Pulse wave measuring device, pulse wave measuring method, and pulse wave measuring program

Abstract

The skin region includes areas where it is easy to obtain a pulse wave signal and areas where it is difficult to obtain a pulse wave signal. Pulse wave measurement devices may experience reduced detection accuracy depending on the area being measured. [Solution] The pulse wave measuring device comprises an imaging unit that images a living body and generates imaging data, a measurement condition setting unit that identifies a facial image included in the imaging data and determines a part of the facial image as a measurement area, and a detection unit that detects the amount of detected light in the measurement area from the imaging data and detects a pulse wave signal of the living body based on the detected light amount.

Description

[Technical Field]

[0001] The present disclosure relates to a pulse wave measurement device, a pulse wave measurement method, and a pulse wave measurement program. [Background technology]

[0002] Pulse wave measuring devices that measure the pulse waves of a living body are known. The pulse wave measuring device described in Patent Document 1 includes a receiving unit, a display control unit, and a measuring unit. The receiving unit receives an instruction to measure a pulse wave signal. The display control unit displays a captured image and a guide frame on the display unit. The measuring unit detects a skin area included in a face area that is contained within the guide frame. The measuring unit measures the pulse wave signal based on the skin area. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2021-183079 Summary of the Invention [Problem to be solved by the invention]

[0004] The skin region includes areas where it is easy to obtain a pulse wave signal and areas where it is difficult to obtain a pulse wave signal. Pulse wave measurement devices may experience reduced detection accuracy depending on the area being measured. [Means for solving the problem]

[0005] The pulse wave measurement device of the present disclosure includes an imaging unit that images a living body and generates imaging data, a measurement condition setting unit that identifies a facial image included in the imaging data and determines a part of the facial image as a measurement area, and a detection unit that detects the amount of detected light in the measurement area from the imaging data and detects a pulse wave signal of the living body based on the detected light amount.

[0006] The pulse wave measurement method disclosed herein captures an image of a living body to generate imaging data, identifies a facial image included in the imaging data, determines a portion of the facial image as a measurement area, detects the amount of detected light in the measurement area from the imaging data, and detects a pulse wave signal of the living body based on the detected amount of light.

[0007] The pulse wave measurement program disclosed herein causes a computer connected to an imaging unit that images a living body and generates imaging data to identify a facial image included in the imaging data, determine a portion of the facial image as a measurement area, detect the amount of detected light in the measurement area from the imaging data, and detect a pulse wave signal of the living body based on the detected amount of light. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing a schematic configuration of a measurement device. [Figure 2] FIG. 2 is a block diagram showing the configuration of a measurement device. [Figure 3] FIG. 2 is a diagram showing a schematic configuration of a plurality of partial regions and a face image region. [Figure 4] FIG. 10 is a diagram showing a captured image including face mesh information. [Figure 5] FIG. 10 is a diagram schematically showing a method for setting a variable region. [Figure 6] FIG. 10 is a diagram schematically showing a method for setting a variable region. [Figure 7] FIG. 10 is a diagram schematically showing a method for setting a variable region. [Figure 8] FIG. 4 is a diagram showing an example of a detection region. [Figure 9] FIG. 4 is a diagram showing an example of a detection area. [Figure 10] FIG. 10 is a diagram showing an example of a gradation detection value. [Figure 11] FIG. 10 is a diagram showing an example of an analysis procedure for detecting a pulse wave signal. [Figure 12] FIG. 10 is a diagram showing an analysis result of a noise-removed signal. [Figure 13] FIG. 4 is a diagram showing an example of a control flow executed by the measurement device. [Figure 14] FIG. 4 is a diagram showing an example of a control flow executed by the measurement device. DETAILED DESCRIPTION OF THE INVENTION

[0009] FIG. 1 shows a schematic configuration of a measurement device 10. The measurement device 10 corresponds to an example of a pulse wave measurement device. The measurement device 10 detects a pulse wave signal of the subject M using moving image data. The pulse wave signal is a signal indicating the pulse wave of the subject M. The subject M corresponds to an example of a living organism. The measurement device 10 calculates biological information of the subject M based on the pulse wave signal. The biological information includes pulse rate, pulse rate variability, oxygen saturation level, blood pressure, etc. The measurement device 10 may also evaluate sleep apnea syndrome, etc., based on the biological information. The measurement device 10 displays the biological information calculated based on the pulse wave signal.

[0010] The measuring device 10 is configured with an information processing device such as a computer. The measuring device 10 shown in FIG. 1 is a laptop computer, but is not limited to this. The measuring device 10 may be any device that has a function for capturing video or is connectable to a device that captures video. The measuring device 10 may be configured with a desktop computer, a tablet terminal, a smartphone, or the like. The measuring device 10 includes an imaging unit 11, a display unit 13, and an input unit 15. The measuring device 10 may also include a communication unit (not shown), etc.

[0011] The imaging unit 11 captures an image of the subject M by receiving reflected light reflected by the subject M, external light, etc. as detection light. The imaging unit 11 generates moving image data including the face of the subject M. The moving image data is made up of multiple image data. The image data is made up of output values ​​of multiple pixels. The image data is data that causes the display unit 13 to display the captured image 100. The captured image 100 is a still image. The imaging unit 11 captures images at a predetermined frame rate and generates moving image data. The imaging unit 11 corresponds to an example of an imaging section. The moving image data corresponds to an example of captured data.

[0012] The imaging unit 11 is, for example, a camera including an optical element, an imaging element, etc. The optical element focuses light on the imaging element. The imaging element converts the detected light into an output value of an electrical signal. The output value of each pixel indicates the light intensity of each pixel. The imaging element generates the output value of each pixel. The imaging element is, for example, a CCD (Charge Coupled Device), a CMOS (Complementary Metal Oxide Semiconductor), etc. The output value includes gradation values ​​of multiple color lights. The imaging unit 11 generates gradation values ​​of multiple color lights for each pixel. For example, the multiple color lights are red light, green light, and blue light. The red light, green light, and blue light are lights having different wavelength bands. The red wavelength band, which is the wavelength band of red light, is 600 nm to 800 nm. The red wavelength band corresponds to an example of a second wavelength band. The green wavelength band, which is the wavelength band of green light, is 520 nm to 550 nm. The green wavelength band corresponds to an example of a first wavelength band. The blue wavelength band, which is the wavelength band of blue light, is 430 nm to 490 nm. The blue wavelength band corresponds to an example of a third wavelength band. The imaging unit 11 may include IR (Infrared) color light, etc. The imaging unit 11 generates a red gradation value that is the gradation value of red light, a green gradation value that is the gradation value of green light, and a blue gradation value that is the gradation value of blue light for each pixel. The image data is composed of output values ​​for each pixel including the red gradation value, the green gradation value, and the blue gradation value. The image data includes the brightness of each pixel, etc.

[0013] 1 is a camera built into the measuring device 10, but is not limited to this. The imaging unit 11 may also be an external camera connected to the measuring device 10. The external camera may be a near-infrared camera, a webcam, a smartphone camera, or the like.

