JP7601985B2 - Electronic device, estimation system, estimation method, and estimation program - Google Patents

Description

The present invention relates to an electronic device, an estimation system, an estimation method, and an estimation program for estimating the health condition of a subject from measured biological information.

Conventionally, blood components and blood fluidity have been measured as a means of estimating the health condition of a subject (user). These measurements are performed using blood drawn from the subject. Electronic devices that measure biological information from a test site such as the subject's wrist are also known. For example, Patent Document 1 describes an electronic device that measures the subject's pulse when worn by the subject on the wrist.

JP 2002-360530 A

However, drawing blood is painful, making it difficult to estimate one's own health condition on a daily basis. Furthermore, the electronic device described in Patent Document 1 only measures the pulse rate and cannot estimate the subject's health condition other than the pulse rate.

In view of the above circumstances, the object of the present invention is to provide an electronic device, an estimation system, an estimation method, and an estimation program that can easily estimate the health condition of a subject.

To solve the above problem, an electronic device according to one embodiment of the present invention includes a control unit that estimates the blood glucose level of a subject based on an estimation equation created based on the blood glucose level and the subject's pulse wave associated with the blood glucose level, and the subject's age, and the control unit uses, as the estimation equation, an equation created based on the results of a regression analysis using the subject's age and an index AI that is expressed as the ratio between the forward wave of the pulse wave and the reflected wave that appears later than the forward wave.

An electronic device according to one embodiment of the present invention includes a control unit that estimates the glucose metabolism of the subject based on an estimation equation created based on the blood glucose level and the subject's pulse wave associated with the blood glucose level, and the subject's age. The control unit uses, as the estimation equation, an equation created based on the results of a regression analysis using the subject's age and an index AI that is expressed as the ratio between the forward wave of the pulse wave and the reflected wave that appears later than the forward wave.

An electronic device according to one embodiment of the present invention includes a control unit that estimates the lipid level of the subject based on an estimation equation created based on the lipid level and the subject's pulse wave associated with the lipid level, and the subject's age, and the control unit uses, as the estimation equation, an equation created based on the results of a regression analysis using the subject's age and an index AI that is expressed as the ratio between the forward wave of the pulse wave and the reflected wave that appears later than the forward wave.

An electronic device according to one embodiment of the present invention includes a control unit that estimates the lipid metabolism of the subject based on an estimation equation created based on the lipid value and the subject's pulse wave associated with the lipid value, and the subject's age, and the control unit uses, as the estimation equation, an equation created based on the results of a regression analysis using the subject's age and an index AI that is expressed as the ratio between the forward wave of the pulse wave and the reflected wave that appears later than the forward wave.

In addition, an estimation method according to one embodiment of the present invention includes an estimation step of estimating the blood glucose level or glucose metabolism of the subject based on an estimation equation created based on the blood glucose level and the subject's pulse wave associated with the blood glucose level, and the age of the subject, and the estimation step uses, as the estimation equation, an equation created based on the results of a regression analysis using the subject's age and an index AI represented by the ratio between the forward wave of the pulse wave and the reflected wave that appears later than the forward wave.

In addition, an estimation method according to one embodiment of the present invention includes an estimation step of estimating the lipid level or lipid metabolism of the subject based on an estimation equation created based on the lipid level and the subject's pulse wave associated with the lipid level, and the age of the subject, and the estimation step uses an estimation equation created based on the results of a regression analysis using the subject's age and an index AI represented by the ratio between the forward wave of the pulse wave and the reflected wave that appears later than the forward wave.

An estimation program according to one embodiment of the present invention causes a computer to function as a control unit that estimates the blood glucose level or glucose metabolism of a subject based on an estimation equation created based on the blood glucose level and the subject's pulse wave associated with the blood glucose level, and the subject's age, and the control unit uses, as the estimation equation, an equation created based on the results of a regression analysis using the subject's age and an index AI that is expressed as the ratio between the forward wave of the pulse wave and the reflected wave that appears later than the forward wave.

An estimation program according to one embodiment of the present invention causes a computer to function as a control unit that estimates the lipid level or lipid metabolism of a subject based on an estimation equation created based on the lipid level and the subject's pulse wave associated with the lipid level, and the subject's age, and the control unit uses, as the estimation equation, an equation created based on the results of a regression analysis using the subject's age and an index AI that is expressed as the ratio between the forward wave of the pulse wave and the reflected wave that appears later than the forward wave.

The present invention provides an electronic device, an estimation system, an estimation method, and an estimation program that can easily estimate the health condition of a subject.

1 is a schematic diagram showing a schematic configuration of an electronic device according to a first embodiment of the present invention; FIG. 2 is a cross-sectional view showing a schematic configuration of a main body portion of FIG. 1 . FIG. 2 is a diagram showing an example of a usage state of the electronic device in FIG. 1 . FIG. 2 is a functional block diagram showing a schematic configuration of the electronic device shown in FIG. 1 . 4 is a diagram illustrating an example of an estimation method based on a change in a pulse wave in the electronic device of FIG. 1. FIG. 4 is a diagram showing an example of an acceleration pulse wave. FIG. 4 is a diagram showing an example of a pulse wave acquired by a sensor unit. 10A and 10B are diagrams illustrating another example of an estimation method based on a change in a pulse wave in the electronic device of FIG. 1 . 2 is a flow chart for creating an estimation formula used by the electronic device of FIG. 1. FIG. 10 is a flow diagram for estimating a subject's postprandial blood glucose level using the estimation equation created by the flow of FIG. 9 . FIG. 10 is a diagram showing a comparison between postprandial blood glucose levels estimated using the estimation equation created by the flow of FIG. 9 and actually measured postprandial blood glucose levels. FIG. 11 is a flow chart for creating an estimation equation used by an electronic device according to a second embodiment of the present invention. 13 is a flow diagram for estimating a subject's postprandial blood glucose level using the estimation equation created by the flow of FIG. 12. FIG. 11 is a flow chart showing the creation of an estimation equation used by an electronic device according to a third embodiment of the present invention. FIG. 15 is a flow diagram for estimating the postprandial lipid level of a subject using the estimation equation created by the flow of FIG. 14. FIG. 15 is a diagram showing a comparison between postprandial lipid levels estimated using the estimation equation created by the flow of FIG. 14 and actually measured postprandial lipid levels. FIG. 2 is a diagram illustrating communication between an electronic device and a blood glucose meter. 1 is a schematic diagram showing a schematic configuration of a system according to an embodiment of the present invention;

The following describes in detail the embodiments of the present invention with reference to the drawings.

