JP7615511B2 - Biometric sensor and biological information measuring system - Google Patents

Description

The present invention relates to a biosensor and a bioinformation measuring system.

A pulse wave signal is a waveform that represents the change in blood vessel volume that occurs when the heart pumps blood through the blood vessels, and a sensor that detects this change in volume is called a pulse wave sensor. Photoplethysmography sensors that measure pulse wave signals have been put to practical use as pulse wave sensors. Patent Document 1 describes a method of estimating blood pressure from a user's pulse wave signal measured using a pulse wave sensor, and correcting the estimated blood pressure based on the difference between the height of the heart and the height of the measurement position of the pulse wave signal.

JP 2017-47016 A

However, the correlation between pulse wave signals and blood pressure has not been clarified in general, and there is also a large degree of individual variation, so it is difficult to estimate blood pressure with sufficient accuracy simply by correcting the estimated blood pressure value based on the difference between the height of the heart and the height of the measurement position of the pulse wave signal.

Therefore, the objective of the present invention is to estimate biometric information from a user's pulse wave signal with sufficient accuracy.

In order to solve the above-mentioned problems, the biosensor of the present invention comprises a sensor body configured to be worn on a user's finger or wrist, a pulse wave sensor that measures the user's pulse wave signal, and a signal processing device that calculates pulse wave features from the pulse wave signal measured by the pulse wave sensor at at least two measurement positions having different heights from the user's heart, and determines the correlation between the user's blood pressure and the pulse wave features from the heights of the at least two measurement positions and the changes in the pulse wave features at the at least two measurement positions.

With the biosensor of the present invention, the correlation between the user's blood pressure and pulse wave features is calculated for each user from the height of at least two measurement positions that are at different heights from the user's heart and the changes in the pulse wave features at at least two measurement positions, thereby making it possible to estimate biometric information from the user's pulse wave signal with sufficient accuracy.

1 is an explanatory diagram showing a configuration of a biological information measuring system according to an embodiment of the present invention. 1 is an explanatory diagram showing a configuration of a biological information measuring system according to an embodiment of the present invention. FIG. 2 is an explanatory diagram of pulse wave feature amounts according to an embodiment of the present invention. 1 is a graph showing the correlation between pulse wave feature amounts and blood pressure according to an embodiment of the present invention. 1 is a graph showing the correlation between pulse wave feature amounts and blood pressure according to an embodiment of the present invention. 1 is a graph showing the correlation between pulse wave feature amounts and blood pressure according to an embodiment of the present invention. 1 is a graph showing the correlation between pulse wave feature amounts and blood pressure according to an embodiment of the present invention. 1 is a graph showing the correlation between pulse wave feature amounts and blood pressure according to an embodiment of the present invention. 1 is a graph showing the correlation between pulse wave feature amounts and blood pressure according to an embodiment of the present invention. 1 is a graph showing the correlation between pulse wave feature amounts and blood pressure according to an embodiment of the present invention. 1 is a graph showing the correlation between pulse wave feature amounts and blood pressure according to an embodiment of the present invention. FIG. 2 is an explanatory diagram of a measurement posture in a sitting position according to an embodiment of the present invention. FIG. 2 is an explanatory diagram of a measurement posture in a sitting position according to an embodiment of the present invention. FIG. 2 is an explanatory diagram of a measurement posture in a sitting position according to an embodiment of the present invention. 10A and 10B are explanatory diagrams of a method for estimating a height difference between at least two measurement positions from an image of a user captured by an image capturing device according to an embodiment of the present invention. 10A and 10B are explanatory diagrams of a method for estimating a height difference between at least two measurement positions from an image of a user captured by an image capturing device according to an embodiment of the present invention. 10A and 10B are explanatory diagrams of a method for estimating a height difference between at least two measurement positions from an image of a user captured by an image capturing device according to an embodiment of the present invention. 10A and 10B are explanatory diagrams of a method for estimating a height difference between at least two measurement positions from an image of a user captured by an image capturing device according to an embodiment of the present invention. 1 is a graph showing changes over time in a feature amount according to an embodiment of the present invention. FIG. 2 is an explanatory diagram of a measurement posture in a supine position according to an embodiment of the present invention. FIG. 2 is an explanatory diagram of a measurement posture in a supine position according to an embodiment of the present invention.

Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Here, the same reference numerals indicate the same components, and duplicate explanations will be omitted.

1 is an explanatory diagram showing the configuration of a bioinformation measuring system 100 according to an embodiment of the present invention. The bioinformation measuring system 100 includes a biosensor 200 that measures a user's bioinformation, and a portable control unit 300 that is configured to be able to communicate with the biosensor 100.

The biosensor 200 includes an annular (ring-shaped or wristband-shaped) sensor body 201 that can be worn on the user's finger or wrist, and a pulse wave sensor 202 that measures the user's pulse wave signal. The sensor body 201 has a hollow cylindrical shape, and the pulse wave sensor 202 is attached to the inner part of the sensor body 201 (i.e., the part facing the body surface such as the user's finger or wrist).

The pulse wave sensor 202 is, for example, a photoelectric pulse wave sensor. For example, a reflective type photoelectric pulse wave sensor can be used. A reflective photoelectric pulse wave sensor irradiates the user's body surface with infrared light, red light, or light with a green wavelength around 550 nm, and measures the light reflected from the user's body surface using a photodiode or phototransistor. Oxygenated hemoglobin is present in the blood of the arteries and has the property of absorbing incident light, so a pulse wave signal can be measured by sensing the blood flow rate (change in blood vessel volume) that changes with the pulsation of the heart in a time series.

The portable control unit 300 includes an imaging device 301 that captures an image of the user, and a display device 302 that displays an image captured by the imaging device 301. The portable control unit 300 is, for example, a multi-function mobile phone terminal called a smartphone. The imaging device 301 is, for example, a digital camera. The display device 302 is, for example, a liquid crystal display.

2 is an explanatory diagram showing the configuration of a bioinformation measuring system 100 according to an embodiment of the present invention. In addition to the sensor body 201 and pulse wave sensor 202 described above, the biosensor 200 includes a signal processing device 203, a communication device 204, and an acceleration sensor 205.

