JP2021118913A - Electronic apparatus, estimation system, estimation method, and estimation program - Google Patents

Abstract

To provide an electronic apparatus, an estimation system, an estimation method, and an estimation program capable of simply estimating the physical condition of a subject.SOLUTION: An electronic apparatus 100 comprises: a sensor 130 for acquiring a pulse wave of a subject; and a control unit 143 which estimates the blood glucose level of the subject on the basis of an estimation formula generated based on the blood glucose level and a pulse wave associated with the blood glucose level and the pulse wave of the subject acquired by the sensor 130. The control unit 143 uses as the estimation formula, a formula generated based on a result of regression analysis using the period of the pulse wave or a pulse rate derived based on the period.SELECTED DRAWING: Figure 10

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 the measured biological information.

Conventionally, blood components and blood fluidity have been measured as means for estimating the health condition of a subject (user). These are measured using blood collected from a subject. In addition, electronic devices that measure biological information from a test site such as the wrist of a test subject are known. For example, Patent Document 1 describes an electronic device that measures the pulse of a subject when the subject wears it on the wrist.

JP-A-2002-360530

However, since blood collection is painful, it is difficult to estimate one's health condition on a daily basis. Further, the electronic device described in Patent Document 1 only measures the pulse, and cannot estimate the health condition of the subject other than the pulse.

An object of the present invention made in view of such circumstances is to provide an electronic device, an estimation system, an estimation method and an estimation program capable of easily estimating the health condition of a subject.

In order to solve the above problem, the electronic device according to the embodiment of the present invention is created based on the blood glucose level and the pulse wave associated with the blood glucose level with the sensor unit that acquires the pulse wave of the subject. The estimation formula is provided with a control unit that estimates the blood glucose level of the subject based on the pulse wave of the subject acquired by the sensor unit, and the control unit serves as the estimation formula. , The one prepared based on the result of the regression analysis using the period of the pulse wave or the pulse rate derived based on the period is used.

Further, the electronic device according to the embodiment of the present invention includes a sensor unit that acquires the pulse wave of the subject, and an estimation formula created based on the blood glucose level and the pulse wave associated with the blood glucose level. A control unit that estimates the glucose metabolism of the subject based on the pulse wave of the subject acquired by the sensor unit is provided, and the control unit comprises, as the estimation formula, the pulse wave of the subject. The one prepared based on the result of the regression analysis using the cycle or the pulse rate derived based on the cycle is used.

Further, the electronic device according to the embodiment of the present invention includes a sensor unit that acquires the pulse wave of the subject, and an estimation formula created based on the lipid value and the pulse wave associated with the lipid value. A control unit that estimates the lipid value of the subject based on the pulse wave of the subject acquired by the sensor unit is provided, and the control unit comprises, as the estimation formula, the pulse wave of the subject. The one prepared based on the result of the regression analysis using the cycle or the pulse rate derived based on the cycle is used.

Further, the electronic device according to the embodiment of the present invention includes a sensor unit that acquires the pulse wave of the subject, and an estimation formula created based on the lipid value and the pulse wave associated with the lipid value. A control unit that estimates the lipid metabolism of the subject based on the pulse wave of the subject acquired by the sensor unit is provided, and the control unit comprises, as the estimation formula, the pulse wave of the subject. The one prepared based on the result of the regression analysis using the cycle or the pulse rate derived based on the cycle is used.

Further, the estimation method according to the embodiment of the present invention is an estimation created based on a step of acquiring a pulse wave of a subject by a sensor unit, a blood glucose level, and a pulse wave associated with the blood glucose level. The estimation step includes an estimation step of estimating the blood glucose level or glucose metabolism of the subject based on the formula and the pulse wave of the subject acquired by the sensor unit, and the estimation step is the estimation formula. , The one prepared based on the result of the regression analysis using the cycle of the pulse wave or the pulse rate derived based on the cycle is used.

Further, the estimation method according to the embodiment of the present invention is an estimation created based on a step of acquiring a pulse wave of a subject by a sensor unit, a lipid value, and a pulse wave associated with the lipid value. The estimation step includes an estimation step of estimating the lipid value or lipid metabolism of the subject based on the formula and the pulse wave of the subject acquired by the sensor unit, and the estimation step is the estimation formula. , The one prepared based on the result of the regression analysis using the period of the pulse wave or the pulse number derived based on the period is used.

Further, the estimation program according to the embodiment of the present invention is created by using a computer based on a sensor unit that acquires a pulse wave of a subject, a blood glucose level, and a pulse wave associated with the blood glucose level. This is an estimation program that functions as a control unit for estimating the blood glucose level or glucose metabolism of the subject based on the estimation formula and the pulse wave of the subject acquired by the sensor unit. Is used as the estimation formula, which is prepared based on the cycle of the pulse wave or the result of regression analysis using the pulse rate derived based on the cycle.