(First embodiment)
Fig. 1 is a schematic diagram showing a schematic configuration of an electronic device according to a first embodiment of the present invention. Electronic device 100 includes a mounting unit 110 and a measurement unit 120. Fig. 1 is a diagram showing electronic device 100 observed from a back surface 120a that contacts a test portion.

The electronic device 100 measures the subject's biometric information while the subject is wearing the electronic device 100. The biometric information measured by the electronic device 100 is the subject's pulse wave, which can be measured by the measurement unit 120. In the present embodiment, the following description will be given assuming that the electronic device 100 is worn on the subject's wrist to acquire the pulse wave, as an example.

In this embodiment, the attachment unit 110 is a straight, elongated band. The pulse wave is measured, for example, with the subject wearing the attachment unit 110 of the electronic device 100 around his/her wrist. Specifically, the subject wraps the attachment unit 110 around his/her wrist so that the back surface 120a of the measurement unit 120 is in contact with the area to be measured, and then measures the pulse wave. The electronic device 100 measures the pulse wave of blood flowing through the ulnar artery or radial artery at the subject's wrist.

Figure 2 is a cross-sectional view showing a schematic configuration of the measurement unit 120 in Figure 1. In Figure 2, in addition to the measurement unit 120, the attachment unit 110 around the measurement unit 120 is also shown.

The measuring unit 120 has a back surface 120a that contacts the subject's wrist when worn, and a front surface 120b opposite the back surface 120a. The measuring unit 120 has an opening 111 on the back surface 120a side. The sensor unit 130 is supported by the measuring unit 120 with one end protruding from the opening 111 toward the back surface 120a side when the elastic body 140 is not pressed. One end of the sensor unit 130 is provided with a pulse contact portion 132. One end of the sensor unit 130 is displaceable in a direction approximately perpendicular to the plane of the back surface 120a. The other end of the sensor unit 130 is supported by the measuring unit 120 by a support portion 133 so that one end of the sensor unit 130 can be displaced.

One end of the sensor unit 130 is in contact with the measurement unit 120 via the elastic body 140 and is displaceable. The elastic body 140 is, for example, a spring. However, the elastic body 140 is not limited to a spring and can be any other elastic body, for example, resin, sponge, etc.

Although not shown, the measurement unit 120 may also include a control unit, a memory unit, a communication unit, a power supply unit, an alarm unit, circuits for operating these units, connecting cables, etc.

The sensor unit 130 includes an angular velocity sensor 131 that detects the displacement of the sensor unit 130. The angular velocity sensor 131 only needs to be able to detect the angular displacement of the sensor unit 130. The sensor included in the sensor unit 130 is not limited to the angular velocity sensor 131, and may be, for example, an acceleration sensor, an angle sensor, or another motion sensor, or may include a plurality of these sensors.

The electronic device 100 includes an input unit 141 on the surface 120b side of the measurement unit 120. The input unit 141 accepts operational input from the subject and is composed of, for example, an operation button (operation key). The input unit 141 may be composed of, for example, a touch screen.

Figure 3 is a diagram showing an example of how the electronic device 100 is used by a subject. The subject uses the electronic device 100 by wrapping it around the wrist. The electronic device 100 is worn with the back surface 120a of the measurement unit 120 in contact with the part to be measured. With the wearing unit 110 wrapped around the wrist, the position of the measurement unit 120 can be adjusted so that the pulse contact unit 132 comes into contact with the position where the ulnar artery or radial artery is located.

In FIG. 3, when the electronic device 100 is worn, one end of the sensor unit 130 is in contact with the skin above the radial artery, which is an artery on the thumb side of the subject's left hand. One end of the sensor unit 130 is in contact with the skin above the radial artery of the subject due to the elastic force of the elastic body 140 arranged between the measurement unit 120 and the sensor unit 130. The sensor unit 130 displaces in response to the movement of the subject's radial artery, i.e., pulsation. The angular velocity sensor 131 detects the displacement of the sensor unit 130 to obtain a pulse wave. A pulse wave is a waveform captured from the body surface that represents the change in blood vessel volume over time caused by the inflow of blood.

Referring again to FIG. 2, the sensor unit 130 has one end protruding from the opening 111 when the elastic body 140 is not pressed. When the electronic device 100 is attached to the subject, one end of the sensor unit 130 is in contact with the skin on the subject's radial artery, and the elastic body 140 expands and contracts in response to pulsation, displacing one end of the sensor unit 130. The elastic body 140 has an appropriate elastic modulus so as not to impede pulsation and to expand and contract in response to pulsation. The opening width W of the opening 111 is sufficiently larger than the blood vessel diameter, which is the radial artery diameter in this embodiment. By providing the opening 111 in the measuring unit 120, the rear surface 120a of the measuring unit 120 does not press against the radial artery when the electronic device 100 is attached. Therefore, the electronic device 100 can acquire a pulse wave with less noise, improving the measurement accuracy.

In FIG. 3, an example is shown in which the electronic device 100 is worn on the wrist and a pulse wave in the radial artery is acquired, but the present invention is not limited to this. For example, the electronic device 100 may acquire a pulse wave of blood flowing through the carotid artery in the subject's neck. Specifically, the subject may measure the pulse wave by lightly pressing the pulse contact portion 132 against the position of the carotid artery. The subject may also wear the attachment portion 110 by wrapping it around the neck so that the pulse contact portion 132 is located at the position of the carotid artery.