The signal processing device 203 performs processing to estimate various biometric information of the user (e.g., blood pressure, blood glucose level, heart rate, oxygen saturation concentration, vascular resistance, blood flow rate, arteriosclerosis level, autonomic nerve function, etc.) from the pulse wave signal measured by the pulse wave sensor 202. For example, the signal processing device 203 can calculate a pulse wave feature from the pulse wave signal and estimate blood pressure based on the pulse wave feature. For example, JP 2016-16295 A is known as a document that refers to a process of calculating a pulse wave feature from a pulse wave signal and estimating blood pressure based on the pulse wave feature. Also, for example, the signal processing device 203 can perform processing to estimate a blood glucose level from a pulse wave signal. For example, Japanese Patent No. 6544751 is known as a document that refers to a process of estimating a blood glucose level from a pulse wave signal. Also, for example, the signal processing device 203 can estimate a heart rate (pulse rate) by determining a period of fluctuation from a pulse wave signal. Also, for example, the signal processing device 203 can calculate an index value of autonomic nerve function by power spectrum analysis of the frequency components of the periodic fluctuation of the heartbeat. Also, for example, the signal processing device 203 can estimate the oxygen saturation concentration in arterial blood by obtaining the pulsation (amount of change) from a pulse wave signal obtained by a photoelectric pulse wave sensor using two wavelengths of light, infrared and red. Also, for example, the signal processing device 203 can estimate the user's activity level and sleep state (sleep/wake distinction and quality of sleep) from the detection value of the acceleration sensor 205. The biosensor 200 may be equipped with a temperature sensor, in which case the signal processing device 203 can obtain the user's body surface temperature from the detection value of the temperature sensor.

In this way, various types of biometric information can be estimated from the pulse wave signal. In the following explanation, we will use the process of estimating blood pressure from the pulse wave signal as an example.

The signal processing device 203 performs a process to correct the estimated blood pressure value taking into account the effects of hydrostatic pressure. In general, if the blood pressure measurement position is higher than the heart, the measured blood pressure value will be lower by the hydrostatic pressure difference in the blood vessels due to gravity. Conversely, if the blood pressure measurement position is lower than the heart, the measured blood pressure value will be higher by the hydrostatic pressure difference in the blood vessels. It is known that if the blood pressure measurement position is raised or lowered by 1 cm from the heart level, the blood pressure (measured value) will change by approximately 0.7 mmHg.

The signal processing device 203, for example, calculates pulse wave feature values from the pulse wave signal measured by the pulse wave sensor 202 at each of at least two measurement positions that are different heights from the user's heart, and performs processing to estimate blood pressure from the pulse wave feature values. The posture in which the user measures the pulse wave signal at at least two measurement positions that are different heights from the user's heart may be a seated posture or a supine posture.

The posture in which the user measures the pulse wave signal at at least two measurement positions at different heights from the user's heart can be, for example, any two or more of the following postures: the user is seated and holds the biosensor 200 horizontally with his/her hands at chest height, the user is seated and holds the biosensor 200 horizontally with his/her hands at face height, and the user is seated and holds the biosensor 200 horizontally with his/her hands at stomach height.

The posture in which the user measures the pulse wave signal at at least two measurement positions at different heights from the user's heart may be, for example, a posture in which the user lies supine on a flat surface and holds the biosensor 200 horizontally with the hand at chest height, and a posture in which the user lies supine on a flat surface and holds the biosensor 200 horizontally with the hand at the height of the flat surface.

The signal processing device 203 determines the correlation between the user's blood pressure and the pulse wave feature amount from the height of at least two measurement positions that are different in height from the user's heart and the change in the pulse wave feature amount at at least two measurement positions. The process of determining this correlation will be described in detail later.

The signal processing device 203 is, for example, an electronic circuit having a processor, a memory, and an input/output interface.

The communication device 204 is a wireless communication circuit for transmitting and receiving various data (e.g., various measurement data or various control data (e.g., commands)) through wireless communication between the biosensor 200 and the portable control unit 300. As the wireless communication between the biosensor 200 and the portable control unit 300, for example, short-range wireless communication such as Bluetooth (registered trademark) can be used.

The acceleration sensor 205 measures the acceleration of the biosensor 200 when the user changes posture to measure the pulse wave signal at at least two measurement positions. The reason for measuring the acceleration of the biosensor 200 will be described later.

In addition to the above-mentioned imaging device 301 and display device 302, the portable control unit 300 is equipped with a communication device 303, a control device 304, and an inclination sensor 305.

The communication device 303 is a wireless communication circuit for transmitting and receiving various data (e.g., various measurement data or various control data (e.g., commands)) through wireless communication between the portable control unit 300 and the biosensor 200.

The control device 304 performs various control processes for controlling the imaging device 301, the display device 302, the communication device 303, and the tilt sensor 305. The control device 304 is, for example, an electronic circuit having a processor, a memory, and an input/output interface. Details of the control processes performed by the control device 304 will be described later.

The tilt sensor 305 detects the tilt of the portable control unit 300 relative to a predetermined reference line (e.g., a vertical line) when the user changes posture to measure the pulse wave signal at at least two measurement positions. The reason for detecting the tilt of the portable control unit 300 relative to the predetermined reference line will be described later.

Next, the pulse wave feature amount will be described with reference to FIG. 3. Reference numeral 401 denotes a velocity pulse wave signal obtained by first-order differentiation of the pulse wave signal. Reference numeral 402 denotes an acceleration pulse wave signal obtained by second-order differentiation of the pulse wave signal. The peaks (maximum peak and minimum peak) of the acceleration pulse wave signal 402 are called a-wave, b-wave, c-wave, d-wave, and e-wave, respectively, as shown in the figure. Reference numeral 403 denotes a volume pulse wave signal. As the pulse wave feature amount, for example, the peak time difference of each peak (a-wave, b-wave, c-wave, d-wave, and e-wave), the height of each peak, the ratio of each peak time difference to the pulse interval, the peak half-width, the ratio of the positive side area to the negative side area of the a-e wave portion of the acceleration pulse wave signal 402, the degree of agreement between the measured pulse wave waveform and the template of the pulse wave waveform, and the like can be used. In addition, as the pulse wave feature amount, not only the pulse wave feature amount for each beat, but also the average value and standard deviation of the pulse wave feature amount for several beats to several tens of beats can be used.

Next, referring to FIG. 4 to FIG. 7, a process for determining the correlation between the user's blood pressure and the pulse wave feature amount from the height of at least two measurement positions that are different from the user's heart (the relationship indicating which of the two measurement positions is relatively high and which is relatively low) and the change in the pulse wave feature amount at at least two measurement positions will be described. For example, the correlation between the blood pressure and the pulse wave feature amount can be determined from the change in the pulse wave feature amount when the blood pressure changes from a high state to a low state (when the measurement position changes from a low state to a high state). Also, for example, the correlation between the blood pressure and the pulse wave feature amount can be determined from the change in the pulse wave feature amount when the blood pressure changes from a low state to a high state (when the measurement position changes from a high state to a low state). In the process for determining the correlation between the user's blood pressure and the pulse wave feature amount, it is sufficient to know the tendency of the change in the pulse wave feature amount relative to the change in blood pressure. In the process of determining the trend of change in pulse wave feature quantity in response to change in blood pressure, the difference in height between the two measurement positions is determined and the difference in blood pressure corresponding to this difference in height is taken into account, thereby making it possible to accurately estimate the trend of change in pulse wave feature quantity in response to change in blood pressure.

