JP2012045191A - Blood sugar level prediction device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a blood sugar level prediction device which predicts an accurate blood sugar level.SOLUTION: The blood sugar level prediction device 10 includes: an intake energy acquisition section 111 for acquiring intake energy information of a user whose blood sugar level is to be predicted; a consumed energy acquisition section 112 for acquiring consumed energy information; a posture measuring part 180 for acquiring posture information; a calculation section 113 for creating a first prediction curve of the blood sugar level of the user from the intake energy information using a first prediction algorithm and creating a second prediction curve of the blood sugar level from the consumed energy information using a second prediction algorithm; an analysis section 114 for acquiring the user information 200 containing the blood sugar level information which changes on the basis of the dietary information and the behavior information, and creating a first model waveform and a second model waveform; a generation section 115 for integrating the first prediction curve and the first model waveform with the second prediction curve and the second model waveform which are transformed on the basis of the posture information, and creating a prediction blood sugar level curve; and a display part 150 for displaying the prediction blood sugar level curve.

Description

  The present invention relates to a blood glucose level prediction apparatus.

  Diabetes mellitus and lifestyle-related diseases are effective for life improvement and treatment by keeping records of biological information, ingested calories, consumed calories, etc. in daily life. The calorie intake can be grasped by recording the meal content and number of times, but the calorie consumption requires measurement of basal metabolism, exercise amount, etc., and it is difficult to accurately grasp at an appropriate time in daily life. is there.

  Therefore, a prediction model for predicting the user's blood glucose level is created based on the past history data of the user's intake calorie and consumption calorie, and the blood glucose level is calculated from the intake calorie and consumption calorie using this prediction model. A blood sugar level predicting device for predicting is disclosed (for example, see Patent Document 1).

JP 2005-328924 A

  In such Patent Document 1, history data is created based on collected data including calorie consumption corresponding to exercise amount, calorie intake corresponding to meal amount, and actually measured blood glucose level of the user, and based on the history data, A change in blood glucose level is predicted from the input calorie consumption and intake calorie, and the calorie consumption is measured and calculated using a pedometer, an acceleration sensor, or the like.

  However, the calorie consumption according to the user's posture (for example, standing, sitting, lying, etc.) cannot be measured. Even in a stationary state, there is a difference in calorie consumption due to the difference in posture of the user, and even in the same movement (exercise), there is a difference in calorie consumption between standing and sitting. Moreover, it may move in a stationary state in daily life. In the prior art, such changes in the user's posture and position cannot be detected, and as a result, accurate blood glucose level prediction is impossible.

  SUMMARY An advantage of some aspects of the invention is to solve at least a part of the problems described above, and the invention can be implemented as the following forms or application examples.

  Application Example 1 A blood glucose level prediction apparatus according to this application example acquires an intake energy acquisition unit that acquires intake energy information consumed by a user who is to predict a blood glucose level, and acquires energy consumption information consumed by the user. A consumption energy acquisition unit, a posture measurement unit that acquires posture information of the user, a first prediction curve of a blood glucose level using the first prediction algorithm for the intake energy information, and a second prediction of the consumption energy information A calculation unit for generating a second prediction curve of blood glucose level using the prediction algorithm, and user information including blood glucose level information that changes based on the user's meal information and behavior information, and a first model waveform; An analysis unit for generating a second model waveform; the first prediction curve; the first model waveform; the second prediction curve modified based on the posture information; A generation unit that creates a predicted blood sugar level curve and the second model waveform, the integrating respectively, characterized by comprising a display unit for displaying at least the predicted blood sugar value curve, a.

  Here, the consumed energy information includes, for example, changes in calorie consumption and blood glucose level corresponding to the type of exercise. In addition, the posture measurement unit adds the change in blood glucose level corresponding to the posture and posture changes such as standing, sitting, and prone positions, compared to the case of measuring only the calories consumed by conventional user exercises. Accurate energy consumption information can be obtained. From this, a more accurate predictive blood sugar level curve can be created.