Further, the estimation program according to the embodiment of the present invention is created by using a computer based on the sensor unit that acquires the pulse wave of the subject, the lipid value, and the pulse wave associated with the lipid value. This is an estimation program that functions as a control unit for estimating the lipid value or lipid metabolism of the subject based on the estimation formula and the pulse wave of the subject acquired by the sensor unit. Is used as the estimation formula, which is prepared based on the cycle of the pulse wave or the result of regression analysis using the pulse rate derived based on the cycle.

According to the present invention, it is possible to provide an electronic device, an estimation system, an estimation method and an estimation program capable of easily estimating the health condition of a subject.

It is a schematic diagram which shows the schematic structure of the electronic device which concerns on 1st Embodiment of this invention. It is sectional drawing which shows the schematic structure of the main body part of FIG. It is a figure which shows an example of the use state of the electronic device of FIG. It is a functional block diagram which shows the schematic structure of the electronic device of FIG. It is a figure explaining an example of the estimation method based on the change of a pulse wave in the electronic device of FIG. It is a figure which shows an example of an acceleration pulse wave. It is a figure which shows an example of the pulse wave acquired by the sensor part. It is a figure explaining another example of the estimation method based on the change of a pulse wave in the electronic device of FIG. It is a creation flow chart of the estimation formula used by the electronic device of FIG. It is a flow chart which estimates the postprandial blood glucose level of a subject using the estimation formula created by the flow of FIG. It is a figure which shows the comparison between the postprandial blood glucose level estimated by using the estimation formula created by the flow of FIG. 9 and the measured postprandial blood glucose level. It is a creation flow chart of the estimation formula used by the electronic device which concerns on 2nd Embodiment of this invention. It is a flow chart which estimates the postprandial blood glucose level of a subject using the estimation formula created by the flow of FIG. It is a creation flow chart of the estimation formula used by the electronic device which concerns on 3rd Embodiment of this invention. It is a flow chart which estimates the postprandial lipid value of a subject using the estimation formula created by the flow of FIG. It is a figure which shows the comparison between the postprandial lipid value estimated by using the estimation formula created by the flow of FIG. 14 and the measured postprandial lipid value. It is a figure which shows typically the communication between an electronic device and a glucose meter. It is a schematic diagram which shows the schematic structure of the system which concerns on one Embodiment of this invention.

Hereinafter, embodiments of the present invention will be described in detail 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. The electronic device 100 includes a mounting unit 110 and a measuring unit 120. FIG. 1 is a view of observing the electronic device 100 from the back surface 120a in contact with the test portion.

The electronic device 100 measures the biological information of the subject while the subject is wearing the electronic device 100. The biological information measured by the electronic device 100 is a pulse wave of the subject that can be measured by the measuring unit 120. In the present embodiment, the electronic device 100 will be described below assuming that the electronic device 100 is attached to the wrist of the subject to acquire the pulse wave as an example.

In the present embodiment, the mounting portion 110 is a linear elongated band-shaped band. The pulse wave is measured, for example, in a state where the subject wraps the mounting portion 110 of the electronic device 100 around his wrist. Specifically, the subject wraps the mounting portion 110 around the wrist so that the back surface 120a of the measuring portion 120 comes into contact with the portion to be examined, and measures the pulse wave. The electronic device 100 measures the pulse wave of blood flowing through the ulnar artery or the radial artery on the wrist of the subject.

FIG. 2 is a cross-sectional view showing a schematic configuration of the measuring unit 120 of FIG. In FIG. 2, not only the measuring unit 120 but also the mounting unit 110 around the measuring unit 120 is shown.

The measuring unit 120 has a back surface 120a that comes into contact with the wrist of the subject when worn, and a front surface 120b that is opposite to 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 in a state where the elastic body 140 is not pressed. A pulse contact portion 132 is provided at one end of the sensor portion 130. One end of the sensor unit 130 can be displaced in a direction substantially 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 the support unit 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 measuring unit 120 via the elastic body 140 and can be displaced. The elastic body 140 is, for example, a spring. However, the elastic body 140 is not limited to the spring, and may be any other elastic body such as resin or sponge.

Although not shown, the measurement unit 120 may be provided with a control unit, a storage unit, a communication unit, a power supply unit, a notification unit, a circuit for operating these units, a cable for connecting them, and the like.

The sensor unit 130 includes an angular velocity sensor 131 that detects the displacement of the sensor unit 130. The angular velocity sensor 131 may 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 other 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 receives an operation input from the subject, and is composed of, for example, an operation button (operation key). The input unit 141 may be configured by, for example, a touch screen.

FIG. 3 is a diagram showing an example of a usage state of the electronic device 100 by the subject. The subject uses the electronic device 100 wrapped around his wrist. The electronic device 100 is mounted with the back surface 120a of the measuring unit 120 in contact with the test unit. With the mounting portion 110 wrapped around the wrist, the measuring portion 120 can adjust the position so that the pulse portion 132 comes into contact with the position where the ulnar artery or the radial artery exists.