For ease of explanation, two pulse wave feature quantities are selected from the various pulse wave feature quantities described above, and one of the two selected pulse wave feature quantities is referred to as pulse wave feature quantity A and the other is referred to as pulse wave feature quantity B.

The default relationship equation (1) showing the correlation between the user's blood pressure and pulse wave features is shown below.

Blood pressure = a x A + b x B + c … (1)

Here, A indicates pulse wave feature A, and B indicates pulse wave feature B. Note that the constants a, b, and c may differ for each user.

Figure 4 is a graph showing the correlation between blood pressure and pulse wave feature A. Figure 5 is a graph showing the correlation between blood pressure and pulse wave feature B. In Figures 4 and 5, reference numeral 501 indicates a graph of relational equation (2), and reference numeral 502 indicates a graph of relational equation (3).

Blood pressure = a1 x A + b1 x B + c1 … (2)
Blood pressure = a2 x A + b2 x B + c2 … (3)

Here, the constants a1, b1, and c1 in relational equation (2) are, for example, specific to a particular user, and the constants a2, b2, and c2 in relational equation (3) are, for example, specific to another particular user.

6 each indicate a measurement point of pulse wave feature A for a user's blood pressure (a user for whom the relationship between blood pressure and pulse wave feature A has not been determined), determined from the measurement results of the pulse wave signal by the pulse wave sensor 202. By performing regression analysis on these measurement points 5031 and 5032, a graph 503 is obtained that shows the correlation between the user's blood pressure and pulse wave feature A.

7 each indicate the measurement points of pulse wave feature quantity B for a user's blood pressure (a user for whom the relationship between blood pressure and pulse wave feature quantity B has not been determined), as determined from the measurement results of the pulse wave signal by pulse wave sensor 202. By performing regression analysis on these measurement points 5033 and 5034, graph 503 is obtained that shows the correlation between the user's blood pressure and pulse wave feature quantity B.

Relational equation (4) representing graph 503 is obtained by calibrating the constants a, b, and c of the default relational equation (1) based on the correlation (correlation between the user's blood pressure and the pulse wave feature) obtained from the difference in the user's blood pressure corresponding to the difference in height between at least two measurement positions that are at different heights from the user's heart and the change in the pulse wave feature at at least two measurement positions, and can be written as follows:

Blood pressure = a3 x A + b3 x B + c3 … (4)

If a formula or table is prepared in advance so that c3 can be calculated from the values of a3 and b3, c3 can be automatically calculated from the values of a3 and b3. To improve the accuracy of c3, a blood pressure value measured at the same time using a conventional oscillometric method or the like may be input. In addition, when the amount of blood pressure fluctuation is more important than the accuracy of the absolute blood pressure value, the blood pressure may be displayed as a relative value.

The user's blood pressure, whose correlation between blood pressure and pulse wave features A and B is given by graph 503, can be determined from the pulse wave features A and B using relational equation (4).

In this way, the default relational expression (1) is calibrated based on the correlation (correlation between the user's blood pressure and the pulse wave feature) calculated from the difference in the user's blood pressure corresponding to the difference in height between at least two measurement positions that are different in height from the user's heart and the amount of change in the pulse wave feature at least two measurement positions, and the user's blood pressure can be estimated from the pulse wave feature based on the calibrated relational expression (4). By optimizing the correlation between the difference in blood pressure and the amount of change in the pulse wave feature for each user, the blood pressure can be estimated from the pulse wave feature with high accuracy.

In the above example, the constants a, b, and c of one default relational equation (1) are optimized for each user. However, if it is not possible to determine a single default relational equation, multiple default relational equations may be prepared in advance, and from the multiple default relational equations, a relational equation that most closely approximates the correlation (the correlation between the difference in blood pressure and the change in the pulse wave feature) obtained from the measurement results of the pulse wave signal by the pulse wave sensor 202 may be selected, and the user's blood pressure may be estimated from the pulse wave feature based on the selected relational equation.

The following equations (5) and (6) are given as examples of multiple default equations.

Blood pressure = a × A + c1 … (5)
Blood pressure = b × B + c2 … (6)

Here, the constants a, b, c1, and c2 in the default relational expressions (5) and (6) may differ for each user. For a user for whom there is a correlation between pulse wave feature quantity A and blood pressure, but no correlation between pulse wave feature quantity B and blood pressure, the default relational expression (5) can be applied. On the other hand, for a user for whom there is a correlation between pulse wave feature quantity B and blood pressure, but no correlation between pulse wave feature quantity A and blood pressure, the default relational expression (6) can be applied.

Figure 8 is a graph showing the correlation between blood pressure and pulse wave feature A. In Figure 8, reference numeral 504 indicates a graph of the default relational equation (5). Figure 9 is a graph showing the correlation between blood pressure and pulse wave feature B. In Figure 9, reference numeral 505 indicates a graph of the default relational equation (6).

Reference numerals 5041 and 5042 in Fig. 10 respectively indicate the measurement points of pulse wave feature quantity A relative to the blood pressure of a user (a user for whom the relationship between blood pressure and pulse wave feature quantity A has not been determined) determined from the measurement results of the pulse wave signal by the pulse wave sensor 202. It can be seen that in the case of this user, even if the blood pressure changes, there is almost no change in pulse wave feature quantity A (weak correlation).

Reference numerals 5051 and 5052 in FIG. 11 respectively indicate the measurement points of pulse wave feature quantity B for the blood pressure of a user (a user for whom the relationship between blood pressure and pulse wave feature quantity B has not been determined) determined from the measurement results of the pulse wave signal by the pulse wave sensor 202. For this user, pulse wave feature quantity B changes with changes in blood pressure, and the correlation is approximate to the default relational equation (6). Therefore, from the default relational equations (5) and (6), the default relational equation (6) can be selected, and the user's blood pressure can be estimated from the pulse wave feature quantity based on the selected relational equation (6).

