JPWO2016163019A1 - Biological information analysis system - Google Patents

Abstract

In an information system that acquires biological information from an image of a body, since the biological information is acquired by capturing a minute signal change in the living body with an image, it is easily affected by external noise, and stable measurement is difficult. Therefore, in the present invention, by analyzing the signal of the difference between the image information of two or three regions adjacent to each other along the blood flow, the pulse rate, pulse waveform, pulse wave velocity, blood pressure can be suppressed while suppressing external noise. Biometric information such as is measured. Thereby, highly accurate biological information can be acquired in a non-contact manner.

Description

  The present invention relates to a system for analyzing biological information.

  There is non-contact monitoring using a camera as a method for acquiring biological information such as a pulse and blood pressure with less discomfort and trouble. There is known a technique for estimating a pulse rate by analyzing a temporal change in the color of blood flow in a face image using a face image taken by a camera.

  Usually, blood pressure is measured by measuring the boundary between the pressure at which blood flow stops and the pressure at which it begins to flow by monitoring the state of blood flow with pressure and sound while applying pressure to the blood vessel. This pressurization is not suitable for high-frequency measurement or constant measurement because it causes discomfort when pressurized. As a method of estimating blood pressure without applying pressure, there is a method of measuring the pulse wave velocity by paying attention to the correlation between the pulse wave velocity and the blood pressure. Patent Document 1 discloses a technique for estimating a pulse wave propagation speed by measuring palm and face images. In addition, the “Moens-Korteweg equation” representing the relationship between the pulse wave velocity (PWV) and the incremental elastic modulus of the arterial wall is described in “Arterial”. It is well known that the blood pressure can be estimated from the pulse wave velocity by using the relational expression between the pressure and the artery inner diameter.

WO2014 / 136310 A1

K Hayashi, S Nagasawa, Y Naruo, A Okumura, “Mechanical properties of human cerebral arteries. Biorheology”, Vol. 17, no. 3, pp. 211-218, 1980.

  As described above, a method using an image can be considered as a means for estimating blood pressure by pulse rate measurement or pulse wave velocity measurement without contact and without discomfort and effort. However, this method needs to detect a slight signal change in a time-series image, and has a problem that it is easily influenced by the external environment when used in an actual environment. Examples of the environment used include a home, a car, a nursing facility, and a hospital. Depending on the weather, the season, and the shooting time, the brightness will vary even indoors. In addition, non-uniform brightness may occur depending on the arrangement of windows and lighting. Changes in brightness due to lighting such as fluorescent lamps and flicker noise of displays are also noise sources. Accordingly, an object of the present invention is to provide means for stably acquiring biological information against external noise in a system that detects biological information using an image.

In order to solve the above problem, the image processing apparatus includes a data processing unit that receives input of image data including color information of physical information and outputs biometric information based on the image data.
The data processing unit calculates each representative color in a plurality of regions in the body based on the color information, extracts each fundamental wave in the plurality of regions based on the representative color, and outputs a fundamental wave difference signal between adjacent regions of the plurality of regions. A biological information analysis system is provided that calculates, calculates pulse information based on a frequency of a difference signal, and outputs the pulse information as biological information.

  According to the present invention, biological values such as pulse waves and blood pressure can be accurately measured by image processing even under noise such as an external environment.

It is an example of the schematic diagram which shows the whole structure of a system. Example 1 It is an example of the processing flow which calculates a pulse rate from the image | photographed image data. Example 1 It is an example of the processing flow which calculates a pulse rate from the image | photographed image data. Example 1 It is an example of the processing flow which calculates a pulse rate from the image | photographed image data. Example 1 It is an example of the processing flow which calculates a pulse wave propagation velocity from the image | photographed image data. Example 1 It is an example of the figure explaining the case of a different measurement site | part. Example 1 It is another example of the figure explaining the case of a different measurement site | part. Example 1 It is another example of the figure explaining the case of a different measurement site | part. Example 1 It is another example of the figure explaining the case of a different measurement site | part. Example 1 It is an example of the figure explaining the data kind stored in a data storage part. Example 1 It is an example of the schematic diagram of the processing flow in the health service system using this system. Example 1 It is the example of the figure which showed the example of the information displayed on a display part in the health service system using this system. Example 1 It is the example of the figure which showed the example of the information displayed on a display part in the health service system using this system. Example 1 It is an example of the schematic diagram which shows the structure of a system. (Example 2) It is an example of the processing flow which calculates biometric information from the image | photographed image data. (Example 2) It is an example of the schematic diagram which shows the structure of a system. (Example 3) It is an example of the schematic diagram which shows the whole structure of a system. Example 4 It is an example of a figure explaining processing of data obtained using this system. Example 4 It is an example of the figure explaining the example which watched this system and was incorporated in the robot. Example 4 It is the example of the figure which showed the example of the information displayed on a display part in the health service system using this system. Example 1 It is the example of the figure which showed the example of the fundamental wave and difference signal which were measured in the health service system using this system. Example 1 It is the example of the figure which showed the example of the information displayed on a display part in the health service system using this system. Example 1

  Embodiments of the present invention will be described below with reference to the accompanying drawings. The accompanying drawings show specific embodiments in accordance with the principle of the present invention, but these are for the understanding of the present invention, and are never used to interpret the present invention in a limited manner. is not.

<System configuration>
FIG. 1 is a diagram showing a device configuration of an information system according to the first embodiment of the present invention. An imaging unit (camera unit) 101, a data processing unit 102, a communication unit 103, and a display unit 104 are included. The data processing unit 102 includes a CPU 111, a memory 112, a storage device 113, and the like connected thereto. Data is processed by reading the program stored in the storage device 113 into the memory 112 and executing it by the CPU 111. The camera 101 of the imaging unit has an independent shape, but may have a shape integrated with the data processing unit 102 and the like. For example, in the smartphone, the imaging unit, the data processing unit, the communication unit, and the display unit are all integrated, but such a device may be used. In this case, biometric information can be easily acquired and analyzed in the local environment. Furthermore, data in the middle of data processing may be transmitted to the outside via the communication unit, and the remaining data processing may be performed externally. In this case, there is an effect that it is not necessary to place computing resources in the local environment. The installation location of the camera is, for example, a daily use location, and it is desirable to install the camera in a form in which the skin of the observation target 105 can easily come in front of the camera 101.

