Measuring device, measuring system, and measuring method

Abstract

A measuring device is provided that can calculate biological information without contact, reflecting the effects of exercise. [Solution] The measuring device comprises an imaging unit that captures an image of a living body by capturing an image, a signal acquisition unit that acquires a biosignal, which is a value related to the living body calculated from the image, an input unit into which information related to the exercise performed by the living body before the imaging unit acquired the image is input as exercise information, and a bioinformation calculation unit that calculates bioinformation related to blood vessels or blood flow from the biosignal using reference information selected from multiple reference information based on the exercise information.

Description

Technical Field

[0004] , , ,

[0001] The present disclosure relates to a measuring device, a measuring system, and a measuring method.

Background Art

[0002] Patent Document 1 discloses a technique for acquiring optical information related to a living body by photographing the living body with a camera, and acquiring the pulse rate, blood pressure, respiration rate, etc. of the living body by analyzing the feature amounts of the living body calculated from the optical information. Specifically, in the technique disclosed in Patent Document 1, the feature amounts of the living body are analyzed by artificial intelligence or machine learning to acquire the pulse rate, blood pressure, respiration rate, etc. of the living body. The feature amounts of the living body in the technique disclosed in Patent Document 1 include feature amounts related to the pulse wave of the living body, feature amounts related to the blood pressure of the living body, feature amounts related to the age of the living body, feature amounts related to the movement of the living body, and the like.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Patent Document 1 does not mention the effects of exercise on blood pressure, etc., and does not specify a method for considering factors due to changes in vascular condition caused by exercise in the estimation of blood pressure, etc. Therefore, the technology disclosed in Patent Document 1 cannot reflect the effects of exercise on blood pressure, etc. due to changes in vascular condition. Furthermore, the technology disclosed in Patent Document 1 outputs a comprehensive health assessment by combining blood pressure values ​​measured with a cuff blood pressure monitor, pulse waves, and exercise status. However, it is a burden for the user to wear a cuff blood pressure monitor. Therefore, one aspect of this disclosure aims to provide a measuring device, measuring system, and measuring method that can calculate biological information non-contact while reflecting the effects of exercise. [Means for solving the problem]

[0006] A measuring device according to one embodiment of the present disclosure comprises: an imaging unit that images a living body and acquires an image; a signal acquisition unit that acquires a biological signal, which is a value related to the living body calculated from the image; an input unit that receives as movement information information about the movement performed by the living body before the imaging unit acquired the image; and a biological information calculation unit that calculates biological information related to blood vessels or blood flow from the biological signal using reference information selected from a plurality of reference pieces of information based on the movement information.

[0007] A measurement system according to one embodiment of the present disclosure comprises: an imaging unit that images a living organism and acquires an image; a signal acquisition unit that acquires a biological signal, which is a value related to the living organism calculated from the image; an input unit that receives as movement information information about the movement the living organism was performing before the imaging unit acquired the image; and a biological information calculation unit that calculates biological information related to blood vessels or blood flow from the biological signal using reference information selected from a plurality of reference pieces of information based on the movement information.

[0008] A measurement method according to one embodiment of the present disclosure includes the steps of: acquiring an image by imaging a living organism; acquiring a biological signal, which is a value related to the living organism calculated from the image; inputting information about the movement performed by the living organism before the image was acquired as movement information; and calculating biological information related to blood vessels or blood flow from the biological signal using reference information selected from a plurality of reference pieces of information based on the movement information. [Brief explanation of the drawing]

[0009] [Figure 1] This figure shows an example of how the measuring device is used. [Figure 2] This is a block diagram showing an example of the configuration of a measuring device according to the first embodiment. [Figure 3] This is a schematic diagram showing an example of an input screen displayed to the user. [Figure 4] This is a flowchart illustrating an example of the operation of the measuring device according to the first embodiment. [Figure 5] This is a block diagram showing an example of the configuration of a measuring device according to the second embodiment. [Figure 6] This flowchart shows an example of the operation of the measuring device according to the second embodiment. [Modes for carrying out the invention]

[0010] (First Embodiment) The first embodiment will be described with reference to Figures 1 to 4. In the drawings, the same or similar elements are denoted by the same reference numerals, and redundant explanations are omitted.