  Application Example 2 In the blood glucose level prediction apparatus according to the application example, it is preferable that the posture measurement unit includes an altitude sensor that detects the posture of the user.

  By including an altitude sensor in the posture measurement unit, if the posture measurement unit is mounted on the user's arm, etc., it is possible to detect standing, sitting, prone positions, etc., and intermediate positions between them. Even when there is no movement, calorie consumption corresponding to the posture difference can be input, and the second blood sugar level prediction curve is deformed by adding posture information, thereby enabling accurate blood sugar level prediction.

  Application Example 3 In the blood glucose level prediction apparatus according to the application example described above, it is preferable that the posture measurement unit includes an altitude sensor and a GPS sensor that detects position information of the user.

  By providing an altitude sensor and a GPS sensor in this way, it is possible to detect changes in posture such as the user's standing position, sitting position, and prone position, as well as positional changes in the plane direction. By using these two sensors in combination, slopes, etc. Can be detected, and by inputting a blood sugar level change corresponding to these posture information, the second predictive curve can be deformed to create a more accurate predictive blood sugar level curve.

  Application Example 4 In the blood sugar level predicting apparatus according to the application example, it is preferable that the altitude sensor is a barometric pressure sensor.

  The altitude sensor includes an atmospheric pressure method, a GPS method, a radio wave method, a laser method, and the like, but is required to be capable of detecting at least a posture difference such as standing, sitting, and lying down with a small size and light weight that can be worn by the user. For this reason, the barometric sensor is most suitable. As an atmospheric pressure sensor, there is one using a quartz element whose resonance frequency changes due to a change in atmospheric pressure. Such an atmospheric pressure sensor can measure an atmospheric pressure change of 0.01 hPa at a height change of 0.1 m under a constant temperature condition, and can detect the change in the posture. Since it can be made small and light, the user can always wear it on his arm.

  Application Example 5 In the blood sugar level prediction apparatus according to the application example, it is preferable that the energy consumption information is measured by an activity meter including an activity amount measurement unit and the posture measurement unit.

  As the activity amount measuring unit, for example, a triaxial acceleration sensor can be adopted. Therefore, if an activity meter including a GPS sensor and an atmospheric pressure sensor (altitude sensor) is used as a three-axis acceleration sensor and an attitude measurement unit, energy consumption including user behavior information and attitude information can be calculated. effective.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a configuration explanatory diagram illustrating a configuration of a blood sugar level prediction apparatus according to a first embodiment. (A) is a table | surface which shows an example of meal menu information, (b) is a table | surface which shows an example of exercise type information. User information is represented, (a) is a figure showing an example of blood glucose level information, (b) is a table showing an example of action information. The function block diagram centering on the function of a control part. (A) is a figure which shows an example of a 1st prediction curve, (b) is a figure which shows an example of a 2nd prediction curve. The conceptual diagram showing a user's attitude | position. Explanatory drawing which represents typically an example of the measurement of the attitude | position change at the time of combining a GPS sensor and an atmospheric pressure sensor. A second prediction curve deformed based on the posture information. The operation | movement flow explanatory drawing of a blood glucose level prediction apparatus. The figure explaining the example which deform | transforms a 1st prediction curve. The figure explaining the example which deform | transforms a 2nd prediction curve. FIG. 6 is a configuration explanatory view showing a configuration of a blood sugar level predicting device according to Modification 1; The figure showing an example of the blood glucose level information among the user information which concerns on the modification 1. FIG. The functional block diagram centering on the control part of the blood glucose level prediction apparatus which concerns on the modification 1. FIG. The specific example by the modification 1 is shown, (a) represents the change of a pulse, (b) is a figure which illustrates changing a 1st prediction curve based on a pulse change.