  Here, as an example, a camera was installed above the mirror in the bathroom. In general, a washroom is often installed in a place where it is not exposed to sunlight or in a place where it is difficult to hit, and the effect of changes in external light due to the weather and time is relatively small, and stable shooting is easy. In addition, at home, near the TV screen in the living room, in the mirror stand, in front of the toilet seat. In the office, install near the PC display. Moreover, you may install an imaging part in the robot which is used for medication support or conversation. Since it faces the robot through medication and conversation, it is easy to capture facial images. The communication unit 103 is connected to the network 106 and can perform data communication with the service server 107 via the network. The service server is connected to the data storage unit 108 and stores measured information, user profile information, parameters for blood pressure estimation set for each user, parameter information for abnormality detection for health management services, and the like. The

By accumulating these data in the data storage unit 108 that can be accessed from the server, necessary parameters can be used even when the user performs measurements at various locations. Here, instead of storing all these pieces of information in the data storage unit 108, data may be stored in an external storage unit, and data may be acquired by communication as necessary. Further, the local system 110 may have a data storage unit connected to the data processing unit, and may hold a part of the information. By having parameters necessary for data processing in the local system, it is possible to speed up data processing. Further, the service server can communicate with the information system 109 of the contracted external organization.
<Data processing>
First, the outline of the present invention will be described. Biological information such as pulse waves can be acquired from the fundamental wave, which is a time-series change signal of the color image of blood flow on the body surface such as the face, but blood flow acquired in the presence of noise sources such as the external environment. The color image includes various noises and affects the estimation of the pulse wave. In the present invention, a representative color that is a representative color in a plurality of regions that are close to each other on the body surface is calculated, and a difference signal that is a difference between fundamental waves that is a signal indicating a temporal change of the representative color is calculated and calculated. Biological information such as a pulse wave is calculated using the difference signal. This is because it can be approximated that the amplitude and noise of each signal are almost the same in the adjacent area, so the influence of noise on the difference signal is suppressed, and biological information such as pulse waves is acquired from the difference signal become able to. Details will be described below.

  FIG. 2 shows a processing flow of image data captured by the camera. FIG. 2 shows a processing flow when the observation target 105 is a face as an example. In FIG. 2, after extracting the region of the observation target 105 shown in the image by a general face detection technique or the like, the representative color calculation unit 201 in the data processing unit 102 calculates the representative color in the region. Here, the representative color is, for example, a signal value for each pixel in the region (luminance value for a monochrome image, R (red), G (green), B (blue), C (cyan for a color image). ), M (magenta), Y (yellow), etc., or a signal value obtained by mixing or averaging multiple colors) and dividing by the number of pixels in the area (that is, the average in the area) Value). When calculating the representative color, an average may be taken after weighting at a position in the area according to the luminance distribution of the area, or a color at a specific position in the area may be taken.

  The fundamental wave is extracted by the fundamental wave extraction unit 202 from the representative color output by the representative color calculation unit 202. Here, the fundamental wave is a signal indicating the time-series change of the output color. Further, harmonics, impulsive noise, and the like may be separated from the representative color signal, and extracted as a signal containing many components close to a sine wave synchronized with the pulse wave. The fundamental wave extraction unit 202 extracts a bandpass filter (BPF) having a pass band of about 1 to 2 Hz close to the frequency of the pulse wave, or a component of about 1 to 2 Hz from the result of Fourier transform, and performs an inverse Fourier transform. Since the conversion can be realized by a general technique, the detailed illustration is omitted.

  Subsequently, the frequency estimation unit 203 estimates the frequency of the fundamental wave using zero cross counting, Fourier transform, autocorrelation, etc., and outputs the pulse rate 204 (that is, the number of cycles of the fundamental wave per minute). To do.

  As an example, a frequency estimation method using zero-crossing is shown in FIG. Zero crossing is a point where the signal changes from positive to negative, or from negative to positive, by measuring the number of zero crossings within the observation time (t0 seconds) and multiplying by 60 / (t0 × 2). The frequency of zero crossing can be converted to the frequency of the fundamental wave (that is, the pulse rate). Here, “60” is the number of seconds per minute, and “2” is the number of zero crossings per cycle.

  In frequency estimation using Fourier transform, the signal is separated into multiple frequency components, and the pulse with the highest amplitude value (= √ (real value squared + imaginary value squared)) is used as the pulse rate. . In this case, if the fundamental wave extraction unit 202 uses the Fourier transform, the frequency can be estimated using the result as it is, and thus the inverse Fourier transform described above is unnecessary.

  Also, in frequency estimation using autocorrelation, the signal is shifted by one sample (one sample point) in time, the correlation value with the signal before shifting is obtained, and the reciprocal of the time difference when the correlation is highest is obtained. , The pulse rate.

  Since the frequency estimation method using zero cross count, Fourier transform, and autocorrelation as described above can be realized by a general technique, detailed illustration is omitted.

  It should be noted that the process of extracting the region of the observation target 105 from the image (face detection in the case of the figure) may be incorporated as part of the data processing unit 102.

  FIG. 3 can accurately estimate the pulse rate even when there is noise due to external light or the like (that is, changes in brightness due to lighting such as a fluorescent lamp as described above or flicker noise (flicker) of the display). The processing flow of possible image data is shown. In FIG. 3, after extracting the region of the observation target 105 shown in the image by a general face detection technique or the like, n (where n is an integer of 2 or more) smaller along the blood flow direction 301. The image data is divided into regions (region # 1 (303) and region # 2 (304) in the figure), and each image data is processed by the data processing unit 102. As this blood flow direction 301, for example, in the part near the surface of the face, it is known that blood that has risen from the carotid artery on the left and right of the neck to the head flows horizontally toward the center of the face, The position and direction (longitudinal direction) of the nose in the image showing the face may be detected, and the direction substantially orthogonal to that direction may be set as the blood flow direction 301. Furthermore, the face (observation target 105) region is divided into two left and right regions with the nose position as the boundary, and either the left or right region is divided into smaller regions 303 and 304 as the new observation target 105. Also good.

  In the data processing unit 102, representative colors for each region are calculated by representative color calculation units 305 and 306 similar to the representative color calculation unit 201 described above. The size of each region may be the same or different. For example, the length (first length) in the direction substantially orthogonal to the blood flow direction 301 of each region is the maximum length that does not protrude from the region of the face (observation target 105), and is substantially parallel to the blood flow direction 301 The length in the direction (second length) may be set so that the area of each region (that is, the product of the first length and the second length) is substantially the same.

  The representative color of each region changes according to the blood flow volume for each region, and also includes noise due to 302 such as external light. At this time, the representative color component that changes according to the blood flow volume has a phase difference according to the blood flow velocity for each region along the blood flow direction, whereas the representative color component that changes according to 302 such as external light. The color component is considered to cause a substantially constant change in the entire observation target 105. By utilizing this property, the pulse rate 311 can be accurately estimated while reducing noise caused by 302 such as external light. The details will be described below.

  In FIG. 3, the fundamental wave extraction units 307 and 308 similar to the fundamental wave extraction unit (202) described above are used for the respective fundamental waves (signal #) from the representative colors output from the representative color calculation units 305 and 306. 1. Extract signal # 2). Each fundamental wave may be extracted within one fundamental wave extraction unit.

