318 , a blood flow information calculation unit  320 , and a pulse calculation unit  322 . Hereinafter, each functional unit included in the processor  300  will be described. 
     (Inter-Signal Decimation Unit  310 ) 
     The inter-signal decimation unit  310  performs a process of decimating some of signals included in the first detection signal in accordance with a predetermined rule on the first detection signal acquired at the first sampling frequency in the first operation to acquire the second detection signal corresponding to the second sampling frequency. The acquired second detection signal is output to the narrowband spectrum signal generation unit  314  to be described below. 
     (Broadband Spectrum Signal Generation Unit  312 ) 
     The broadband spectrum signal generation unit  312  performs a process on the first detection signal detected by the detection unit  102  of the measurement module  10  to generate a first power spectrum  810  (see  FIG. 10 ). In particular, the broadband spectrum signal generation unit  312  performs the FFT on the first detection signal to generate the first power spectrum  810  (the first processing method). Alternatively, the broadband spectrum signal generation unit  312  calculates an autocorrelation function from the first detection signal and performs the FFT on the calculated autocorrelation function to generate the first power spectrum  810  (the second processing method). 
     More specifically, the broadband spectrum signal generation unit  312  performs a process on the first detection signal obtained at the first sampling frequency in the first operation to generate the first power spectrum  810 . The generated first power spectrum  810  is output to the learning unit  316  to be described below so that the first power spectrum  810  is supplied for machine learning. Note that in the first power spectrum  810 , an adverse influence of a folding noise component on the calculation of the blood flow information is small to the extent that the adverse influence is negligible since the first sampling frequency is sufficiently high. 
     (Narrowband Spectrum Signal Generation Unit  314 ) 
     The narrowband spectrum signal generation unit  314  performs a process on the second detection signal processed by the inter-signal decimation unit  310  to generate a second power spectrum  820  (see  FIG. 10 ), in particular, the narrowband spectrum signal generation unit  314  performs the FFT on the second detection signal processed by the inter-signal decimation unit  310  to generate the second power spectrum  820  as in the above-described broadband spectrum signal generation unit  312  (the first processing method). Alternatively, the narrowband spectrum signal generation unit  314  calculates an autocorrelation function from the second detection signal and performs the FFT on the calculated autocorrelation function to generate the second power spectrum (the second processing method). On the other hand, the narrowband spectrum signal generation unit  314  performs a process on the third detection signal detected at the second sampling frequency in the second operation to generate a third power spectrum  830  (see  FIG. 10 ). Note that a folding noise component is greatly superimposed on the third power spectrum  830  to the extent that the folding noise component has an adverse influence on the calculation of the flood flow information since the second sampling frequency is low. The generated third power spectrum  830  is output to the spectrum signal estimation unit  318  to be described below in order to estimate a fourth power spectrum  840  (see  FIG. 10 ) which is in a state in which the folding noise component is small to the extent that the adverse influence is negligible. 
     More specifically, the narrowband spectrum signal generation unit  314  performs a process on the second detection signal corresponding to the second sampling frequency and processed by the inter-signal decimation unit  310  in the first operation to generate the second power spectrum  820 . The generated second power spectrum  820  is output to the learning unit  316  to be described below so that the second power spectrum  820  is supplied for the machine learning. Note that a folding noise component is greatly superimposed on the second power spectrum  820  to the extent that the folding noise component has an adverse influence on the calculation of the blood flow information since the second sampling frequency is low. On the other hand, in the second operation, the narrowband spectrum signal generation unit  314  performs a process on the third detection signal obtained at the second sampling frequency to generate the third power spectrum  830 . The generated third power spectrum  830  is supplied to the spectrum signal estimation unit  318  so that the fourth power spectrum  840  is estimated. 
     (Learning Unit  316 ) 
     The learning unit  316  performs the machine learning using the first power spectrum  810  output from the broadband spectrum signal generation unit  312  and the second power spectrum  820  output from the narrowband spectrum signal generation unit  314  in the first operation. Then, information (relation information) obtained through the machine learning in the learning unit  316  is stored in the storage unit  302  so that the information is used in the spectrum signal estimation unit  318  to be described below. 
     In particular, the first power spectrum  810  output from the broadband spectrum signal generation unit  312  is the power spectrum generated from the first detection signal corresponding to the first sampling frequency, as described above. Accordingly, in the first power spectrum  810 , a folding noise component is small to the extent that the adverse influence of the folding noise component on the calculation of the blood flow information is negligible since the first sampling frequency is sufficiently high. As a result, by processing the first power spectrum  810  in the state in which the folding noise component is small to the extent that the adverse influence is negligible, it is possible to acquire the accurate blood flow information. In contrast, the second power spectrum  820  output from the narrowband spectrum signal generation unit  314  is a power spectrum generated from a signal corresponding to the second sampling frequency, as described above. Accordingly, a folding noise component is superimposed on the second power spectrum  820  to the extent that the folding noise component has an adverse influence on the calculation of the blood flow information since the second sampling frequency is low. As a result, as described, it is difficult to directly process the second power spectrum  820  on which the folding noise component is greatly superimposed and acquire the accurate blood flow information. 
     Accordingly, the learning unit  316  performs the machine learning of the relation between the first power spectrum  810  and the second power spectrum  820 . Specifically, the learning unit  316  performs leaning in a supervised leaner such as a support vector regression or a deep neural network using the first power spectrum  810  and the second power spectrum  820  as a supervised signal and an input signal, respectively, in accordance with a predetermined rule. Further, the learning unit  316  acquires relation information indicating a relation between the first power spectrum  810  and the second power spectrum  820  by the above-described learning. Since the velocity distribution of the particles during the blood flow has a particular nature and there is limitation on the distribution shape of the power spectrum, it is possible to find specific relation information from the above-described machine learning. For example, as illustrated in the upper stage of  FIG. 10 , the learning unit  316  acquires the relation information by the machine learning using one power spectrum pair  700  formed by the first power spectrum  810  and the second power spectrum  820  or a plurality of power spectrum pairs  700 . 
     (Spectrum Signal Estimation Unit  318 ) 
     The spectrum signal estimation unit  318  estimates the fourth power spectrum  840  from the third power spectrum  830  acquired by the blood flow measurement on the basis of the relation information regarding the blood flow information obtained by the learning unit  316  in the second operation. In particular, the third power spectrum includes a folding noise component and the estimated fourth power spectrum  840  corresponds to the third power spectrum. Then, the fourth power spectrum  840  estimated by the spectrum signal estimation unit  318  is output to the blood flow information calculation unit  320  to be described below. In particular, the spectrum signal estimation unit  318  acquires the third power spectrum  830  corresponding to the second sampling frequency and output from the narrowband spectrum signal generation unit  314  as an input signal in the second operation. Then, on the basis of the relation information obtained through the machine learning of the learning unit  316 , the spectrum signal estimation unit  318  estimates the fourth power spectrum (the power spectrum corresponding to the first sampling frequency)  840  in the state in which the adverse influence of the folding noise component is negligible, from the third power spectrum  830 . For example, the spectrum signal estimation unit  318  estimates each fourth power spectrum  840  illustrated in the lower stage of  FIG. 10  on the basis of relation learning (see the upper stage of  FIG. 10 ) from each third power spectrum  830  illustrated in the lower stage of  FIG. 10 . 