Embodiments according to the present invention will be described below with reference to the drawings.
(Embodiment 1)

  FIG. 1 is a configuration explanatory diagram illustrating a configuration of a blood sugar level prediction apparatus according to the first embodiment. The blood glucose level prediction apparatus 10 includes a control unit 110, an activity amount measurement unit 120, a posture measurement unit 180, an operation unit 130, a storage unit 140, a display unit 150, and a time measuring unit 160 as main components. ing.

  The control unit 110 includes a CPU (Central Processing Unit), a ROM (Read Only Memory), and a RAM (Random Access Memory) memory, and by executing a control program stored in the ROM in advance using the RAM as a working area, Each unit connected to the control unit 110 is controlled. Details of the control unit 110 will be described later with reference to FIG.

The activity amount measuring unit 120 obtains calorie consumption consumed by the user, and sends the obtained calorie consumption to the control unit 110 as consumption energy information. The activity amount measuring unit 120 includes, for example, sensors such as an acceleration sensor and a speed sensor for detecting the user's exercise state, and outputs an output signal from the sensor detected in advance by the user's walking or exercise. Convert to energy consumption using the defined formula. The acceleration sensor is preferably a three-axis acceleration sensor in order to provide a variety of detection directions.
In addition, in order to obtain calorie consumption with high accuracy, biological data such as pulse wave RR intervals, body temperature, blood pressure, and sleep may be detected using optical detection, electrical signal detection, pressure detection, and the like.

  The posture measurement unit 180 obtains energy consumption information resulting from the posture of the user. For example, whether the posture measurement unit 180 is exercising while standing, exercising while sitting, etc. Determine. Therefore, the attitude measurement unit 180 includes a GPS (Global Positioning System) sensor that measures planar position information, an altitude sensor that measures a position in the vertical direction, and the like. An atmospheric pressure sensor can be used as the altitude sensor. The obtained energy consumption information is sent to the control unit 110.

  The operation unit 130 includes, for example, an operation button group having input keys such as numbers and characters, and sends an operation signal corresponding to the input key operated by the user to the control unit 110. In the present embodiment, in particular, the operation unit 130 inputs the measured user's blood glucose level data, and also includes meal menu information indicating the contents of meals taken by the user and exercise type information data indicating the contents of exercises performed by the user. Is input to the control unit 110.

  The storage unit 140 is configured by a non-volatile storage medium, and stores tables such as user information 200 including information related to the user's past blood glucose level, meal menu information 211, and exercise type information 212. Details of the meal menu information 211, the exercise type information 212, and the user information 200 will be described later with reference to FIGS.

The display unit 150 is configured by a display device such as a liquid crystal display, and displays various images such as an input screen of meal menu information 211 and exercise type information 212 and a blood glucose level prediction curve under the control of the control unit 110.
The timer 160 counts a predetermined clock and measures time.

Subsequently, the meal menu information 211, the exercise type information 212, and the user information 200 included in the storage unit 140 will be described in order.
FIG. 2A is a table showing an example of the meal menu information 211. In the meal menu information 211, a meal menu such as a single food name or a dish name and intake calories corresponding to the meal menu are stored. The meal menu information 211 is referred to when the user inputs meal information.
FIG. 2B is a table showing an example of the exercise type information 212. The exercise type information 212 stores an exercise type and exercise content. The exercise type information 212 is referred to when the user inputs exercise information.

Next, the user information 200 will be described.
FIG. 3 shows user information, (a) is a table showing an example of blood glucose level information, and (b) is a table showing an example of behavior information. The user information 200 includes blood glucose level information 200a shown in FIG. 3A and behavior information 200b shown in FIG.
The blood glucose level information 200a stores, for example, daily blood glucose levels and action histories (meal and exercise histories) measured during the educational hospitalization period, such as when a user has previously been admitted to education regarding diabetes. .

  FIG. 3A shows blood glucose level information 200a of the user on February 1, 2010 and February 2, 2010 as an example. A waveform 41 in this figure represents a time-series change in the blood glucose level of the user. “Breakfast”, “walk”, “lunch”, etc. on the time axis indicate action histories such as meals taken by the user and timing of exercise.