  Subsequently, a difference signal between the signal # 1 and the signal # 2 is generated by the subtractor 309, and the frequency of the difference signal (that is, the pulse rate 311) is generated using the frequency estimation unit 310 similar to the frequency estimation unit 203 described above. By estimating the noise, noise caused by 302 such as outside light can be reduced.

  Here, the signal # 1 composed of the fundamental wave (sine wave) is represented as A1cos (ωt + θ1) + n1, and the signal # 2 is represented as A2cos (ωt + θ2) + n2. A1 and A2 represent the amplitude of each signal, θ1 and θ2 represent the phase (initial phase) of each signal, and n1 and n2 represent noise due to external light included in each signal. Note that ω is the angular frequency of the fundamental wave, and is common to the signal # 1 and the signal # 2.

At this time, it may be considered that the amplitude of each signal is substantially equal between the plurality of adjacent regions, and the magnitude of the noise component due to external light or the like is also substantially equal. This approximation can be sufficiently approximated as long as it is a face size or less. Therefore, if approximately A1 = A2 = A and n1 = n2 = n, the difference signal between signal # 1 and signal # 2 (signal # 2-signal # 1) is calculated as can do.
(Equation 1)
Difference signal (Signal # 2-Signal # 1) = (A2cos (ωt + θ2) + n2)-(A1cos (ωt + θ1) + n1)
≒ A (cos (ωt + θ2) -cos (ωt + θ1)) + (nn)
= -2Asin (ωt + (θ2 + θ1) / 2) sin ((θ2-θ1) / 2)
Since the phase difference δ (= θ2-θ1) between the signal # 1 and the signal # 2 is almost proportional to the distance (positional difference) between the region # 1 and the region # 2 along the blood flow direction (301), The value of sin ((θ2-θ1) / 2) and the value of (-2Asin ((θ2-θ1) / 2)) may always be considered to be almost constant (const.). Therefore, since the difference signal≈sin (ωt + (θ2 + θ1) / 2) × const., It is possible to estimate the pulse rate using the frequency estimation unit 310 while canceling noise caused by 302 such as outside light.

  FIG. 4 shows an example of the data processing unit 102 that further reduces the influence of random noise (hereinafter referred to as random noise) such as shot noise and thermal noise in the imaging element of the camera by further utilizing the blood flow direction. As described above, it is known that blood that has risen from the carotid arteries on the left and right sides of the neck to the head flows horizontally toward the center of the face near the face surface. Therefore, as shown in FIG. 4, when the vertical line along the longitudinal direction of the nose at the center (nose) of the face is taken as the axis 401, the blood flow direction is almost symmetrical. Therefore, as shown in FIG. 4, the face axis is extracted by the axis extraction processing 400, and the area # 1A (402) and the area # 1B (403) are compared between the areas close to the axis 401 by the representative color calculation unit 406. The representative color calculation unit 407 calculates a representative color that combines region # 2A (404) and region # 2B (405) in a region far from the axis (401), By estimating the pulse rate 408 by the fundamental wave extraction units 307 and 308, the subtractor 309, and the frequency estimation unit 310 described above, the phase relationship between the signal # 1 and the signal # 2 shown in FIG. It becomes possible to increase the number of pixels when calculating each representative color. At this time, the change amount of the representative color that changes according to the blood flow is substantially the same in the region # 1A (402) and the region # 1B (403), whereas the change amount of the representative color that changes according to the random noise. Since region # 1A (402) and region # 1B (403) are uncorrelated, for example, if the area (number of pixels) of region # 1A (402) and region # 1B (403) is the same, the SN ratio Is improved by about 3 dB and can be made more robust against random noise.

  In addition, when the left-right symmetry of the face to be observed is low, or when the detection accuracy of the axis 401 along the longitudinal direction of the nose is insufficient at the center (nose) of the face, the left and right blood flow rates of the face differ greatly In some cases, it is better not to use the representative colors of the left and right areas of the face as in the configuration shown in FIG. 4, but to use the representative colors of only the left and right areas of the face as in the configuration shown in FIG. The pulse rate can be estimated with high accuracy. Therefore, before the representative color calculators (406) and (407), the representative colors of the region # 1A (402) and the region # 1B (403) are compared, or the region # 2A (404) and the region # 2B (405) ) Is used to calculate the degree of symmetry (409) for comparing each representative color, and only when the comparison result (absolute value of the difference) is smaller than a predetermined threshold, the representative colors of both the left and right regions of the face are used. When each comparison result (absolute value of the difference) is larger than a predetermined threshold, the data processing unit 102 may be configured to use the representative color of either the left or right region of the face.

  FIG. 5 shows a processing flow for calculating blood pressure while suppressing the influence of noise due to external light or the like. The calculation of blood pressure requires a pulse wave velocity, but when calculating the pulse wave velocity by measuring the pulse wave only on the skin surface of the face, the distance between the two points is close. Since the propagation time of the signal becomes shorter and the signal phase difference between the two points becomes extremely small, it is affected by noise caused by external light. For example, the position (time) of the zero cross 205 shown in FIG. 2 moves to the left (ie, the phase advances) when noise having a positive value is added to the signal. ), The position of the zero cross at the falling edge of the signal waveform moves to the right (that is, the phase is delayed), and an error is included in the pulse wave propagation velocity obtained from the phase difference. Hereinafter, a technique for suppressing the influence of noise caused by external light by measuring pulse waves at three or more points will be described.

  In FIG. 5, the region of the observation target 105 shown in the image is represented by m (where m is an integer of 3 or more) smaller regions (FIG. 5) arranged at approximately equal intervals along the blood flow direction. Region # 1 (501), region # 2 (502), region # 3 (503)), and the image processing unit 102 processes each image data. In the data processing unit 102, representative colors for each region are calculated by representative color calculation units 504, 505, and 506 similar to the representative color calculation unit 201 described above. The size of each region may be the same or different. From the representative colors for each region output from the representative color calculators 504, 505, and 506, the fundamental waves (signal # 1, signal # 1, Signal # 2 and signal # 3) are extracted.

  Subsequently, the subtracter 509 generates a difference signal #a (= signal # 2−signal # 1) between the signal # 1 and the signal # 2, and the subtracter 510 generates a difference signal #b between the signal # 2 and the signal # 3. (= Signal # 3−signal # 2) is generated, and the time difference estimation unit 511 obtains the time difference between the difference signal #a and the difference signal #b.