FIG. 4 is a functional block diagram showing a schematic configuration of electronic device 100. Electronic device 100 includes sensor unit 130, input unit 141, control unit 143, power supply unit 144, memory unit 145, communication unit 146, and notification unit 147. In this embodiment, control unit 143, power supply unit 144, memory unit 145, communication unit 146, and notification unit 147 are included inside measurement unit 120 or attachment unit 110.

The sensor unit 130 includes an angular velocity sensor 131 and detects pulsation from the test area to obtain a pulse wave.

The control unit 143 is a processor that controls and manages the entire electronic device 100, including each functional block of the electronic device 100. The control unit 143 is also a processor that estimates the subject's blood glucose level from the acquired pulse wave. The control unit 143 is composed of a processor such as a CPU (Central Processing Unit) that executes a program that defines the control procedure and a program that estimates the subject's blood glucose level, and such programs are stored in a storage medium such as the storage unit 145. The control unit 143 also estimates the subject's state regarding glucose metabolism, lipid metabolism, etc., based on the calculated index. The control unit 143 also notifies the notification unit 147 of the data.

The power supply unit 144 includes, for example, a lithium ion battery and a control circuit for charging and discharging the battery, and supplies power to the entire electronic device 100.

The storage unit 145 stores programs and data. The storage unit 145 may include any non-transitory storage medium, such as a semiconductor storage medium and a magnetic storage medium. The storage unit 145 may include multiple types of storage media. The storage unit 145 may include a combination of a portable storage medium, such as a memory card, an optical disk, or a magneto-optical disk, and a storage medium reader. The storage unit 145 may include a storage device used as a temporary storage area, such as a RAM (Random Access Memory). The storage unit 145 stores various information and programs for operating the electronic device 100, and also functions as a work memory. The storage unit 145 may store, for example, the measurement results of the pulse wave acquired by the sensor unit 130.

The communication unit 146 transmits and receives various data by performing wired or wireless communication with an external device. For example, the communication unit 146 communicates with an external device that stores the subject's biological information to manage the health condition, and transmits the measurement results of the pulse wave measured by the electronic device 100 and the health condition estimated by the electronic device 100 to the external device.

The notification unit 147 notifies information by sound, vibration, image, etc. The notification unit 147 may include a speaker, a vibrator, and a display device such as a liquid crystal display (LCD), an organic electro-luminescence display (OELD), or an inorganic electro-luminescence display (IELD). In this embodiment, the notification unit 147 notifies, for example, the state of the glucose metabolism or lipid metabolism of the subject.

The electronic device 100 estimates the blood glucose level of the subject based on an estimation equation created by regression analysis. The electronic device 100 stores, for example, in advance in the memory unit 145, estimation equations for estimating the blood glucose level based on the pulse wave. The electronic device 100 estimates the blood glucose level using these estimation equations.

Here, the estimation theory for estimating blood glucose levels based on pulse waves will be described. After a meal, the blood glucose level rises, causing a decrease in blood fluidity (increase in viscosity), vascular dilation, and an increase in circulating blood volume, and vascular and hemodynamic conditions are determined so that these conditions are in equilibrium. The decrease in blood fluidity occurs, for example, due to an increase in the viscosity of plasma or a decrease in the deformability of red blood cells. In addition, vascular dilation occurs due to the secretion of insulin, the secretion of digestive hormones, and an increase in body temperature. When blood vessels dilate, the pulse rate increases to suppress a decrease in blood pressure. In addition, an increase in circulating blood volume compensates for blood consumption for digestion and absorption. The changes in vascular and hemodynamic conditions before and after a meal due to these factors are also reflected in the pulse wave. Therefore, the electronic device 100 can acquire a pulse wave and estimate a blood glucose level based on changes in the waveform of the acquired pulse wave.

Based on the above estimation theory, an estimation equation for estimating blood glucose levels can be created by performing regression analysis based on sample data of pre- and post-prandial blood glucose levels and pulse waves obtained from multiple subjects. During estimation, the blood glucose level of the subject can be estimated by applying the created estimation equation to an index based on the subject's pulse wave. In particular, by performing regression analysis using sample data in which the variation in blood glucose levels is close to a normal distribution, the blood glucose level of the subject being tested can be estimated regardless of whether it is before or after a meal.

Figure 5 is a diagram for explaining an example of an estimation method based on changes in the pulse wave, showing an example of a pulse wave. The estimation formula for estimating the blood glucose level is created, for example, by regression analysis of an index (rising index) Sl indicating the onset of the pulse wave, an Augmentation Index (AI), and the pulse rate PR.

The rise index Sl is derived based on the waveform shown in region D1 in FIG. 5. Specifically, the rise index Sl is the ratio of the first minimum value to the first maximum value in the accelerated pulse wave derived by differentiating the pulse wave twice. For example, in the accelerated pulse wave shown as an example in FIG. 6, the rise index Sl is expressed as -b/a. The rise index Sl decreases due to a decrease in blood fluidity after a meal, the secretion of insulin, and the expansion (relaxation) of blood vessels due to an increase in body temperature.

AI is an index expressed as the ratio of the magnitude of the forward wave and the reflected wave of the pulse wave. A method for deriving AI will be described with reference to FIG. 7. FIG. 7 is a diagram showing an example of a pulse wave acquired at the wrist using electronic device 100. FIG. 7 shows a case where angular velocity sensor 131 is used as a pulsation detection means. FIG. 7 shows the time integration of the angular velocity acquired by angular velocity sensor 131, with the horizontal axis representing time and the vertical axis representing angle. The acquired pulse wave may contain noise due to, for example, the subject's body movement, so correction may be performed using a filter that removes DC (Direct Current) components to extract only the pulsation component.