     (Blood Flow Information Calculation Unit  320 ) 
     The blood flow information calculation unit  320  calculates blood flow information (a blood flow velocity, a particle density in blood vessels within a predetermined velocity range, or the like) using the fourth power spectrum  840  estimated by the spectrum signal estimation unit  318  in the second operation. Then, the blood flow information calculated by the blood flow information calculation unit  320  is output to the pulse calculation unit  322  to be described below. In particular, the blood flow information calculation unit  320  can obtain the blood flow velocity or the like by integrating values obtained by taking products of frequencies having proportional relations with particle velocities in the entire power spectrum in the fourth power spectrum  840  and subsequently performing normalization. Then, the blood flow information calculation unit  320  acquires a waveform indicating a change in the blood flow velocity over time by acquiring the plurality of blood flow velocities from the plurality of fourth power spectra  840  (the first processing method). Alternatively, the blood flow information calculation unit  320  can obtain a relative density of the particles in a predetermined velocity range in the blood vessels by performing the integration on the fourth power spectrum  840  in a predetermined frequency range. Further, the blood flow information calculation unit  320  acquires a waveform indicating a relative density of the particles over time by acquiring the relative density of the plurality of particles from the plurality of fourth power spectra  840  calculated from a plurality of different time ranges (the second processing method). 
     (Pulse Calculation Unit  322 ) 
     The pulse calculation unit  322  calculates a pulse waveform by performing an interpolation process or the like on the waveform obtained from the blood flow information calculation unit  320  and calculates a pulse rate from the pulse waveform in the above-described second operation. The pulse rate or the like obtained by the pulse calculation unit  322  may be output to the storage unit  302  or may be output to the above-described information posting apparatus (not illustrated). 
     &lt;4.2 Information Processing Method According to First Embodiment&gt; 
     Next, an information processing method according to the first embodiment of the present disclosure will be described. The information processing method according to the embodiment can be broadly divided into the first operation and the second operation described above. In the first operation, the first power spectrum  810  corresponding to the high first sampling frequency and the second power spectrum  820  corresponding to the low second sampling frequency are acquired and machine learning of this relation is performed. In addition, in the second operation, the third power spectrum  830  corresponding to the second sampling frequency is acquired. Then, the fourth power spectrum  840  which is in a state the adverse influence of the folding noise component is negligible, that is, which corresponds to the first sampling frequency, is estimated from the third power spectrum  830 . Further, in the second operation, desired blood flow information is acquired from the estimated fourth power spectrum  840 . Accordingly, hereinafter, the information processing method according to the embodiment is divided into the first operation and the second operation for the description. 
     &lt;4.2.1 First Operation&gt; 
     First, a first operation of the information processing method according to the embodiment will be described with reference to  FIG. 11 .  FIG. 11  is a diagram illustrating of a flowchart of the first operation in the information processing method according to the embodiment. As illustrated in  FIG. 11 , the first operation in the information processing method according to the embodiment includes step S 101  to step S 107 . Hereinafter, each step of the first operation will be described. 
     First, before the first operation starts, for example, as illustrated in  FIG. 4 , the above-described measurement module  10  is mounted on a wrist or the like of the measured person. Note that, for example, the first operation may be performed when the measurement module  10  is mounted on a part of the body of the measured person or may be performed when the measured person performs first measurement. In addition, the first operation may be performed when the blood flow measurement starts or may be performed for each predetermined period (10 minutes or 20 minutes) in the blood flow measurement. Alternatively, the first operation may be performed when a manipulation by a user using the information processing system  1  is received. 
     (Step S 101 ) 
     When it is detected that the measurement module  10  is mounted on a part of the body of the measured person, or the like, the measurement module  10  starts the blood flow measurement. Then, the information processing apparatus  30  acquires a first detection signal corresponding to the first sampling frequency. Note that the blood flow measurement may be performed a plurality of times by repeating step S 101 . 
     (Step S 103 ) 
     The information processing apparatus  30  performs the decimation process on the first detection signal corresponding to the first sampling frequency and detected in step S 101  on the basis of a predetermined rule to acquire a second detection signal corresponding to the second sampling frequency. 
     (Step S 105 ) 
     The information processing apparatus  30  performs a process on the first detection signal corresponding to the first sampling frequency and detected in step S 101  to generate the first power spectrum  810 . Further, the information processing apparatus  30  performs a process on the second detection signal corresponding to the second sampling frequency and acquired in step S 103  to generate the second power spectrum  820 . Note that in a case in which the blood flow measurement is performed a plurality of times by repeating the above-described step S 101 , step S 103  and step S 105  are repeated the plurality of times. 
     In this way, by performing step S 101  to step S 105  once or a plurality of times, it is possible to obtain one power spectrum pair  700  formed by the first power spectrum  810  and the second power spectrum  820 , as illustrated in the upper stage of  FIG. 10  or the plurality of power spectrum pairs  700 . 
     (Step S 107 ) 
     The information processing apparatus  30  performs machine learning of the relation between the first power spectrum  810  and the second power spectrum  820  using the power spectrum pair  700  generated in step S 105 . Then, the information processing apparatus  30  stores the relation information regarding the first power spectrum  810  and the second power spectrum  820  obtained through the machine learning as information which is used for the second operation. Note that the number of power spectrum pairs  700  used for the learning of step S 107  may be 1 or plural and the power spectrum pair  700  can be selected in accordance with the accuracy of the desired blood flow information or the like. 
     Note that the above-described first operation may not be performed in the information processing system  1  and the relation information acquired in another information processing system  1  may be stored in advance in the storage unit  302  when the information processing apparatus  30  is manufactured or shipped, or the like. In this case, the second operation, that is, the normal blood flow measurement, can be immediately performed without performing the first operation. In addition, by repeating the above-described first operation using data measured in measured people with a plurality of different features, it is possible to obtain performance for stably estimating power spectra for diverse measured people. Further, in the embodiment, by performing the first operation at a specific timing during the blood flow measurement, it is also possible to perform the learning in real time. In this case, since the learning in accordance with the blood flow features of the measured people can be performed, the accuracy of the estimation can be further improved than in a case in which the estimation is performed on the basis of only learning results of other measured people. Furthermore, by performing the first operation when the measurement module  10  is mounted, the learning can be performed in accordance with a mounting state (the measurement module  10  is mounted tightly or mounted loosely on a part of the body of the measured person, or the like) or a mounted part of the measurement module  10 . Therefore, it is possible to further improve accuracy of the estimation. 
     &lt;4.2.2 Second Operation&gt; 
     Next, the second operation of the information processing method according to the embodiment will be described with reference to  FIG. 12 .  FIG. 12  is a diagram illustrating of a flowchart of the second operation in the information processing method according to the embodiment. As illustrated in  FIG. 12 , the second operation in the information processing method according to the embodiment includes step S 201  to step S 209 . Hereinafter, each step of the second operation will be described. 
     First, before the second operation starts, for example, as illustrated in  FIG. 4 , the above-described measurement module  10  is mounted on a wrist or the like of the measured person. 
     (Step S 201 ) 
     When it is detected that the measurement module  10  is mounted on a part of the body of the measured person, or the like, the measurement module  10  starts the blood flow measurement. Then, the information processing apparatus  30  acquires the third detection signal at the second sampling frequency. Note that the blood flow measurement can be continuously performed by continuously repeating step S 201 . 
     (Step S 203 ) 
     The information processing apparatus  30  performs a process on the third detection signal corresponding to the second sampling frequency and detected in step S 201  to generate the third power spectrum  830 . Note that in a case in which the blood flow measurement is performed a plurality of times by repeating the above-described step S 201 , step S 203  is repeated the plurality of times. In this way, by performing step S 201  to step S 203  the plurality of times, it is possible to obtain the plurality of third power spectra  830 , as illustrated in the lower stage of  FIG. 10 . Thus, it is possible to continuously acquire the third power spectra  830  chronologically. 
     (Step S 205 ) 
     The information processing apparatus  30  estimates the fourth power spectrum  840  from the third power spectrum  830  on which a folding noise component is superimposed and which is obtained in step S 203  on the basis of the relation information obtained in step S 107  of the first operation. The estimated fourth power spectrum  840 , the adverse influence of the folding noise component is small to the extent that the folding noise component is negligible. 
     (Step S 207 ) 
     The information processing apparatus  30  acquires the desired blood flow information using the fourth power spectrum  840  estimated in step S 205 . 