FIG. 3B shows action information 200b corresponding to each action history of FIG. 3A, and action contents (meal contents, exercise contents) corresponding to each action history and intake corresponding to the action contents. Calories and calories burned are associated.
For example, the meal content of the breakfast on February 1, 2010 in FIG. 3A is “Japanese food A”, and the intake calorie of Japanese food A is “500 kcal”. Further, it is shown that the calorie consumption of “walk” performed in the morning of February 1, 2010 in FIG. 3A was “50 kcal”.
As described above, the user information 200 stores the change in the user's past blood glucose level and the calories ingested and consumed with respect to the user's behavior.

Next, the functional configuration of the control unit 110 described above will be described with reference to the drawings.
FIG. 4 is a functional configuration diagram centering on the function of the control unit. The control unit 110 includes an intake energy acquisition unit 111, a consumption energy acquisition unit 112, a calculation unit 113, an analysis unit 114, and a generation unit 115.

  The intake energy acquisition unit 111 acquires blood glucose level data input by the user and meal information input by the user as intake energy information from the operation unit 130, and based on the acquired meal information and meal menu information 211, the meal information is converted into meal information. Find the corresponding calorie intake.

  The consumed energy acquisition unit 112 acquires the calorie consumption as the user's consumed energy information from the activity amount measuring unit 120 at regular intervals. The calculation unit 113 predicts a change in blood glucose level with respect to calorie intake (hereinafter referred to as a first prediction curve) based on the calorie intake determined by the energy intake acquisition unit 111 and a predetermined first prediction algorithm. ). Further, the calculation unit 113 predicts a change in blood sugar level relative to the calorie consumption based on the calorie consumption acquired by the energy consumption acquisition unit 112 and a predetermined second prediction algorithm (hereinafter, second prediction). Create a curve).

Here, creation of the first prediction curve and the second prediction curve will be described.
Fig.5 (a) is a figure which shows an example of the 1st prediction curve in this embodiment. The first prediction curve is obtained based on the calorie intake and the first prediction algorithm. The first prediction curve has a delay period d1, a rising period d2, an equilibrium period d3, and a falling period d4. Hereinafter, an example of the 1st prediction algorithm which calculates | requires the blood glucose level curve in each period is demonstrated.

  The delay period d1 indicates a period from the start of the meal to the blood glucose level (reference value) C0 at the start of the meal. In the delay period d1, a predetermined time (for example, 15 minutes) from the start of the meal is set, and the blood glucose level C0 at the start of the meal is maintained. Note that the blood glucose level C0 at the start of the meal uses the blood glucose level measured by the user at the time, but if the measurement cannot be made, for example, a preset standard value of the user's blood glucose level is used. May be.

The rising period d2 indicates a period starting from the end of the delay period d1 until the blood sugar level starts to rise and reaches a peak value (peak value). The peak value is a value obtained by adding the blood glucose level increase h11 to the blood glucose level C0 at the start of the meal, with the blood glucose level increasing at the slope S1.
The blood glucose level increase amount h11 is obtained, for example, by h11 = (calorie intake) × (insulin secretion amount) × (coefficient α). In this embodiment, the insulin secretion amount and the coefficient α (where α> 0) are fixed values set in advance according to the user. The insulin secretion amount and the coefficient may be not only fixed values set in advance, but also values or variable values determined according to user attributes (age, sex, height, weight).

  The equilibrium period d3 is a period during which the peak value of the blood sugar level is maintained from the end of the rising period d2, and a fixed value defined in advance is set in this embodiment. For example, a value obtained by multiplying the calorie intake by a user-specific coefficient is divided by a coefficient corresponding to the difference from the previous calorie intake, and the equilibrium period d3 is calculated using the calorie intake and a predetermined arithmetic expression. You may make it ask.