Pulse wave propagation is a phenomenon in which the pulsation of blood pushed out from the heart travels through the walls of arteries and blood. The pulsation of blood pushed out from the heart reaches the extremities of the limbs as a forward wave, and part of it is reflected at the branching of the blood vessels, the change in the diameter of the blood vessels, etc., and returns as a reflected wave. AI is the magnitude of this reflected wave divided by the magnitude of the forward wave, and is expressed as AIn = (PRn - PSn) / (PFn - PSn). Here, AIn is the AI for each pulse. AI may be calculated by, for example, measuring the pulse wave for several seconds and calculating the average value AIave of AIn (n = an integer from 1 to n) for each pulse. AI is derived based on the waveform shown in area D2 in Figure 5. AI decreases after a meal due to the decrease in blood fluidity and the expansion of blood vessels due to an increase in body temperature.

The pulse rate PR is derived based on the period _TPR of the pulse wave shown in Fig. 5. The pulse rate PR increases after a meal.

The electronic device 100 can estimate the blood glucose level using an estimation equation created based on these rising indexes Sl, AI, and pulse rate PR.

Figure 8 is a diagram illustrating another example of an estimation method based on changes in a pulse wave. Figure 8(a) shows a pulse wave, and Figure 8(b) shows the result of FFT (Fast Fourier Transform) of the pulse wave in Figure 8(a). An estimation equation for estimating blood glucose levels is created, for example, by regression analysis of fundamental and harmonic components (Fourier coefficients) derived by FFT. The peak value in the FFT result shown in Figure 8(b) changes based on changes in the waveform of the pulse wave. Therefore, blood glucose levels can be estimated using an estimation equation created based on the Fourier coefficients.

The electronic device 100 estimates the subject's blood glucose level using an estimation formula based on the above-mentioned rising index Sl, AI, pulse rate PR, and Fourier coefficients, etc.

Here, a method for creating an estimation formula used by the electronic device 100 when estimating the blood glucose level of a subject will be described. The estimation formula does not need to be created by the electronic device 100, but may be created in advance using another computer or the like. In this specification, the device that creates the estimation formula will be referred to as an estimation formula creation device. The created estimation formula is stored in advance, for example, in the memory unit 145, before the subject estimates the blood glucose level using the electronic device 100.

Figure 9 is a flow diagram for creating an estimation formula used by the electronic device 100 of Figure 1. The estimation formula is created by measuring the subject's pre- and post-prandial pulse waves using a pulse wave meter, and measuring the subject's pre- and post-prandial blood glucose levels using a blood glucose meter, and performing regression analysis based on the sample data acquired by the measurements. Note that "pre-prandial" refers to the subject's fasting state, and "post-prandial" refers to the time a predetermined time after a meal when blood glucose levels rise (for example, about one hour after starting a meal). The sample data acquired is not limited to pre- and post-prandial data, and may be data from a time period when blood glucose levels fluctuate greatly.

In creating the estimation equation, first, information on the subject's pre-prandial blood glucose level and the pulse wave associated with the blood glucose level, measured by a blood glucose meter and a pulse wave meter, respectively, is input to the estimation equation creation device (step S101).

In addition, information on the subject's postprandial blood glucose level and the pulse wave associated with the blood glucose level, measured by a blood glucose meter and a pulse wave meter, respectively, are input to the estimation equation creation device (step S102). The blood glucose level input in steps S101 and S102 is measured by the blood glucose meter, for example, by taking a blood sample. In addition, in step S101 or step S102, the age of the subject of each sample data is also input.

The estimation formula creation device determines whether the number of samples of the sample data input in steps S101 and S102 is equal to or greater than N, which is sufficient for performing regression analysis (step S103). The number of samples N can be determined appropriately, and can be set to 100, for example. If the estimation formula creation device determines that the number of samples is less than N (if No), it repeats steps S101 and S102 until the number of samples is equal to or greater than N. On the other hand, if the estimation formula creation device determines that the number of samples is equal to or greater than N (if Yes), it proceeds to step S104 and executes the calculation of the estimation formula.

In calculating the estimation formula, the estimation formula creation device analyzes the input pre- and post-prandial pulse waves (step S104). In this embodiment, the estimation formula creation device analyzes the onset index Sl, AI, and pulse rate PR of the pre- and post-prandial pulse waves. The estimation formula creation device may also perform FFT analysis to analyze the pulse waves.

The estimation formula creation device then executes a regression analysis (step S105). The objective variable in the regression analysis is the postprandial blood glucose level. The explanatory variables in the regression analysis are the age input in step S101 or step S102, and the preprandial and postprandial pulse wave onset indexes Sl, AI, and pulse rate PR analyzed in step S104. Note that when the estimation formula creation device performs an FFT analysis in step S104, the explanatory variables may be, for example, Fourier coefficients calculated as a result of the FFT analysis.

The estimation formula creation device creates an estimation formula for estimating postprandial blood glucose levels based on the results of the regression analysis (step S106). An example of an estimation formula for estimating postprandial blood glucose levels is shown in the following formula (1).

In formula (1), GLa indicates the postprandial blood glucose level. age indicates age, PRb indicates the preprandial pulse rate PR, AIb indicates the preprandial AI, Slb indicates the preprandial rise index Sl, PRa indicates the postprandial pulse rate PR, AIa indicates the postprandial AI, Sla indicates the postprandial rise index Sl, and BLG indicates the blood glucose level input by the subject (measured by drawing blood). The blood glucose level BLG input by the subject is a blood glucose level measured at a different time from the estimated blood glucose level GLa. In this embodiment, the blood glucose level BLG input by the subject is a blood glucose level measured by drawing blood before a meal. By using the blood glucose level BLG measured by drawing blood in the estimation formula, the accuracy of blood glucose level estimation is improved.

Next, the flow of estimating the subject's blood glucose level using the estimation equation will be described. FIG. 10 is a flow diagram for estimating the subject's postprandial blood glucose level using the estimation equation created by the flow in FIG. 9. Here, a case will be described in which the subject inputs the preprandial blood glucose level measured using a blood glucose meter from the input unit 141 of the electronic device 100.

First, the electronic device 100 inputs the subject's age based on the subject's operation of the input unit 141 (step S201).

The electronic device 100 also inputs the pre-prandial blood glucose level measured by the subject using a blood glucose meter based on the subject's operation of the input unit 141 (step S202).

The electronic device 100 also measures the subject's pre-meal pulse wave based on the subject's operation (step S203).