In FIG. 3, one end of the sensor unit 130 is in contact with the skin on the radial artery, which is the artery on the thumb side of the subject's left hand, when the electronic device 100 is attached. Due to the elastic force of the elastic body 140 arranged between the measuring unit 120 and the sensor unit 130, one end of the sensor unit 130 is in contact with the skin on the radial artery of the subject. The sensor unit 130 is displaced according to the movement of the radial artery of the subject, that is, the pulsation. The angular velocity sensor 131 acquires a pulse wave by detecting the displacement of the sensor unit 130. A pulse wave is a waveform obtained from the surface of the body that changes in volume and time of a blood vessel caused by the inflow of blood.

Referring to FIG. 2 again, the sensor unit 130 is in a state in which one end protrudes from the opening 111 in a state where 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 radial artery of the subject, and the elastic body 140 expands and contracts according to the pulsation, and one end of the sensor unit 130. Displaces. As the elastic body 140, one having an appropriate elastic modulus is used so as not to interfere with the pulsation and to expand and contract according to the pulsation. The opening width W of the opening 111 has a width sufficiently larger than the blood vessel diameter, that is, the radial artery diameter in the present embodiment. By providing the opening 111 in the measuring unit 120, the back surface 120a of the measuring unit 120 does not press the radial artery when the electronic device 100 is attached. Therefore, the electronic device 100 can acquire a pulse wave with less noise, and the measurement accuracy is improved.

FIG. 3 shows an example in which the electronic device 100 is worn on the wrist to acquire a pulse wave in the radial artery, but the present invention is not limited thereto. For example, the electronic device 100 may acquire a pulse wave of blood flowing through the carotid artery at the neck of the subject. Specifically, the subject may lightly press the pulse pad 132 against the position of the carotid artery to measure the pulse wave. In addition, the subject may wear the wearing part 110 by wrapping it around the neck so that the pulse contact part 132 comes to the position of the carotid artery.

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

The sensor unit 130 includes an angular velocity sensor 131, detects a pulsation from the test site, and acquires 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. Further, the control unit 143 is a processor that estimates the blood glucose level of the subject 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 a control procedure and a program that estimates a blood glucose level of a subject, and such a program is a storage medium such as a storage unit 145 or the like. Stored in. In addition, the control unit 143 estimates the state of the subject regarding glucose metabolism, lipid metabolism, and the like based on the calculated index. The control unit 143 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 lithium ion battery, and supplies electric 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 a plurality of 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 reading device for the storage medium. 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, a program for operating the electronic device 100, and the like, and also functions as a work memory. The storage unit 145 may store, for example, the measurement result of the pulse wave acquired by the sensor unit 130.

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

The notification unit 147 notifies information by sound, vibration, an image, or the like. The notification unit 147 displays a speaker, an oscillator, a liquid crystal display (LCD), an organic EL display (OELD: Organic Electro-Luminescence Display), an inorganic EL display (IELD: Inorganic Electro-Luminescence Display), or the like. It may be equipped with a device. In the present embodiment, the notification unit 147 notifies, for example, the state of glucose metabolism or lipid metabolism of the subject.

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

Here, the estimation theory regarding the estimation of the blood glucose level based on the pulse wave will be described. After a meal, an increase in blood glucose level causes a decrease in blood fluidity (increase in viscosity), dilation of blood vessels and an increase in circulating blood volume, and vasodynamics and blood so that these states are balanced. The dynamics are determined. The decrease in blood fluidity is caused, for example, by increasing the viscosity of plasma or decreasing the deformability of red blood cells. In addition, vasodilation is caused by insulin secretion, digestive hormone secretion, and an increase in body temperature. When blood vessels dilate, the pulse rate increases because it suppresses the decrease in blood pressure. Also, the increase in circulating blood volume compensates for blood consumption for digestion and absorption. Changes in vascular and hemodynamics before and after a meal due to these factors are also reflected in the pulse wave. Therefore, the electronic device 100 can acquire the pulse wave and estimate the blood glucose level based on the change in the waveform of the acquired pulse wave.

Based on the above estimation theory, an estimation formula for estimating the blood glucose level can be created by performing regression analysis based on sample data of blood glucose levels and pulse waves before and after meals obtained from a plurality of subjects. can. At the time of estimation, the blood glucose level of the subject can be estimated by applying the created estimation formula to the index based on the pulse wave of the subject. In the preparation of the estimation formula, in particular, by performing regression analysis using sample data in which the variation in blood glucose level is close to the normal distribution and creating the estimation formula, the subject to be examined regardless of whether it is before or after meals. The blood glucose level can be estimated.