In the above description, an example was shown in which the relational expression (6) that most closely approximates the correlation (between the user's blood pressure and the pulse wave feature) obtained from the measurement results of the pulse wave signal by the pulse wave sensor 202 was selected from among the multiple default relational expressions (5) and (6), and the user's blood pressure was estimated from the pulse wave feature based on the selected relational expression (6). However, this selected relational expression (6) may be calibrated based on the difference in the user's blood pressure corresponding to the difference in height between at least two measurement positions and the change in the pulse wave feature at at least two measurement positions, and the user's blood pressure may be estimated from the pulse wave feature based on the calibrated relational expression (6). This calibration process is similar to the process of calibrating the default relational expression (1) to obtain the relational expression (4). In addition, the relational expression (6) that most closely approximates the correlation (between the user's blood pressure and the pulse wave feature) obtained from the measurement results of the pulse wave signal by the pulse wave sensor 202 may be selected from among the multiple default relational expressions (5) and (6), taking into account the difference in the user's blood pressure corresponding to the difference in height between at least two measurement positions.

For convenience of explanation, an example has been described in which the number of pulse wave features used to determine the correlation between the difference in blood pressure and the change in pulse wave features is two, but the number of pulse wave features used to determine the correlation between the difference in blood pressure and the change in pulse wave features is not limited to two, and may be, for example, one or three or more. In addition, the default relational equation may be stored in advance in the memory of the control device 304, or the portable control unit 300 may read the default relational equation stored in a server on the cloud via the network, and the signal processing device 203 may use the read default relational equation.

Next, referring to Figures 12 to 14, a seated posture in which a user measures a pulse wave signal at at least two measurement positions at different heights from the user's heart will be described. In Figures 12 to 14, reference numeral 600 indicates a user, reference numeral 601 indicates the user's face (more specifically, the forehead or eyes), reference numeral 602 indicates the user's chest (more specifically, the nipples), and reference numeral 603 indicates the user's abdomen (more specifically, the navel).

Figure 12 shows a posture in which a user 600 is sitting and holds the biosensor 200 horizontally with his/her hand at the height of the face 601. The measurement position in this posture is the position of the face 601 of the user 600 who is sitting.

Figure 13 shows a posture in which a user 600 is sitting and holds the biosensor 200 horizontally with his/her hands at chest 602 height. The measurement position in this posture is the position of the chest 602 of the user 600 who is sitting.

Figure 14 shows a posture in which a user 600 is sitting and holds the biosensor 200 horizontally with his/her hands at the height of the abdomen 603. The measurement position in this posture is the position of the abdomen 603 of the user 600 who is sitting.

12 to 14 as the sitting posture in which the user 600 measures the pulse wave signal at at least two measurement positions at different heights from the user's heart, a posture with high reproducibility can be realized, and there is no need to use strength in the hands to maintain such a posture, which can suppress hand tremors. In these postures, the palm of the user 600 may be placed on the face 601, chest 602, or abdomen 603, and the finger of the user 600 (e.g., index finger) may be placed on the face 601, chest 602, or abdomen 603.

In addition, if the height from the heart is the same in a posture where the user 600's hand is held horizontally (a posture where the hand faces horizontally) and a posture where the user 600's hand is held vertically (a posture where the hand faces vertically), the blood pressure will be the same, but there may be a difference in the pulse wave signal. In the posture where the user 600 holds the biosensor 200 at the height of the face 601 while sitting, the posture where the user 600 holds the biosensor 200 at the height of the chest 602 while sitting, and the posture where the user 600 holds the biosensor 200 at the height of the abdomen 603 while sitting, the hand of the user 600 faces horizontally in all postures, which is effective in suppressing the variation in the measured value of the pulse wave signal. The signal processing device 203 may determine whether the hand on which the biosensor 200 is attached is held horizontally at each measurement position (i.e., whether the hand is not moving in a direction that deviates from the horizontal direction) from the acceleration of the biosensor 200 measured by the acceleration sensor 205.

Methods for estimating the height difference between at least two measurement positions at different heights from the heart of a user 600 in a seated position include (1) a method of estimating from information indicating the user's physical characteristics, (2) a method of estimating from the acceleration of the biosensor 200 measured by the acceleration sensor 205, and (3) a method of estimating from an image of the user 600 captured by the imaging device 301. These methods are described below.

First, we describe a method for estimating the height difference between at least two measurement positions at different heights from the heart of a user 600 in a seated position from information indicating the physical characteristics of the user 600.

The height of the user 600's forehead, nipples, and navel can be statistically estimated from the height of the user 600. Information indicating the physical characteristics of the user 600 (information such as height) may be stored in advance in a memory in the signal processing device 203, or information indicating the physical characteristics of the user 600 (information such as height) stored in a server on the cloud may be read from the portable control unit 300 via a network, and the read information may be transferred to the signal processing device 203 through wireless communication between the biosensor 200 and the portable control unit 300. When information indicating the physical characteristics of the user 600 (information such as height) is not available, the height of the user 600's forehead, nipples, and navel may be estimated from the statistical average values of the heights of the forehead, nipples, and navel of humans.

The human body dimension database stores information such as a person's height, entocanthion height, nipple height, bust height, and navel height. For example, if the nipple height value is used as the heart height, the difference in height between the heart and navel can be estimated from the difference between the nipple height and navel height. Also, since the difference between the forehead and entocanthion height is around 5 cm, the forehead height can be estimated by adding the difference between the forehead and entocanthion height to the difference between the height between the entocanthion height and the heart.

The signal processing device 203 can calculate a statistical average value of the height difference between at least two measurement positions at different heights from the heart of the user 600 in a seated position from information indicating the physical characteristics of the user 600.

Next, we will explain a method for estimating the height difference between at least two measurement positions at different heights from the heart of a user 600 in a seated position, based on the acceleration of the biosensor 200 measured by the acceleration sensor 205.

The biosensor 200 measures a pulse wave signal in a stationary state at one measurement position (e.g., the measurement position shown in FIG. 12), and then measures a pulse wave signal in a stationary state at another measurement position (e.g., the measurement position shown in FIG. 13). When the user 600 changes his/her posture to measure the pulse wave signal at the two measurement positions, the signal processing device 203 can calculate the difference in height between the two measurement positions (the vertical movement distance of the biosensor 200) by performing a second-order integration of the acceleration of the biosensor 200 measured by the acceleration sensor 205, taking into account the effect of gravitational acceleration. In this way, the signal processing device 203 can calculate the difference in height between at least two measurement positions from the acceleration of the biosensor 200 measured by the acceleration sensor 205 when the user 600 changes his/her posture to measure the pulse wave signal at at least two measurement positions.

Next, we will explain a first method of estimating the height difference between at least two measurement positions from an image of the user 600 captured by the imaging device 301.