Then, after the subject has eaten, the electronic device 100 measures the subject's postprandial pulse wave based on the subject's operation (step S204).

Next, the electronic device 100 analyzes the measured pulse wave (step S205). Specifically, the electronic device 100 analyzes, for example, the rising index Sl, AI, and pulse rate PR related to the measured pulse wave.

The electronic device 100 estimates the subject's postprandial blood glucose level by applying the preprandial blood glucose level input in step S202, the rise index Sl, AI, and pulse rate PR analyzed in step S205, and the subject's age, for example, to the above-mentioned formula (1) (step S206). The estimated postprandial blood glucose level is notified to the subject, for example, by the notification unit 147 of the electronic device 100.

Figure 11 is a diagram showing a comparison between postprandial blood glucose levels estimated using the estimation equation created by the flow in Figure 9 and actual postprandial blood glucose levels. In the graph shown in Figure 11, the horizontal axis shows postprandial blood glucose measurements (actual measurements) and the vertical axis shows postprandial blood glucose estimates. The blood glucose measurements were taken using a MedisafeFit blood glucose meter manufactured by Terumo Corporation. As shown in Figure 11, the measured and estimated values are generally within a range of ±20%. In other words, it can be said that the estimation accuracy using the estimation equation is within 20%.

In this way, the electronic device 100 can estimate the postprandial blood glucose level non-invasively and in a short time based on the preprandial blood glucose level measured by blood sampling from the subject. In this embodiment, the estimation formula is created using the preprandial and postprandial blood glucose levels and pulse waves, but the creation of the estimation formula is not limited to this, and the estimation formula may be created using either the preprandial or postprandial blood glucose level and pulse wave. Furthermore, the electronic device 100 may estimate the subject's blood glucose level at any timing, not limited to the postprandial blood glucose level. The electronic device 100 can also estimate the blood glucose level at any timing non-invasively and in a short time.

The electronic device 100 according to this embodiment may update the estimation formula stored in the memory unit 145 based on the subject's pre-prandial blood glucose level and pulse wave acquired in steps S202 and S203 in estimating the blood glucose level. That is, the electronic device 100 can use the pre-prandial blood glucose level and pulse wave acquired when estimating the blood glucose level as sample data for updating the estimation formula. This allows the estimation formula to be updated every time the subject estimates the blood glucose level, improving the accuracy of estimating the post-prandial blood glucose level using the estimation formula.

Second Embodiment
In the first embodiment, a case where the estimation formula is created based on the subject's pre- and post-prandial blood glucose levels and pulse waves is described. In the second embodiment, an example where the estimation formula is created based on the subject's own pre- and post-prandial blood glucose levels and pulse waves is described.

FIG. 12 is a flow diagram of creating an estimation formula used by electronic device 100 according to this embodiment. In this embodiment, the estimation formula is described as being created by electronic device 100. Note that the estimation formula may be created by an estimation formula creation device different from electronic device 100, as described in the first embodiment.

First, the electronic device 100 inputs the pre-prandial blood glucose level measured by the subject using a blood glucose meter based on the subject's operation of the input unit 141 (step S301).

The electronic device 100 also measures the subject's pre-meal pulse wave based on the subject's operation (step S302).

After the subject has eaten, the electronic device 100 inputs the postprandial blood glucose level measured by the subject using the blood glucose meter based on the subject's operation of the input unit 141 (step S303). The blood glucose level input in steps S301 and S303 is measured by the blood glucose meter, for example, by the subject taking a blood sample.

The electronic device 100 also measures the subject's postprandial pulse wave based on the subject's operation (step S304).

The electronic device 100 determines whether the number of samples of the sample data input in steps S301 to S304 is equal to or greater than N, which is sufficient for performing regression analysis (step S305). The number of samples N can be determined appropriately, and can be set to 5, for example. If the estimation formula creation device determines that the number of samples is less than N (if No), it repeats steps S301 to S304 until the number of samples is equal to or greater than N. On the other hand, if the estimation formula creation device determines that the number of samples is equal to or greater than N (if Yes), it proceeds to step S306 and executes the calculation of the estimation formula.

The method of calculating the estimation formula in steps S306 to S308 is similar to steps S104 to S106 in FIG. 9, and therefore a detailed description thereof will be omitted here. The estimation formula created by electronic device 100 according to the flow shown in FIG. 12 is, for example, a formula (1) in which each coefficient is different.

Next, the flow of estimating the subject's blood glucose level using the estimation equation will be described. FIG. 13 is a flow diagram for estimating the subject's postprandial blood glucose level using the estimation equation created by the flow in FIG. 12. Here, a case will be described in which the subject inputs the blood glucose level measured using a blood glucose meter from the input unit 141 of the electronic device 100.

First, the electronic device 100 inputs the subject's age based on the subject's operation of the input unit 141 (step S401).

The electronic device 100 also inputs the pre-prandial blood glucose level measured by the subject using a blood glucose meter based on the subject's operation of the input unit 141 (step S402).

The electronic device 100 also measures the subject's pre-meal pulse wave based on the subject's operation (step S403).

Then, after the subject has eaten, the electronic device 100 measures the subject's postprandial pulse wave based on the subject's operation (step S404).

Next, the electronic device 100 analyzes the measured pulse wave (step S405). Specifically, the electronic device 100 analyzes, for example, the rising index Sl, AI, and pulse rate PR related to the measured pulse wave.

The electronic device 100 estimates the subject's postprandial blood glucose level by applying the rising index Sl, AI, and pulse rate PR analyzed in step S405 and the subject's age to the estimation formula created in the flow diagram of FIG. 12 (step S406). The estimated postprandial blood glucose level is notified to the subject, for example, by the notification unit 147 of the electronic device 100.

In this way, the electronic device 100 can non-invasively and quickly estimate the postprandial blood glucose level based on the preprandial blood glucose level measured by blood sampling from the subject. In this embodiment, the estimation formula for estimating the postprandial blood glucose level is created based on sample data obtained from the subject, improving the accuracy of estimating the subject's postprandial blood glucose level.