At this time, as described above, it may be considered that the amplitude of each signal is substantially equal between the plurality of adjacent regions, and the magnitude of the noise component due to external light or the like is approximately equal, so approximately A1 = A2 = A , And n1 = n2 = n, the difference signal #a can be expressed as follows.
(Equation 2)
Difference signal #a = (A2 cos (ωt + θ2) + n2)-(A1 cos (ωt + θ1) + n1)
≒ A (cos (ωt + θ2) -cos (ωt + θ1)) + (nn)
= -2Asin (ωt + (θ2 + θ1) / 2) sin ((θ2-θ1) / 2)
= Sin (ωt + (θ2 + θ1) / 2)) × const. # A
Here, const. # A = -2Asin ((θ2-θ1) / 2).
Similarly, when A2 = A3 = A and n2 = n3 = n, the difference signal #b can be expressed as follows.
(Equation 3)
Difference signal #b = (A3 cos (ωt + θ3) + n3)-(A2 cos (ωt + θ2) + n2)
≒ A (cos (ωt + θ3) -cos (ωt + θ2)) + (nn)
= -2Asin (ωt + (θ3 + θ2) / 2) sin ((θ3-θ2) / 2)
= Sin (ωt + (θ3 + θ2) / 2)) × const. # B
Similarly, const. # B = -2Asin ((θ3-θ2) / 2).
Here, if region # 1 (501), region # 2 (502), region # 3 (503)) are arranged at approximately equal intervals, -2Asin ((θ2-θ1) / 2) ≈-2Asin ( (θ3-θ2) / 2), that is, const. # a≈const. # b, and in difference signal #a and difference signal #b, noise due to 302 such as outside light is canceled. By using the zero-cross position, Fourier transform, cross-correlation, etc., which will be described later, the phase difference between the difference signal #a and the difference signal #b (that is, (θ3 + θ2) / 2-(θ2 + θ1) / 2 = (θ3-θ1) / 2) can be obtained accurately, and the difference is obtained by dividing the phase difference (unit: radians) by the fundamental wave (ie, pulse) frequency (unit: Hz) and then dividing by 2π. The time difference between the signal #a and the difference signal #b can be obtained.

  For example, in the method using the zero cross position, the difference in position (time) of the zero cross 205 as shown in FIG. 2 is obtained between the difference signal #a and the difference signal #b, and the average value of the differences between the plurality of zero cross positions is obtained. Is a time difference. In the method using the Fourier transform, the difference signal #a and the difference signal #b are each decomposed into frequency components, and the phase difference of the frequency component having the largest amplitude value (= arctan (the maximum amplitude frequency of the difference signal #b (Component imaginary value / real number) -arctan (imaginary value / real number of maximum amplitude frequency component of difference signal #a)) is divided by the value of the maximum amplitude frequency, and further divided by 2π to obtain the time difference. In the method using cross-correlation, the difference signal #b is shifted by one sample (one sample point) in time, the correlation value with the difference signal #a is obtained, and the time difference when the correlation is the highest is obtained. You can ask for. Since the estimation method of these phase differences or time differences can be realized by a general technique, detailed illustration is omitted.

  Pulse wave propagation velocity calculation unit 512 obtains the pulse wave propagation velocity by dividing the actual distance between the regions by the time difference obtained by time difference estimation unit 511 described above.

  The number of pixels between the two eyes of the observed observation object 105, the number of pixels along the blood flow direction between the center of gravity positions of region # 1 and region # 2, and the center positions of region # 2 and region # 3 The actual distance between region # 1 and region # 2 and the actual distance between region # 2 and region # 3 along the blood flow direction It can be calculated. For example, assuming that the distance between both eyes of an average human face is about 6.5 cm, the number of pixels between both eyes is 100 pixels, and the blood flow direction between each central position of region # 1 and region # 2 Assuming that the number of pixels along 30 is 30 pixels, the actual distance between the region # 1 and the region # 2 along the blood flow direction can be calculated as about 2 cm (= 6.5 × 30/100). You may obtain | require the distance between both eyes from race, age, sex, image processing, etc.

  As described above, the time difference obtained from the phase difference ((θ3-θ1) / 2) between the difference signal #a and the difference signal #b indicates the time difference between the region # 3 and the region # 1. Calculate the pulse wave velocity by calculating the actual distance between region # 3 and region # 1 along the direction of blood flow and dividing this distance by the time difference between region # 3 and region # 1. The pulse wave velocity can be calculated in part 512. The conversion from the pulse wave velocity thus obtained to the blood pressure 513 may be performed using, for example, the aforementioned Moens-Korteweg equation or the relationship between the arterial pressure and the inner diameter of the artery.

  As described above, the pulse rate, pulse waveform, pulse wave velocity, etc. are canceled by analyzing the difference signal between the image information of two or three regions along the blood flow, and canceling noise caused by external light. Can be measured with high accuracy.

  FIG. 6 shows an example in which the measurement site is taken separately including other than the face. The measurement site is taken where the skin is exposed. Possible parts that are not covered with everyday clothes are the neck, top, hands, and feet. However, this does not apply when other parts are exposed during bathing. Further, as will be described later, a plurality of locations that are not necessarily continuous may be taken as the measurement site. When the measurement site is not continuous, the accuracy of calculation of the pulse wave velocity is improved as described below. Even when a non-continuous skin surface is taken as a measurement site, it is desirable to perform imaging so as to be within the field of view of the same camera. This is because it is easy to synchronize between signals. Moreover, it is because it can be expected that the influence of noise due to external light or the like will be the same between measurement sites. A camera is installed beside the face, and the measurement areas are defined as two cheek areas t1 and t2 and two neck areas t3 and t4. The difference signals of the images of the cheek two areas t1 and t2 and the difference signals of the neck two areas t3 and t4 are analyzed. The pulse rate can be obtained using the angular velocity ω of the fundamental frequency component. Further, the pulse wave propagation time ΔT can be obtained from the phase difference between the two difference signals and the angular velocity ω.

  The pulse wave velocity is, for example, the distance of blood flow divided by ΔT between the midpoint of the two regions t1 and t2 and the midpoint of the two regions t3 and t4. Here, the distance is calculated by adding two sides of a right triangle t5 including the midpoint of the two regions t1 and t2 and the midpoint of the two regions t3 and t4. In the human body, the blood flow (blood flow direction t51) flowing from the heart and flowing through the carotid artery changes the flow to a substantially right angle on the side of the face and flows laterally on the cheek (blood flow direction t50). Therefore, the calculation is based on the sum of two sides sandwiching a right angle, not the hypotenuse of a right triangle. Compared to the case where multiple measurement parts were originally taken only in the cheek, the distance increases when one is taken on the neck, but here it is not only that, but the sum of the two sides instead of the hypotenuse increases the distance. There is a feature that becomes. For example, when the right triangle is a substantially right isosceles triangle, the distance is 1.4 times longer than the hypotenuse. When the distance is large, the error is relatively small even if there is a certain amount of error in the distance estimation, and as a result, the calculation error of the pulse wave propagation velocity is also small. Further, the phase difference between the two differential signals also increases, and as a result, the calculation error of the phase difference, and hence the calculation error of the pulse wave propagation time ΔT, also decreases. For this reason, the measurement error of the pulse wave velocity is reduced, and the calculation error of the blood pressure is also reduced.