[0014] The display unit 13 displays various types of information such as the captured image 100. The display unit 13 displays various types of biometric information based on the pulse wave signal. The display unit 13 may also display comments based on the biometric information. The display unit 13 is configured with a liquid crystal panel, an organic EL (electro-luminescence) panel, or the like. The display unit 13 may have a touch input function. When the display unit 13 has the touch input function, the display unit 13 functions as the input unit 15. The display unit 13 shown in FIG. 1 is included in the measurement device 10, but is not limited to this. The display unit 13 may also be a display external to the measurement device 10.

[0015] The input unit 15 accepts various input operations by the measurer M. The input unit 15 generates various input signals in response to the input operations. The input unit 15 shown in FIG. 1 is a keyboard included in the measurement device 10, but is not limited to this. The input unit 15 may also be a mouse, keyboard, touch panel, pen tablet, etc. connected to the measurement device 10.

[0016] The subject M operates the measuring device 10 at a position facing the imaging unit 11 of the measuring device 10. The subject M operates the measuring device 10 when he / she wants the measuring device 10 to detect his / her biometric information. The subject M may also operate the measuring device 10 when performing a task such as document creation. The measuring device 10 detects the biometric information of the subject M in the background while the subject M is performing a task such as document creation. By detecting the biometric information in the background, the measuring device 10 can detect the biometric information of the subject M in a normal active state.

[0017] 2 shows a block configuration of the measuring device 10. The measuring device 10 includes an imaging unit 11, a display unit 13, an input unit 15, a storage unit 21, and a control unit 31.

[0018] The imaging unit 11 transmits moving image data to the control unit 31. The imaging unit 11 transmits the moving image data to the control unit 31 at a predetermined timing. The imaging unit 11 may transmit image data included in the moving image data to the control unit 31 at predetermined time intervals. The imaging unit 11 transmits image data including red gradation values, green gradation values, and blue gradation values ​​for each pixel to the control unit 31. The imaging unit 11 may transmit the moving image data to the storage unit 21, and store the moving image data in the storage unit 21.

[0019] The display unit 13 displays various images under the control of the control unit 31. The display unit 13 receives display data from the control unit 31 and displays various images based on the display data. The display unit 13 may display moving images captured by the imaging unit 11 based on moving image data. The display unit 13 may display captured images 100 based on image data included in the moving image data.

[0020] The input unit 15 transmits an input signal to the control unit 31. By transmitting the input signal to the control unit 31, the input unit 15 causes the control unit 31 to perform various controls. As an example, the input unit 15 transmits a display instruction signal to the control unit 31 as an input signal to display biological information. Based on the display instruction signal, the control unit 31 generates biological information display data that causes the display unit 13 to display biological information based on the pulse wave signal. The control unit 31 transmits the biological information display data to the display unit 13. Based on the biological information display data, the display unit 13 displays a screen including the biological information.

[0021] The memory unit 21 stores various programs, various data, etc. The memory unit 21 stores a biometric analysis program PG and set region information 101. The memory unit 21 stores a word processing program, a spreadsheet program, etc. The memory unit 21 may store various detected data such as moving image data and biometric information. The memory unit 21 may store a facial image database, which will be described later. The memory unit 21 is configured with semiconductor memory such as RAM (Random Access Memory) and ROM (Read Only Memory). The memory unit 21 may have an HDD (Hard Disk Drive). The memory unit 21 may function as a work area for the control unit 31. The memory unit 21 corresponds to an example of a memory section.

[0022] The biological analysis program PG is a program that causes the measurement device 10 to detect a pulse wave signal. The biological analysis program PG is executed by the control unit 31. When the biological analysis program PG is executed by the control unit 31, the control unit 31 functions as various functional units. The biological analysis program PG causes various types of biological information to be detected based on the pulse wave signal. The biological analysis program PG may be executed in the background while the control unit 31 is executing a document creation program or the like. The biological analysis program PG corresponds to an example of a pulse wave measurement program.

[0023] The set area information 101 is information related to the out-of-detection area. The set area information 101 corresponds to an example of area information. The out-of-detection area is used when the control unit 31 determines the detection area 201, which will be described later. The out-of-detection area is included in the face image area 121, which will be described later. The control unit 31 determines the detection area 201 by excluding the out-of-detection area from the face image area 121. The detection area 201 corresponds to an example of a measurement area. The out-of-detection area includes one or more body part areas 115. The set area information 101 is information indicating that one or more body part areas 115 are out-of-detection areas. The out-of-detection area corresponds to an example of a predetermined area.

[0024] FIG. 3 shows a schematic configuration of the multiple part regions 115 and face image region 121. FIG. 3 shows the multiple part regions 115 and face image region 121 on the captured image 100. The multiple part regions 115 and face image region 121 are set by face recognition processing, which will be described later. FIG. 3 also shows the shapes and sizes of the multiple part regions 115 and face image region 121. The shapes and sizes of the multiple part regions 115 and face image region 121 are set appropriately for each subject M. FIG. 3 shows, as examples of part regions 115, peripheral region 115a, head region 115b, hair region 115c, eye socket region 115d, nasal cavity region 115e, lip region 115f, forehead region 115g, and chin region 115h.

[0025] The marginal region 115a is a region including marginal parts such as the outline of the face and the border with the hair. The head region 115b is a region corresponding to the head. The hair region 115c is a region including the hair. The eye socket region 115d is a region corresponding to the eye socket. The nasal cavity region 115e is a region corresponding to the nasal cavity. The lip region 115f is a region corresponding to the lips. The forehead region 115g is a region corresponding to the forehead. The lower jaw region 115h is a region corresponding to the lower jaw. The non-detection region includes at least one of the marginal region 115a, the head region 115b, the hair region 115c, the eye socket region 115d, the nasal cavity region 115e, the lip region 115f, the forehead region 115g, and the lower jaw region 115h. The set area information 101 indicates that at least one of the peripheral area 115a, head area 115b, hair area 115c, eye socket area 115d, nasal cavity area 115e, lip area 115f, forehead area 115g, and lower jaw area 115h is an undetected area.

[0026] The set region information 101 is stored in advance in the storage unit 21. The set region information 101 is stored in the storage unit 21 when the biological analysis program PG is installed in the measurement device 10. The measurement device 10 may generate moving image data during pre-measurement operations before detecting a pulse wave signal, and generate the set region information 101 based on the moving image data. The measurement device 10 stores the generated set region information 101 in the storage unit 21.

[0027] The control unit 31 shown in FIG. 2 is a control controller that controls the operations of various units. As an example, the control unit 31 is a processor having a CPU (Central Processing Unit). The control unit 31 is composed of one or more processors. The control unit 31 is communicably connected to the imaging unit 11, the display unit 13, etc. By executing the biometric analysis program PG, the control unit 31 functions as a data processing unit 33, a region setting unit 35, an analysis unit 37, and a display control unit 39. By executing the biometric analysis program PG, the control unit 31 may function as functional units other than the data processing unit 33, the region setting unit 35, the analysis unit 37, and the display control unit 39. The control unit 31 corresponds to an example of a computer.