As described in the first embodiment, in the electronic device 100 according to this embodiment, the estimation formula stored in the memory unit 145 may be updated based on the subject's preprandial blood glucose level and pulse wave acquired in steps S402 and S403 in estimating the blood glucose level. This allows the estimation formula to be updated every time the subject estimates the blood glucose level, improving the accuracy of estimating the postprandial blood glucose level using the estimation formula.

In addition, when a sufficient number of sample data can be collected from the subject, the electronic device 100 may estimate the blood glucose level based on the subject's pulse wave, without using the blood glucose level measured by blood sampling. For example, the electronic device 100 estimates the subject's pre-prandial blood glucose level based on the subject's pre-prandial pulse wave. In this way, the subject uses the electronic device 100 to measure the pre-prandial pulse wave, and the electronic device 100 can estimate the subject's pre-prandial blood glucose level using an estimation formula based on the pre-prandial pulse wave. In this case, the electronic device 100 can also estimate the pre-prandial blood glucose level non-invasively and in a short time. Note that sufficient sample data refers to an amount of data that can create an estimation formula that can estimate the subject's pre-prandial blood glucose level with a predetermined accuracy or higher based on the pre-prandial pulse wave. In addition, the estimated blood glucose level is not limited to pre-prandial, and the post-prandial blood glucose level may be estimated based on the post-prandial pulse wave. In addition, the estimated blood glucose level is not limited to before and after a meal, and the blood glucose level at any timing may be estimated based on the pulse wave measured at any timing.

Third Embodiment
In the first embodiment, a case where the electronic device 100 estimates a postprandial blood glucose level of a subject has been described. In the third embodiment, an example of a case where the electronic device 100 estimates a postprandial lipid level of a subject will be described. Here, the lipid level includes neutral fat, total cholesterol, HDL cholesterol, LDL cholesterol, etc. In the description of this embodiment, the description of the same points as in the first embodiment will be omitted as appropriate.

The electronic device 100 stores, for example, in advance in the storage unit 145, estimation equations for estimating lipid levels based on pulse waves. The electronic device 100 estimates lipid levels using these estimation equations.

The estimation theory for estimating lipid levels based on pulse waves is the same as the estimation theory for blood glucose levels described in the first embodiment. That is, changes in lipid levels in the blood are also reflected in changes in the waveform of the pulse wave. Therefore, electronic device 100 can acquire pulse waves and estimate lipid levels based on changes in the acquired pulse waves. Electronic device 100 improves the accuracy of estimating lipid levels by inputting blood glucose levels along with the pulse waves at the time of lipid estimation.

Figure 14 is a flow diagram for creating an estimation formula used by electronic device 100 according to this embodiment. In this embodiment as well, the estimation formula is created by performing regression analysis based on sample data. In this embodiment, the estimation formula is created based on the sample data, such as pre-prandial pulse waves, lipid levels, and blood glucose levels. In this embodiment, "pre-prandial" refers to the subject's fasting state. In addition, "post-prandial" refers to the time after a meal when lipid levels are high (e.g., about 3 hours after starting a meal). In particular, by performing regression analysis using sample data in which the variation in lipid levels is close to a normal distribution, the lipid levels of the subject to be tested at any timing, regardless of whether it is before or after a meal, can be estimated.

In creating the estimation equation, first, information on the subject's pre-prandial blood glucose level, as well as the pulse wave and lipid values associated with the blood glucose level, measured by a blood glucose meter, a pulse wave meter, and a lipid measurement device, is input to the estimation equation creation device (step S501).

In addition, information on the subject's postprandial blood glucose level, as well as the pulse wave and lipid levels associated with the blood glucose level, measured by the blood glucose meter, pulse wave meter, and lipid measurement device, respectively, is input to the estimation equation creation device (step S502). The blood glucose level input in steps S501 and S502 is measured by the blood glucose meter, for example, by taking a blood sample. In addition, in step S501 or step S502, the age of the subject of each sample data is also input.

The estimation formula creation device determines whether the number of samples of the sample data input in steps S501 and S502 is equal to or greater than N, which is sufficient for performing regression analysis (step S503). The number of samples N can be determined appropriately, and can be set to 100, for example. If the estimation formula creation device determines that the number of samples is less than N (if No), it repeats steps S501 and S502 until the number of samples is equal to or greater than N. On the other hand, if the estimation formula creation device determines that the number of samples is equal to or greater than N (if Yes), it proceeds to step S504 and executes the calculation of the estimation formula.

In calculating the estimation formula, the estimation formula creation device analyzes the input pre- and post-prandial pulse waves (step S504). In this embodiment, the estimation formula creation device analyzes the onset index Sl, AI, and pulse rate PR of the pre- and post-prandial pulse waves. The estimation formula creation device may also perform FFT analysis to analyze the pulse waves.

The estimation formula creation device then executes a regression analysis (step S505). The objective variable in the regression analysis is the postprandial lipid value. The explanatory variables in the regression analysis are the age input in step S501 or step S502, and the preprandial and postprandial pulse wave onset indexes Sl, AI, and pulse rate PR analyzed in step S504. If the estimation formula creation device performs an FFT analysis in step S504, the explanatory variables may be, for example, Fourier coefficients calculated as a result of the FFT analysis.

The estimation formula creation device creates an estimation formula for estimating postprandial lipid levels based on the results of the regression analysis (step S506).

Next, the flow of estimating the lipid level of a subject using the estimation equation will be described. FIG. 15 is a flow diagram for estimating the postprandial lipid level of a subject using the estimation equation created by the flow of FIG. 14. Here, a case will be described in which the subject inputs the blood glucose level measured using a blood glucose meter from the input unit 141 of the electronic device 100.

First, the electronic device 100 inputs the subject's age based on the subject's operation of the input unit 141 (step S601).

The electronic device 100 also inputs the pre-meal blood glucose level measured by the subject using a blood glucose meter based on the subject's operation of the input unit 141 (step S602).