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

The rising index Sl is derived based on the waveform shown in the region D1 of FIG. Specifically, the rising index Sl is the ratio of the first local minimum value to the first maximum value in the acceleration pulse wave derived by differentiating the pulse wave twice. The rise index Sl is represented by −b / a in the acceleration pulse wave shown as an example in FIG. 6, for example. The rise index Sl becomes smaller due to a decrease in blood fluidity after a meal, insulin secretion, and vasodilation (relaxation) due to an increase in body temperature.

AI is an index expressed by the ratio of the magnitude of the forward wave and the reflected wave of the pulse wave. The method of deriving AI will be described with reference to FIG. 7. FIG. 7 is a diagram showing an example of a pulse wave acquired on the wrist using the electronic device 100. FIG. 7 shows a case where the angular velocity sensor 131 is used as a pulsation detecting means. In FIG. 7, the angular velocity acquired by the angular velocity sensor 131 is time-integrated, and the horizontal axis represents time and the vertical axis represents angle. Since the acquired pulse wave may contain noise caused by the body movement of the subject, for example, it may be corrected by a filter that removes the DC (Direct Current) component to extract only the pulsation component.

Pulse wave propagation is a phenomenon in which the pulsation of blood pushed out of the heart is transmitted through the walls of arteries and blood. The pulsation caused by the blood extruded from the heart reaches the periphery of the limbs as a forward wave, and a part of it is reflected at the bifurcation of the blood vessel, the change in the blood vessel diameter, etc. and returns as the reflected wave. AI is obtained by dividing the magnitude of the reflected wave by the magnitude of the forward wave, and is represented by AI _n = (P _Rn −P _Sn ) / (P _Fn −P _Sn ). Here, AI _n is the AI for each pulse. As the AI, for example, the pulse wave may be measured for several seconds, and _{the average value AI ave of AI n} (an integer of n = 1 to n) for each pulse may be calculated. AI is derived based on the waveform shown in region D2 of FIG. AI decreases due to a decrease in blood fluidity after meals and vasodilation due to an increase in body temperature.

The pulse rate PR is derived based on the _{pulse wave period T PR shown in FIG.} Pulse rate PR increases after a meal.

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

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

The electronic device 100 estimates the blood glucose level of the subject by using an estimation formula based on the above-mentioned rise index Sl, AI, pulse rate PR, Fourier coefficient, and the like.

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

FIG. 9 is a flow chart for creating an estimation formula used by the electronic device 100 of FIG. In the estimation formula, the pre-meal and post-meal pulse waves of the subject are measured using a pulse wave meter, and the pre-meal and post-meal blood glucose levels of the subject are measured using a glucose meter, and based on the sample data obtained by the measurement, the estimation formula is used. Created by performing regression analysis. Before meals, it means the subject's fasting time, and after meals, it means the time when the blood glucose level rises after a predetermined time after meals (for example, about 1 hour after the start of meals). The sample data to be acquired is not limited to before and after meals, and may be data in a time zone in which the blood glucose level fluctuates greatly.

In creating the estimation formula, first, information on the blood glucose level of the subject before meals and the pulse wave associated with the blood glucose level, which are measured by the glucose meter and the pulse wave meter, respectively, is input to the estimation formula creation device (step S101). ).

In addition, information on the blood glucose level of the subject after meals and the pulse wave associated with the blood glucose level, which are measured by the blood glucose meter and the pulse wave meter, respectively, is input to the estimation formula creation device (step S102). The blood glucose level input in steps S101 and S102 is measured by a glucose meter, for example, by collecting blood. Further, 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 or not the number of samples of the sample data input in steps S101 and S102 is N or more sufficient for performing regression analysis (step S103). The number of samples N can be appropriately determined, for example, 100. When the estimation formula creation device determines that the number of samples is less than N (No), the estimation formula creating apparatus repeats steps S101 and S102 until the number of samples becomes N or more. On the other hand, when it is determined that the number of samples is N or more (in the case of Yes), the estimation formula creation device proceeds to step S104 and executes the calculation of the estimation formula.

In the calculation of the estimation formula, the estimation formula creation device analyzes the input pre-meal and post-meal pulse waves (step S104). In the present embodiment, the estimation formula creating device analyzes the rise index Sl, AI and pulse rate PR of the pulse wave before and after the meal. The estimation formula creation device may perform FFT analysis as the analysis of the pulse wave.

Then, the estimation formula creation device executes regression analysis (step S105). The objective variable in regression analysis is postprandial blood glucose. The explanatory variables in the regression analysis are the age input in step S101 or step S102, and the pre-meal and post-meal pulse wave rise indexes Sl, AI, and pulse rate PR analyzed in step S104. When the estimation formula creation device performs the FFT analysis in step S104, the explanatory variable may be, for example, the Fourier coefficient calculated as a result of the FFT analysis.