[0011] Figure 1 shows an example of how the measuring device 100 is used. As illustrated in Figure 1, the measuring device 100 includes an imaging unit 101.

[0012] The measuring device 100 obtains biological information by measuring the time-series changes in the state of the surface or inside of the skin of the living organism 102 from the image acquired by the imaging unit 101. For example, the measuring device 100 is a PC (Personal Computer), smartphone, tablet terminal, dedicated biological information measurement terminal, or a monitoring robot equipped with an imaging unit 101. When the living organism is irradiated with lighting or natural light, it is possible to measure the state inside the skin, such as blood pressure, pulse rate, blood oxygen saturation, and other vital signs related to blood vessels or blood flow, by measuring the light transmitted or reflected by the skin. In this embodiment, blood pressure is used as an example of a vital sign, but it is not limited to blood pressure as long as it is a vital sign related to blood vessels or blood flow. In Figure 1, the measuring device 100 is not held by hand, but this is not the only example; for example, if it is a smartphone or tablet, it is also possible to hold it by hand when taking pictures.

[0013] The imaging unit 101 captures images of the living body 102 and acquires images. In this disclosure, still images and moving images extracted from continuous or discontinuous live recordings that reflect the state of the blood vessels of the living body 102 captured by the imaging unit 101 are referred to as images.

[0014] The imaging unit 101 is installed in a position that allows imaging of exposed areas of the body surface of the living organism 102. These exposed areas of the body surface include the forehead, cheeks, fingertips, wrists, and palms of the living organism 102. For example, the imaging unit 101 can be installed on a PC, smartphone, tablet, display, mirror, or washbasin.

[0015] The imaging unit 101 is a camera including a CCD (Charged Coupled Device), a CMOS (Complementary Metal Oxide Semiconductor) image sensor, and a lens. The imaging unit 101 may be composed of an image sensor for a camera including RGB filters. For example, the imaging unit 101 may be equipped with an RGB Bayer array color filter to detect minute biological changes in the skin of a living organism 102. Alternatively, the imaging unit 101 may be equipped with color filters such as RGBCy, RGBIR, etc. Color filters such as RGBCy, RGBIR, etc. are suitable for observing increases or decreases in blood volume indicated by reflected light transmitted into the skin.

[0016] Figure 2 is a block diagram showing an example of the configuration of the measuring device 100.

[0017] The measuring device 100 includes an imaging unit 101, an input unit 201, an output unit 202, a storage unit 203, a control unit 204, and the like. The imaging unit 101, the input unit 201, the output unit 202, and the storage unit 203 are electrically connected to the control unit 204.

[0018] The imaging unit 101 captures an image 211 of the user, the living body 102, and transmits the acquired image 211 to the control unit 204. For example, the imaging unit 101 captures an image 211 of the living body 102 at 30 to 60 fps (frames per second). The image 211 includes an image of the surface of the living body 102.

[0019] Motion information 210 is input to the input unit 201. The motion information 210 in this embodiment is information regarding the motion performed by the living body 102 before the imaging unit 101 acquires the image 211. Further, in addition to the user's motion information 210, the input unit 201 accepts input of information necessary for the measuring device 100. For example, the input unit 201 is a keyboard, a mouse, a touch panel, or the like.

[0020] The output unit 202 outputs the image 211, the report 212 in which the control unit 204 summarizes the biological information according to the user's needs, a message for the user, the date and time, and the like. For example, the output unit 202 includes a display, a speaker, and the like.

[0021] The control unit 204 executes various processes according to the programs and data stored in the storage unit 203. The control unit 204 is constituted by a processor such as a CPU (Central Processing Unit), a GPU (Graphic Processing Unit), or the like.

[0022] The control unit 204 includes a signal acquisition unit 205, a biological information calculation unit 206, and a model selection unit 207.