[0028] The data processing unit 33 acquires moving image data transmitted from the imaging unit 11. The data processing unit 33 acquires multiple image data included in the moving image data. The data processing unit 33 acquires the red gradation value, green gradation value, blue gradation value, pixel brightness, etc. of each pixel included in the image data. The data processing unit 33 appropriately performs data processing such as correction processing on the red gradation value, green gradation value, blue gradation value, etc. The data processing unit 33 transmits the red gradation value, green gradation value, blue gradation value, etc. to the analysis unit 37.

[0029] The area setting unit 35 acquires moving image data transmitted from the imaging unit 11. The area setting unit 35 acquires multiple pieces of image data included in the moving image data. The area setting unit 35 performs face recognition processing for each piece of image data. The face recognition processing detects feature points included in the image data and extracts a face image area 121 by matching the feature points with a pre-registered face image database. The face image database is a database that stores information related to feature points used for face recognition. Examples of feature points included in the image data include the positions and contours of the eyes, nose, and mouth. The face image database is pre-stored in the storage unit 21. If the measurement device 10 is connected to a server via a network, the face image database may be pre-stored in the server. The face image area 121 is an area where the face of the subject M is displayed. The area setting unit 35 identifies the face image area 121 by performing face recognition processing. The area setting unit 35 corresponds to an example of a measurement condition setting unit. The face image area 121 corresponds to an example of a face image.

[0030] As an example, a face mesh is used in the face recognition process. The face mesh is a machine learning model that detects facial key points from an image. The area setting unit 35 performs face recognition processing using the face mesh on the image data, and acquires a face image area 121 and face mesh information 131. The face mesh information 131 is represented by a plurality of key points included in the face image area 121.

[0031] Fig. 4 shows a captured image 100 including face mesh information 131. Fig. 4 shows an example of the face mesh information 131. The face mesh information 131 differs depending on the subject M. Fig. 4 shows mesh points 131a and mesh lines 131b that indicate the face mesh information 131.

[0032] The mesh points 131a are points corresponding to key points. For example, the number of key points is 468. The key points include three-dimensional coordinate points and edges. The mesh lines 131b are lines connecting two adjacent mesh points 131a. The mesh lines 131b form a triangular mesh area 141.

[0033] 2 acquires the face image region 121 and then divides the face image region 121 into a plurality of divided regions. As an example, the region setting unit 35 sets the divided regions based on a mesh region 141 formed in face mesh information 131 acquired by the face recognition processing.

[0034] The region setting unit 35 determines whether or not an image showing a predetermined part is included in the multiple divided regions. The predetermined parts are the peripheral part, head, hair, eye sockets, nostrils, lips, forehead, and chin. The region setting unit 35 sets a region including a predetermined part from the multiple divided regions as a part region 115. The region setting unit 35 sets multiple part regions 115. The region setting unit 35 sets a divided region including a peripheral part as a peripheral region 115a. The region setting unit 35 sets a divided region including the head as a head region 115b. The region setting unit 35 sets a divided region including hair as a hair region 115c. The region setting unit 35 sets a divided region including the eye sockets as an eye socket region 115d. The region setting unit 35 sets a divided region including the nasal cavity as a nasal cavity region 115e. The region setting unit 35 sets a divided region including the lips as a lip region 115f. The region setting unit 35 sets the divided region including the forehead as a forehead region 115g, and the divided region including the lower jaw as a lower jaw region 115h.

[0035] Region setting unit 35 sets some of the multiple divided regions as part regions 115, and then sets the non-detection region. Region setting unit 35 sets the non-detection region based on set region information 101. Region setting unit 35 sets part region 115 indicated by set region information 101 as the non-detection region.

[0036] The region setting unit 35 may set the variable region 151 based on the video data. The variable region 151 is a region within the face image region 121. The variable region 151 is a region that may move out of the imaging range IR of the imaging unit 11 for a predetermined time period during measurement of the pulse wave signal due to the movement of the face of the subject M. The variable region 151 is a region that is not imaged for a predetermined time period during measurement of the biological information. The time period is set in advance. For example, the predetermined time period is a range in which the time during which no image is captured is 30 to 100% of the imaging time. The variable region 151 corresponds to an example of a non-imaged region. The region setting unit 35 sets the variable region 151 in a preparation step before detecting the pulse wave signal. The variable region 151 may include the peripheral region 115a or may coincide with the peripheral region 115a. The region setting unit 35 stores the variable region 151 set in the preparation step in the set region information 101 in the storage unit 21. The region setting unit 35 stores information indicating that the variable region 151 is a non-detection region in the set region information 101. The variable region 151 may be included in the set region information 101 in advance when the measurement device 10 is shipped. In this case, the set region information 101 includes, as an example, information indicating that the peripheral region 115a corresponding to the variable region 151 is a non-detection region.

[0037] Figures 5, 6, and 7 schematically show a method for setting the variable area 151. Figures 5, 6, and 7 schematically show the imaging unit 11 and the face of the person being measured M. Figures 5, 6, and 7 show the imaging range IR of the person being measured M by the imaging unit 11. Figures 5, 6, and 7 show the imaging unit 11 and the person being measured M in a planar view from above. Figures 5, 6, and 7 show the imaging unit 11 using a camera image.

[0038] 5 shows a state in which the subject M faces the imaging unit 11. When the subject M faces the imaging unit 11, the imaging unit 11 captures an image of the area between a first point P1 and a second point P2. The first point P1 and the second point P2 are points within the facial image area 121. FIG. 5 shows a first virtual line VL1 that connects the first point P1 and the second point P2 with a straight line.

[0039] When the subject M faces the imaging unit 11, the imaging unit 11 captures an image of the area between the first point P1 and the second point P2. The area between the first point P1 and the second point P2 becomes the imaging area PA.

[0040] FIG. 6 shows a state when the subject M faces right relative to the imaging unit 11. When the subject M faces right relative to the imaging unit 11, the imaging unit 11 captures an image of the area between a third point P3 and a fourth point P4. The third point P3 is a point different from the first point P1. The fourth point P4 is a point different from the second point P2. FIG. 6 shows a second virtual line VL2 that connects the third point P3 and the fourth point P4 with a straight line.

[0041] FIG. 6 shows a first point P1 and a second point P2. When the subject M turns to the right with respect to the imaging unit 11, the first point P1 and the second point P2 move. The first point P1 moves into the imaging range IR. The second point P2 moves out of the imaging range IR. The imaging unit 11 captures an image of the area between the third point P3 and the fourth point P4. The area between the third point P3 and the fourth point P4 becomes the imaging area PA.

[0042] The area between the fourth point P4 and the second point P2 is a non-imaging area NR. The non-imaging area NR is an area that moves out of the imaging area IR when the subject M moves their face. The area between the fourth point P4 and the second point P2 is not imaged by the imaging unit 11.

[0043] 7 shows a state in which the subject M faces left relative to the imaging unit 11. When the subject M faces left relative to the imaging unit 11, the imaging unit 11 captures an image of the area between the fifth point P5 and the sixth point P6. The fifth point P5 is a point different from the first point P1 and the third point P3. The sixth point P6 is a point different from the second point P2 and the fourth point P4. FIG. 7 shows a third virtual line VL3 that connects the fifth point P5 and the sixth point P6 with a straight line.