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

In formula (1), GLa indicates the postprandial blood glucose level. In addition, age is age, PRb is pre-meal pulse rate PR, AIb is pre-meal AI, Slb is pre-meal pulse rate PR, PRa is post-meal pulse rate PR, AIa is post-meal AI, and Sl is post-meal rise index Sl. BLG indicates the blood glucose level input (measured by collecting blood) by the subject. The blood glucose level BLG input by the subject is a blood glucose level measured at a timing different from the estimated blood glucose level GLa. In the present embodiment, the blood glucose level BLG input by the subject is the blood glucose level measured by collecting blood before meals. By using the blood glucose level BLG measured by collecting blood in the estimation formula, the estimation accuracy of the blood glucose level is improved.

Next, the flow of estimating the blood glucose level of the subject using the estimation formula will be described. FIG. 10 is a flow chart for estimating the postprandial blood glucose level of the subject using the estimation formula created by the flow of FIG. Here, a case where the subject inputs the pre-meal blood glucose level measured by using a glucose meter from the input unit 141 of the electronic device 100 will be described.

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

Further, the electronic device 100 inputs the pre-meal blood glucose level measured by the subject using the glucose meter based on the operation of the input unit 141 by the subject (step S202).

Further, the electronic device 100 measures the pulse wave before meals of the subject based on the operation by the subject (step S203).

Then, after the subject has eaten, the electronic device 100 measures the postprandial pulse wave of the subject based on the operation by the subject (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 applies the pre-meal blood glucose level input in step S202, the rise index Sl, AI and pulse rate PR analyzed in step S205, and the age of the subject to, for example, the above equation (1). Then, the postprandial blood glucose level of the subject is estimated (step S206). The estimated postprandial blood glucose level is notified to the subject from, for example, the notification unit 147 of the electronic device 100.

FIG. 11 is a diagram showing a comparison between the postprandial blood glucose level estimated using the estimation formula created by the flow of FIG. 9 and the actually measured postprandial blood glucose level. In the graph shown in FIG. 11, the horizontal axis shows the measured value (measured value) of the postprandial blood glucose level, and the vertical axis shows the estimated value of the postprandial blood glucose level. The measured blood glucose level was measured using a Terumo blood glucose meter Medisafe Fit. As shown in FIG. 11, the measured value and the estimated value are included in the range of approximately ± 20%. That is, it can be said that the estimation accuracy by the estimation formula is within 20%.

In this way, the electronic device 100 can estimate the postprandial blood glucose level in a non-invasive and short time based on the premeal blood glucose level measured by the subject by collecting blood. In the present embodiment, the estimation formula is created using the blood glucose level and pulse wave before and after meal, but the preparation of the estimation formula is not limited to this, and the estimation formula is estimated using either the blood glucose level and pulse wave before or after meal. You may create an expression. Further, the electronic device 100 is not limited to the postprandial blood glucose level, and may estimate the blood glucose level of the subject at any timing. The electronic device 100 can estimate the blood glucose level at an arbitrary timing in a non-invasive manner in a short time.

The electronic device 100 according to the present embodiment updates the estimation formula stored in the storage unit 145 based on the pre-meal blood glucose level and pulse wave of the subject acquired in steps S202 and S203 in the estimation of the blood glucose level. You may. That is, the electronic device 100 can use the pre-meal blood glucose level and the pulse wave acquired at the time of estimating the blood glucose level as sample data for updating the estimation formula. As a result, the estimation formula is updated every time the subject estimates the blood glucose level, and the accuracy of estimating the postprandial blood glucose level using the estimation formula is improved.

(Second Embodiment)
In the first embodiment, the case where the estimation formula is created based on the blood glucose level and the pulse wave before and after the meal of the subject has been described. In the second embodiment, an example will be described in which the estimation formula is created based on the blood glucose level and pulse wave before and after meals of the subject himself / herself.

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

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

Further, the electronic device 100 measures the pulse wave before meals of the subject based on the operation by the subject (step S302).

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

Further, the electronic device 100 measures the postprandial pulse wave of the subject based on the operation by the subject (step S304).

The electronic device 100 determines whether or not the number of samples of the sample data input in steps S301 to S304 is N or more sufficient for performing regression analysis (step S305). The number of samples N can be appropriately determined, and can be, for example, 5. When the estimation formula creation device determines that the number of samples is less than N (No), the estimation formula creating apparatus repeats steps S301 to S304 until the number of samples becomes N or more. On the other hand, when it is determined that the number of samples is N or more (in the case of Yes), the estimation formula creation device proceeds to step S306 and executes the calculation of the estimation formula.

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

Next, the flow of estimating the blood glucose level of the subject using the estimation formula will be described. FIG. 13 is a flow chart for estimating the postprandial blood glucose level of the subject using the estimation formula created by the flow of FIG. Here, a case where the subject inputs the blood glucose level measured by using the glucose meter from the input unit 141 of the electronic device 100 will be described.