  FIG. 7 shows an example in which another measurement site is taken. An imaging unit is installed at a position where a finger is observed. The method for measuring the pulse rate and the pulse wave velocity from the images of the continuous two regions t14 and t15 and the images of the three regions t14, t15 and t16 is as described above. Compared to the case of using a face image, it is less susceptible to the effects of hairstyle, makeup, and the like. In addition, while the face changes in skin color due to sunburn or the like, the color of the inside of a finger usually does not change so much, so that it can be measured stably. At this time, personal authentication using the imaging unit and the finger is possible, and using the fingerprint pattern t23 and the finger vein pattern observed by infrared irradiation, the identification of the observation target and the vital information acquisition can be performed only by measuring the finger. Both are possible. For example, as shown in FIG. 7, the data processing unit accepts input of image data of a finger part (701), and calculates biological information such as a pulse wave, a pulse wave propagation speed, and blood pressure from the image data by the above-described method ( 702) Further, the personal authentication is performed from the image data (703), and the biometric information and the authenticated individual are output in association with each other (704). This facilitates health information management in an environment where it is difficult to manage by name, for example, in an environment where the hospital database is not well-developed or where name identification is complicated. FIG. 8 shows an example in which measurement regions are taken in two regions t17 and t18 of the finger and two regions t19 and t20 of the wrist. Compared with the case of measuring with only the finger shown in FIG. 7, the distance between the measurement parts is larger, so that the measurement error of the pulse wave velocity is reduced as in the description of FIG. 6.

FIG. 9 is also an example in which another measurement site is taken. Bring your palm next to your face and measure. Since the face measurement region t21 and the palm measurement region t22 are greatly different from each other in the distance from the heart, the measurement error of the pulse wave velocity is also reduced as in the description of FIG. The face and palm are parts that can be easily photographed side by side in the same screen, although the distance from the heart is greatly different. The information processing of the present invention is also effective when photographing a plurality of observation sites with different cameras, but in order to obtain a more accurate result, the imaging system is configured so that the time of the image to be photographed can be accurately adjusted. Good. On the other hand, if the measurement objects can be photographed side by side in the same screen as shown in FIG. 9, there is no need to synchronize the time of different images, and the system can be simplified.
<Example of health management service system>
Next, an example of a data processing technique for detected biological information and a service system using the living body will be described. As an example, a case where a face image is used will be described. However, the service flow is the same for other measurement sites. The authentication method is performed using a face image, but in the case of a finger or the like, the means may be replaced with fingerprint authentication or finger vein authentication. First, data contents stored in the data storage unit 108 will be described with reference to FIG. Characteristic data for personal authentication distinguished by ID for each individual User profile Blood pressure estimation parameters, information about subscribed external services, judgment parameters for detecting abnormalities, past vital data, past facial color data, Other physical condition data and past message history are stored.

  Here, a method for registering parameters for blood pressure estimation will be described. A blood pressure measurement device as a reference is prepared, and the pulse wave and blood pressure are measured simultaneously. By performing such measurement at different timings, the correspondence relationship between the pulse wave velocity and the blood pressure is learned. This association parameter represents the state of the blood vessel such as the degree of arteriosclerosis of the measurement subject, and can be used as an index of health. The parameter for blood pressure estimation is used for adjusting each coefficient when calculating blood pressure from the pulse wave. Since it differs depending on the individual, it is recommended to obtain it for each individual. Further, the blood pressure may be estimated from the pulse wave by using the average value of the population as a parameter without using this parameter.

  Next, the processing flow of the service system will be described with reference to FIG. The local system 110 first detects a face area t25, extracts the feature amount, encrypts it, and sends it to the service server. Authentication processing is performed by comparing the feature amount data held in the data storage unit 108 in the service server. If the feature quantity is not registered, a message indicating that the person is a non-registered person is created. Note that when the number of users of the local system 110 is limited to one, the authentication process can be omitted, and the personal ID can be associated with the device ID of the system. For example, when the local system 110 is a smartphone, a normal user can be identified by one person. Alternatively, the user may more simply input the ID and password, and use the input ID to link the personal ID. From this point on, processing is performed using the data unique to the person associated with the authenticated ID.

  The measured data is subjected to image data processing in the local system 110, and the facial color feature information such as the representative color, the pulse rate, and the pulse wave velocity are calculated t28 while reducing the influence of noise as described above. The result is sent to the service server 107 via the network 106 via the communication unit 103. A blood pressure estimation t29 is performed on the transmitted data using the pulse wave propagation speed and the parameter t27 for estimating the blood pressure of the person. Data such as facial color features, pulse rate and pulse wave velocity, estimated blood pressure, measurement time and location information is stored in the data storage unit t30. At this time, environmental data such as weather, temperature, atmospheric pressure, and humidity may be accumulated together. These environmental data may be actually measured by incorporating a sensor into the system, or may be obtained from an external service simply using time and location information. By recording these environmental data together and analyzing the collected data, it is possible to find the hidden factors of physical condition change from more accurate biological information by suppressing noise, and linked to the preventive action of the person New medical knowledge can be obtained.

  Visualization processing t31 is performed using these latest data and past history data. Here, the abnormality determination t32 is performed using the measurement result or the calculated feature amount according to the abnormality determination algorithm and the determination reference value accumulated in the data storage unit. For example, the lower limit and upper limit of the pulse rate and the lower limit and upper limit of the estimated blood pressure are set for each individual, and a warning is issued when the lower limit is exceeded or the upper limit is exceeded, and a preset external organization is notified. The lower and upper limits are determined by a doctor or the like in consideration of individual characteristics. In addition, as an abnormality detection algorithm, the amount of change from the previous measured value of pulse rate and blood pressure, the amount of change from the long-term average value, the amount of change from the long-term average in the same time zone is more than a predetermined amount It is good also as abnormal when it becomes. If it is determined that there is an abnormality, an abnormality notification t33 is sent to the information system 109 of the external organization based on the subscribed external service information. Examples of the external organization 109 include a hospital, a security company, and a family home. In FIG. 1, the notification to the external organization is made via the network, but means different from the network for collecting biometric information such as a telephone line may be used. In this way, it is possible to efficiently manage the physical condition of an individual, starting from obtaining more accurate biological information while suppressing noise.

  Next, a message for the user is created both in an abnormal case and in a normal case, and displayed on the display unit 104 of the local system 110 together with the visualization processing result. Here, a display method will be described.