As shown in FIG. 15, the display device 302 presents to the user 600, who is holding the portable control unit 300 in one hand (e.g., the right hand), instructions to move the other hand (e.g., the left hand) on which the biosensor 200 is attached to at least two measurement positions, and instructions to photograph the other hand (e.g., the left hand) on which the biosensor 200 is attached and the user's 600 face 601 with the imaging device 301 when the other hand (e.g., the left hand) on which the biosensor 200 is attached is located at each measurement position, and displays the images captured by the imaging device 301.

For example, the control device 304 distinguishes and recognizes the face 601 of the user 600 from the other hand (e.g., the left hand) on which the biosensor 200 is attached from the image captured by the imaging device 301. At each measurement position, the control device 304 estimates the height difference between at least two measurement positions based on the relative positional relationship between the other hand (e.g., the left hand) on which the biosensor 200 is attached and the face 601 from the image captured by the imaging device 301. The control device 304 transmits the estimated result of the height difference between the at least two measurement positions to the signal processing device 203 via wireless communication.

In this way, by estimating the size of the user's 600 face (total head height, head width, etc.) from information indicating the physical characteristics of the user 600, the accuracy of estimating the height difference between two measurement positions from the size of the face in the image can be improved.

Also, for example, the control device 304 distinguishes and recognizes the face 601 of the user 600 from the biometric sensor 200 from the image captured by the imaging device 301. The control device 304 estimates the height difference between at least two measurement positions based on the relative positional relationship between the biometric sensor 200 and the face 601 from the image captured by the imaging device 301 at each measurement position. The control device 304 transmits the estimated result of the height difference between the at least two measurement positions to the signal processing device 203 via wireless communication.

16, the display device 302 may graphically display a display target range 700 indicating the target position and display target size of the face 601 so as to be superimposed on the face 601 displayed on the display device 302. The display target range 700 may be, for example, a shape such as an ellipse or a rectangle. By adjusting the positional relationship between the face 601 and the display device 301 so that the display position and display size of the face 601 match the target position and display target size of the face 601, respectively, from the image captured by the imaging device 301, the height difference between at least two measurement positions can be estimated based on (1) the relative positional relationship between the other hand (e.g., the left hand) on which the biosensor 200 is attached and the face 601, or (2) the relative positional relationship between the biosensor 200 and the face 601.

Next, we will explain a second method of estimating the height difference between at least two measurement positions from an image of the user 600 captured by the imaging device 301.

As shown in FIG. 17, the display device 302 presents to the user 600, who is holding the portable control unit 300 with the hand (e.g., the right hand) on which the biosensor 200 is attached, instructions to move the hand (e.g., the right hand) on which the biosensor 200 is attached to at least two measurement positions, and instructions to photograph the face 601 of the user 600 with the imaging device 301 when the hand (e.g., the right hand) on which the biosensor 200 is attached is positioned at each measurement position, and displays the images captured by the imaging device 301.

The control device 304 estimates the height difference between at least two measurement positions based on the positional relationship between the face 601 of the user 600 and the portable control unit 300 and the inclination of the portable control unit 300 relative to a predetermined reference line (e.g., a vertical line) from the images captured by the image capture device 301 at each measurement position. The tilt sensor 305 detects the inclination of the portable control unit 300 relative to a predetermined reference line (e.g., a vertical line) when the user changes his/her posture to measure the pulse wave signal at at least two measurement positions.

18, the display device 302 may graphically display a display target range 700 indicating the target position and display target size of the face 601 so as to be superimposed on the face 601 displayed on the display device 302. By adjusting the positional relationship between the face 601 and the display device 301 so that the display position and display size of the face 601 match the target position and display target size of the face 601, respectively, the height difference between at least two measurement positions can be estimated from the image captured by the imaging device 301 based on the positional relationship between the face 601 of the user 600 and the portable control unit 300 and the inclination of the portable control unit 300 with respect to a predetermined reference line (e.g., a vertical line). The control device 304 transmits the estimated result of the height difference between at least two measurement positions to the signal processing device 203 via wireless communication.

In this way, by estimating the size of the user's 600 face (total head height, head width, etc.) from information indicating the physical characteristics of the user 600, the accuracy of estimating the height difference between two measurement positions from the size of the face in the image can be improved.

The control device 304 may determine whether the user 600 is holding the portable control unit 300 with the hand on which the biosensor 200 is attached. This can prevent the user 600 from measuring the pulse wave signal while holding the portable control unit 300 with a hand (e.g., left hand) other than the hand on which the biosensor 200 is attached (e.g., right hand). The control device 304 can determine whether the user 600 is holding the portable control unit 300 with the hand on which the biosensor 200 is attached, based on whether short-range wireless communication such as NFC between the biosensor 200 and the portable control unit 300 is possible, or whether the reception strength of the wireless signal from the biosensor 200 is equal to or greater than a threshold.

Next, the stable state of the pulse wave signal will be described. When user 600 changes his/her posture to measure the pulse wave signal at at least two measurement positions at different heights from user 600's heart, the pulse wave signal may fluctuate unstably for a certain period of time (approximately 10 to 20 seconds) after the posture change. For this reason, it is desirable to determine whether the pulse wave signal is stable for a certain period of time after user 600's posture changes, and to start deriving the correlation between blood pressure and pulse wave features once it is determined that the pulse wave signal is in a stable state.

There are two methods for determining whether a pulse wave signal is stable: (1) determining the stable state based on the time elapsed since the user 600's posture changed, and (2) determining the stable state based on whether the fluctuation value of the pulse wave signal has converged below a threshold. These methods are described below.

First, a method for determining a stable state based on the time elapsed since the user 600 changes posture will be described. In this method, the pulse wave signal measured during the initial period (e.g., initial 0-15 seconds, preferably initial 0-20 seconds) of the pulse wave signal after the user 600 assumes each posture is not stable, and it is determined that the pulse wave signal is in a stable state after the initial period has elapsed. The time at which each posture is assumed may be, for example, the time at which the user 600 performs an operation to start measurement, or a time automatically determined from an image captured by the portable control unit 300.

Figure 19 plots the changes in pulse wave features extracted from the pulse waveform measured when the hand wearing the biosensor 200 is held at navel height for 30 seconds, then raised to chest height and held there for 30 seconds, and then returned to navel height and held there for 30 seconds. The average value over 10 seconds was used as this pulse wave feature. The dotted line graph shows the average value (0-10 sec average) over the 10 seconds immediately after the user 600's posture changes. The solid line graph shows the average value (15-25 sec average) over the 10 seconds 15 seconds after the user 600's posture changes.