[0044] FIG. 7 shows a first point P1 and a second point P2. When the subject M turns to the left with respect to the imaging unit 11, the first point P1 and the second point P2 move. The first point P1 moves out of the imaging range IR. The second point P2 moves into the imaging range IR. The imaging unit 11 captures an image of the area between the fifth point P5 and the sixth point P6. The area between the fifth point P5 and the sixth point P6 becomes the imaging area PA.

[0045] The area between the first point P1 and the fifth point P5 is a non-imaged area NR. The area between the first point P1 and the fifth point P5 is not imaged by the imaging unit 11. When the subject M turns to the right or left with respect to the imaging unit 11, the non-imaged area NR occurs.

[0046] In a preparation step, the region setting unit 35 shown in FIG. 2 causes the imaging unit 11 to capture an image of the face of the subject M for a predetermined time. The predetermined time is set appropriately. The region setting unit 35 acquires moving image data and detects a non-image capture region NR for each image data. The region setting unit 35 sets the face image region 121 that is included in the non-image capture region NR for a predetermined time percentage as a variable region 151. The variable region 151 is a region that is included in the non-image capture region NR. The region setting unit 35 stores information indicating that the variable region 151 is a non-detection region in the set region information 101.

[0047] The region setting unit 35 may set an expression change region 161 based on moving image data. The expression change region 161 is a region within the face image region 121. The expression change region 161 is a region including coordinates where a coordinate displacement of a predetermined amount or more occurs when the subject M changes their facial expression, etc. The coordinates are coordinates within the mesh point 131a or the mesh region 141. The coordinate displacement corresponds to an example of body movement. The predetermined amount is set in advance. As an example, the predetermined amount is 0.5 mm to 5 cm. The expression change region 161 corresponds to an example of a body movement region. In a preparation step, the region setting unit 35 measures the coordinate displacement of each coordinate within the face image region 121 using moving image data, thereby determining some of the divided regions as the expression change region 161. The region setting unit 35 may determine some of the multiple part regions 115 as the expression change region 161 by measuring the coordinate displacement of each coordinate within the face image region 121.

[0048] The region setting unit 35 generates information that designates the determined facial expression change region 161 as an out-of-detection region, and stores this in the set region information 101. The region setting unit 35 generates information that designates a divided region corresponding to the facial expression change region 161 or the part region 115 as an out-of-detection region, and stores this in the set region information 101. As an example, the region setting unit 35 generates information indicating that the lip region 115f is an out-of-detection region, and stores this in the set region information 101.

[0049] The region setting unit 35 may set a non-standard region 171 based on the video data. The non-standard region 171 is a region within the face image region 121. The non-standard region 171 is a region captured with pixel luminance values ​​that differ from the standard value range. The pixel luminance values ​​vary depending on the luminance of external light on the subject M, the direction of the external light, the contours of the face, etc. If the luminance of external light is too high, the pixel luminance values ​​will be higher than the standard value range. Depending on the direction of external light and the contours of the face, the pixel luminance values ​​will be lower than the standard value range. The region setting unit 35 determines a region where the pixel luminance values ​​are outside the standard range as the non-standard region 171. The non-standard region 171 corresponds to an example of a non-luminance region. The region setting unit 35 generates information that designates the determined non-standard region 171 as a non-detection region, and stores this information in the set region information 101.

[0050] The normalized value range is a predetermined threshold range for the representative brightness value. The representative brightness value is the average value, median, quartile, etc. of the brightness values ​​of the pixels included in the face image area 121. The threshold is the standard deviation value, an integer multiple of the standard deviation value, etc. The normalized value range may be the interquartile range. The interquartile range is the range from the first quartile to the third quartile. The normalized value range corresponds to an example of a predetermined range. The representative brightness value and normalized value range are set as appropriate.

[0051] The region setting unit 35 determines a part of the face image region 121 as the detection region 201. The region setting unit 35 determines the detection region 201 based on the set region information 101. The region setting unit 35 determines the detection region 201 by excluding one or more part regions 115 based on the set region information 101. The region setting unit 35 may determine the detection region 201 by excluding the variation region 151 from the face image region 121. The region setting unit 35 may determine the detection region 201 by excluding the expression variation region 161 from the face image region 121. The region setting unit 35 may determine the detection region 201 by excluding the nonstandard region 171 from the face image region 121.

[0052] 8 shows an example of the detection area 201. The detection area 201 is determined by the area setting unit 35 by excluding the non-detection area from the face image area 121. The area setting unit 35 determines a part of the face image area 121 as the detection area 201. FIG. 8 shows the face image area 121 and multiple part areas 115.

[0053] 8 shows a detection area 201 determined by excluding a plurality of part areas 115 from a face image area 121. The plurality of part areas 115 are a peripheral area 115a, a head area 115b, a hair area 115c, an eye socket area 115d, a nostril area 115e, a lip area 115f, a forehead area 115g, and a lower jaw area 115h. The plurality of part areas 115 are set as non-detection areas based on set area information 101.

[0054] 8 is determined by excluding a plurality of part regions 115 from the face image region 121, but is not limited to this. The detection region 201 is determined by excluding any one or more of the plurality of part regions 115. A portion of the face image region 121 is set as the detection region 201. By setting a portion of the face image region 121 as the detection region 201, it is possible to reduce the influence of noise occurring in regions where it is difficult to detect a pulse wave signal. It is also possible to remove noise components such as body movement, enabling more accurate detection of a pulse wave signal.

[0055] 9 shows an example of the detection area 201. The detection area 201 is determined by the area setting unit 35 by excluding non-detection areas from the face image area 121. The area setting unit 35 determines a part of the face image area 121 as the detection area 201. FIG. 9 shows the face image area 121, multiple body part areas 115, a variation area 151, an expression variation area 161, and a non-standard area 171.

[0056] 9 shows a detection area 201 determined by excluding a plurality of body part areas 115, a variation area 151, an expression change area 161, and an out-of-standard area 171 from a face image area 121. The body part areas 115 are an eye socket area 115d, a nasal cavity area 115e, and a forehead area 115g. The body part areas 115 are set as out-of-detection areas based on set area information 101.

[0057] 9 is set based on moving image data captured in the preparation step. The region setting unit 35 may set the peripheral region 115a as the variable region 151. In this case, the set region information 101 includes information that associates the peripheral region 115a with the variable region 151.

[0058] 9 is set based on moving image data captured in the preparation step. The region setting unit 35 may set the lip region 115f and the lower jaw region 115h as the facial expression change region 161. In this case, the set region information 101 includes information that associates the lip region 115f and the lower jaw region 115h with the facial expression change region 161.

[0059] The non-standard area 171 shown in Fig. 9 is set based on the moving image data captured in the preparation process. The non-standard area 171 shown in Fig. 9 is an area where the pixel brightness value is outside the interquartile range of the median. The non-standard area 171 shown in Fig. 9 indicates an area where the brightness value is lower than the standard value range depending on the direction of incidence of external light on the face of the subject M and the height of the nose.