[0023] The signal acquisition unit 205 acquires a biological signal 216 from the image 211. The biological signal 216 is a value related to the living body 102 calculated from the image 211. Specifically, the signal acquisition unit 205 acquires the biological signal 216 from a signal such as the RGB pixel value included in the image 211. For example, in this embodiment, the biological signal 216 is data representing the state of the living body such as the volume pulse wave of blood vessels. Since the pulse wave reflects the state of blood vessels and blood flow, biological information can be obtained from the pulse wave. The pulse wave is not limited to the volume pulse wave, and other signals or indices such as the pressure pulse wave or the pulse wave propagation velocity may be used as long as they are biological signals reflecting the state of blood vessels or blood flow.

[0024] In this embodiment, image 211 is a face image. Generally, the location of the region of interest within the face image can be extracted using a face detection algorithm such as pattern recognition or machine learning. The biosignal 216 is then calculated using the pixel values ​​or time-series data of the pixel values ​​within the extracted region of interest.

[0025] The biological information calculation unit 206 calculates biological information related to blood vessels or blood flow from the biological signal 216. In this embodiment, blood pressure is used as an example of biological information.

[0026] The model selection unit 207 selects an appropriate model from among the blood pressure calculation models stored in the model storage unit 215 based on the exercise information 210 received by the input unit 201. In this disclosure, a model includes machine learning models, regression equations, mathematical formulas, or tables for calculating biological information. Features generated from the exercise information 210 can be used in the model. For example, the following can be used as features in the exercise information 210: "type of exercise, intensity, or load, the severity of the exercise or the burden on the body," "duration or load, amount, and number of repetitions of the exercise," and "exercise completion time or elapsed time from the end of the exercise until measurement by the measuring device 100." For example, a polynomial consisting of this exercise information 210 and coefficients multiplied by each may be created, or if a machine learning model using a decision tree is used, this exercise information 210 may be used as branching conditions.

[0027] Furthermore, instead of using the exercise information 210 as features directly, new features may be generated. For example, a feature may be created by combining multiple pieces of exercise information 210 using principal component analysis. Here, the definition of "model" also includes a correction formula for the feature values. For example, a correction formula can be created to correct the feature values ​​using the following: "the intensity of the exercise or the burden on the body, such as the type of exercise, intensity, or load," "the duration, load, amount, and number of repetitions of the exercise," and "the end time of the exercise or the elapsed time from the end of the exercise until it is measured by the measuring device 100." Information on which model the model selection unit 207 has selected is output as the judgment result 217.

[0028] The biological information calculation unit 206 receives the judgment result 217 and calculates the biological information using the model selected based on the judgment result 217.

[0029] The output unit 202 receives the report 212 created by the control unit 204 from the biological information and outputs it as needed. The report 212 is the biological information itself or the biological information processed or modified according to the user's needs. Specifically, it may include the biological information values ​​themselves, graphs of the changes in the biological information, analysis results, evaluations, or summaries of the biological information, or logs or trends of the user's exercise information. The procedures for such processing or modification are stored in advance in the storage unit 203.

[0030] The storage unit 203 is a recording medium capable of storing various data, programs, etc., and is composed of a hard disk, SSD (Solid State Drive), semiconductor memory, etc. The storage unit 203 includes a measurement information storage unit 213, a biometric information storage unit 214, and a model storage unit 215. Figure 2 illustrates the configuration of the storage unit 203 in the measurement device 100, but the storage unit 203 may also be composed of a server connected to the measurement device 100 via a network. For example, the network may be the Internet, a LAN (Local Area Network), etc.

[0031] The measurement information storage unit 213 stores pre-programmed information necessary for measuring biological information, information registered by the user, and other similar information. The measurement information storage unit 213 stores, for example, calculation formulas for converting images 211 to pulse waves, signal processing algorithms for reducing noise in pulse waves, measurement conditions such as the measurement time required for calculating biological information, biological signals 216, and analysis results of biological signals 216. For example, the user may be the administrator of the living organism 102 or the measuring device 100, or the manufacturer of the measuring device 100.