The falling period d4 indicates a period from the end of the equilibrium period d3 until the blood sugar level starts to decrease at the slope S2 and reaches the reference value. That is, the descent period d4 is a period until the blood glucose level returns from the peak value to the reference value (blood glucose level C0 at the start of the meal).
The slope S2 is obtained by, for example, S2 = (calorie intake) × (coefficient β). In this embodiment, the coefficient β is a fixed value (β <0) determined in advance according to the user, but is a value determined in advance according to the user's attributes (age, gender, height, weight). Or a variable value.

Next, the second prediction curve will be described.
FIG. 5B is a diagram illustrating an example of the second prediction curve in the present embodiment. The second prediction curve is obtained based on the calorie consumption and the second prediction algorithm. The second prediction curve includes a delay period e1 and a falling period e2. Hereinafter, an example of the 2nd prediction algorithm which calculates | requires the blood glucose level curve in each period is demonstrated.

The delay period e1 indicates a period from the start of exercise until the blood sugar level starts to decrease, and is a period during which the blood sugar level at the start of exercise is maintained. In the present embodiment, a predetermined period (for example, 2 minutes) is set as the delay period e1. The falling period e2 is a period during which the blood sugar level falls with a slope S3 (the amount of decrease in blood sugar level per unit time ΔC) from the end of the delay period e1.
The blood glucose level reduction amount ΔC is obtained, for example, by ΔC = (calorie consumption) × (insulin secretion amount) × (coefficient γ). The calorie consumption is the user's calorie consumption measured by the activity amount measuring unit 120. In the present embodiment, the user's calorie consumption is calculated by the activity amount measuring unit 120 even during normal operation when the user is not conscious of exercise. Calculated and sequentially input.

  The amount of insulin secretion is a fixed value set in advance according to the user, and the coefficient γ (where γ <0) may be a variable value according to the blood glucose level, or the user's attributes (age, It may be a fixed value determined according to sex, height, weight).

Next, the analysis unit 114 will be described with reference to FIG.
The analysis unit 114 extracts the user information 200 using the extraction condition for extracting the user information 200 corresponding to the meal information (meal menu information 211) as the intake energy information input from the operation unit 130, and extracts the extracted user information. 200 is used to analyze the change tendency of the blood glucose level of the user with respect to the meal information.

  Specifically, for example, when the input meal menu is Japanese food A, the next action (meal or exercise) is performed from the time when Japanese food A is ingested as a waveform representing a change in blood glucose level when Japanese food A is ingested. Waveform data for a period until it is displayed is extracted. In the example of the blood glucose level information 200a shown in FIG. 3A, the waveform data for the period from the time when the breakfast on February 1, 2010 was ingested until the next action, that is, until the walk is performed, is extracted. If a walk is not performed, waveform data for a period from when breakfast is consumed until lunch is extracted is extracted. In this way, a waveform representing a change in blood glucose level when the same meal content is ingested is extracted as the first model waveform. In addition, when a plurality of waveforms are extracted, the analysis unit 114 performs processing such as averaging the plurality of extracted waveforms, and generates a first model waveform indicating a change tendency of the blood sugar level with respect to the meal information. To do.

  Further, the analysis unit 114 extracts the user information 200 using the extraction condition for extracting the user information 200 corresponding to the exercise information (exercise type information) input from the operation unit 130, and uses the extracted user information 200. Analyzes changes in user's blood glucose level relative to exercise information. In the present embodiment, the calorie consumption of the user is sequentially calculated by the activity amount measuring unit 120, but it is distinguished what operation the calculated calorie consumption is performed. Therefore, for an exercise other than the normal operation, the exercise type is input before the user exercises.

  Specifically, for example, when the input exercise content is walking, a waveform representing a change in blood glucose level when walking is performed as a waveform representing the change in blood glucose level in the blood glucose level information 200a shown in FIG. Waveform data is extracted from the start of walking on the 1st month until the next action, that is, taking a snack. In this way, a waveform representing a change in blood glucose level when the same exercise is performed is extracted as the second model waveform. In addition, when a plurality of waveforms are extracted, the analysis unit 114 performs processing such as averaging the plurality of extracted waveforms, and generates a second model waveform indicating a blood glucose level change tendency with respect to the exercise information. Generate.