     (Step S 209 ) 
     The information processing apparatus  30  calculates a pulse rate from the blood flow information obtained in step S 207 . Note that the pulse rate obtained in step S 209  may be output to the storage unit  302  or may be output to the above-described information posting apparatus (not illustrated). 
     As described above, in the embodiment, the third power spectrum  830  on which the folding noise component is superimposed since the sampling frequency is lowered is detected. Accordingly, in the embodiment, the machine learning is performed on the relation between the first power spectrum  810  which is in the state in which the adverse influence of the folding noise component is negligible and the second power spectrum  820  which corresponds to the first power spectrum  810  and on which the folding noise component is greatly superimposed. Then, in the embodiment, the relation information indicating the relation is acquired. Further, on the basis of the relation information, the fourth power spectrum  840  which is in the state in which the adverse influence of the folding noise component is negligible is estimated from the foregoing third power spectrum  830  and the desired blood flow information (a pulse rate or the like) is acquired using the estimated fourth power spectrum  840 . Accordingly, in the embodiment, it is possible to obtain the highly accurate blood flow information from the third power spectrum  830  on which the folding noise component is superimposed since the sampling frequency is lowered, while suppressing the power consumption of the measurement module  10  by lowering the sampling frequency. 
     &lt;4.3 Modification Example of First Embodiment&gt; 
     In the above-described first embodiment, in step S 103  of the first operation, the decimation process has been performed on the first detection signal to obtain the second detection signal corresponding to the second sampling frequency. However, in the embodiment, the present disclosure is not limited to this form. For example, two detection units  102  may be caused to operate simultaneously to acquire the first detection signal corresponding to the first sampling frequency and the second detection signal corresponding to the second sampling frequency. In this case, the processor  300  can reduce a processing amount of the processor  300  since the signal decimation process is not performed. Hereinafter, this embodiment will be described as a modification example of the above-described first embodiment. 
     In the modification example, compared to the configuration according to the first embodiment, two detection units  102  are provided and further the inter-signal decimation unit  310  is omitted in a processor  300   a.  In particular, as illustrated in  FIG. 13  which is a block diagram illustrating a functional configuration of the processor  300   a  according to the modification example, a first detection unit  102   a  and a second detection unit  102   b  are provided and the inter-signal decimation unit  310  is not provided in the processor  300   a  in the modification example. The first detection unit  102   a  performs the blood flow measurement at the first sampling frequency in the first operation and outputs the first detection signal to the broadband spectrum signal generation unit  312 . Further, the first detection unit  102   a  performs the blood flow measurement at the second sampling frequency in the second operation and outputs the third detection signal to the narrowband spectrum signal generation unit  314 . In addition, the second detection unit  102   b  performs the blood flow measurement at the second sampling frequency in the first operation and directly outputs the second detection signal to the narrowband spectrum signal generation unit  314 . That is, in the modification example, in step S 101  in  FIG. 11  illustrating a flowchart of the first operation of the information processing method according to the above-described first embodiment, the blood flow measurement at the first and second sampling frequencies is performed. Further, in the modification example, the process subsequently proceeds to step S 105  of generating the power spectrum without performing step S 103  in  FIG. 11 . 
     For example, equipment in which the two detection units  102   a  and  102   b  according to the modification example illustrated in  FIG. 13  are provided is assumed to be used, for example, when learning data for machine learning gathers in advance. Further, as products to be mass-produced, equipment in which one detection unit  102   b  performing the blood flow measurement at the second sampling frequency is provided is assumed. In this way, in the mass-produced products, one detection unit  102  that operates at the low second sampling frequency may be provided. Therefore, it is possible to decrease manufacturing cost of the products. 
     5. Second Embodiment 
     In the above-described first embodiment, the power spectrum (the fourth power spectrum  830 ) has been estimated. In an embodiment, however, blood flow information such as a blood flow velocity may be estimated without being limited to the estimation of the power spectra. Hereinafter, the embodiment will be described as a second embodiment. In the following description, an average blood flow velocity will be estimated. In the embodiment, by directly estimating an average blood flow velocity, it is possible to reduce a processing amount in the second operation. Note that since the configurations of the information processing system  1 , the measurement module  10 , and the information processing apparatus  30  according to the embodiment have been described above, the description thereof will be omitted. 
     &lt;5.1 Configuration of Processor  300   b  According to Second Embodiment&gt; 
     In the embodiment, each functional unit included in the processor  300   b  is different from that of the processor  300  according to the first embodiment. Hereinafter, a configuration of the processor  300   b  according to the embodiment will be described with reference to  FIGS. 14 and 15 .  FIG. 14  is a block diagram illustrating a functional configuration of the processor  300   b  according to the embodiment.  FIG. 15  is an explanatory diagram illustrating an information processing method according to a second embodiment. 
     As illustrated in  FIG. 14 , the processor  300   b  mainly includes the inter-signal decimation unit  310 , the broadband spectrum signal generation unit  312 , the narrowband spectrum signal generation unit  314 , a learning unit  316   a,  the blood flow information calculation unit  320 , the pulse calculation unit  322 , and a blood flow information estimation unit  324 . That is, in the embodiment, the learning unit  316   a  and the blood flow information estimation unit  324  are different from those of the first embodiment. Accordingly, the description of the common functional units to those of the first embodiment will be omitted and only an association relation between the functional units, the learning unit  316   a,  and the blood flow information estimation unit  324  will be described. 
     (Association Relation Between Functional Units in Processor  300   b  According to Second Embodiment) 
     In the second embodiment, a calculation result of the broadband spectrum signal generation unit  312  is output to the blood flow information calculation unit  320 . In addition, a calculation result of the narrowband spectrum signal generation unit  314  is output to the learning unit  316   a  and the blood flow information estimation unit  324 . A calculation result of the blood flow information calculation unit  320  is output to the learning unit  316   a.  A calculation result of the learning unit  316   a  is used when the blood flow information estimation unit  324  performs estimation via the storage unit  302 . Further, an estimation result of the blood flow information estimation unit  324  is output to the pulse calculation unit  322 . 
     (Learning Unit  316   a ) 
     The learning unit  316   a  performs machine learning using an average flood flow velocity (a first average blood flow velocity) output from the blood flow information calculation unit  320  and the second power spectrum  820  output from the narrowband spectrum signal generation unit  314  in the above-described first operation. Then, relation information between the average blood flow velocity and the second power spectrum  820  obtained through the machine learning in the learning unit  316   a  is stored in the storage unit  302  to be used in the blood flow information estimation unit  324  to be described below. Note that the average blood flow velocity output to the learning unit  316   a  is obtained by processing the first detection signal in which a folding noise component is small to the extent that an adverse influence of the folding noise component is negligible. More specifically, as illustrated in the upper stage of  FIG. 15 , the learning unit  316   a  acquires an average blood flow velocity from a blood flow velocity distribution  710  acquired from the first power spectrum  810 . Further, the learning unit  316   a  acquires the second power spectrum  820  forming the power spectrum pair  700  along with the first power spectrum  810 . Then, the learning unit  316   a  learns a relation between the average blood flow velocity and the second power spectrum  820 . In other words, the learning unit  316   a  learns a relation between the second power spectrum  820  on which the folding noise component is superimposed and the average blood flow velocity on which there is no influence of the folding noise component. Note that one second power spectrum  820  with which a correspondent relation is learned by the learning unit  316   a  may be used or the plurality of second power spectra  820  with different time ranges may be used. In an example of the blood flow information estimation unit  324  to be described below, a relation between one average blood flow velocity and the second power spectra  820  with five different time ranges is learned (see  FIG. 15 ). 