[0060] 9 is determined by excluding a plurality of part regions 115, variation regions 151, facial expression change regions 161, and irregular region 171 from face image region 121, but is not limited to this. Detection region 201 is determined by excluding one or more of part regions 115, variation regions 151, facial expression change regions 161, and irregular region 171. A portion of face image region 121 is set as detection region 201. Setting a portion of face image region 121 as detection region 201 reduces the influence of noise occurring in regions where it is difficult to detect a pulse wave signal. Furthermore, noise components such as body movement can be removed, enabling more accurate detection of a pulse wave signal.

[0061] The analysis unit 37 shown in FIG. 2 analyzes biological information based on the pulse wave signal. The analysis unit 37 acquires image data from the imaging unit 11, the image data being composed of output values ​​for each pixel, including red, green, and blue gradations. The analysis unit 37 acquires the detection area 201 from the area setting unit 35. The analysis unit 37 detects the output values ​​in the detection area 201 from the image data, and detects the pulse wave signal of the subject M based on the output values. The analysis unit 37 identifies multiple pixels included in the detection area 201. The analysis unit 37 detects the output values ​​of each of the identified multiple pixels. The analysis unit 37 generates a detection value using the multiple output values. The detection value corresponds to an example of the detected light amount. The detection value includes a gradation detection value calculated using each gradation value. The detection value is the sum, average, etc. of the output values ​​of the pixels in the detection area 201. The gradation detection value is the sum, average, etc. of the gradation values ​​included in the output value. The detection value is calculated for each image data in the video image data. Analysis unit 37 uses the gradation detection value to detect a pulse wave signal of subject M. Analysis unit 37 corresponds to an example of a detection unit.

[0062] The analysis unit 37 detects the pulse wave signal using each gradation detection value included in the detection value. As an example, the analysis unit 37 detects the pulse wave signal using at least one of the red gradation detection value Dr, the green gradation detection value Dg, and the blue gradation detection value Db. The red gradation detection value Dr is calculated using the red gradation value. The green gradation detection value Dg is calculated using the green gradation value. The blue gradation detection value Db is calculated using the blue gradation value. The pulse wave signal is detected based on the green gradation detection value Dg. The pulse wave signal is detected based on the difference between the green gradation detection value Dg and at least one of the red gradation detection value Dr and the blue gradation detection value Db.

[0063] FIG. 10 shows an example of gradation detection values. FIG. 10 shows red gradation detection values ​​Dr, green gradation detection values ​​Dg, and blue gradation detection values ​​Db. FIG. 10 shows waveform signals of the red gradation detection values ​​Dr, green gradation detection values ​​Dg, and blue gradation detection values ​​Db over time. FIG. 10 shows the red gradation detection values ​​Dr, green gradation detection values ​​Dg, and blue gradation detection values ​​Db in a body movement section S1 and the red gradation detection values ​​Dr, green gradation detection values ​​Dg, and blue gradation detection values ​​Db in a resting section S2. The body movement section S1 is a section in which facial movement and facial expression changes occur. The resting section S2 is a section in which facial movement and facial expression changes are smaller than a predetermined amount of change.

[0064] 10 shows the green gradation detection value Dg. The green gradation detection value Dg corresponds to the light intensity value of green light contained in the detected light in the detection area 201 captured by the imaging unit 11. The green light corresponds to an example of the first light. The green gradation detection value Dg corresponds to an example of the first detected light intensity. The green gradation detection value Dg is included in the detection value.

[0065] As shown in Figure 10, in the body movement section S1, the green gradation detection value Dg fluctuates due to the influence of body movement. The pulse wave signal included in the green gradation detection value Dg is difficult to detect due to the fluctuating noise. In the resting section S2, the influence of the fluctuating noise due to body movement on the green gradation detection value Dg decreases, making it possible to detect the pulse wave signal.

[0066] 10 shows the red gradation detection value Dr. The red gradation detection value Dr corresponds to the light intensity value of red light contained in the detected light in the detection area 201 captured by the imaging unit 11. The red light corresponds to an example of the second light. The red gradation detection value Dr corresponds to an example of the second detected light intensity. The red gradation detection value Dr is included in the detection value.

[0067] As shown in Figure 10, in the body movement section S1, the red gradation detection value Dr fluctuates due to the influence of body movement. The pulse wave signal included in the red gradation detection value Dr is difficult to detect due to the fluctuating noise. In the resting section S2, the influence of the fluctuating noise due to body movement is reduced in the red gradation detection value Dr, but the signal-to-noise ratio is small, making it difficult to detect the pulse wave signal.

[0068] 10 shows the blue gradation detection value Db. The blue gradation detection value Db corresponds to the light intensity value of blue light contained in the detected light in the detection area 201 captured by the imaging unit 11. The blue light corresponds to an example of the third light. The blue gradation detection value Db corresponds to an example of the third detected light intensity. The blue gradation detection value Db is included in the detection value.

[0069] As shown in Figure 10, in the body movement section S1, the blue gradation detection value Db fluctuates due to the influence of body movement. The pulse wave signal included in the blue gradation detection value Db is difficult to detect due to the fluctuating noise. In the resting section S2, the influence of the fluctuating noise due to body movement on the blue gradation detection value Db is reduced, but the signal-to-noise ratio is small, making it difficult to detect the pulse wave signal.

[0070] Analysis unit 37 detects the pulse wave signal using red gradation detection value Dr, green gradation detection value Dg, and blue gradation detection value Db shown in Fig. 10. As an example, analysis unit 37 detects the pulse wave signal using the analysis procedure shown in Fig. 11.

[0071] Fig. 11 shows an example of an analysis procedure for detecting a pulse wave signal. Fig. 11 is a flowchart showing an example of the analysis procedure. The analysis procedure shown in Fig. 11 is executed by analysis unit 37. The analysis procedure shown in Fig. 11 detects a pulse wave signal using a red gradation detection value Dr, a green gradation detection value Dg, and a blue gradation detection value Db.

[0072] In step S101, the analysis unit 37 samples each gradation detection value at a predetermined time interval. The time interval and sampling frequency are set appropriately. The time interval is preferably a time that includes one or more pulse wave pulses. As an example, the time interval is 3 to 10 seconds. As an example, the sampling frequency is 10 Hz or more and 50 Hz or less. The analysis unit 37 acquires sampling data by performing sampling. The sampling data includes the sampled red gradation detection value Dr, green gradation detection value Dg, and blue gradation detection value Db.

[0073] After performing the sampling, the analysis unit 37 normalizes the sampled data in step S103. The analysis unit 37 normalizes the plurality of green gradation detection values ​​Dg included in the sampled data.

[0074] The analysis unit 37 calculates a green mean value Gmean, which is the average value of the multiple green gradation detection values ​​Dg, and a green standard deviation value Gstd, which is the standard deviation value of the multiple green gradation detection values ​​Dg. The analysis unit 37 normalizes each green gradation detection value Dg using the following formula (1). Gnorm n =(Gn-Gmean) / Gstd (1) Here, n is an arbitrary integer equal to or greater than 1. Gn is the n-th green gradation detection value Dg. Gnorm n is the normalized value of the n-th green gradation detection value Dg.