Further, the energy consumption information corresponding to the posture of the user is input to the analysis unit 114 from the posture measurement unit 180. First, the attitude measurement unit will be described.
FIG. 6 is a conceptual diagram showing the posture of the user. A represents a standing position, B represents a sitting position, and C represents a prone position. The attitude measurement unit 180 includes a GPS sensor 181 and an atmospheric pressure sensor 182 as an altitude sensor. The GPS sensor 181 can measure the movement of the user in the planar direction (planar position), and the atmospheric pressure sensor can measure changes in the user's posture such as standing, sitting, and lying.
The calorie consumption varies depending on the posture of the user, such as standing, sitting, or lying, even if the same kind of exercise or resting state.

  A barometric sensor that can measure with high accuracy using a crystal element is adopted. For example, if a change in pressure of 0.01 hPa can be measured when the height changes by 0.1 m under a constant temperature condition, the change in posture can be detected. However, the altitude measurement of the barometric sensor is a relative altitude measurement. Therefore, a change in posture can be determined by combining with a GPS sensor. This will be described with reference to FIG.

  FIG. 7 is an explanatory diagram schematically illustrating an example of measurement of posture change when a GPS sensor and an atmospheric pressure sensor are combined. The elapsed time (T) is shown on the horizontal axis, and the measured value of the atmospheric pressure sensor and the measured value of the GPS sensor are shown on the vertical axis. In FIG. 7, in the P1 region, the barometric sensor indicates that the altitude is relatively rising, and at the same time, the GPS sensor is moving. That is, the user is standing and moving. Here, when the activity amount measuring unit 120 does not detect, it can be estimated that the vehicle is moving by a vehicle such as an automobile or a train.

  In the P2 region, the barometric sensor indicates that the altitude is relatively increased, and the GPS sensor does not detect movement. That is, it shows that the robot is standing and standing still from the sitting or lying position.

  Here, in order to distinguish an altitude change by the atmospheric pressure sensor from a change in the atmospheric pressure itself, a time axis may be added. For example, if there is an altitude change of 0.5 m to 1.0 m (corresponding to a change in pressure of 0.05 hPa to 0, 1 hPa) in one second, it is determined that there has been a change in posture, and the same pressure in one hour When there is a change, it is determined that there is a change in the atmospheric pressure itself.

  Note that the atmospheric pressure on the floor may be measured when measurement is desired, and the change in posture may be determined from the atmospheric pressure change amount from the reference value with the atmospheric pressure as a reference value (that is, the altitude reference value).

Next, the relationship between the calorie consumption accompanying the posture change and the blood glucose level will be described.
FIG. 8 is a second prediction curve deformed based on the posture information. The relationship between calories burned and blood glucose level corresponds to the second prediction curve. Therefore, the influence of the posture change may be added to the second prediction curve shown in FIG. For example, when the posture measurement unit 180 rises and moves, the coefficient γ of the second prediction algorithm is adjusted so that the blood glucose level falls at the slope S4 determined by the posture in the falling period e2.

  In the present embodiment, even during a normal operation in which the user is not conscious of changes in posture, the posture measurement unit 180 calculates and sequentially inputs the user's calorie consumption.

  Note that the amount of insulin secretion in the second prediction algorithm is a fixed value set in advance according to the user, and the adjustment of the coefficient γ may be a variable value according to the blood glucose level, or the user attribute (age , Gender, height, weight) may be fixed values determined according to.

Next, the generation unit 115 will be described.
The generation unit 115 deforms the first prediction curve and the second prediction curve calculated by the calculation unit 113 based on the analysis result of the analysis unit 114, and integrates the deformed first prediction curve and the second prediction curve. Generate a predictive blood glucose curve.