     (Blood Flow Information Estimation Unit  324 ) 
     The blood flow information estimation unit  324  estimates an average blood flow velocity from the third power spectrum  830  on which a folding noise component is superimposed on the basis of the relation information obtained by the learning unit  316   a  in the second operation. Then, the average blood flow velocity estimated by the blood flow information estimation unit  324  is output to the above-described pulse calculation unit  322   a.  For example, the blood flow information estimation unit  324  estimates an average blood flow velocity illustrated in the lower stage of  FIG. 15  on the basis of relation learning (see the upper stage of  FIG. 15 ) from each third power spectrum  830  illustrated in the lower stage of  FIG. 15 . Here, for example, the blood flow information estimation unit  324  estimates the corresponding average blood flow velocity from the five power spectra  830  with the different time ranges. 
     &lt;5.2 Information Processing Method According to Second Embodiment&gt; 
     Next, an information processing method according to the embodiment will be described. The information processing method according to the embodiment can be divided into a first operation and a second operation as in the first embodiment. First, the first operation of the information processing method according to the embodiment will be described with reference to  FIG. 11  which is a flowchart of the first operation according to the first embodiment. The first operation according to the embodiment is different from that of the first embodiment in that the average blood flow velocity is calculated in addition to generation of the power spectrum in step S 105  illustrated in  FIG. 11 . Further, the first operation according to the embodiment is different from that of the first embodiment in that in step S 107  illustrated in  FIG. 11 , the relation between the first power spectrum  810  and the second power spectrum  820  is not learned, and the relation between the average blood flow velocity and the second power spectrum  820  is learned. 
     Next, the second operation of the information processing method according to the embodiment will be described with reference to  FIG. 12  which is the flowchart of the second operation according to the first embodiment. The second operation according to the embodiment is different from that of the first embodiment in that the average blood flow velocity is estimated instead of estimating the third power spectrum  830  in step S 205  illustrated in  FIG. 12 . 
     As described above, in the embodiment, the third power spectrum  830  on which the folding noise component is superimposed since the sampling frequency is lowered is detected as in the first embodiment. Accordingly, in the embodiment, the average blood flow velocity obtained from the first power spectrum  810  in which the folding noise component is small to the extent that the adverse influence of the folding noise component is negligible is acquired. Further, the relation information is acquired by performing the machine learning of the relation between the average blood flow velocity and the power spectrum  820  on which the folding noise component is superimposed and which corresponds to the first power spectrum  810 . Then, in the embodiment, on the basis of the relation information, the average blood flow velocity on which there is no influence of the folding noise component is estimated from the foregoing third power spectrum  830  and desired blood flow information (pulse rate or the like) is acquired using the estimated average blood flow velocity. Accordingly, in the embodiment, it is possible to obtain the accurate blood flow information from the power spectrum  830  on which the folding noise component is superimposed since the sampling frequency is lowered, while suppressing the power consumption of the measurement module  10  by lowering the sampling frequency Furthermore, in the embodiment, it is possible to reduce a processing amount in the second operation because of the direct estimation of the average blood flow velocity. 
     &lt;5.3 Modification Example of Second Embodiment&gt; 
     In the above-described second embodiment, the average blood flow velocity has been estimated. However, the embodiment is not limited to this form. For example, a relative density of particles in a predetermined velocity range may be estimated. Hereinafter, a modification example of the embodiment in which a relative density of the particles is estimated will be described with reference to  FIG. 16  which is an explanatory diagram illustrating an information processing method according to the modification example of the embodiment. 
     In the first operation according to the modification example, as illustrated in the upper stage of  FIG. 16 , the first detection signal corresponding to the first sampling frequency is acquired, a process is performed on the first detection signal, and a distribution  720  of a particle density in blood vessels is acquired. Further, in the modification example, a relative density (a first specific velocity range particle relative density) of the particles in the blood vessels in a predetermined velocity range is calculated from the particle density distribution  720 . In addition, in the modification example, the second detection signal corresponding to the second sampling frequency is acquired and the second power spectrum  820  is generated as in the first and second embodiments. Then, in the modification example, machine learning of a relation between the second power spectrum  820  and the first specific velocity range particle relative density is performed. 
     Further, in the second operation according to the modification example, the third detection signal corresponding to the second sampling frequency is acquired and the acquired third detection signal is processed to acquire the third power spectrum  830  as in the first and second embodiments (see the lower stage of  FIG. 16 ). Then, in the modification example, a specific velocity range particle relative density (a second specific velocity range particle relative density) is estimated from the third power spectrum  830  on the basis of the relation information (see the upper stage of  FIG. 16 ) obtained through the machine learning (see the lower stage of  FIG. 16 ). 
     6. Third Embodiment 
     In the above-described first and second embodiments, the power spectrum, the average blood flow velocity, and the like have been estimated. In an embodiment, however, a pulse rate may be directly estimated without being limited to the estimation of the power spectrum or the like. Hereinafter, the embodiment will be described as a third embodiment. In the embodiment, by directly estimating a pulse rate, it is possible to reduce a processing amount in the second operation. Note that since the configurations of the information processing system  1 , the measurement module  10 , and the information processing apparatus  30  according to the embodiment have been described above, the description thereof will be omitted. 
     &lt;6.1 Configuration of Processor  300   c  According to Third Embodiment&gt; 
     In the embodiment, each functional unit included in the processor  300   c  is different from that of the processors  300  and  300   b  according to the first and second embodiments. Hereinafter, a configuration of the processor  300   c  according to the embodiment will be described with reference to  FIGS. 17 and 18 .  FIG. 17  is a block diagram illustrating a functional configuration of the processor  300   c  according to the embodiment.  FIG. 18  is an explanatory diagram illustrating an information processing method according to the embodiment. 
     As illustrated in  FIG. 17 , the processor  300   c  mainly includes the inter-signal decimation unit  310 , the broadband spectrum signal generation unit  312 , the narrowband spectrum signal generation unit  314 , a learning unit  316   b,  the blood flow information calculation unit  320 , the pulse calculation unit  322 , and a pulse estimation unit  326 . That is, in the embodiment, the learning unit  316   b  and the pulse estimation unit  326  are different from those of the second embodiment. Accordingly, the description of the common functional units to those of the second embodiment will be omitted and only an association relation between the functional units, the learning unit  316   b,  and the pulse estimation unit  326  will be described. 
     (Association Relation Between Functional Units in Processor  300   c  According to Third Embodiment) 
     In the third embodiment, a calculation result of the blood flow information calculation unit  320  is output to the pulse calculation unit  322 . A calculation result of the pulse calculation unit  322  is output to the learning unit  316   b.  A calculation result of the narrow band spectrum signal generation unit  314  is output to the learning unit  316   b  and the pulse estimation unit  326 . A learning result of the learning unit  316   b  is used when the pulse estimation unit  326  performs estimation via the storage unit  302 . 
     (Learning Unit  316   b ) 
     The learning unit  316   b  performs machine learning using a pulse rate output from the pulse calculation unit  322  and the second power spectrum  820  output from the narrowband spectrum signal generation unit  314  in the first operation. Then, relation information between the pulse rate and the second power spectrum  820  obtained through the machine learning in the learning unit  316   b  is stored in the storage unit  302  to be used in the pulse estimation unit  326  to be described below Note that the pulse rate output to the learning unit  316   b  is obtained by processing the plurality of first detection signals in which a folding noise component is small to the extent that an adverse influence of the folding noise component is negligible. More specifically, as illustrated in the upper stage of  FIG. 18 , the learning unit  316   b  acquires a pulse rate from a pulse rate waveform  730  acquired from the first power spectrum  810 . Further, the learning unit  316   b  acquires the second power spectrum  820  forming the power spectrum pair  700  along with the first power spectrum  810 . Then, the learning unit  316   b  learns a relation between the pulse rate and the second power spectrum  820 . In other words, the learning unit  316   b  learns a relation between the second power spectrum  820  on which the folding noise component is superimposed and the pulse rate on which there is no influence of the folding noise component. Note that in order for the learning unit  316   b  to perform the learning efficiently, it is preferably to input the plurality of second power spectra  820  with different time ranges to the learning unit  316   b  throughout a period in which a difference in the time range is longer than a pulse interval of the pulse. Note that the learning unit  316   b  can also perform the learning by inputting a heartbeat rate measured using another measurer from the outside of the processor  300   c  to the learning unit  316   b  as an alternative of the information from the pulse calculation unit  322 . In this case, the estimation by the pulse estimation unit is a heartbeat rate rather than a pulse rate in theory of machine learning, but is equal to estimation of a pulse rate in practice because of estimation on the basis of blood flow information. By realizing such modification, an additional measurer is necessary, but it is possible to further improve accuracy of a supervised signal used for the learning. 