[0075] The analysis unit 37 normalizes the multiple red gradation detection values ​​Dr and multiple blue gradation detection values ​​Db included in the sampling data, similar to the green gradation detection values ​​Dg. The analysis unit 37 calculates a red mean value Rmean, which is the average value of the multiple red gradation detection values ​​Dr, and a red standard deviation value Rstd, which is the standard deviation value of the multiple red gradation detection values ​​Dr. The analysis unit 37 calculates a blue mean value Bmean, which is the average value of the multiple blue gradation detection values ​​Db, and a blue standard deviation value Bstd, which is the standard deviation value of the multiple blue gradation detection values ​​Db. The analysis unit 37 normalizes each red gradation detection value Dr and each blue gradation detection value Db using the following equations (2) and (3). Rnorm n =(Rn-Rmean) / Rstd (2) Bnorm n =(Bn-Bmean) / Bstd (3) Here, n is an arbitrary integer equal to or greater than 1. Rn is the n-th red gradation detection value Dr. Rnorm n is the normalized value of the n-th red gradation detection value Dr. Bn is the n-th blue gradation detection value Db. Bnorm n is the normalized value of the n-th blue gradation detection value Db.

[0076] After normalizing the sampling data, the analysis unit 37 performs noise removal processing in step S105. The analysis unit 37 performs noise removal processing using the normalized green gradation detection value Dg, the normalized red gradation detection value Dr, and the normalized blue gradation detection value Db. The analysis unit 37 performs noise removal processing using the following equation (4) to generate a noise-removed signal S. Sn=Gnorm n +αBnorm n +βRnorm n (4) Here, n is an arbitrary integer equal to or greater than 1. Sn is the n-th noise-removed signal S. α is the first coefficient, and β is the second coefficient.

[0077] For example, α and β are −0.5 and −0.5, respectively. When α and β are negative values, the analysis unit 37 detects the noise-removed signal S by subtracting the normalized red gradation detection value Dr and the normalized blue gradation detection value Db from the normalized green gradation detection value Dg. At least one of α and β may be 0. When α=0 and β=−0.5, the analysis unit 37 detects the noise-removed signal S by calculating the difference between the green gradation detection value Dg and the red gradation detection value Dr. When α=−0.5 and β=0, the analysis unit 37 detects the noise-removed signal S by calculating the difference between the green gradation detection value Dg and the blue gradation detection value Db. α and β are set appropriately depending on the noise removal situation.

[0078] Fig. 12 shows the analysis results of the noise-removed signal S. Fig. 12 is analyzed based on the red gradation detection value Dr, green gradation detection value Dg, and blue gradation detection value Db shown in Fig. 10. Fig. 12 shows the noise-removed signal S when α = -0.5 and β = -0.5 are substituted into equation (4). Fig. 12 shows the noise-removed signal S for the body movement section S1 and the rest section S2.

[0079] As shown in FIG. 12, the noise-removed signal S corresponds to a pulse wave signal. Noise components such as body movement are removed from the noise-removed signal S. The analysis unit 37 detects the noise-removed signal S as a pulse wave signal. The signal waveform of the noise-removed signal S in the resting section S2 is detected more clearly than that of the green gradation detection value Dg. The noise-removed signal S in the body movement section S1 is adjusted to a signal waveform corresponding to the pulse wave signal. By performing the noise removal process, the analysis unit 37 can detect the pulse wave signal in the body movement section S1 and the resting section S2.

[0080] The analysis unit 37 may calculate biological information such as a pulse wave using the noise-removed signal S. The analysis unit 37 acquires the noise-removed signal S as a pulse wave signal. The analysis unit 37 calculates biological information such as a pulse rate by calculating the period, amplitude, etc. of the pulse wave signal. The analysis unit 37 transmits the biological information including the pulse wave signal to the display control unit 39. The analysis unit 37 may store the biological information, etc. in the storage unit 21.

[0081] The display control unit 39 shown in FIG. 2 controls the display by the display unit 13. The display control unit 39 acquires biological information including a pulse wave signal from the analysis unit 37. The display control unit 39 generates display data including the biological information. The display control unit 39 transmits the display data to the display unit 13. The display control unit 39 displays the display data on the display unit 13. By displaying the display data on the display unit 13, the display control unit 39 can notify the subject M of the detection results of the biological information.

[0082] The display control unit 39 may generate message data indicating the operating status of the biometric analysis program PG. The message data is a start message, an execution message, an end message, etc. The start message indicates that detection of biometric information has started. The execution message indicates that detection of biometric information is in progress. The end message indicates that detection of biometric information has ended. The display control unit 39 transmits the message data to the display unit 13. The display control unit 39 causes the display unit 13 to display the message data.

[0083] The measuring device 10 includes an imaging unit 11 that images the subject M and generates moving image data, an area setting unit 35 that identifies a facial image area 121 included in the moving image data and determines a part of the facial image area 121 as a detection area 201, and an analysis unit 37 that detects the detection value of the detection light in the detection area 201 from the moving image data and detects the pulse wave signal of the subject M based on the detection value. Measurement device 10 can improve the detection accuracy of the pulse wave signal by acquiring detection values ​​from a part of face image area 121 as detection area 201.

[0084] The detected light includes green light in a green wavelength band and red light in a red wavelength band different from the green wavelength band. The output value includes a green gradation detection value Dg of the green light and a red gradation detection value Dr of the red light. Analysis unit 37 detects the green gradation detection value Dg and the red gradation detection value Dr, and detects a pulse wave signal based on the difference between the green gradation detection value Dg and the red gradation detection value Dr. The measuring device 10 can eliminate the influence of noise such as body movement, etc. The measuring device 10 can detect a pulse wave signal while suppressing the influence of noise.

[0085] The measurement device 10 includes a storage unit 21 that stores set area information 101 related to a non-detection area included in a predetermined face image area 121. It is preferable that the area setting unit 35 determines the detection area 201 by excluding the non-detection area from the face image area 121 based on the set area information 101. The measurement device 10 can set the detection area 201 using set area information 101 stored in advance.

[0086] The non-detection region preferably includes at least one of the peripheral region, the head, the hair, the eye sockets, the nasal cavity, the lips, the forehead, and the lower jaw. Measurement device 10 can improve the detection accuracy of the pulse wave signal by treating areas that reduce detection accuracy as non-detection areas.

[0087] It is preferable that the region setting unit 35 determines the detection region 201 by excluding, from the face image region 121, a variable region 151 that is not imaged for a predetermined period of time based on the moving image data. By excluding fluctuation region 151 from detection region 201, measurement device 10 can improve the detection accuracy of the pulse wave signal.

[0088] The variable region 151 preferably includes a peripheral region. By including the peripheral area in the variable region 151, the measurement device 10 can easily set the detection region 201.

[0089] It is preferable that the area setting unit 35 determines the detection area 201 by determining the expression change area 161 where coordinate displacement of a predetermined amount or more occurs based on the moving image data and excluding the expression change area from the face image area 121. Measurement device 10 can improve the detection accuracy of the pulse wave signal by excluding facial expression change region 161, where measurement errors are likely to occur, from detection region 201.