  Specifically, the generation unit 115 increases the amount h11 of the first prediction curve (see FIG. 5A) up to the peak value of the blood glucose level during the increase period d2 and the same meal content output from the analysis unit 114. The amount of increase in the first prediction curve is compared with the amount of increase up to the peak value of the first model waveform representing the change in blood glucose level when ingesting The coefficient α is adjusted so that h11 becomes the increase amount of the first model waveform.

  Further, the generation unit 115 compares the equilibrium period d3 of the first prediction curve with the continuation period in which the peak value of the blood sugar level continues in the first model waveform, and the difference between the equilibrium period d3 and the continuation period is determined in advance. If it is equal to or greater than the threshold, the equilibrium period d3 is set so that the equilibrium period d3 of the first prediction curve matches the duration. In the first model waveform, the period when the peak value of the blood glucose level falls within the predetermined threshold value ΔCth is assumed to be continued, and the time point when the peak value is below the threshold value ΔCth is continued. Judging by the end of

  In addition, the generation unit 115 compares the amount of decrease (h11) in which the blood sugar level has decreased from the peak value in the falling period d4 of the first prediction curve with the amount of decrease in which the blood sugar level has decreased from the peak value of the first model waveform. When the difference between the decrease amounts is equal to or greater than a predetermined threshold, the coefficient β of the slope S2 is set so that the decrease amount (h11) in the decrease period d4 of the first prediction curve becomes the decrease amount in the first model waveform. Adjust.

  The generation unit 115 also deforms the second prediction curve (see FIG. 5B) in the same manner as the first prediction curve. Specifically, the generation unit 115 represents the change in blood glucose level per unit time of the blood glucose level during the falling period e2 of the second prediction curve and the change in the blood glucose level when the same exercise is performed. The amount of decrease in blood glucose level per unit time in the waveform is compared. If the difference in amount of decrease is equal to or greater than a predetermined threshold, the amount of decrease ΔC in the second prediction curve is the amount of decrease in the second model waveform. The coefficient γ is adjusted as follows.

  Further, the generation unit 115 deforms the second prediction curve using the energy consumption information corresponding to the posture of the user input to the analysis unit 114. That is, as shown in FIG. 8, in the falling period e2, the coefficient γ of the second prediction algorithm is adjusted so that the blood glucose level decreases with the slope S4 determined by the posture.

  The generation unit 115 deforms the first prediction curve and the second prediction curve as described above, generates a predicted blood glucose level curve that integrates the deformed first prediction curve and the second prediction curve, and generates the generated prediction. The blood glucose level curve is output to the display unit 150.

  In the present embodiment, the process of deforming the prediction curve based on the analysis result refers to the user's waveform (first model waveform, second model waveform) based on the analysis result of exercise information, posture information, and intake energy information. A parameter indicating a characteristic amount of blood glucose level change is extracted, a parameter corresponding to this characteristic amount is extracted in a prediction curve (first prediction curve, second prediction curve), and the parameter of the prediction curve becomes a parameter of the analysis result It is a process to approach.

Subsequently, the operation of the blood sugar level prediction apparatus 10 in the present embodiment will be described.
FIG. 9 is an explanatory diagram of an operation flow of the blood sugar level predicting apparatus in the present embodiment. In this embodiment, the blood glucose level is actually measured by the user before breakfast every day. The blood glucose level prediction device 10 predicts a blood glucose level each time meal information is input or a user's consumed calories are measured using the actual measurement value, and outputs a predicted blood glucose level curve.
The user inputs actually measured blood glucose level data via the operation unit 130 of the blood glucose level prediction apparatus 10. When the control unit 110 receives the blood glucose level data input via the operation unit 130 together with the input time (step S11), the control unit 110 sets the input blood glucose level data as the reference value C0 (see FIG. 5A). Then, the blood sugar level prediction process is started.

  When the user performs an operation for displaying a meal information input screen via the operation unit 130, the control unit 110 displays the meal menu of the meal menu information 211 on the display unit 150 and accepts an input from the user (step S12). .