  FIG. 12 shows an example of the display method. The outlines of the observation areas 501, 502, and 503 are drawn on the photographed face image, and the color is sequentially changed in each area at a speed corresponding to the measured pulse wave propagation speed. In addition, information such as the pulse rate and estimated blood pressure calculated from the measurement result is displayed on the message part t36. Further, a message from the system is also displayed in the message part t36. In FIG. 12, an icon t37 for switching to a screen displaying a past history as a graph and an icon t38 for notifying the outside when there is a consciousness of poor physical condition are arranged. When the notification icon t38 to the outside is selected, the notification is made to the external organization in the same manner as the automatically determined abnormality notification t33. In addition, when the notification icon t38 is selected, a function of selecting a notification destination, notification content, and the like may be prepared. In this way, the user can grasp the observation area and the biological information, and at the same time use the notification function or check the past history.

  FIG. 13 shows another example of a display method that is intuitively easy to understand. In order to display the pulse wave propagation velocity, visualization is made with the arrow t39 instead of the observation regions 501, 502, and 503 itself. Here, as an example, the display is performed so that the color moves from the tail part t40 of the arrow to the tip part t41 at a speed corresponding to the pulse wave propagation speed. This makes it possible to display blood flow intuitively and easily. In addition, although it was set as the shape of arrow t39 here, other shapes, such as a rectangle, may be sufficient.

  FIG. 20 shows the waveform of the measurement result as a waveform form t52 as a display method different from FIG. If the waveform signal # 1 in the region # 1 (501) or the waveform signal # 2 in the region # 2 is displayed as it is, the pulse waveform becomes difficult to see due to the noise t53 due to outside light, and the pulse rate and pulse It also interferes with wave propagation velocity calculations. By displaying the differential waveform t52 in accordance with the method of the present embodiment, it is possible to display a waveform that is easy to see by reducing noise, and at the same time, an accurate pulse rate and pulse wave velocity can be calculated.

  FIG. 22 shows a screen display example when the switch icon t37 to the screen for displaying the graph is selected. It is the graph which showed the estimated blood pressure. Range 1 (t54), range 2 (t55), and range 3 (t56) set under the guidance of a doctor or the like indicate a region where the blood pressure is low, a normal region, and a region where the blood pressure is high. The blood pressure was measured twice a day in the morning and evening, taking into account diurnal fluctuations. The horizontal axis is the date, and the tendency of blood pressure fluctuation can be grasped with a graph. In accordance with this range, a message such as “There is a possibility that blood pressure is high” is displayed on the message part t36, and for example, a more accurate measurement is urged by a blood pressure monitor. Here, although the range of about one week is shown in the example of FIG. 22, a longer range may be displayed. For example, a past blood pressure plot may be set to an average value for a week or the like, and a longer period range may be displayed while keeping the visibility by reducing the number of plots. In this way, it is possible to easily understand the more accurate relationship between the estimated blood pressure fluctuation and the predetermined range.

  The biometric information continuously stored is managed so that it can be viewed by the person or organization authorized by the person, in addition to the person himself / herself. For example, if you have chronic illness and regularly visit a hospital, a doctor checks the status of your home from the previous visit to this time and uses it to assist you in determining your status. Since blood pressure changes from time to time and there are daily fluctuations, it is characterized in that the state can be grasped in more detail than using only the measurement data at the time of consultation. Moreover, it is possible to create a table of daily fluctuations of the pulse rate and estimated blood pressure by measuring a plurality of times a day. In particular, it is known that the peculiar pattern of circadian variation in blood pressure is related to the risk of diseases such as complications such as cerebrovascular disorders and cardiac hypertrophy and organ disorders, making it easier to grasp blood pressure fluctuations more accurately. This is effective for early detection of the risk of illness.

  FIG. 14 is a diagram illustrating a device configuration of the information system according to the second embodiment of the present invention. The information system according to the present embodiment includes a first imaging unit N1, a second imaging unit N3, and a data processing unit N2. The first imaging unit N1 is a camera installed in the vehicle interior, and the imaging target N5 is a driver of a vehicle such as a car. The second imaging unit N3 is a camera installed outside and inside the vehicle interior, and the imaging target N4 is a background of vehicles, pedestrians, sky, buildings, roads, etc. around the host vehicle. Since the camera image in the passenger compartment is affected by disturbance caused by changes in time zone and weather, it is difficult to measure the face color while suppressing the influence of noise. In the present embodiment, the second imaging unit N3 determines the time zone and the weather that cause the disturbance, and performs the noise reduction process suitable for each to suitably measure the pulse rate and the pulse wave velocity. Hereinafter, a detailed processing flow will be described.

  FIG. 15 shows a flowchart of the operation processing of the data processing unit N2 in this embodiment.

  In step N2001, the data processing unit N2 reads the image output from the first imaging unit N1. In step N2002, the data processing unit N2 detects the face area by the same method as in the first embodiment, for example. In step N2003, the data processing unit N2 detects a seat area (for example, a headrest part or an upper seat) in the vicinity of the face area detected in step N2002. Specifically, first, the image output from the first imaging unit N1 is subtracted from the background image (vehicle interior image where the driver is not sitting) created at the time of factory shipment or camera installation, and only the moving object is subtracted. Create an image of Next, the image of only the moving object created is subtracted from the image output from the first imaging unit N1, thereby creating an image of only a stationary object. Finally, a region of interest corresponding to the headrest or the upper part of the seat is cut out from the created image of only the stationary object to create a seat region image.

  In steps N2004 to N2007, the camera image outside the vehicle compartment is processed. In step N2004, the data processing unit N2 reads the image output from the second imaging unit N3. In step N2002, the data processing unit N2 determines a time zone (day / night) by analyzing a time-series change in the average luminance of the image. For example, when the average luminance continues to be low for a certain time, it is determined that the night. It is also possible to subdivide and judge according to the usage environment such as morning / daytime / evening / night. In step N2006, the data processing unit N2 determines the weather (sunny / rainy) by analyzing the presence of raindrops, high-intensity areas, smear (out-of-white), and the like. For example, when a water droplet adheres to the lens surface, it is determined as rain, and when smear due to sunlight occurs, it is determined as clear. Also in this case, it may be determined by subdividing such as clear / cloudy / rain / mist. In step N2007, the data processing unit N2 reads, as a reference image, an image created in advance as a seat area image for each determined time zone or weather, or a generated image.

  In step N2008, the data processing unit N2 calculates an average luminance difference between the read reference image and the created seat area image, thereby calculating a noise component on the current face image. Also, the seat area image is stored in the RAM according to the associated time zone and weather. In step N2009, the data processing unit N2 subtracts the calculated noise component (RGB luminance value) from the face image. In step N2010, the data processing unit N2 calculates a pulse wave and a blood pressure value by the method described in the first embodiment.