[0032] The biological information storage unit 214 stores information related to the biological organism 102, such as input data, measured data, and calculated results. The biological information storage unit 214 also stores necessary information, such as images 211, movement information 210, biological information calculated from images 211, reports 212, biological signals 216, and analysis results of biological signals 216.

[0033] The model storage unit 215 stores multiple reference information. In this embodiment, the multiple reference information represents multiple models used to calculate biological information. Specifically, the model storage unit 215 stores multiple models, pre-stored programs related to those models, and information registered by the user regarding those models. Multiple models are stored and can be selected by the model selection unit 207. Furthermore, the stored contents can be modified by the user.

[0034] Figure 3 is a schematic diagram showing an example of an input screen displayed to the user. In this embodiment, the user's movement information 210 is input by the user from the input unit 201.

[0035] The input exercise information 210 mainly consists of "the type, intensity, or load of the exercise, the severity of the exercise or the burden it places on the body," "the duration or load, amount, and number of repetitions of the exercise," and "the end time of the exercise (the time when the exercise is finished) or the elapsed time from the end of the exercise until it is measured by the measuring device 100." The elapsed time from the end of the exercise until it is measured by the measuring device 100 is the elapsed time from the end of the exercise until the imaging unit 101 takes an image of the body 102.

[0036] During exercise, blood vessels dilate to pump a large amount of blood throughout the body, but this state can persist after exercise, leading to continued changes in the vascular condition. The degree and duration of this change vary depending on the type of exercise; some reports indicate that after strenuous or prolonged exercise, the vascular condition may not return to normal for several hours. Furthermore, the manifestation of this change may differ depending on whether high-intensity exercise or exercise that puts a strain on the heart is performed regularly. Therefore, machine learning models created using vascular data measured during non-exercise periods, or vascular data from subjects who do not regularly exercise, may have reduced accuracy in calculating post-exercise biometric information where changes in vascular condition are present. Thus, it is desirable to use exercise information data 210 and select and use an appropriate model that takes into account the changes in vascular condition caused by exercise.

[0037] Therefore, it is desirable that the input exercise information 210 includes three pieces of information: "type of exercise, intensity or load, etc., the intensity of the exercise or the burden it places on the body," "duration or load, volume, and number of repetitions of the exercise," and "time of the exercise's end or the time from the end of the exercise to measurement by the measuring device 100." While the accuracy of calculating the biological information can be improved with just one of these pieces of information, the accuracy can be further improved if all three pieces of information are included. Note that "type of exercise, intensity or load" and "duration or load, volume, and number of repetitions of the exercise" may be combined into a single comprehensive index by multiplying these two together. Examples of comprehensive indexes include "METs" or "total load," which will be described later.

[0038] Figure 3 illustrates the input items for obtaining these three pieces of information. "METs of exercise performed today" is an item that assumes a single comprehensive index combining "type, intensity, or load of exercise" and "type of exercise performed today and each duration or load, volume, and number of repetitions." "Type of exercise performed today and each duration or load, volume, and number of repetitions" is an item that assumes the duration of exercise. And "Elapsed time from the end of exercise to measurement or exercise completion time" is an item that assumes the exercise completion time or the elapsed time from the end of exercise until measurement by the measuring device 100. Furthermore, since it is desirable to gather information such as whether the person habitually engages in high-intensity exercise or exercise that puts a strain on the heart in order to improve the accuracy of calculating biometric information, Figure 3 includes an input item called "exercise habits." Figure 3 is merely an example, and it is possible to improve the calculation accuracy even if information on all items is not necessarily obtained, but the calculation accuracy can be improved if this information is available.

[0039] Figure 3 shows "METs," which indicates the intensity of activity by comparing the energy consumed during activity to that consumed during rest (set as 1). METs are widely used as "MET·hours," which is calculated by multiplying the value of MET (Metabolic Equivalent), a unit indicating exercise intensity, by the exercise time (s).