The electronic device 100 also measures the subject's pre-meal pulse wave based on the subject's operation (step S603).

Then, after the subject has eaten, the electronic device 100 inputs the postprandial blood glucose level measured by the subject using the blood glucose meter based on the subject's operation (step S604).

The electronic device 100 also measures the subject's postprandial pulse wave based on the subject's operation (step S605).

Next, the electronic device 100 analyzes the measured pulse wave (step S606). Specifically, the electronic device 100 analyzes, for example, the rising index Sl, AI, and pulse rate PR related to the measured pulse wave.

The electronic device 100 estimates the subject's postprandial lipid level by applying the rise index Sl, AI, and pulse rate PR analyzed in step S606 and the subject's age to the estimation formula created in the flow diagram of FIG. 14 (step S607). The estimated postprandial lipid level is notified to the subject, for example, by the notification unit 147 of the electronic device 100.

Figure 16 is a diagram showing a comparison between postprandial lipid values estimated using the estimation equation created by the flow in Figure 14 and actually measured postprandial lipid values. In the graph shown in Figure 16, the horizontal axis shows the measured postprandial lipid values (actual values), and the vertical axis shows the estimated postprandial lipid values. The measured lipid values were measured using a Cobas b101 manufactured by Roche Diagnostics. As shown in Figure 16, the measured values and estimated values are generally within a range of ±20%. In other words, it can be said that the estimation accuracy using the estimation equation is within 20%.

In this way, the electronic device 100 can estimate postprandial lipid levels based on the preprandial and postprandial blood glucose levels measured by taking blood from the subject.

The electronic device 100 also estimates the lipid level using the blood glucose levels before and after a meal. Therefore, the electronic device 100 can estimate the lipid level by correcting (removing) the effect of the blood glucose level on the pulse wave after a meal. This allows the electronic device 100 to improve the accuracy of estimating the lipid level.

In this embodiment, the estimation formula was created using pre- and post-prandial blood glucose levels, pulse waves, and lipid levels, but the creation of the estimation formula is not limited to this, and the estimation formula may be created using either pre- or post-prandial blood glucose levels, pulse waves, and lipid levels. Furthermore, the electronic device 100 may estimate the subject's lipid level at any timing, not limited to the post-prandial lipid level. The electronic device 100 can also estimate the lipid level at any timing non-invasively and in a short time.

As with the first embodiment, in the electronic device 100 according to this embodiment, the estimation formula stored in the storage unit 145 may be updated based on the subject's pre-prandial blood glucose level and pulse wave and post-prandial blood glucose level and pulse wave acquired in steps S602 to S605 in estimating the lipid level. This allows the estimation formula to be updated every time the subject estimates the blood glucose level, improving the accuracy of estimating the post-prandial lipid level using the estimation formula.

In the above first and second embodiments, an example has been described in which, when estimating a postprandial blood glucose level using the electronic device 100, the subject inputs the preprandial blood glucose level measured using a blood glucose meter using the input unit 141 of the electronic device 100. However, the preprandial blood glucose level may be automatically input to the electronic device 100 from, for example, the blood glucose meter.

Figure 17 is a diagram showing a schematic of communication between the electronic device 100 and the blood glucose meter 160. The blood glucose meter 160 has a communication unit and can transmit and receive information via the communication unit 146 of the electronic device 100. For example, when the blood glucose meter 160 measures a blood glucose level (pre-prandial blood glucose level) based on the subject's operation, it transmits the blood glucose level as a measurement result to the electronic device 100. The electronic device 100 uses the blood glucose level acquired from the blood glucose meter 160 to estimate the subject's post-prandial blood glucose level, for example, according to the flow shown in Figure 10 or Figure 13.

Similarly, in the third embodiment, the electronic device 100 may obtain the blood glucose level from a blood glucose meter 160 with which the electronic device 100 can communicate. In this case, the electronic device 100 can estimate the lipid level based on the blood glucose level obtained from the blood glucose meter 160.

In addition, in the above embodiment, an example in which the estimation of blood glucose levels and lipid levels is performed by the electronic device 100 has been described, but the estimation of blood glucose levels and lipid levels does not necessarily have to be performed by the electronic device 100. An example in which the estimation of blood glucose levels and lipid levels is performed by a device other than the electronic device 100 will be described.

FIG. 18 is a schematic diagram showing a schematic configuration of a system according to an embodiment of the present invention. The system of the embodiment shown in FIG. 18 includes an electronic device 100, a server 151, a mobile terminal 150, and a communication network. As shown in FIG. 18, the pulse wave measured by the electronic device 100 is transmitted to the server 151 through the communication network and stored in the server 151 as the subject's personal information. The server 151 estimates the subject's blood glucose level or lipid level by comparing with the subject's previously acquired information and various databases. The server 151 may further create optimal advice for the subject. The server 151 returns the estimation result and advice to the mobile terminal 150 owned by the subject. A system can be constructed in which the mobile terminal 150 notifies the received estimation result and advice from the display unit of the mobile terminal 150. By utilizing the communication function of the electronic device 100, the server 151 can collect information from multiple users, thereby further improving the accuracy of the estimation. Furthermore, since the mobile terminal 150 is used as the notification means, the electronic device 100 does not require the notification unit 147, and is further miniaturized. Furthermore, since the blood glucose level or lipid level of the subject is estimated by the server 151, the computational burden on the control unit 143 of the electronic device 100 can be reduced. Furthermore, since the previously acquired information of the subject can be stored by the server 151, the burden on the storage unit 145 of the electronic device 100 can be reduced. Therefore, the electronic device 100 can be further miniaturized and simplified. Furthermore, the computational processing speed is also improved.

The system according to the present embodiment has been shown to be configured in such a way that the electronic device 100 and the mobile terminal 150 are connected via a communication network via the server 151, but the system according to the present invention is not limited to this. The system may also be configured in such a way that the electronic device 100 and the mobile terminal 150 are directly connected via a communication network without using the server 151.