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

Further, the electronic device 100 inputs the pre-meal blood glucose level measured by the subject using the glucose meter based on the operation of the input unit 141 by the subject (step S402).

Further, the electronic device 100 measures the pulse wave before meals of the subject based on the operation by the subject (step S403).

Then, after the subject has eaten, the electronic device 100 measures the postprandial pulse wave of the subject based on the operation by the subject (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 applies the rise index Sl, AI and pulse rate PR analyzed in step S405 and the age of the subject to the estimation formula created in the flow chart of FIG. Estimate the blood glucose level (step S406). The estimated postprandial blood glucose level is notified to the subject from, for example, the notification unit 147 of the electronic device 100.

In this way, the electronic device 100 can estimate the postprandial blood glucose level in a non-invasive and short time based on the premeal blood glucose level measured by the subject by collecting blood. In the present embodiment, since the estimation formula for estimating the postprandial blood glucose level is created based on the sample data obtained from the subject, the accuracy of estimating the postprandial blood glucose level of the subject is improved. do.

As for the electronic device 100 according to the present embodiment, the blood glucose level and the pulse wave before meals of the subject acquired in steps S402 and S403 in the estimation of the blood glucose level are obtained in the same manner as described in the first embodiment. Based on this, the estimation formula stored in the storage unit 145 may be updated. As a result, the estimation formula is updated every time the subject estimates the blood glucose level, and the accuracy of estimating the postprandial blood glucose level using the estimation formula is improved.

Further, when the electronic device 100 can collect sample data of a sufficient number of samples from the subject, the blood glucose level is based on the pulse wave of the subject without using the blood glucose level measured by collecting blood. May be estimated. For example, the electronic device 100 estimates the pre-meal blood glucose level of the subject based on the pre-meal pulse wave of the subject. In this way, the subject measures the pre-meal pulse wave using the electronic device 100, and the electronic device 100 uses the estimation formula based on the pre-meal pulse wave to measure the pre-meal blood glucose of the subject. The value can be estimated. In this case, the electronic device 100 can also estimate the blood glucose level before meals in a non-invasive manner in a short time. Sufficient sample data refers to an amount of data that can be used to create an estimation formula that can estimate the pre-meal blood glucose level of a subject based on the pre-meal pulse wave with an accuracy higher than a predetermined accuracy. Further, the estimated blood glucose level is not limited to before meals, and the postprandial blood glucose level may be estimated based on the pulse wave after meals. Further, the estimated blood glucose level is not limited to before and after a meal, and the blood glucose level at an arbitrary timing may be estimated based on the pulse wave measured at an arbitrary timing.

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

The electronic device 100 stores, for example, an estimation formula for estimating the lipid value based on the pulse wave in the storage unit 145 in advance. The electronic device 100 estimates the lipid value using these estimation formulas.

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

FIG. 14 is a flow chart for creating an estimation formula used by the electronic device 100 according to the present embodiment. Also in this embodiment, the estimation formula is created by performing regression analysis based on the sample data. In the present embodiment, as sample data, an estimation formula is created based on the pulse wave, lipid level, and blood glucose level before meals. In the present embodiment, before meals means the subject's fasting. In addition, after a meal, it means a time when the lipid level becomes high after a predetermined time after a meal (for example, about 3 hours after the start of a meal). In the preparation of the estimation formula, in particular, by performing regression analysis using sample data in which the variation in lipid values is close to the normal distribution, the estimation formula is prepared, and the subject to be inspected regardless of whether it is before or after meals. The lipid level can be estimated at any timing.

In the preparation of the estimation formula, first, the information on the blood glucose level of the subject before meals measured by the blood glucose meter, the pulse wave meter, and the lipid measuring device, and the pulse wave and the lipid value associated with the blood glucose level are created. It is input to the device (step S501).

In addition, information on the blood glucose level of the subject after meals measured by the blood glucose meter, the pulse wave meter, and the lipid measuring device, and the pulse wave and the lipid level associated with the blood glucose level are input to the estimation formula creation device ( Step S502). The blood glucose level input in steps S501 and S502 is measured by a glucose meter, for example, by collecting blood. Further, 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 or not the number of samples of the sample data input in steps S501 and S502 is N or more sufficient for performing regression analysis (step S503). The number of samples N can be appropriately determined, for example, 100. When the estimation formula creation device determines that the number of samples is less than N (No), the estimation formula creating apparatus repeats steps S501 and S502 until the number of samples becomes N or more. On the other hand, when it is determined that the number of samples is N or more (in the case of Yes), the estimation formula creation device proceeds to step S504 and executes the calculation of the estimation formula.

In the calculation of the estimation formula, the estimation formula creation device analyzes the input pre-meal and post-meal pulse waves (step S504). In the present embodiment, the estimation formula creating device analyzes the rise index Sl, AI and pulse rate PR of the pulse wave before and after the meal. The estimation formula creation device may perform FFT analysis as the analysis of the pulse wave.