[0040] "The type of exercise performed today" is not limited to running and weightlifting, as exemplified in Figure 3. For example, if used in a gym, the name of a training machine such as a treadmill would also be acceptable.

[0041] For "Each exercise duration or load, volume, and repetitions," enter the duration for running, or the number of repetitions for exercises where repetitions are important, such as weight training. As shown in Figure 3, it is desirable to enter the data for each type of exercise, but if you cannot enter it accurately, you can enter the duration of all exercises. However, since the load on the body differs for each type of exercise, it is desirable to enter the data for each type. Note that if you can enter an index that combines the intensity and volume of exercise using indicators such as METs or total load, you can substitute the exercise duration and volume fields, so these fields are not necessarily required.

[0042] The two items mentioned above are not limited to the examples in Figure 3, and should be appropriately selected depending on the type of exercise. For example, exercise intensity can be expressed using heart rate, or for strength training, an index such as RM (Repetition Maximum) can be used, or a comprehensive index that combines multiple items, such as total load, can be used. Furthermore, exercise intensity can be expressed as an absolute value, or it can be based on the physical capacity of the body, for example, subjective exercise intensity (Rate of Perceived Exertion).

[0043] The "time the exercise ended today or the elapsed time from the end of exercise to measurement" does not need to be entered, as the measuring device 100 has a clock function and can calculate the elapsed time from the end of exercise to measurement by the measuring device 100 using the exercise end time. In the diagram, the "elapsed time from the end of exercise to measurement" is entered in minutes, but it can also be entered in hours or seconds.

[0044] The "exercise habit" field was created to obtain information on how changes in blood flow or blood vessels after exercise manifest differently depending on whether the exercise is strenuous, whether it is a type of exercise that heavily utilizes cardiovascular function such as track and field, and whether the exercise is performed frequently. In Figure 3, the user is asked to input how many times a week they participate in club activities, but this is not the only way to express it; other notations are acceptable as long as the aforementioned information can be obtained.

[0045] While it is possible to improve the accuracy of biometric data calculations without entering information in the "exercise habits" section, entering this information will further enhance the accuracy of the calculations.

[0046] While Figure 3 only shows exercise as input, it is not limited to exercise; any activity that places a load on the body or heart is acceptable. For example, fields for activities such as eating, sleeping, climbing stairs, and talking may be included. Also, while Figure 3 shows all items entered, this is not the only option; the input screen allows users to leave fields blank if they cannot be filled in.

[0047] Figure 4 is a flowchart showing an example of the operation of the measuring device 100 according to this embodiment.

[0048] In step S401, the user inputs exercise information 210. The input unit 201 then receives the exercise information 210 input by the user.

[0049] In step S402, the imaging unit 101 acquires image 211. In this embodiment, for illustrative purposes, the motion information 210 is input first in step 401, but this is not the only way. The acquisition of image 211 does not necessarily have to be after the input unit 201 receives the motion information, as long as the accuracy of calculating the biological information can be improved using the motion information 210. For example, if a conversation such as "Is it okay to calculate the biological information using this image?" is held between the user and the measuring device 100 after acquiring image 211, the motion information 210 may be input during that conversation. Also, for example, if the user remembers that they forgot to input the motion information 210 after acquiring image 211, they may be able to input it again later. Furthermore, for example, if the user feels that the biological information calculated after simply inputting the motion information 210 is incorrect, there may be a function such as a recalculation mode or high-precision mode that allows the user to input more detailed motion information 210 and recalculate the biological information to further improve accuracy.

[0050] In step S403, the signal acquisition unit 205 acquires a biological signal 216 from the image 211 acquired by the imaging unit 101. For example, it calculates a pulse wave from the RGB pixel values ​​of the region of interest in the image 211 using a calculation formula that has been stored in the storage unit 203 beforehand. At this time, the biological signal 216 is time-series data such as a pulse wave. It is desirable to appropriately determine the means for acquiring the biological signal 216 according to the vital sign to be measured. For example, the pulse wave may be acquired as a time change of a value calculated by substituting the brightness value of the image 211 into a predetermined mathematical formula, or it may be acquired as a pulse wave converted to absorbance, or the biological signal may be extracted and acquired using an independent component analysis method, etc. For example, when calculating blood pressure, the hemoglobin concentration is correlated with the degree to which blood vessels absorb light, and a volume pulse wave can be obtained from a conversion formula using absorbance, and blood pressure can be calculated from the state of the volume pulse wave.