[0090] It is preferable that the area setting unit 35 determines the detection area 201 by identifying a non-standard area 171 that is captured at a brightness value that differs from the standard value range for the representative brightness value based on the moving image data, and excluding the non-standard area 171 from the face image area 121. By excluding non-standard area 171, where measurement errors are likely to occur, from detection area 201, measurement device 10 can improve the detection accuracy of the pulse wave signal.

[0091] The representative value of the luminance is preferably any one of the average value of the luminance, the median value of the luminance, and the quartile value of the luminance. Measurement device 10 can improve the detection accuracy of the pulse wave signal by setting non-standard region 171 using the average value of brightness or the like as a representative value of brightness.

[0092] FIG. 13 shows an example of a control flow executed by measurement device 10. FIG. 13 shows a control flow for determining detection area 201 using set area information 101 and detecting a pulse wave signal. The control flow is executed by control unit 31 running biological analysis program PG. The control flow corresponds to an example of a pulse wave measurement method. FIG. 13 shows the control flow in a flowchart.

[0093] In step S201, the measurement device 10 reads out the set region information 101. The region setting section 35 of the control unit 31 reads out the set region information 101 stored in advance from the storage unit 21. The region setting section 35 acquires the set region information 101.

[0094] In step S203, the measuring device 10 acquires moving image data. The measuring device 10 causes the imaging unit 11 to capture the moving image data. The imaging unit 11 captures an image of the subject M and generates the moving image data. The measuring device 10 acquires the moving image data by causing the imaging unit 11 to generate the moving image data. The imaging unit 11 transmits the moving image data to the control unit 31.

[0095] After acquiring the video data, the measuring device 10 executes face recognition processing in step S205. The area setting unit 35 of the control unit 31 identifies a face image area 121 included in the video data. The area setting unit 35 identifies the face image area 121 for each piece of image data included in the video data. As an example, the area setting unit 35 detects feature points included in the image data and extracts the face image area 121 by matching the feature points with a pre-registered face image database. The area setting unit 35 divides the face image area 121 into a plurality of divided areas. The area setting unit 35 determines whether the plurality of divided areas includes an image showing a predetermined body part. The area setting unit 35 sets an area including the predetermined body part among the plurality of divided areas as a body part area 115.

[0096] After executing face recognition processing, the measuring device 10 determines a detection area 201 in step S207. The area setting unit 35 determines the detection area 201 using the set area information 101. The area setting unit 35 acquires information indicating the out-of-detection area based on the set area information 101. The area setting unit 35 sets a part of the multiple part areas 115 as the out-of-detection area based on the set area information 101. The area setting unit 35 determines the detection area 201 by excluding the out-of-detection area from the face image area 121. The area setting unit 35 determines a part of the face image area 121 as the detection area 201.

[0097] After determining the detection area 201, the measurement device 10 detects each gradation detection value in step S209. The analysis unit 37 of the measurement device 10 acquires moving image data including multiple image data. The image data is composed of output values ​​for each pixel, including red gradation values, green gradation values, and blue gradation values. The analysis unit 37 acquires the detection area 201 from the area setting unit 35. The analysis unit 37 detects the output value of the detection light in the detection area 201 from the image data. The detection light includes red light, green light, and blue light. The analysis unit 37 identifies multiple pixels included in the detection area 201. The analysis unit 37 detects the output value of each of the identified multiple pixels. The analysis unit 37 generates a detection value using the multiple output values. The detection value includes a gradation detection value calculated using each gradation value. The detection value is calculated for each image data in the moving image data. The analysis unit 37 detects a red gradation detection value Dr, a green gradation detection value Dg, and a blue gradation detection value Db as the gradation detection values.

[0098] After detecting each gradation detection value, measurement device 10 detects a pulse wave signal in step S211. Analysis unit 37 detects the pulse wave signal based on the detection values. Analysis unit 37 detects a noise-removed signal S using red gradation detection value Dr, green gradation detection value Dg, and blue gradation detection value Db. Analysis unit 37 normalizes each gradation detection value, performs noise removal processing, and detects noise-removed signal S. Analysis unit 37 detects noise-removed signal S as a pulse wave signal. Analysis unit 37 may use noise-removed signal S to calculate biological information such as pulse rate.

[0099] The control flow captures an image of the subject M to generate moving image data, identifies a facial image area 121 included in the moving image data, determines a part of the facial image area 121 as a detection area 201, detects the detection value of the detection light in the detection area 201 from the moving image data, and detects the pulse wave signal of the subject M based on the detection value. Measurement device 10 can improve the detection accuracy of the pulse wave signal by acquiring detection values ​​from a part of face image area 121 as detection area 201.

[0100] The biometric analysis program PG causes the control unit 31, which is connected to the imaging unit 11 that images the subject M and generates moving image data, to identify the facial image area 121 included in the moving image data, determine a part of the facial image area 121 as the detection area 201, detect the detection value of the detection light in the detection area 201 from the moving image data, and detect the pulse wave signal of the subject M based on the detection value. The biological analysis program PG can improve the detection accuracy of the pulse wave signal by causing the control unit 31 to acquire detection values ​​using a part of the face image area 121 as the detection area 201.

[0101] Fig. 14 shows an example of a control flow executed by measurement device 10. Fig. 14 shows a control flow in which moving image data is captured in a preparation step before detecting a pulse wave signal, detection area 201 is determined based on the moving image data, and then the pulse wave signal is detected using the moving image data for detection. The control flow is executed by control unit 31 running bioanalysis program PG. Fig. 14 shows the control flow in a flowchart.

[0102] In step S301, the measuring device 10 acquires moving image data for determining the region. In a preparation step before detecting a pulse wave signal, the measuring device 10 causes the imaging unit 11 to capture an image of the subject M, thereby acquiring the moving image data. The measuring device 10 acquires the moving image data used when determining the detection region 201. The measuring device 10 images the subject M for a predetermined period of time. When capturing the image of the subject M, the measuring device 10 may have the subject M perform a predetermined action.

[0103] After acquiring the moving image data for region determination, the measuring device 10 executes face recognition processing in step S303. The region setting unit 35 of the control unit 31 identifies a face image region 121 included in the moving image data. The region setting unit 35 identifies the face image region 121 for each piece of image data included in the moving image data. As an example, the region setting unit 35 detects feature points included in the image data and extracts the face image region 121 by matching the feature points with a pre-registered face image database. The region setting unit 35 divides the face image region 121 into a plurality of divided regions. The region setting unit 35 determines whether the plurality of divided regions includes an image showing a predetermined body part. The region setting unit 35 sets the region including the predetermined body part as the body part region 115 among the plurality of divided regions.

[0104] After executing the face recognition process, the measuring device 10 determines the detection area 201 in step S305. As an example, the area setting unit 35 sets the part area 115 as the out-of-detection area. The area setting unit 35 may detect the variable area 151, the facial expression change area 161, and the non-standard area 171 based on the video image data. The area setting unit 35 determines at least one of the variable area 151, the facial expression change area 161, and the non-standard area 171 as the out-of-detection area. The area setting unit 35 determines the detection area 201 by excluding the out-of-detection area from the face image area 121. The area setting unit 35 determines a part of the face image area 121 as the detection area 201.