Then, the estimation formula creation device executes regression analysis (step S505). The objective variable in regression analysis is the postprandial lipid level. The explanatory variables in the regression analysis are the age input in step S501 or step S502, and the pre-meal and post-meal pulse wave rise indexes Sl, AI, and pulse rate PR analyzed in step S504. When the estimation formula creation device performs the FFT analysis in step S504, the explanatory variable may be, for example, the Fourier coefficient calculated as a result of the FFT analysis.

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

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

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

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

Further, the electronic device 100 measures the pulse wave before meals of the subject based on the operation by the subject (step S603).

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

In addition, the electronic device 100 measures the postprandial pulse wave of the subject based on the operation by the subject (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 applies the rise index Sl, AI and pulse rate PR analyzed in step S606 and the age of the subject to the estimation formula created in the flow chart of FIG. 14, after the subject's meal. The lipid level is estimated (step S607). The estimated postprandial lipid level is notified to the subject from, for example, the notification unit 147 of the electronic device 100.

FIG. 16 is a diagram showing a comparison between the postprandial lipid value estimated using the estimation formula created by the flow of FIG. 14 and the measured postprandial lipid value. In the graph shown in FIG. 16, the horizontal axis shows the measured value (measured value) of the postprandial lipid value, and the vertical axis shows the estimated value of the postprandial lipid value. The measured lipid value was measured using Cobas b101 manufactured by Roche Diagnostics. As shown in FIG. 16, the measured value and the estimated value are included in the range of approximately ± 20%. That is, it can be said that the estimation accuracy by the estimation formula is within 20%.

In this way, the electronic device 100 can estimate the postprandial lipid level based on the pre-meal and post-meal blood glucose levels measured by the subject by collecting blood.

In addition, the electronic device 100 estimates the lipid level using the blood glucose levels before and after a meal. Therefore, the electronic device 100 can correct (remove) the influence of the blood glucose level on the pulse wave after a meal and estimate the lipid level. As a result, according to the electronic device 100, the accuracy of estimating the lipid value is improved.

In the present embodiment, an estimation formula is created using the blood glucose level, pulse wave, and lipid level before and after a meal, but the estimation formula is not limited to this, and the blood glucose level and pulse wave of either before or after a meal are created. , An estimation formula may be created using the lipid value. Further, the electronic device 100 may estimate the lipid value of the subject at any timing, not limited to the lipid value after meals. The electronic device 100 can estimate the lipid value at an arbitrary timing in a non-invasive manner in a short time.

As for the electronic device 100 according to the present embodiment, the blood glucose level and the pulse wave before meals of the subject acquired in steps S602 to S605 in the estimation of the lipid value are obtained in the same manner as described in the first embodiment. , The estimation formula stored in the storage unit 145 may be updated based on the postprandial blood glucose level and the pulse wave. As a result, the estimation formula is updated every time the subject estimates the blood glucose level, and the accuracy of estimating the postprandial lipid level using the estimation formula is improved.

In the first and second embodiments, when the postprandial blood glucose level is estimated using the electronic device 100, the pre-meal blood glucose level measured by the subject using a glucose meter is measured by the electronic device 100. An example of input using the input unit 141 of the above has been described. However, the pre-meal blood glucose level may be automatically input to the electronic device 100 from, for example, a glucose meter.

FIG. 17 is a diagram schematically showing communication between the electronic device 100 and the glucose meter 160. The glucose meter 160 includes 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 the blood glucose level (pre-meal blood glucose level) based on the operation of the subject, the blood glucose meter 160 transmits the blood glucose level as the measurement result to the electronic device 100. The electronic device 100 estimates the postprandial blood glucose level of the subject using the blood glucose level obtained from the glucose meter 160, for example, by the flow shown in FIG. 10 or FIG.

Similarly, in the case of the third embodiment, the electronic device 100 may acquire the blood glucose level from the communicable glucose meter 160. 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.

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

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 personal information of the subject. The server 151 estimates the blood glucose level or lipid level of the subject by comparing it with the past acquired information of the subject and various databases. Server 151 may also create optimal advice for the subject. The server 151 returns the estimation result and the advice to the mobile terminal 150 owned by the subject. The mobile terminal 150 can construct a system in which the received estimation result and advice are notified from the display unit of the mobile terminal 150. By using the communication function of the electronic device 100, information from a plurality of users can be collected in the server 151, so that the estimation accuracy is further improved. Further, 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. Further, since the blood glucose level or the lipid level of the subject is estimated by the server 151, the calculation load of the control unit 143 of the electronic device 100 can be reduced. Further, since the past acquired information of the subject can be stored in 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. In addition, the processing speed of calculation is also improved.