[0051] In step S404, the control unit 204 stores the biosignal 216 in the measurement information storage unit 213 and the biosignal storage unit 214. The data is stored with the advantages of being able to perform new analyses using the biosignal 216 again at a later date, recalculate using updated models, and use it as a log, but it is not necessarily required to store it. The server may collect exercise information and calculated biosignal information for multiple users from the measurement device 100. For example, the server is connected to the measurement device 100 via a network. The server may then update multiple models based on the collected exercise information and biosignal information. In that case, the control unit 204 obtains the updated multiple models from the server and stores the obtained multiple models in the model storage unit 215.

[0052] In step S405, the model selection unit 207 selects the most suitable model from those stored in the model storage unit 215 based on the exercise information 210 received in step S401. The criteria or formulas for the determination are pre-stored in the model storage unit 215. For example, if the METs are 7 or higher and the person was exercising up to 30 minutes before the measurement, a model that takes post-exercise hypotension into consideration may be selected.

[0053] In step S406, the biological information calculation unit 206 calculates biological information from the biological signal 216 using the model selected by the model selection unit 207. In other words, the biological information calculation unit 206 calculates biological information from the biological signal 216 using reference information selected from a plurality of reference information stored in the memory unit 203 based on the motor information 210.

[0054] In step S407, the control unit 204 creates a report 212, which contains information to be displayed to the user as needed, based on the biometric information, and outputs it to the output unit 202. The output unit 202 outputs the report 212 to the user as needed.

[0055] In step S408, the control unit 204 stores the biological information and motor information 210 in the storage unit 203 as needed.

[0056] Thus, the measuring device 100 according to this embodiment can perform measurements in a non-contact manner using only a camera, without using a cuff blood pressure monitor, a contact-type acceleration sensor, or the like. Therefore, the measuring device 100 according to this embodiment can reduce the burden on the user compared to using a cuff blood pressure monitor, a contact-type acceleration sensor, or the like.

[0057] Furthermore, the measuring device 100 according to this embodiment calculates biological information related to blood vessels and blood flow by reflecting information about the exercise performed by the user before the measurement. As a result, the measuring device 100 according to this embodiment can accurately calculate biological information without contact, taking into account the effects of exercise on blood vessels and blood flow.

[0058] (Second embodiment) The second embodiment will be described with reference to Figures 5 and 6. In the drawings, the same or equivalent elements are denoted by the same reference numerals, and redundant explanations are omitted.

[0059] Figure 5 is a block diagram showing an example of the configuration of the measuring device 500 according to this embodiment. The difference between the measuring device 500 illustrated in Figure 5 and the measuring device 100 illustrated in Figure 2 is that, instead of the biological information calculation unit 206, model selection unit 207, and model storage unit 215 in Figure 2, the measuring device 500 is equipped with a biological information calculation unit 506, a correction information selection unit 507, and a correction information storage unit 515 as shown in Figure 5.

[0060] In the first embodiment, the measuring device 100 selected a model based on the motion information 210 received by the input unit 201. However, in this embodiment, the measuring device 500 does not select a model, but instead calculates uncorrected biological information related to blood vessels or blood flow from the biological signal 216 using a predetermined model, and then calculates biological information by correcting the uncorrected biological information using correction information selected based on the motion information 210.

[0061] Multiple reference pieces of information are stored in the correction information storage unit 515. In this embodiment, the multiple reference pieces of information represent multiple correction pieces of information used to correct the pre-correction biological information. In other words, the correction information storage unit 515 stores multiple correction pieces of information.