  In the second embodiment described above, since the noise component on the face color can be calculated by providing the second imaging unit and analyzing the weather / time zone, the blood pressure value is more preferably used even on an in-vehicle system with a large disturbance. Can be calculated. Further, by using the blood pressure calculation method described in the present embodiment, it can be used as a health check system before driving, an abnormality detection system during driving, and the like.

  FIG. 16 is a diagram illustrating the imaging unit of the information system according to the third embodiment of the present invention. The imaging unit (camera unit) t7 is an infrared camera. In addition to the imaging unit t7, the information system includes the data processing unit 102, the display unit 104, the communication unit 103, and the like described in the first embodiment. An infrared camera can shoot in dark places. Even for an image captured by detecting light in the infrared region, data processing of the captured image may be performed in the same manner as in the first embodiment. The present invention is more effective because the signal strength is small in a dark environment and the noise resistance is reduced. Further, in order to obtain a clear image including the surroundings, it may be combined with the infrared irradiator t6. Since it can be measured even if the surrounding environment is not bright, it is suitable for constant monitoring of the person t8 lying on the bed. For example, it is possible to perform continuous monitoring at the time of going to bed in an inpatient, a home or a care facility. In addition, by using an infrared irradiator as a random dot pattern, three-dimensional measurement using a known depth camera technique becomes possible. In this case, since the time series change of the chest shape can be observed, the respiration rate can be measured by the same frequency analysis method as described above.

  FIG. 17 is a diagram for explaining the imaging unit and the data processing unit of the information system according to the fourth embodiment of the present invention. The combination is an imaging unit (camera unit) 101 and an infrared sensor array t9. Here, as an example, a sensor array in which infrared sensors using pyroelectric elements are arranged in an 8 × 8 matrix is used. The type of element, the number of sensor arrays, and how to arrange them may be different from those on the left. The imaging unit (camera unit) 1 and the infrared sensor array t9 are aligned so that the images are taken in the same direction. The pyroelectric element can measure the temperature of the object by performing calibration. Therefore, it is possible to measure the body surface temperature in addition to the pulse rate, the estimated blood pressure, and the respiratory rate that are measured while suppressing noise by the methods described in the first to third embodiments. Vital information can be acquired. Although a pyroelectric element is used here as an infrared sensor, other types of infrared sensors may be used. Here, the body surface temperature is different from so-called body temperature and is affected by the outside air, so the accuracy is inferior, but it is clear that it has a strong correlation with the body temperature. Therefore, changes in health conditions such as fever can be captured. In particular, capturing changes by monitoring the body surface of the same part each time is effective in detecting changes in physical condition.

  FIG. 18 shows an example in which the camera image and the image t11 obtained by the sensor array are displayed in a superimposed manner. The result t10 of face recognition on the camera image is also shown. By using the image recognition result for the part, it is possible to associate which part the temperature is measured by the infrared sensor. Therefore, an input for selecting the position of the part or the matrix is accepted, and the measurement result corresponding to the desired part is used from the 8 × 8 matrix. Here, the average of the measurement results of the parts t12 and t13 corresponding to the forehead was used to monitor the physical condition. Other parts, such as the cheeks or the neck when facing sideways, may be used for the monitor, and are stored together with the information on the measurement part.

  Since the system of this embodiment can acquire biological information while reducing noise remotely, it is also suitable for telemedicine. For example, when talking remotely with a doctor on a videophone, the measurement information is displayed on the doctor side, so that the doctor can make an inquiry while grasping quantitative state data. In addition, by displaying the accumulated information, it is possible to grasp information from the previous conversation until this time, and more accurately grasp the state of the target person.

  Taking biometric information while reducing noise without contact is also effective, for example, in the treatment of virus infection. Staff such as doctors and nurses can obtain information without touching the patient. In addition, since the information can always be acquired, it is possible to automatically detect early when an abnormality occurs, and to take quick action. A similar method of use is primary screening during influenza epidemics. In a small room that isolates a visitor who is suspected of being infected, a vital check is performed using the system of this example, and the person who is suspected of influenza and a patient with a common cold are separated and passed to another room. It can prevent contact between an unaffected person and an influenza patient, and can prevent infection in a hospital. Further, instead of a small room such as a hospital, the system of this embodiment may be placed at home to make a determination. At this time, it is advisable to separate the hospital counter for influenza and other counters. By making judgments before going to the hospital, it is possible to prevent infection in the hospital by going to the appropriate window from the beginning. Furthermore, it is also effective to install the system of this embodiment in a waiting room of a hospital and check vital information before the examination. Vital information can be checked after reducing noise with less burden on the visitor, and the hospital side can save time and effort to measure heat and pulse at the time of the examination, so the examination efficiency can be improved.

  FIG. 19 shows an example in which the system shown in FIG. 17 is integrated with a watching robot (hereinafter referred to as watching robot) for a person or the like. The watching robot t40 includes a camera 101, an infrared sensor array t9, and a display unit 104, and further includes a microphone t41, a speaker t42, a medicine outlet t43, and a communication unit t44. A data processing unit may be included inside, or information may be transmitted to the outside via the communication unit 104, and information processed outside may be acquired and displayed on the display unit 104. Since the robot has an interactive function, the observation target person faces the robot for a certain period of time, so that biological information can be acquired by taking an image. FIG. 19 shows an example of a stationary robot in a living room or the like, but it may have a moving function. If there is a moving function, it is possible to acquire image information in different life scenes, and there is a feature that more information can be acquired. Further, the degree of symmetry of the face in the image data acquired from the camera can be evaluated, and the camera can be moved to a position suitable for photographing so as to satisfy a predetermined condition. In this way, it is possible to shoot while avoiding as much as possible an environment where a lot of noise is on the image, such as sunlight and vibration, and using the camera in an environment where the influence of noise has been reduced in advance, Embodiments 1 to 3 are used. It is possible to obtain biometric information by further reducing noise by this method.

  Next, the medication support function will be described. For example, a helper or a dispensary pharmacy staff watches and sets necessary medicines inside the robot. When it is time to take medicine, the robot calls out from the speaker t42 and displays on the display unit 104. When the person arrives in front of the robot t40 and can confirm the identity by image authentication or the like, the amount of medicine to be taken once comes out from the medicine outlet and records in the medication history. Here, for example, there are cases where couples who live together are taking medicine, so by performing identity authentication, it is possible to avoid taking medicine by mistake while using the same medication robot it can. Information on the medication history and how much medicine is currently left inside the robot is transmitted to the outside via the communication unit, and can be remotely confirmed by a helper, a dispensing pharmacy, a family doctor, or the like. In addition to being able to acquire vital information while suppressing noise, this system can acquire audio information using the microphone t41. By continuously recording the feature amount of the voice as well as other information, it is possible to capture the physical condition change from more information, and the accuracy of abnormality detection is improved. In this way, it is possible to obtain data that continuously accumulates highly accurate biological information that suppresses the influence of medication history and noise, and it is possible to analyze the effects and side effects of medium- to long-term medication by performing data analysis. is there.