     (Pulse Estimation Unit  326 ) 
     The pulse estimation unit  326  estimates a pulse rate from the third power spectrum  830  on which a folding noise component is superimposed on the basis of the relation information obtained by the learning unit  316   b  in the second operation. The pulse rate obtained by the pulse estimation unit  326  may be output to the storage unit  302  or may be output to the above-described information posting apparatus (not illustrated). More specifically, the pulse estimation unit  326  estimates a pulse rate illustrated in the lower stage of  FIG. 18  on the basis of the relation learning (see the upper stage of  FIG. 18 ) from each third power spectrum  830  illustrated in the lower stage of  FIG. 18 . 
     &lt;6.2 Information Processing Method According to Third Embodiment&gt; 
     Next, an information processing method according to the embodiment will be described. The information processing method according to the embodiment can be divided into a first operation and a second operation as in the first embodiment. First, the first operation of the information processing method according to the embodiment will be described with reference to  FIG. 11  which is a flowchart of the first operation according to the first embodiment. 
     The first operation according to the embodiment is different from that of the first embodiment in that blood flow information and a pulse rate (first pulse rate) are calculated in addition to the generation of the power spectrum in step S 105  illustrated in  FIG. 11 . Further, the first operation according to the embodiment is different from that of the first embodiment in that in step S 107  illustrated in  FIG. 11 , the relation between the first power spectrum  810  and the second power spectrum  820  is not learned, and the relation between the pulse rate and the second power spectrum  820  is learned. 
     Next, the second operation of the information processing method according to the embodiment will be described with reference to  FIG. 12  which is the flowchart of the second operation according to the first embodiment. The second operation according to the embodiment is different from that of the first embodiment in that step S 205  and step S 207  illustrated in  FIG. 12  are skipped and the pulse rate (second pulse rate) is estimated from the third power spectrum  830  instead of calculating the pulse rate from the blood flow information in step S 209  illustrated in  FIG. 12 . 
     As described above, in the embodiment, the third power spectrum  830  on which the folding noise component is superimposed since the sampling frequency is lowered is detected as in the first embodiment. Accordingly, in the embodiment, the pulse rate obtained from the first power spectrum  810  in which the folding noise component is small to the extent that the adverse influence of the folding noise component is negligible is acquired. Further, the relation information is acquired by performing the machine learning of the relation between the pulse rate and the power spectrum  820  on which the folding noise component is superimposed and which corresponds to the first power spectrum  810 . Then, in the embodiment, on the basis of the relation information, the pulse rate on which there is no influence of the folding noise component is estimated from the foregoing power spectrum  830 . Accordingly, in the embodiment, it is possible to obtain the accurate blood flow information from the power spectrum  830  on which the folding noise component is superimposed since the sampling frequency is lowered, while suppressing the power consumption of the measurement module  10  by lowering the sampling frequency. Furthermore, in the embodiment, it is possible to reduce a processing amount in the second operation because of the direct estimation of the pulse rate. 
     Note that embodiments of the present disclosure are not limited to the above-described first to third embodiments. Another piece of blood flow information or information such as a function with a specific relation such as proportion or inverse proportion to specific blood flow information may be estimated and the present disclosure is not particularly limited. For example, in an embodiment of the present disclosure, a relation between blood flow information or the like (first blood flow information) obtained at the high first sampling frequency and blood flow information (second blood flow information) obtained at the low second sampling frequency is learned (a relation between two different kinds of blood flow information or the like is learned). In this case, on the basis of the learned relation information, blood flow information or the like (fourth blood flow information) which is obtained at the high first sampling frequency and in Which a folding noise component is small to the extent that the adverse influence of the folding noise component is negligible is estimated from blood flow information or the like (third blood flow information) obtained at the low second sampling frequency in another blood flow measurement. That is, in the embodiment of the present disclosure, another kind of blood flow information or the like corresponding to one kind of blood flow information or the like is estimated from the one kind of blood flow information or the like. In addition, for example, in the embodiment of the present disclosure, a relation between blood flow information or the like on which there is no influence of noise (noise is not superimposed) and blood flow information on which there is an influence of noise (noise is superimposed) may be learned. In this case, on the basis of the learning, blood flow information or the like on which there is no influence of noise is estimated from blood flow information or the like on which there is an influence of noise and which is obtained in another flood flow measurement. 
     7. Fourth Embodiment 
     To improve accuracy of the estimation performed in the above-described first to third embodiments, it is preferable to use the detection signal or the blood flow information on which other noise components are not superimposed as a matter of course. As an instance in which other noise components are removed, for example, an instance in which an exercise in which the measured person shakes his or her arm when blood flow measurement is performed to obtain a pulse rate is performed is known. In this instance, since acceleration occurring in the exercise is used to change blood flow and noise components are caused to arise, the noise components causing a change in the blood flow are cancelled using information of an accelerometer. However, for example, when a finger moves at the time of measurement of a signal in the arm, noise components caused due to the movement may be superimposed on a detection signal or the like in some cases. More specifically, a blood flow velocity of the arm of the measured person is modulated due to a physical change in a blood flow passage before the arm caused due to the movement of the finger at the time of blood flow measurement, the blood flow velocity is mixed with a variation by heartbeat, and an accurate heartbeat rate (heartbeat waveform) may not be acquired in some cases. However, in this case, since acceleration of the arm is scarcely changed, an obtainable effect is small when noise components are cancelled using the above-described accelerometer. Accordingly, in a fourth embodiment, an embodiment in which accurate blood flow information can be obtained by removing noise components caused due to a body motion of the measured person from a detection signal, blood flow information, or the like will be proposed. For example, in the embodiment, by combining the above-described first to third embodiments, it is possible to further improve accuracy of the learning or the estimation in each embodiment. 
     In particular, there are various body motions of the measured person corresponding to the above-described example. For example, in a case in which the measurement module  10  is considered to be mounted on a wrist of the measured person, an exercise in which an arm on which the measurement module  10  is mounted is shaken, a typing exercise of fingertips, and the like can be exemplified. Then, blood flow is variously influenced due to a difference in the body motion. Specifically, for example, in a case in which a body motion is an exercise in which an arm is shaken, a blood flow velocity in an artery of the arm is modulated due to the body motion and blood flow information detected from the artery contains noise components caused due to the body motion. In the present disclosure, since a measurement part is not a broad region, the measurement part is moved as one substantially integrated part and acceleration by the exercise is almost uniform. On the other hand, a resistant constant received from a blood vessel serving as a flow passage differs depending on a kind of blood vessel in which there is blood. Since an actually caused change in an exercise of particles in a blood vessel is made by a resultant force of both parties, a tendency of the change differs for each kind of blood vessel in which there are the particles. In addition, in a case in which a body motion is a typing exercise, propagation of an influence of deformation of fingertips differs for each kind of blood vessel. Therefore, a tendency of exercises of particles also differs for each kind of blood vessel. That is, depending on a body motion, an influence of the body motion differs for each kind of blood vessel in the body. 