[0062] The correction information selection unit 507 selects correction information from a plurality of correction information stored in the correction information storage unit 515 based on the motion information 210. In other words, the correction information selection unit 507 selects appropriate correction information from a plurality of correction information according to the motion information 210.

[0063] Correction information includes, for example, correction formulas, correction conditions, and correction tables. Rules, conditions, criteria, and formulas used to select correction information are pre-stored in the correction information storage unit 515.

[0064] The server may collect exercise information and calculated biometric information for multiple users from the measuring device 500. The server may then update multiple correction information based on the collected exercise information and biometric information. In this case, the control unit 204 obtains the updated correction information from the server and stores the obtained correction information in the correction information storage unit 515.

[0065] Alternatively, the server may collect exercise information and calculated biometric information linked to user identification information. The server may then update correction information for each user based on the collected exercise information and biometric information. The control unit 204 obtains the correction information from the server linked to user identification information and stores the obtained correction information in the correction information storage unit 515 linked to user identification information.

[0066] The biological information calculation unit 506 according to this embodiment calculates uncorrected biological information from the biological signal 216 and calculates biological information by correcting the uncorrected biological information using selected correction information.

[0067] For example, if the exercise information 210 indicates that METs are 7 or higher and that the person was exercising up to 30 minutes prior to the measurement, the correction information selection unit 507 selects correction information that shows a correction formula using these values ​​indicated by the exercise information 210. The biological information calculation unit 506 then calculates the biological information by correcting the pre-correction biological information using the correction formula shown by the selected correction information.

[0068] For example, if multiple correction formulas are created by pre-measuring the time elapsed of blood pressure after exercise for each user and stored in the correction information storage unit 515, the correction information selection unit 507 can select a correction formula from the multiple formulas based on the exercise information 210. Alternatively, instead of mathematical formulas, a correspondence table showing the correction values ​​to be used according to the content of the exercise information may be stored in the correction information storage unit 515. In this case, the correction information selection unit 507 refers to the correspondence table stored in the correction information storage unit 515 and selects the correction value to be used based on the exercise information 210. The biological information calculation unit 506 then corrects the pre-correction biological information using the selected correction value.

[0069] Figure 6 is a flowchart showing an example of the operation of the measuring device 500 according to this embodiment. Detailed explanations of steps similar to those illustrated in Figure 4 are omitted.

[0070] In step S605, the biological information calculation unit 506 calculates uncorrected biological information from the biological signal 216. Specifically, the biological information calculation unit 506 calculates uncorrected biological information using a predetermined model.

[0071] In step S606, the correction information selection unit 507 selects correction information corresponding to the motion information 210. Specifically, based on the motion information 210, the correction information selection unit 507 determines which correction information to use from the correction information stored in the correction information storage unit 515.

[0072] In step S607, the biological information calculation unit 506 calculates the biological information output by the output unit 202 by correcting the uncorrected biological information calculated in step S605 using the selected correction information. The subsequent processing is the same as the processing illustrated in Figure 4 and is therefore omitted.

[0073] Thus, the measuring device 500 according to this embodiment corrects biological information related to blood vessels and blood flow by reflecting information about the exercise performed by the user before the measurement. As a result, the measuring device 500 according to this embodiment can correct biological information by taking into account the effect of exercise on blood vessels and blood flow, and can calculate biological information accurately without contact.

[0074] The processes performed in the above embodiments are not limited to the processing modes exemplified in each embodiment. The functional blocks described above may be implemented using either logic circuits (hardware) formed on integrated circuits or software using a CPU. The processes performed in the above embodiments may be executed on multiple computers. For example, some of the processes performed in each functional block of the measuring device 100 may be performed on other computers, or all of the processes may be shared and executed on multiple computers. In other words, a measurement system composed of multiple computers may be configured to include an imaging unit 101, an input unit 201, an output unit 202, a storage unit 203, a signal acquisition unit 205, a bio-information calculation unit 506, and a model selection unit 207. Alternatively, a measurement system composed of multiple computers may be configured to include an imaging unit 101, an input unit 201, an output unit 202, a storage unit 503, a signal acquisition unit 205, a bio-information calculation unit 506, and a correction information selection unit 507.