The system according to the present embodiment shows a configuration in which the electronic device 100 and the mobile terminal 150 are connected by a communication network via the server 151, but the system according to the present invention is not limited to this. The electronic device 100 and the mobile terminal 150 may be directly connected to each other via a communication network without using the server 151.

In order to fully and clearly disclose the present invention, characteristic examples have been described. However, the accompanying claims are not limited to the above embodiments, and all modifications and alternatives that can be created by those skilled in the art within the scope of the basic matters set forth herein. It should be configured to embody possible configurations.

For example, in the above-described embodiment, the case where the sensor unit 130 is provided with the angular velocity sensor 131 has been described, but the electronic device 100 according to the present invention is not limited to this. The sensor unit 130 may include an optical pulse wave sensor including a light emitting unit and a light receiving unit, or may include a pressure sensor. Further, the electronic device 100 is not limited to the wrist. The sensor unit 130 may be arranged on an artery such as the neck, ankle, thigh, and ear.

100 Electronic equipment 110 Mounting part 120 Measuring part 120a Back side 120b Front side 111 Opening part 130 Sensor part 131 Angular velocity sensor 132 Pulse contact part 133 Support part 140 Elastic body 141 Input part 143 Control part 144 Power supply part 145 Storage part 146 Communication part 147 Notification part 150 Mobile terminal 151 Server 160 Glucose meter

Claims (9)

The sensor unit that acquires the pulse wave of the subject and
The blood glucose level of the subject is estimated based on the blood glucose level and the estimation formula created based on the pulse wave associated with the blood glucose level and the pulse wave of the subject acquired by the sensor unit. With a control unit to
The control unit is used as the estimation formula.
An electronic device using the period of the pulse wave or one created based on the result of regression analysis using the pulse rate derived based on the period. The sensor unit that acquires the pulse wave of the subject and
The glucose metabolism of the subject is estimated based on the blood glucose level and the estimation formula created based on the pulse wave associated with the blood glucose level and the pulse wave of the subject acquired by the sensor unit. With a control unit to
The control unit is used as the estimation formula.
An electronic device using the period of the pulse wave or one created based on the result of regression analysis using the pulse rate derived based on the period. The sensor unit that acquires the pulse wave of the subject and
The lipid value of the subject is estimated based on the lipid value and the estimation formula created based on the pulse wave associated with the lipid value and the pulse wave of the subject acquired by the sensor unit. With a control unit
The control unit is used as the estimation formula.
An electronic device using the period of the pulse wave or one created based on the result of regression analysis using the pulse rate derived based on the period. The sensor unit that acquires the pulse wave of the subject and
The lipid metabolism of the subject is estimated based on the lipid value and the estimation formula created based on the pulse wave associated with the lipid value and the pulse wave of the subject acquired by the sensor unit. Control unit and
With
The control unit is used as the estimation formula.
An electronic device using the period of the pulse wave or one created based on the result of regression analysis using the pulse rate derived based on the period. A glucose meter that measures the blood glucose level of the subject, and
The electronic device according to any one of claims 1 to 4, and the electronic device.
Estimate system with. The process of acquiring the pulse wave of the subject by the sensor unit,
The blood glucose level or sugar of the subject based on the blood glucose level and the estimation formula created based on the pulse wave associated with the blood glucose level and the pulse wave of the subject acquired by the sensor unit. The estimation process for estimating metabolism and
With
The estimation step is performed as the estimation formula.
An estimation method using the period of the pulse wave or the one prepared based on the result of regression analysis using the pulse rate derived based on the period. The process of acquiring the pulse wave of the subject by the sensor unit,
Based on the lipid value and the estimation formula created based on the pulse wave associated with the lipid value, and the pulse wave of the subject acquired by the sensor unit, the lipid value or lipid of the subject The estimation process for estimating metabolism and
With
The estimation step is performed as the estimation formula.
An estimation method using the period of the pulse wave or the one prepared based on the result of regression analysis using the pulse rate derived based on the period. Computer,
The sensor unit that acquires the pulse wave of the subject, and
The blood glucose level or sugar of the subject based on the blood glucose level and the estimation formula created based on the pulse wave associated with the blood glucose level and the pulse wave of the subject acquired by the sensor unit. An estimation program that functions as a control unit that estimates metabolism.
The control unit is used as the estimation formula.
An estimation program using the period of the pulse wave or the one created based on the result of regression analysis using the pulse rate derived based on the period. Computer,
The sensor unit that acquires the pulse wave of the subject, and
Based on the lipid value and the estimation formula created based on the pulse wave associated with the lipid value, and the pulse wave of the subject acquired by the sensor unit, the lipid value or lipid of the subject An estimation program that functions as a control unit that estimates metabolism.
The control unit is used as the estimation formula.
An estimation program using the period of the pulse wave or the one created based on the result of regression analysis using the pulse rate derived based on the period.

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