     Incidentally, in a blood flow information measurement method used in the embodiment, as described above, blood flow information in blood vessels in a biological tissue within a range in which radiated light arrives can be acquired at one time. More specifically, in the blood flow information measurement method used in the embodiment, blood flow information regarding not only capillaries near the surface of skin of the measured person but also inner arterioles or the like can be acquired at one time. Accordingly, a detection signal detected in the embodiment includes a plurality of signal components obtained from the various blood vessels. Moreover, in a method of obtaining a relative particle density in blood flow in a predetermined velocity range used in the embodiment, blood flow velocities of flowing blood differ for each kind of blood vessel (for example, about several to tens of mm/sec in arterioles and about hundreds of μm/sec in capillaries). Therefore, the foregoing detection signal is processed in a range suitable for a blood flow velocity range for each kind of blood vessel. As a result, in the method, it is possible to obtain an independent signal component for each kind of blood vessel. 
     Accordingly, in the embodiment, noise components caused due to a body motion are calculated using a plurality of independent signal components obtained by processing power spectra obtained from one detection signal in a plurality of ranges and the calculated noise components are removed from the detection signal. In particular, for example, in a case in Which noise components caused due to a body motion are superimposed as an irregular signal on one of the plurality of signal components, the signal component on which the irregular signal is superimposed is compared to a signal component on which the irregular signal is not superimposed. Then, by extracting the irregular signal on the basis of the comparison, the noise components can be calculated. In addition, for example, in a case in which noise components caused due to a body motion are superimposed as an irregular signal at a specific ratio in a plurality of signal components among the plurality of signal components, the noise components can be calculated by comparing signal components on which the irregular signal is superimposed and extracting the common irregular signal. Further, as in the above-described first to third embodiments, a relation between each signal component and noise components caused to a body motion may be learned and the noise components may be estimated from a plurality of signal components included in detection signals obtained from other blood flow measurements on the basis of the relation information obtained through the learning. In addition, in a case in which an acceleration sensor (not illustrated) is included in the information processing system according to the embodiment, accuracy of the calculation of the noise components may be improved by additionally using a detection result obtained by the acceleration sensor. 
     According to the embodiment, the accurate blood flow information can be obtained by using the detection signal from which the noise components are removed. Hereinafter, this embodiment will be described as the fourth embodiment. Note that since the configurations of the information processing system  1 , the measurement module  10 , and the information processing apparatus  30  according to the embodiment have been described above, the description thereof will be omitted herein. Further, in the embodiment, the information processing system  1  may include an acceleration sensor or the like, as described above. The acceleration sensor is contained in, for example, the measurement module  10 . Alternatively, apart from the measurement module  10 , one acceleration sensor or a plurality of acceleration sensors are mounted at predetermined spots of the body of the measured person to detect a motion of a part of the body of the measured person. 
     &lt;7.1 Configuration of Processor  300   d  According to Fourth Embodiment&gt; 
     In the embodiment, each functional unit included in the processor  300   d  is different from that of the processor  300  according to the first embodiment. Hereinafter, a configuration of the processor  300   d  according to the embodiment will be described with reference to  FIGS. 19 and 20 .  FIG. 19  is a block diagram illustrating a functional configuration of the processor  300   d  according to the embodiment.  FIG. 20  is an explanatory diagram illustrating an information processing method according to the embodiment. As illustrated in  FIG. 19 , the processor  300   d  mainly includes a spectrum signal generation unit  312   a,  a first blood flow information calculation unit  320   a,  a second blood flow information calculation unit  320   b,  a pulse calculation unit  322   a,  and an adaptive filter unit  328 . Hereinafter, each functional unit included in the processor  300   d  will be described. 
     (Spectrum Signal Generation Unit  312   a ) 
     The spectrum signal generation unit  312   a  performs a process on a detection signal detected by the detection unit  102  of the measurement module  10  to generate power spectra and outputs the power spectra to the first blood flow information calculation unit  320   a,  the second blood flow information calculation unit  320   b,  and the adaptive filter unit  328  to be described below. 
     (First Blood Flow Information Calculation Unit  320   a  and Second Blood Flow Information Calculation Unit  320   b ) 
     The first blood flow information calculation unit  320   a  and the second blood flow information calculation unit  320   b  perform processes on the power spectra output from the spectrum signal generation unit  312   a  in mutually different frequency ranges to obtain mutually different signal components. In particular, as illustrated in  FIG. 20 , the first blood flow information calculation unit  320   a  and the second blood flow information calculation unit  320   b  perform processes on the plurality of power spectra  850  output from the spectrum signal generation unit  312   a.  More specifically, the first blood flow information calculation unit  320   a  performs a process on each power spectrum  850  in a first frequency range  740   a  to acquire a first signal component. In addition, the second blood flow information calculation unit  320   b  performs a process on each power spectrum  850  in a second frequency range  740   b  which is a different range from the first frequency range  740   a  to acquire a second signal component. Then, the acquired first and second signal components are output to the adaptive filter unit  328  to be described below. 
     Note that the first frequency range  740   a  and the second frequency range  740   b  are set to ranges in accordance with a range of blood flow velocity of each kind of blood vessel, as described above. Alternatively, the first frequency range  740   a  and the second frequency range  740   b  may be set to any ranges rather than the ranges in accordance with the range of the blood flow velocity of each kind of blood vessel. In addition, the number of blood flow information calculation units according to the embodiment is not limited to two, as illustrated in  FIG. 19  and three or more blood flow information calculation units may be installed. In addition, in order to improve accuracy of the calculation of noise components caused due to a body motion, it is preferable to use independent signal components as many as possible, and thus it is preferable to provide many blood flow information calculation units  320 . 
     (Pulse Calculation Unit  322   a ) 
     The pulse calculation unit  322   a  calculates a pulse rate using blood flow information from which the noise components caused due to the body motion are removed by the adaptive filter unit  328  to be described below. 
     (Adaptive Filter Unit  328 ) 
     The adaptive filter unit  328  calculates the noise components caused due to the body motion using the power spectra output from the spectrum signal generation unit  312   a  and two independent first and second signal components obtained from the first blood flow information calculation unit  320   a  and the second blood flow information calculation unit  320   b.  Further, the adaptive filter unit  328  removes the calculated noise components from the first and second signal components from the first blood flow information calculation unit  320   a  and the second blood flow information calculation unit  320   b  and acquires combination values so that an adverse influence of the noise components and the combination is small. Then, the adaptive filter unit  328  outputs the acquired values to the above-described pulse calculation unit  322   c.    
     &lt;7.2 Information Processing Method According to Fourth Embodiment&gt; 
     Next, an information processing method according to the embodiment will be described with reference to  FIG. 21 .  FIG. 21  is a diagram illustrating of a flowchart of the information processing method according to the embodiment. As illustrated in  FIG. 21 , the information processing method according to the embodiment includes step S 301  to step S 309 . Hereinafter, each step of the information processing method according to the embodiment will be described. 
     First, before the information processing method according to the embodiment starts, for example, as illustrated in  FIG. 4 , the above-described measurement module  10  is mounted on a wrist or the like of the measured person. 
     (Step S 301 ) 
     When it is detected that the measurement module  10  is mounted on a part of the body of the measured person, or the like, the measurement module  10  starts the blood flow measurement. Then, the information processing apparatus  30  acquires a detection signal. Note that the blood flow measurement may be performed a plurality of times by repeating step S 301 . 
     (Step S 303 ) 
     The information processing apparatus  30  performs a process on the detection signal detected in step S 301  to generate the power spectra  850 . Note that in a case in which the blood flow measurement is performed the plurality of times by repeating the above-described step S 301 , step S 303  is repeated the plurality of times. 
     (Step S 305 ) 
     The information processing apparatus  30  performs a process on the power spectra  850  generated in step S 303  to acquire a plurality of signal components. 
     (Step S 307 ) 
     The information processing apparatus  30  calculates noise components caused due to a body motion using the power spectra  850  acquired in step S 303  and the plurality of signal components acquired in step S 305  and removes the noise components calculated from the signal components generated in step S 305 . 
     (Step S 309 ) 
     The information processing apparatus  30  calculates a pulse rate using the signal components from which the noise components caused due to the body motion are removed and which are acquired in step S 307 . 