[0075] This disclosure is not limited to the embodiments described above, and may be replaced with configurations substantially identical to those shown in the embodiments, configurations that produce the same effects, or configurations that can achieve the same objectives. This disclosure also includes embodiments obtained by appropriately combining the technical means disclosed in different embodiments. Furthermore, new technical features can be formed by combining the technical means disclosed in each embodiment. [Explanation of Symbols]

[0076] 100 Measurement device, 101 Imaging unit, 102 Biological device, 201 Input unit, 202 Output unit, 203 Storage unit, 204 Control unit, 205 Signal acquisition unit, 206 Biological information calculation unit, 207 Model selection unit, 210 Motion information, 211 Image, 212 Report, 213 Measurement information storage unit, 214 Biological information storage unit, 215 Model storage unit, 216 Biological signal, 217 Judgment result, 500 Measurement device, 503 Storage unit, 506 Biological information calculation unit, 507 Correction information selection unit, 515 Correction information storage unit

Claims

1. An imaging unit that captures an image of a living organism after the exercise the organism has been performing has finished, A signal acquisition unit that acquires a biological signal, which is a value related to the living organism calculated from the aforementioned image, Before the imaging unit acquires the image, the information regarding the motion is input to the input unit as motion information, A biological information calculation unit calculates biological information related to blood vessels or blood flow from the biological signal using reference information selected from multiple reference pieces based on the motion information, Equipped with, The motion information includes the elapsed time from the end of the motion until the imaging unit captures an image of the living body. The selected reference information is selected from the plurality of reference information based at least on the elapsed time. Measuring device.

2. The exercise information includes at least one selected from (i) the group consisting of type, intensity, and load, and (ii) the group consisting of duration, number of repetitions, and amount of the exercise. The measuring device according to claim 1.

3. The aforementioned multiple reference pieces represent multiple models, A storage unit that stores the aforementioned multiple models, A model selection unit that selects the model that is the selected reference information from the plurality of models, Furthermore, The biological information calculation unit calculates the biological information from the biological signal using the model. The measuring device according to claim 1 or 2.

4. The aforementioned model uses features generated from the motion information. The measuring device according to claim 3.

5. The aforementioned model includes a correction formula for the value of the feature. The measuring device according to claim 4.

6. The aforementioned multiple reference pieces indicate multiple correction pieces, A storage unit that stores the aforementioned multiple correction information, A correction information selection unit selects the correction information that is the selected reference information from the plurality of correction information, Furthermore, The biological information calculation unit calculates uncorrected biological information related to blood vessels or blood flow from the biological signal, and calculates the biological information by correcting the uncorrected biological information using the corrected information. The measuring device according to claim 1 or 2.

7. An imaging unit that captures an image of a living organism after the exercise the organism has performed has finished, A signal acquisition unit that acquires a biological signal, which is a value related to the living organism calculated from the aforementioned image, Before the imaging unit acquires the image, the information regarding the motion is input to the input unit as motion information, A biological information calculation unit calculates biological information related to blood vessels or blood flow from the biological signal using reference information selected from multiple reference pieces based on the motion information, Equipped with, The motion information includes the elapsed time from the end of the motion until the imaging unit captures an image of the living body. The selected reference information is selected from the plurality of reference information based at least on the elapsed time. Measurement system.

8. A step of imaging the living body after the exercise it has been performing has finished and acquiring an image of the living body, A step of acquiring a biological signal, which is a value related to the living organism calculated from the aforementioned image, The process of inputting information about the movement as movement information before the aforementioned image is acquired, A step of calculating biological information related to blood vessels or blood flow from the biological signal using reference information selected from multiple reference pieces based on the motion information, Includes, The motion information includes the elapsed time from the end of the motion until the living body is imaged. The selected reference information is selected from the plurality of reference information based at least on the elapsed time. Measurement method.

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