When blood pressure is measured at navel height, the measured blood pressure value is high, and when blood pressure is measured at chest height, the measured blood pressure value is low. The 15-25 sec average is strongly correlated with changes in blood pressure, whereas the 0-10 sec average is weakly correlated with changes in blood pressure. It can be seen that the pulse wave feature value is not stable immediately after a change in posture. This is due to a human physiological response, and it is impossible to suppress the temporary instability of the pulse wave signal immediately after a change in posture. Since the time it takes for the pulse wave signal to stabilize varies from person to person, the pulse wave signal is not stable in the initial period (0-15 seconds, or 0-20 seconds) immediately after a change in posture, and it is desirable to determine that the pulse wave signal is in a stable state after the initial period has elapsed.

Next, we will explain a method for determining whether the stable state is reached based on whether the fluctuation value of the pulse wave signal has converged below a threshold. In this method, the pulse wave signal is determined to be in a stable state when any of the rate of change, speed of change, or range of fluctuation (variation) of the pulse wave feature of the pulse wave signal has converged below a threshold.

The signal processing device 203 determines whether the pulse wave signal is stable after the posture of the user 600 changes, according to the above-mentioned determination method (1) or (2). After determining that the pulse wave signal is stable, the signal processing device 203 obtains a correlation between blood pressure and pulse wave feature amounts.

Next, with reference to Figures 20 and 21, we will explain the supine position in which user 600 measures a pulse wave signal at two measurement positions at different heights from user 600's heart.

Figure 20 shows a user 600 lying supine on a flat surface 800, holding the biosensor 200 horizontally with his/her hand at the height of the flat surface 800. The measurement position in this position is the position of the back of the user 600 lying supine (the position of the flat surface 800 in contact with the back of the user 600). The flat surface 800 is, for example, bedding or a floor.

Figure 21 shows a posture in which a user 600 lies supine on a flat surface 800 and holds the biosensor 200 horizontally with his/her hands at chest 602 height. The measurement position in this posture is the position of the chest 602 of the user 600 in a supine position.

20 and 21 as the supine posture in which the user 600 measures the pulse wave signal at two measurement positions at different heights from the user's heart, a posture with high reproducibility can be realized, and there is no need to use force in the hands to maintain such a posture, which can suppress hand tremors. In these postures, the palm of the user 600 may be placed on the chest 602 or the flat surface 800, or the finger (e.g., the index finger) of the user 600 may be placed on the chest 602 or the flat surface 800.

In addition, if the height from the heart is the same in a posture where the user 600's hand is held horizontally (a posture where the hand faces horizontally) and a posture where the user 600's hand is held vertically (a posture where the hand faces vertically), the blood pressure will be the same, but there may be a difference in the pulse wave signal. In a posture where the user 600 lies supine on the flat surface 800 and holds the biosensor 200 horizontally at the height of the flat surface 800, and a posture where the user 600 lies supine on the flat surface 800 and holds the biosensor 200 horizontally at the height of the chest 602, the hand of the user 600 faces horizontally in both postures, which is effective in suppressing the variation in the measured value of the pulse wave signal. The signal processing device 203 may determine whether the hand on which the biosensor 200 is attached is held horizontally at each measurement position (i.e., whether the hand is not moving in a direction deviating from the horizontal direction) from the acceleration of the biosensor 200 measured by the acceleration sensor 205.

Methods for estimating the height difference between two measurement positions at different heights from the heart of a user 600 in a supine position include (1) a method of estimating it from information indicating the user's physical characteristics, and (2) a method of estimating it from the acceleration of the biosensor 200 measured by the acceleration sensor 205. These methods are described below.

First, we will explain a method for estimating the height difference between two measurement positions at different heights from the heart of a user 600 in a supine position from information indicating the user's physical characteristics.

When user 600 in a supine position places his/her hands on flat surface 800, the height of user 600's hands is lower than the height of the heart of user 600. Since user 600's heart is located approximately in the center of user 600's chest, in the supine position, the height of user 600's hands is lower than the height of user 600's heart by approximately half the height of user 600's chest thickness.
The chest thickness of the user 600 can be statistically estimated from the height of the user 600 .

Information indicating the physical characteristics of the user 600 (such as height) may be pre-stored in memory within the signal processing device 203, or information indicating the physical characteristics of the user 600 (such as height) stored on a server on the cloud may be read from the portable control unit 300 via a network, and the read information may be transferred to the signal processing device 203 via wireless communication between the biosensor 200 and the portable control unit 300.

The signal processing device 203 can calculate the statistical average value of the height difference between two measurement positions at different heights from the heart of the user 600 in a supine position from information indicating the physical characteristics of the user 600.

The method for estimating the difference in height between two measurement positions at different heights from the heart of a user 600 in a supine position from the acceleration of the biosensor 200 measured by the acceleration sensor 205 is the same as the method for estimating the difference in height between two measurement positions at different heights from the heart of a user 600 in a sitting position from the acceleration of the biosensor 200 measured by the acceleration sensor 205, so a detailed explanation of this method will be omitted.

In the above description, a process for estimating blood pressure from a pulse wave signal has been exemplified, but the present invention is not limited to this example. For example, vascular resistance can also be estimated from a pulse wave signal. When estimating vascular resistance, the pulse wave signal is affected by blood pressure, so by measuring the pulse wave signal at the height of the heart, the variability of the measured value can be reduced. In addition, the accuracy of estimating vascular resistance can be improved by measuring the pulse wave signal at at least two measurement positions with different heights from the heart and determining the correlation between the difference in blood pressure of the user 600 corresponding to the difference in height between the at least two measurement positions and the amount of change in the pulse wave feature at the at least two measurement positions.

Similarly, when estimating blood glucose level, degree of arteriosclerosis, and blood flow rate from a pulse wave signal, the accuracy of estimating blood glucose level, degree of arteriosclerosis, and blood flow rate can be improved by measuring the pulse wave signal at at least two measurement positions at different heights from the heart and determining the correlation between the difference in blood pressure of the user 600 corresponding to the difference in height between the at least two measurement positions and the amount of change in the pulse wave features at the at least two measurement positions.

In addition, since the posture of the user 600 affects the pulse rate, blood flow, body surface temperature, and respiration, the variability in the measurement values can be reduced by measuring the pulse wave signal in a predetermined posture (e.g., the postures shown in Figures 12, 13, 14, 20, and 21).

The following equation (7) shows the correlation between pulse wave feature A and blood pressure and vascular resistance.

A = d x blood pressure + e x vascular resistance + f … (7)

The following equation (8) shows the correlation between pulse wave feature B and blood pressure and vascular resistance.