     As described above, in the embodiment, the noise components caused due to the body motion are calculated using the plurality of independent signal components obtained by processing one detection signal in the plurality of ranges, and the calculated noise components are removed from the detection signal. Accordingly, according to the embodiment, by using the detection signal from which the noise components caused due to the body motion are removed, it is possible to obtain the accurate blood flow information. Further, according to the embodiment, by processing one detection signal in the plurality of ranges, it is possible to obtain the plurality of independent signal components and avoid providing the plurality of radiation units  100  and the plurality of detection units  102 . As a result, since the plurality of radiation units  100  and the plurality of detection units  102  are not provided, it is possible to suppress an increase in the size of the measurement module  10  and manufacturing cost of the measurement module  10 . Furthermore, by combining the embodiment with the above-described first to third embodiments, it is possible to further improve accuracy of the learning or the estimation in each embodiment. In addition, in the embodiment, for example, the measurement modules  10  may be mounted on both arms of the measured person so that learning can be performed using a detection signal (blood flow information) obtained from one stopping arm and a detection signal (blood flow information) obtained from the other arm affected by disturbance caused due to a body motion as a supervised signal and an input signal, respectively. In this case, the detection signal (blood flow information) of the other arm which is not affected by the disturbance caused due to the body motion, that is, on which noise components are not superimposed, can be estimated in accordance with the relation information obtained through the learning. Then, for example, by applying the estimated detection signal on which the noise components caused due to the body motion are not superimposed as the supervised signal to the second or third embodiment, it is possible to improve accuracy of the learning or the estimation in the second or third embodiment. 
     Note that in the above-described embodiment, the plurality of independent signal components have been obtained by processing the power spectra in the plurality of ranges, as described above, but the embodiment is not limited thereto. For example, by processing the power spectra to acquire the blood flow information and processing blood flow information acquired under a predetermined condition, the plurality of independent signal components included in the blood flow information may be obtained. 
     In addition, in the embodiment, the finally obtained blood flow information (for example, a pulse rate) may be analyzed, the noise components caused due to the body motion and superimposed on the blood flow information be partitioned, and calculation or removal of the noise may be dynamically controlled on the basis of the result. In particular, in the embodiment, for example, as the control, a frequency range used at the time of acquisition of the signal components is caused to be dynamically changed in accordance with the calculated noise components. By performing the feedback control, it is also possible to optimize the frequency range or the like and decrease or remove the noise components included in the finally obtained blood flow information. 
     8. Hardware Configuration 
       FIG. 22  is an explanatory diagram illustrating an example of a hardware configuration of an information processing apparatus  900  according to an embodiment of the present disclosure. In  FIG. 22 , the information processing apparatus  900  is an example of a hardware configuration of the above-described information processing apparatus  30 . 
     The information processing apparatus  900  includes, for example, a CPU  950 , a ROM  952 , a RAM  954 , a recording medium  956 , an input/output interface  958 , and a manipulation input device  960 . Further, the information processing apparatus  900  includes a display device  962 , a communication interface  968 , and a sensor  980 . Further, in the information processing apparatus  900 , for example, the respective components are connected by a bus  970  serving as a data transmission path. 
     (CPU  950 ) 
     The CPU  950  includes, for example, one processor or two or more processors configured as an arithmetic circuit such as a CPU, various processing circuits, and the like, functions as a controller (not illustrated) that controls the entire information processing apparatus  900 , and functions as the processor  300  that processes a detection result. 
     (ROM  952  and RAM  954 ) 
     The ROM  952  stores a program, control data such as operation parameters, and the like used by the CPU  950 . The RAM  954  temporarily stores, for example, a program executed by the CPU  950 . The ROM  952  and the RAM  954  achieve, for example, the function of the above-described storage unit  302  in the information processing apparatus  900 . 
     (Recording Medium  956 ) 
     The recording medium  956  functions as the storage unit  302 , and stores, for example, data related to the information processing method according to the present embodiment and various data such as various kinds of applications. Here, examples of the recording medium  956  include a magnetic recording medium such as a hard disk and a non-volatile memory such as a flash memory. Further, the recording medium  956  may be removable from the information processing apparatus  900 . 
     (Input/Output Interface  958 , Manipulation Input Device  960 , and Display Device  962 ) 
     The input/output interface  958  connects, for example, the manipulation input device  960 , the display device  962 , and the like. Examples of the input/output interface  958  include a universal serial bus (USB) terminal, a digital visual interface (DVI) terminal, a high-definition multimedia interface (HDMI) (registered trademark) terminal, and various kinds of processing circuits. 
     The manipulation input device  960  functions as a manipulating unit (not illustrated), is installed in, for example, the information processing apparatus  900 , and is connected with the input/output interface  958  in the information processing apparatus  900 . Examples of the manipulation input device  960  include a button, a directional key, a rotary selector such as a jog dial, a touch panel, and a combination thereof. 
     The display device  962  functions as the information presenting section (not shown) including the display apparatus, and is installed, for example, in the information processing apparatus  900  and connected with the input/output interface  958  in the information processing apparatus  900 . Examples of the display device  962  include a liquid crystal display, an organic electro-luminescence (EL) display, and the like. 
     Further, it goes without saying that the input/output interface  958  can also be connected with an external device such as a manipulation input device (for example, a keyboard, a mouse, or the like) outside the information processing apparatus  900  or an external display device. 
     (Communication Interface  968 ) 
     A communication interface  968  is a communication unit installed in the information processing apparatus  900  and functions as a communication section (not illustrated) for performing wireless or wired communication with an external apparatus such as a server via a network (or directly). Here, examples of the communication interface  968  include a communication antenna and a radio frequency (RF) circuit (wireless communication), an IEEE 802.15.1 port and a transceiving circuit (wireless communication), an IEEE 802.11 port and a transceiving circuit (wireless communication), and a local area network (LAN) terminal and a transceiving circuit (wired communication). 
     (Sensor  980 ) 
     The sensor  980  is a sensor that functions as the measurement module  10  and detects blood flow signals in accordance with an arbitrary scheme capable of detecting the blood flow signals or the like caused by the blood flow. The sensor  980  includes, for example, the radiation unit  100  that emits light and the detection unit  102  that generates a signal in response to received light. The radiation unit  100  includes, as described earlier, for example, one or more light sources such as a laser. Further, the detection unit  102  also includes, for example, a photodiode, an amplifier circuit, a filter circuit, and an analog-to-digital converter. 
     Further, the sensor  980  may include, for example, one sensor or two or more sensors capable of detecting a motion of the body of a measured person, such as an acceleration sensor, a gyro sensor, or the like. Further, the sensor  980  may include a pressure sensor or the like capable of detecting a mounting state of the above-described measurement module  10 . Note that a sensor included in the sensor  980  is not limited to the above-described example. 
     Note that a hardware configuration of the information processing apparatus  900  is not limited to the configuration illustrated in  FIG. 22 . For example, in this case in which the information processing apparatus  900  communicates with an external apparatus or the like via an external communication device connected thereto or in a case in which the information processing apparatus  900  is configured to perform standalone processing, the information processing apparatus  900  may not include the communication interface  968 . Further, the communication interface  968  may have a configuration capable of communicating with one or more external apparatuses in accordance with a plurality of communication schemes. Further, the information processing apparatus  900  can also have, for example, a configuration in which the recording medium  956 , the manipulation input device  960 , the display device  962 , or the like is not included. 
     While the information processing apparatus has been described as the present invention, the present embodiment is not limited to such an embodiment. The present embodiment can be applied to various devices capable of performing processing related to the information processing method according to the present embodiment such as a communication apparatus such as a cellular phone or the like. 
     Further, the information processing apparatus according to the present embodiment may be applied to a system including a plurality of apparatuses based on a connection to a network (or communication between respective apparatuses) as in cloud computing or the like. In other words, the information processing apparatus according to the present embodiment can also be realized as, for example, an information processing system which performs processing related to the information processing method according to the present embodiment through a plurality of apparatuses. 