The present invention has been described with respect to specific embodiments in order to fully and clearly disclose the present invention. However, the appended claims should not be limited to the above-described embodiments, but should be construed to embody all modifications and alternative configurations that may be made by those skilled in the art within the scope of the basic subject matter presented in this specification.

For example, in the above embodiment, the sensor unit 130 is provided with an angular velocity sensor 131, but the electronic device 100 according to the present invention is not limited to this. The sensor unit 130 may be provided with an optical pulse wave sensor consisting of a light-emitting unit and a light-receiving unit, or may be provided with a pressure sensor. Furthermore, the electronic device 100 is not limited to being worn on the wrist. The sensor unit 130 may be placed on an artery, such as the neck, ankle, thigh, or ear.

REFERENCE SIGNS LIST 100 Electronic device 110 Mounting section 120 Measurement section 120a Back surface 120b Front surface 111 Opening 130 Sensor section 131 Angular velocity sensor 132 Pulse contact section 133 Support section 140 Elastic body 141 Input section 143 Control section 144 Power supply section 145 Storage section 146 Communication section 147 Notification section 150 Portable terminal 151 Server 160 Blood glucose meter

Claims (13)

a control unit that estimates a postprandial blood glucose level or glucose metabolism of a subject based on an estimation equation created based on a blood glucose level and a pulse wave associated with the blood glucose level, and a preprandial blood glucose level of the subject and a preprandial and postprandial pulse wave of the subject,
The control unit uses, as the estimation equation, an estimation equation created based on the results of regression analysis using an index AI expressed as the ratio of the magnitude of the forward wave and the reflected wave of the pulse wave. The estimation device according to claim 1, wherein the estimation formula is created based on the regression analysis using sample data in which the blood glucose level variation is close to a normal distribution. The estimation device according to claim 1, wherein the estimation formula is created based on the subject's blood glucose level and a pulse wave associated with the blood glucose level. The estimation device according to any one of claims 1 to 3, wherein the control unit updates the estimation formula based on the subject's preprandial blood glucose level and pulse wave. The estimation device according to any one of claims 1 to 4, wherein the estimation formula is created using at least one of pre-meal and post-meal blood glucose levels. a control unit that estimates a postprandial blood glucose level or glucose metabolism of a subject based on an estimation equation created based on a blood glucose level and a pulse wave associated with the blood glucose level, and a preprandial blood glucose level of the subject and a preprandial and postprandial pulse wave of the subject,
The control unit of the estimation device creates an estimation equation for estimating blood glucose level or glucose metabolism based on the results of regression analysis using an index AI represented by the ratio of the magnitude of the forward wave and the reflected wave of the pulse wave. The estimation device according to claim 6, wherein the estimation formula is created using at least one of pre-meal and post-meal blood glucose levels. On the computer,
estimating the subject's postprandial blood glucose level or glucose metabolism based on an estimation equation created based on the blood glucose level and the pulse wave associated with the blood glucose level, the subject 's preprandial blood glucose level, and the subject's preprandial and postprandial pulse waves;
The program causes the computer to use, as the estimation equation, an equation created based on the results of regression analysis using an index AI expressed as the ratio of the magnitude of the forward wave and the reflected wave of the pulse wave. On the computer,
estimating the subject's postprandial blood glucose level or glucose metabolism based on an estimation equation created based on the blood glucose level and the pulse wave associated with the blood glucose level, the subject 's preprandial blood glucose level, and the subject's preprandial and postprandial pulse waves;
A program that creates an estimation equation for estimating blood glucose levels or glucose metabolism based on the results of regression analysis using an index AI that is expressed as the ratio of the magnitude of the forward wave and the reflected wave of the pulse wave. A sensor for acquiring a pulse wave of a subject;
An estimation device including a control unit that estimates a blood glucose level or glucose metabolism of the subject,
The estimation device estimates the subject 's postprandial blood glucose level or glucose metabolism based on an estimation equation created based on the blood glucose level and the pulse wave associated with the blood glucose level, and the subject's preprandial blood glucose level and the subject's preprandial and postprandial pulse waves, and uses, as the estimation equation, an equation created based on the results of a regression analysis using an index AI expressed as the ratio of the magnitude of a forward wave to a reflected wave of the pulse wave.
Estimation system. A sensor for acquiring a pulse wave of a subject;
An estimation device including a control unit that estimates a blood glucose level or glucose metabolism of the subject,
the estimation device estimates the subject 's postprandial blood glucose level or glucose metabolism based on an estimation equation created based on the blood glucose level and the pulse wave associated with the blood glucose level, the subject's preprandial blood glucose level, and the subject's preprandial and postprandial pulse waves;
The estimation formula is created based on the results of a regression analysis using an index AI that is expressed by the ratio of the magnitude of a forward wave and a reflected wave of the pulse wave.
Estimation system. a blood glucose meter for measuring a blood glucose level of a subject;
A sensor for acquiring a pulse wave of the subject;
An estimation device for estimating a blood glucose level or glucose metabolism of the subject,
The estimation device includes:
estimating a postprandial blood glucose level or glucose metabolism of the subject based on an estimation equation created based on the blood glucose level and the pulse wave associated with the blood glucose level, the preprandial blood glucose level of the subject measured by the blood glucose meter, and the preprandial and postprandial pulse waves of the subject acquired by the sensor;
creating an estimation formula for estimating the blood glucose level or glucose metabolism based on an index AI represented by a ratio of the magnitude of a forward wave and a reflected wave of the pulse wave;
Estimation system. a blood glucose meter for measuring a blood glucose level of a subject;
A sensor for acquiring a pulse wave of the subject;
An estimation device for estimating a lipid level of the subject,
The estimation device includes:
estimating a postprandial lipid level of the subject based on an estimation equation created based on the blood glucose level, the pulse wave and the lipid level associated with the blood glucose level, the preprandial and postprandial blood glucose levels of the subject measured by the blood glucose meter, and the preprandial and postprandial pulse waves of the subject acquired by the sensor;
creating an estimation formula for estimating the lipid level based on an index AI represented by a ratio of the magnitude of a forward wave and a reflected wave of the pulse wave;
Estimation system.

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