B = g × blood pressure + h × vascular resistance + i … (8)

The constants d, e, f, g, h, and i in equations (7) and (8) may vary for each user.

When blood pressure and vascular resistance are expressed using pulse wave features A and B and constants d, e, f, g, h, and i, equations (9) and (10) are obtained.

Blood pressure = -h/(e×g-d×h)×A + e/(e×g-d×h)×B + (f×h-e×i)/(e×g-d×h) … (9)

Vascular resistance = g/(e×g-d×h)×A-d/(e×g-d×h)×B-(f×g-d×i)/(e×g-d×h) ... (10)

The various processes described above are executed by the signal processing device 203 (e.g., (1) a process of estimating various biometric information of the user 600 (e.g., blood pressure, blood glucose level, heart rate, oxygen saturation concentration, vascular resistance, blood flow rate, arteriosclerosis level, autonomic nerve function, etc.) from the pulse wave signal measured by the pulse wave sensor 202, (2) a process of determining the correlation between the blood pressure of the user 600 and the pulse wave feature amount, (3) a process of calibrating a default relational equation showing the correlation between the blood pressure of the user 600 and the pulse wave feature amount, (4) a process of selecting from among multiple default relational equations showing the correlation between the blood pressure of the user 600 and the pulse wave feature amount, The control device 304 of the portable control unit 300 may execute all or part of the following processes instead of the signal processing device 203: (1) a process for selecting a relational expression that most closely approximates the correlation obtained from the measurement results of the pulse wave signal by the pulse wave sensor 202, (2) a process for determining whether the pulse wave signal is stable after the posture of the user 600 has changed, (3) a process for calculating the height difference between at least two measurement positions that are different in height from the heart of the user 600, and (4) a process for determining whether the hand on which the biosensor 200 is attached is held horizontally at at least two measurement positions. The memory of the control device 304 may store a program for causing the processor of the control device 304 to execute all or part of the various processes described above executed by the signal processing device 203. The processor of the control device 304 can execute all or part of the various processes described above executed by the signal processing device 203 instead of the signal processing device 203 by executing this program.

Next, we explain the effect that fluctuations in the user's blood circulation condition have on the correlation between the user's blood pressure and pulse wave features.

The constants a, b, and c in the default relational equation (1) above, the constants a and c1 in relational equation (5), and the constants b and c2 in relational equation (6) are unique to each user. Research by the inventor has revealed that these constants a, b, c, c1, and c2 can vary to different values depending on the user's blood circulation condition. For this reason, the constants a, b, c, c1, and c2 can be treated as functions of the blood circulation condition.

The pulse wave sensor 202 measures the pulse wave signal at at least two measurement positions at different heights from the user's heart in each of different blood circulation states of the user. This allows the signal processing device 203 to estimate a function that defines the relationship between the blood circulation state and the constants a, b, c, c1, and c2.

The user's blood circulation state can be estimated, for example, from the user's pulse wave feature. The blood circulation state is correlated with vascular age, and the vascular age can be estimated from the waveforms of the b and d waves of the acceleration pulse wave signal, so the blood circulation state can be estimated using the pulse wave feature of the b and d waves. The signal processing device 203 estimates the constants a, b, c, c1, and c2 based on a function (a function that defines the relationship between the blood circulation state and the constants a, b, c, c1, and c2) from the blood circulation state estimated in this way, and can estimate the user's blood pressure from the default relational expressions (1), (5), and (6). This can improve the accuracy of blood pressure estimation.

The different blood circulation states of the user may include, for example, the user's blood circulation state before a meal and the user's blood circulation state after a meal. It is known that when humans eat, their metabolic rate increases and blood circulation improves, and such a physiological response is called meal-induced thermogenesis. By measuring the pulse wave signal at at least two measurement positions at different heights from the user's heart in each of the user's blood circulation state before a meal and the user's blood circulation state after a meal, a function that defines the relationship between the blood circulation state and the constants a, b, c, c1, and c2 can be estimated.

Methods for distinguishing between the user's blood circulation state before a meal and the user's blood circulation state after a meal include, for example, a method in which the user inputs the distinction between before and after a meal into the portable control unit 300 when measuring the pulse wave signal, or a method in which the biosensor 200 or portable control unit 300 distinguishes between before and after a meal based on the user's activity level and blood circulation state (for example, a method in which the user is determined to have eaten a meal if the user's blood circulation state improves even though the user's activity level has not increased).

The different blood circulation states of the user may include, for example, the blood circulation state of the user before exercise and the blood circulation state of the user after exercise. It is known that human blood circulation improves when the person exercises (particularly aerobic exercise). By measuring the pulse wave signal at each of at least two measurement positions at different heights from the user's heart in each of the blood circulation state of the user before exercise and the blood circulation state of the user after exercise, a function that defines the relationship between the blood circulation state and the constants a, b, c, c1, and c2 can be estimated.

Methods for distinguishing between the user's blood circulation state before exercise and the user's blood circulation state after exercise include, for example, a method in which the user inputs the distinction between before and after exercise into the portable control unit 300 when measuring the pulse wave signal, or a method in which the biosensor 200 or portable control unit 300 distinguishes between before and after exercise based on the user's activity level and blood circulation state (for example, a method in which the user is determined to have exercised when the user's activity level increases and their blood circulation state improves).

The different blood circulation states of the user may include, for example, the blood circulation state before the user falls asleep and the blood circulation state after the user wakes up. Before falling asleep, humans tend to have a decreased blood flow to the brain and an increased blood flow to the extremities. Also, humans tend to have a decreased blood pressure during sleep and an increased blood pressure upon waking up. Thus, the blood circulation state before falling asleep and the blood circulation state after waking up tend to differ. By measuring the pulse wave signal at at least two measurement positions at different heights from the user's heart in each of the blood circulation state before the user falls asleep and the blood circulation state after the user wakes up, a function that defines the relationship between the blood circulation state and the constants a, b, c, c1, and c2 can be estimated.

Methods for distinguishing between the blood circulation state before the user falls asleep and the blood circulation state after the user wakes up include, for example, a method in which the user inputs into the portable control unit 300 the distinction between before falling asleep and after waking up when measuring the pulse wave signal, and a method in which the biosensor 200 or the portable control unit 300 distinguishes between before falling asleep and after waking up based on changes in the user's activity level over time (for example, a method in which the user is determined to have fallen asleep when the activity level has decreased for a certain period of time).