101 imaging unit, 102 data processing unit,
103 Communication unit, 104 Display unit, 105 Observation target, 106 Network
107 Service server, 108 Data storage, 109 External organization
301 Blood flow direction, 302 Noise sources such as external light
303, 304 Measurement area, 305, 306 Representative color calculation processing
307, 308 fundamental wave extraction processing, 309 subtractor, 310 frequency estimation processing

Claims (15)

A data processing unit that receives input of image data including color information and outputs biometric information based on the image data;
The data processing unit is configured to calculate a representative color in each of a plurality of regions based on the color information;
A fundamental wave extraction unit that extracts a fundamental wave indicating temporal change of the representative color in the plurality of regions based on the representative color;
Calculating a difference signal of the fundamental wave between adjacent regions of the plurality of regions, and calculating a pulse information based on a frequency of the difference signal;
An output unit for outputting the pulse information as the biological information;
A biological information analysis system comprising: The biological information analysis system according to claim 1,
The image data includes facial part data,
The data processing unit further includes an axis extracting unit that extracts an axis along the longitudinal direction of the nose in the face part,
The representative color calculation unit sets the plurality of regions at two positions at approximately equal distances from the axis on both sides of the axis along the vertical direction of the axis,
When calculating the representative color, the biological information analysis system is characterized in that, among the plurality of regions, the representative color is calculated using representative colors of regions provided on both sides of the axis and substantially equidistant from the axis. . The biological information analysis system according to claim 1,
The data processing unit
An axis extraction unit for extracting an axis along the longitudinal direction of the nose in the face part;
A degree-of-symmetry calculator that calculates a degree of symmetry based on either or both of the face shape and blood flow in the extraction of the axis, and
The representative color calculation unit sets each of the plurality of regions to two positions at approximately equidistant positions on both sides of the axis along the vertical direction of the axis when the degree of symmetry is higher than a predetermined threshold. Set the location,
The biological information analysis system according to claim 1, wherein the representative color is calculated using representative colors of two regions that are substantially equidistant from the axis provided on both sides of the axis. A data processing unit that receives input of image data including color information and outputs biometric information based on the image data;
The data processing unit is configured to calculate a representative color in each of a plurality of regions based on the color information;
A fundamental wave extraction unit that extracts a fundamental wave indicating temporal change of the representative color in the plurality of regions based on the representative color;
Calculate two difference signals of the fundamental wave between adjacent regions of the plurality of regions, calculate a phase difference between the two calculated difference signals, calculate a distance between the plurality of regions, A blood pressure calculation unit that calculates the pulse propagation velocity based on the phase difference and the distance, and calculates the blood pressure based on the pulse propagation velocity;
An output unit for outputting the blood pressure as the biological information;
A biological information analysis system comprising: The biological information analysis system according to claim 4,
The image data includes data relating to a facial part,
The representative color calculation unit extracts an axis along the longitudinal direction of the nose in the face part from the image data, and sets the plurality of areas as three areas that contact one side of the axis along the vertical direction of the axis. And the representative color is calculated for each of the plurality of regions based on the color information. The biological information analysis system according to claim 4,
The image data includes data relating to a facial part, and data relating to a face external position that is a part other than the face,
The representative color calculation unit sets two or more regions in the face part and the face external position from the image data, and sets the plurality of regions in the face part and the face in the setting. Arranging two or more regions along the blood flow direction in each of the plurality of regions, and calculating a representative color of each region in the face part and the face external position based on the color information,
The blood pressure calculation unit, based on the calculated fundamental wave, a facial part difference signal that is a difference signal in a plurality of regions within the facial part and a facial external position that is a difference signal in a plurality of regions within the facial external position. And the difference signal of
Calculating a phase difference between the difference signal of the facial part and the difference signal of the external face position, calculating a distance between the facial part and the external face position, and transmitting a pulse based on the phase difference and the distance A biological information analysis system characterized by calculating a velocity and calculating the blood pressure based on the pulse propagation velocity. The biological information analysis system according to claim 1,
The image data includes image data of a finger part,
The plurality of regions includes a region in the finger part,
The biometric information analysis system, wherein the data processing unit performs personal authentication based on image data of the finger part, and outputs the authenticated individual and the biometric information in association with each other. The biological information analysis system according to claim 4,
The image data includes image data of a finger part and image data of a wrist part,
The representative color calculation unit sets the plurality of regions in two or more regions in the finger part and the wrist part,
In the setting, a plurality of regions in the finger region and a plurality of regions in the wrist region are arranged in two or more regions along the blood flow direction in each of the plurality of regions,
Based on the color information, to calculate the representative color of each region in the finger part and the wrist part, respectively,
The blood pressure calculation unit, based on the calculated fundamental wave, a difference between a finger part that is a difference signal in a plurality of areas in the finger part and a difference between a wrist part that is a difference signal in a plurality of areas in the wrist part Signal, and
The output unit performs personal authentication based on image data of the finger part, and outputs the authenticated individual and the blood pressure in association with each other. The biological information analysis system according to claim 6,
The face external position is a palm part,
The biological information analysis system, wherein the image data is captured by a single imaging device. The biological information analysis system according to claim 1,
The data processing unit is characterized by notifying that the biological information is in an abnormal state when the biological information does not satisfy a predetermined condition. The biological information analysis system according to claim 10,
The data processing unit displays the plurality of areas, the biological information, and an abnormal transmission icon for notifying an abnormal state on a screen. The biological information analysis system according to claim 1,
The biological information analysis system, wherein the data processing unit outputs the difference signal. The biological information analysis system according to claim 1,
The image data includes first image data relating to the inside of the vehicle, and second image data relating to the outside of the vehicle,
The first image data includes data relating to a facial part,
The data processing unit
Detecting a seating area from the first image data;
Obtain either or both of the time information and weather information from the second image data, read a reference image based on either or both of the time information and weather information,
Correcting the image data of the seat area of the first image data using the reference image,
The biological information analysis system, wherein the biological information is output based on the corrected image data of the seat area. The biological information analysis system according to claim 1,
In addition, an infrared sensor array
The data processing unit acquires a temperature information including a region and a temperature in the region from the infrared sensor array, and outputs the temperature information and the biological information. The biological information analysis system according to claim 3,
Furthermore, an imaging unit that images the image data, and a moving unit that moves the imaging unit,
The biological information analysis system, wherein the moving unit moves the imaging unit when the degree of symmetry is lower than a predetermined value.

Images