[0105] In step S307, the measurement device 10 acquires moving image data for detection. The measurement device 10 causes the imaging unit 11 to capture the moving image data. The imaging unit 11 captures an image of the subject M and generates the moving image data. The measurement device 10 acquires moving image data for pulse wave signal detection by causing the imaging unit 11 to generate the moving image data. The imaging unit 11 transmits the moving image data to the control unit 31.

[0106] After acquiring the moving image data for detection, the measuring device 10 executes face recognition processing in step S309. The area setting unit 35 of the control unit 31 identifies a face image area 121 included in the moving image data. The area setting unit 35 identifies a face image area 121 for each piece of image data included in the moving image data. The area setting unit 35 detects feature points included in the image data and extracts the face image area 121 by matching the feature points with a facial image database registered in advance.

[0107] After executing face recognition processing, the measuring device 10 determines a detection area 201 in step S311. The area setting unit 35 determines the detection area 201 of the image data for detection by applying the detection area 201 determined in step S305 to image data included in the video data for detection. The area setting unit 35 determines the detection area 201 of the image data for detection by excluding the non-detection area set in step S305 from the face image area 121. The area setting unit 35 determines a part of the face image area 121 as the detection area 201.

[0108] After determining the detection area 201, the measurement device 10 detects each gradation detection value in step S313. The analysis unit 37 of the measurement device 10 acquires moving image data including multiple image data. The image data is composed of output values ​​for each pixel, including red gradation values, green gradation values, and blue gradation values. The analysis unit 37 acquires the detection area 201 from the area setting unit 35. The analysis unit 37 detects the output value of the detection light in the detection area 201 from the image data. The detection light includes red light, green light, and blue light. The analysis unit 37 identifies multiple pixels included in the detection area 201. The analysis unit 37 detects the output value of each of the identified multiple pixels. The analysis unit 37 generates a detection value using the multiple output values. The detection value includes a gradation detection value calculated using each gradation value. The detection value is calculated for each image data in the moving image data. The analysis unit 37 detects a red gradation detection value Dr, a green gradation detection value Dg, and a blue gradation detection value Db as the gradation detection values.

[0109] After detecting each gradation detection value, measurement device 10 detects a pulse wave signal in step S315. Analysis unit 37 detects the pulse wave signal based on the detection values. Analysis unit 37 detects a noise-removed signal S using red gradation detection value Dr, green gradation detection value Dg, and blue gradation detection value Db. Analysis unit 37 normalizes each gradation detection value, performs noise removal processing, and detects noise-removed signal S. Analysis unit 37 detects noise-removed signal S as a pulse wave signal. Analysis unit 37 may use noise-removed signal S to calculate biological information such as pulse rate.

[0110] 14 illustrates determining the detection region 201 using moving image data for region determination, but this is not limiting. The measurement device 10 may determine the detection region 201 using set region information 101 and moving image data for region determination. The region setting unit 35 uses the set region information 101 to set a part of the part region 115 as an out-of-detection region. The region setting unit 35 uses the moving image data for region determination to detect at least one of the variable region 151, facial expression change region 161, and non-standard region 171. The region setting unit 35 sets at least one of the variable region 151, facial expression change region 161, and non-standard region 171 as an out-of-detection region. The region setting unit 35 determines the detection region 201 by excluding the out-of-detection region from the face image region 121. [Explanation of symbols]

[0111] 10...measuring device, 11...imaging unit, 13...display unit, 15...input unit, 21...storage unit, 31...control unit, 33...data processing unit, 35...area setting unit, 37...analysis unit, 39...display control unit, 100...captured image, 101...set area information, 115...part area, 115a...periphery area, 115b...head area, 115c...hair area, 115d...eye socket area, 115e...nasal cavity area, 115f...lip area, 115g...forehead area, 115h...mandibular area, 121...face image area, 131...face mesh information, 131a...mesh point, 1 31b...mesh line, 141...mesh area, 151...variation area, 161...facial expression displacement area, 171...non-standard area, 201...detection area, Db...blue gradation detection value, Dg...green gradation detection value, Dr...red gradation detection value, IR...imaging range, M...measurer, NR...non-imaging area, PA...imaging area, P1...first point, P2...second point, P3...third point, P4...fourth point, P5...fifth point, P6...sixth point, PG...biometric analysis program, S...noise removed signal, S1...body movement section, S2...rest section, VL1...first virtual line, VL2...second virtual line, VL3...third virtual line.

Claims

1. an imaging unit that captures an image of a living body and generates imaging data; a measurement condition setting unit that identifies a face image included in the imaging data and determines a part of the face image as a measurement area; a detection unit that detects an amount of detected light in the measurement region from the imaging data and detects a pulse wave signal of the living body based on the detected amount of light; A pulse wave measuring device comprising:

2. the detection light includes first light in a first wavelength band and second light in a second wavelength band different from the first wavelength band; the detected light amounts include a first detected light amount of the first light and a second detected light amount of the second light, The detection unit Detecting the first detected light amount and the second detected light amount; detecting the pulse wave signal based on a difference between the first detected light amount and the second detected light amount; The pulse wave measuring device according to claim 1 .

3. a storage unit that stores area information relating to a predetermined area included in the facial image, The measurement condition setting unit determining the measurement area by excluding the predetermined area from the face image based on the area information; The pulse wave measuring device according to claim 1 or 2.

4. The predetermined area includes at least one of a peripheral region, a head, hair, an eye socket, a nasal cavity, lips, a forehead, and a lower jaw. The pulse wave measuring device according to claim 3 .

5. the measurement condition setting unit determines the measurement area by excluding, from the face image, an out-of-image area that is not imaged for a predetermined time period based on the imaging data. The pulse wave measuring device according to claim 1 or 2.

6. The non-imaged region includes a peripheral region. The pulse wave measuring device according to claim 5 .

7. The measurement condition setting unit Based on the imaging data, a body movement region where a body movement of a predetermined amount or more occurs is determined; determining the measurement area by excluding the body movement area from the face image; The pulse wave measuring device according to claim 1 or 2.

8. The measurement condition setting unit Based on the image data, an out-of-luminance area is identified, which is imaged with a luminance value that differs from a predetermined range with respect to a representative luminance value; determining the measurement region by excluding the out-of-luminance region from the face image; The pulse wave measuring device according to claim 1 or 2.

9. the representative value of the luminance is any one of an average value of the luminance, a median value of the luminance, and a quartile value of the luminance; The pulse wave measuring device according to claim 8.

10. capturing an image of a living body to generate imaging data; Identifying a facial image included in the imaging data; determining a portion of the face image as a measurement area; Detecting the amount of detected light in the measurement area from the imaging data; detecting a pulse wave signal of the living body based on the detected light amount; Pulse wave measurement method.

11. A computer connected to an imaging unit that captures images of a living body and generates imaging data, identifying a face image included in the imaging data; determining a part of the face image as a measurement area; Detecting the amount of detected light in the measurement area from the imaging data; detecting a pulse wave signal of the living body based on the detected light amount; Pulse wave measurement program.

Images