Note that the above-described embodiments are intended to facilitate understanding of the present invention and are not intended to limit the present invention. The present invention can be modified/improved without departing from the spirit thereof, and equivalents are also included in the present invention. In other words, even if a person skilled in the art makes appropriate design changes to the embodiments, they will be included in the scope of the present invention as long as they have the characteristics of the present invention. Furthermore, the elements of the embodiments can be combined to the extent technically possible, and combinations of these will be included in the scope of the present invention as long as they have the characteristics of the present invention.

100... Biometric information measuring system 200... Biometric sensor 201... Sensor body 202... Pulse wave sensor 203... Signal processing device 204... Communication device 205... Acceleration sensor 300... Portable control unit 301... Photographing device 302... Display device 303... Communication device 304... Control device 305... Tilt sensor 600... User 700... Display target range 800... Flat surface

Claims (19)

A sensor body configured to be wearable on a finger or wrist of a user;
a pulse wave sensor for measuring a pulse wave signal of the user;
a signal processing device that calculates a pulse wave feature amount from a pulse wave signal measured by the pulse wave sensor at at least two measurement positions each having a different height from the user's heart, determines a correlation between the user's blood pressure and the pulse wave feature amount from the heights of the at least two measurement positions and a change in the pulse wave feature amount at the at least two measurement positions, and estimates a blood circulation state of the user from the change in the correlation ;
A biosensor comprising: The biosensor according to claim 1 ,
The signal processing device has a default relational equation that indicates the correlation, calibrates the default relational equation based on a difference in the user's blood pressure corresponding to a difference in height between the at least two measurement positions and an amount of change in pulse wave features at the at least two measurement positions, and estimates the user's blood pressure from the pulse wave features based on the calibrated relational equation. The biosensor according to claim 1 ,
The signal processing device selects, from a plurality of default relational equations indicating the correlation, a relational equation that most closely approximates the correlation obtained from the measurement results of the pulse wave signal by the pulse wave sensor, and estimates the user's blood pressure from the pulse wave feature based on the selected relational equation. The biosensor according to claim 3 ,
The signal processing device calibrates the selected relational equation based on the difference in the user's blood pressure corresponding to the difference in height between the at least two measurement positions and the amount of change in the pulse wave feature at the at least two measurement positions, and estimates the user's blood pressure from the pulse wave feature based on the calibrated relational equation. The biosensor according to claim 1 ,
The signal processing device determines whether the pulse wave signal is in a stable state after the user's posture changes, and calculates the correlation after determining that the pulse wave signal is in a stable state. The biosensor according to claim 1 ,
The posture in which the user measures the pulse wave signal at the at least two measurement positions is
A posture in which the user is in a sitting position and holds the biosensor horizontally with his/her hands at chest height;
A posture in which the user is in a sitting position and holds the biosensor horizontally with his/her hands at face height; and A posture in which the user is in a sitting position and holds the biosensor horizontally with his/her hands at stomach height.
The biosensor is in any two or more of the above postures. The biosensor according to claim 1 ,
an acceleration sensor that measures an acceleration of the biosensor when the user changes posture to measure the pulse wave signal at the at least two measurement positions;
The signal processing device calculates a difference in height between the at least two measurement positions from the acceleration of the biosensor measured by the acceleration sensor. The biosensor according to claim 7,
The signal processing device calculates a statistical average value of the height differences between the at least two measurement positions from information indicating the user's physical characteristics. A bioinformation measuring system comprising the biosensor according to claim 1 and a portable control unit configured to be able to communicate with the biosensor,
The portable control unit includes:
A photographing device;
a display device that presents to a user, who is holding the portable control unit in one hand, an instruction to move the other hand, on which the biosensor is attached, to the at least two measurement positions, and an instruction to photograph the other hand, on which the biosensor is attached, and the user's face, with the photographing device when the other hand, on which the biosensor is attached, is located at each measurement position, and displays images photographed by the photographing device;
a control device that estimates a height difference between the at least two measurement positions based on a relative positional relationship between the other hand on which the biosensor is attached and the face from images captured by the photographing device at each measurement position; and
A biological information measuring system comprising: A bioinformation measuring system comprising the biosensor according to claim 1 and a portable control unit configured to be able to communicate with the biosensor,
The portable control unit includes:
A photographing device;
a display device that presents to a user, who is holding the portable control unit in one hand, an instruction to move the other hand, on which the biosensor is attached, to the at least two measurement positions, and an instruction to photograph the other hand, on which the biosensor is attached, and the user's face, with the photographing device, when the other hand, on which the biosensor is attached, is located at each measurement position, and displays images photographed by the photographing device;
a control device that estimates a height difference between the at least two measurement positions based on a relative positional relationship between the biometric sensor and the face from images captured by the photographing device at each measurement position; and
A biological information measuring system comprising: A bioinformation measuring system comprising the biosensor according to claim 1 and a portable control unit configured to be able to communicate with the biosensor,
The portable control unit includes:
A photographing device;
a display device that presents to a user, who is holding the portable control unit with the hand on which the biosensor is attached, an instruction to move the hand on which the biosensor is attached to the at least two measurement positions and an instruction to photograph the face of the user with the photographing device when the hand on which the biosensor is attached is located at each measurement position, and displays an image photographed by the photographing device;
a control device that estimates a height difference between the at least two measurement positions based on a positional relationship between the user's face and the portable control unit and an inclination of the portable control unit from images captured by the photographing device at each measurement position; and
A biological information measuring system comprising: The biological information measuring system according to claim 11,
The control device determines whether the user is holding the portable control unit in the hand in which the biosensor is attached. The biological information measuring system according to claim 9,
The display device graphically displays the target position and target display size of the face. The biosensor according to any one of claims 1 to 5,
The posture in which the user measures the pulse wave signal at the at least two measurement positions is
a posture in which the user lies supine on a flat surface and holds the biosensor horizontally with their hands at chest height; and a posture in which the user lies supine on a flat surface and holds the biosensor horizontally with their hands at the height of the flat surface. The biosensor according to claim 7,
The signal processing device determines, from the acceleration of the biosensor measured by the acceleration sensor, whether or not the hand on which the biosensor is attached is held horizontally at the at least two measurement positions. The biosensor according to claim 1 ,
The pulse wave sensor is a biosensor that measures a pulse wave signal at each of at least two measurement positions at different heights from the user's heart in each of different blood circulation states of the user. 17. The biosensor according to claim 16,
The different blood circulation states of the user include a blood circulation state of the user before a meal and a blood circulation state of the user after a meal. 17. The biosensor according to claim 16,
The different blood circulation states of the user include a blood circulation state of the user before exercise and a blood circulation state of the user after exercise. 17. The biosensor according to claim 16,
The different blood circulation states of the user include a blood circulation state before the user falls asleep and a blood circulation state after the user wakes up.

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