     The example of the hardware configuration of the information processing apparatus  900  has been described above. Each of the components may be constituted using a general-purpose member or may be constituted by hardware specialized for the function of each component. Such a configuration can be appropriately changed in accordance with a technical level at the time of implementation. 
     9. Supplement 
     Further, the embodiments of the present disclosures described above may include, for example, a program causing a computer to function as the information processing apparatus according to the present embodiment and a non-transitory tangible medium having a program recorded therein. Further, the program may be distributed via a communication line such as the Internet (including wireless communication). 
     Further, the steps in the process of each of the above-described embodiments may not necessarily be processed in the described order. For example, the order of the steps may be appropriately modified so that the steps are processed. Further, some of the steps may be processed in parallel or individually instead of being processed chronologically. Moreover, a processing method of the steps may not necessarily be processed in accordance with the described method and may be processed in accordance with another method by other functional units, for example. 
     The preferred embodiment(s) of the present disclosure has/have been described above with reference to the accompanying drawings, whilst the present disclosure is not limited to the above examples. A person skilled in the art may find various alterations and modifications within the scope of the appended claims, and it should be understood that they will naturally come under the technical scope of the present disclosure. 
     Further, the effects described in this specification are merely illustrative or exemplified effects, and are not limitative. That is, with or in the place of the above effects, the technology according to the present disclosure may achieve other effects that are clear to those skilled in the art from the description of this specification. 
     Additionally, the present technology may also be configured as below. 
     (1) 
     An information processing apparatus including: 
     an estimation unit configured to estimate another kind of blood flow information associated with one kind of blood flow information from the one kind of blood flow information obtained through blood flow measurement on the basis of relation information indicating a relation between the two different kinds of blood flow information. 
     (2) 
     The information processing apparatus according to (1), further including: 
     a learning unit configured to learn the relation information. 
     (3) 
     The information processing apparatus according to (1) or (2), further including: 
     a storage unit that stores the relation information. 
     (4) 
     The information processing apparatus according to any one of (1) to (3), further including: 
     a pulse calculation unit configured to calculate a pulse rate or a heart rate from the other kind of blood flow information estimated by the estimation unit. 
     (5) 
     The information processing apparatus according to any one of (1) to (3), 
     in which the relation information is information indicating a relation between first blood flow information obtained through another blood flow measurement at a first sampling frequency and second blood flow information obtained through the other blood flow measurement at a second sampling frequency lower than the first sampling frequency, and 
     the estimation unit estimates fourth blood flow information corresponding to the first sampling frequency from third blood flow information obtained through the blood flow measurement at the second sampling frequency. 
     (6) 
     The information processing apparatus according to any one of (1) to (3), further including: 
     a signal decimation unit configured to perform a signal decimation process on a first blood flow signal obtained through another blood flow measurement at a first sampling frequency to generate a second blood flow signal corresponding to a second sampling frequency lower than the first sampling frequency, 
     in which the relation information is information indicating a relation between first blood flow information generated from the first blood flow signal and second blood flow information generated from the second blood flow signal, and 
     the estimation unit estimates fourth blood flow information corresponding to the first sampling frequency from third blood flow information obtained through the blood flow measurement at the second sampling frequency. 
     (7) 
     The information processing apparatus according to (6), further including: 
     a signal generation unit configured to generate a power spectrum from a blood flow signal, 
     in which the relation information is information indicating a relation between a first power spectrum generated from the first blood flow signal and a second power spectrum generated from the second blood flow signal, and the estimation unit estimates a fourth power spectrum corresponding to the first sampling frequency from a third power spectrum obtained through the blood flow measurement at the second sampling frequency. 
     (8) 
     The information processing apparatus according to (6), further including: a signal generation unit configured to generate a power spectrum from a blood flow signal; and 
     a calculation unit configured to calculate an average blood flow velocity or a relative density of particles in a predetermined velocity range from the power spectrum, 
     in which the relation information is information indicating a relation between a first average blood flow velocity or a relative density of particles in a first predetermined velocity range calculated from the first blood flow signal and a second power spectrum generated from the second blood flow signal, and 
     the estimation unit estimates a second average blood flow velocity or a relative density of particles in a second predetermined velocity range corresponding to the first sampling frequency from a third power spectrum obtained through the blood flow measurement at the second sampling frequency. 
     (9) 
     The information processing apparatus according to (6), further including: 
     a signal generation unit configured to generate a power spectrum from a blood flow signal; and 
     a pulse calculation unit configured to calculate a pulse rate from a plurality of the power spectra, 
     in which the relation information is information indicating a relation between a first pulse rate calculated from a plurality of the first blood flow signals and a plurality of second power spectra generated from a plurality of the second blood flow signals, and 
     the estimation unit estimates a second pulse rate corresponding to the first sampling frequency from a plurality of third power spectra obtained through the blood flow measurement at the second sampling frequency. 
     (10) 
     The information processing apparatus according to any one of (7) to (9), in which the signal generation unit performs frequency analysis on the blood flow signal to generate the power spectrum. 
     (11) 
     The information processing apparatus according to any one of (7) to (9), in which the signal generation unit generates the power spectrum by calculating an autocorrelation function from the blood flow signal and performing an integration process on the autocorrelation function. 
     (12) 
     The information processing apparatus according to any one of (1) to (11), further including: 
     a measurement unit configured to be mounted on a part of a body of a measured person and perform the blood flow measurement on the measured person. 
     (13) 
     The information processing apparatus according to (12), in which the measurement unit includes 
     a radiation unit configured to radiate light to the part of the body of the measured person, 
     a detection unit configured to detect light from the part of the body of the measured person, and 
     a controller configured to control a sampling frequency that decides a radiation timing of the radiation unit and a reading timing for reading a detection result of the detection unit. 
     (14) 
     The information processing apparatus according to (13), 
     in which the radiation unit radiates light with a predetermined frequency, and 
     the detection unit detects interference light between light with the predetermined frequency and light with a frequency different from the predetermined frequency. 
     (15) 
     An information processing method including: 
     estimating another kind of blood flow information associated with one kind of blood flow information from the one kind of blood flow information obtained through blood flow measurement on the basis of relation information indicating a relation between the two different kinds of blood flow information. 
     (16) 
     A program causing a computer to realize: 
     a function of estimating another kind of blood flow information associated with one kind of blood flow information from the one kind of blood flow information obtained through blood flow measurement on the basis of relation information indicating a relation between the two different kinds of blood flow information. 
     REFERENCE SIGNS LIST 
     
         
           1  information processing system 
           10  measurement module 
           30  information processing apparatus 
           70  stationary tissue 
           72  scattering substance 
           100  radiation unit 
           102 ,  102   a,    102   b  detection unit 
           104  controller 
           110  band unit 
           112  control unit 
           114  measurement unit 
           116  adhesive layer 
           118  fixing unit 
           300 ,  300   a,    300   b,    300   c,    300   d  processor 
           302  storage unit 
           310  inter-signal decimation unit 
           312 ,  312   a,    314  spectrum signal generation unit 
           316 ,  316   a,    316   b  learning unit 
           318  spectrum signal estimation unit 
           320 ,  320   a,    320   b  blood flow information calculation unit 
           322 ,  322   a  pulse calculation unit 
           324  blood flow information estimation unit 
           326  pulse estimation unit 
           328  adaptive filter unit 
           500  window 
           502  detection signal 
           600 ,  606 ,  810 ,  820 ,  830 ,  840 ,  850  power spectrum 
           602 ,  604  folding noise component 
           700  power spectrum pair 
           710  blood flow velocity distribution 
           720  particle density distribution 
           730  pulse waveform 
           740   a,    740   b  frequency range 
           900  information processing apparatus 
           950  CPU 
           952  ROM 
           954  RAM 
           956  recording medium 
           958  input/output interface 
           960  manipulation input device 
           962  display device 
           968  communication interface 
           970  bus 
           980  sensor