  When the meal menu is input by the user via the operation unit 130 (step S12: YES), the control unit 110 selects the intake calorie corresponding to the input meal menu from the meal menu information 211 as the intake energy information. The first prediction curve for the selected calorie intake is calculated using the first prediction algorithm (step S13). The control unit 110 extracts the user information 200 corresponding to the meal menu input in step S12, analyzes the change tendency of the past blood glucose level with respect to the meal information, and generates a first model waveform (step S14).

The control unit 110 compares the blood glucose level change of each period (d1, d2, d3, d4) in the first prediction curve calculated in step S13 with the blood glucose level change of the first model waveform generated in step S14. The first prediction curve is deformed according to the analysis result (step S15).
Moreover, the control part 110 acquires the user's calorie consumption measured by the activity amount measurement part 120 for every fixed time from the activity amount measurement part 120 as consumption energy information (step S16), and 2nd with respect to the acquired consumption calorie. A prediction curve is calculated using the second prediction algorithm (step S17).

  When the user performs an operation to display the exercise information input screen via the operation unit 130, the control unit 110 displays the exercise type information 212 on the display unit 150 and inputs the exercise information (step S18). When the exercise type is input by the user via the operation unit 130 as the exercise information (step S18: YES), the control unit 110 extracts the user information 200 corresponding to the input exercise information, and the past with respect to the exercise information. A second model waveform is generated by analyzing the change tendency of the blood glucose level (step S19).

  The control unit 110 performs a comparative analysis of the blood glucose level change in the falling period e2 in the second prediction curve calculated in step S17 and the blood glucose level change of the second model waveform generated in step S19, and according to the analysis result. The second prediction curve is deformed (step S20).

  Here, it is determined whether or not posture information has been input (step S21). When there is an input from the posture measurement unit 180 (GPS sensor 181, barometric pressure sensor 182) (for example, when standing, step S <b> 21: YES), the generation unit 115 generates a second prediction curve based on the posture information. Deform (step S22).

  The control unit 110 generates a predictive blood sugar level curve obtained by integrating the first predictive curve and the second predictive curve on the same time axis, and displays an image indicating the generated predictive blood sugar level curve on the display unit 150 (step 150). S23). In step S12, when the meal information is not input by the user (step S12: NO), the control unit 110 performs the process of step S16. Moreover, when exercise information is not input by the user in step S18 (step S18: NO), the control part 110 performs the process of step S21. If no posture information is input (step S21: NO), the control unit 110 performs the process of step S23.

  As described above, in the present embodiment, the first prediction curve and the second prediction curve are calculated every time the intake energy information and the consumption energy information are input, and based on the calculated first prediction curve and second prediction curve. To generate a predictive blood sugar level curve. The blood glucose level data, meal information, and exercise information input by the user may be stored as user information 200 in the storage unit 140 by the control unit 110 as information on the user's past blood glucose level.

Next, a specific operation example of the blood glucose level prediction apparatus 10 will be described according to the above-described operation flow. The description will be made with reference to FIGS. 9, 10, and 11.
FIGS. 10A to 10C are diagrams illustrating an example in which the first prediction curve is deformed.
FIGS. 11A to 11C are diagrams illustrating an example of deforming the second prediction curve.
The user inputs blood sugar level data “130” obtained by measuring the blood sugar level before taking breakfast through the operation unit 130 (step S11), and performs an operation of displaying a meal information input screen. "C" is input (step S12). Control unit 110 selects “500 kcal” from meal menu information 211 as the calorie intake corresponding to “Western food C”. The control unit 110 sets the input blood glucose level data “130” as the reference value C0, and uses the calorie intake “500 (kcal)” and the first prediction algorithm to show the first prediction curve shown in FIG. Is calculated and created (step S13).

  Subsequently, the control unit 110 extracts a waveform including the meal information “Western food C” from the blood sugar level information 200a illustrated in FIG. In the example of the blood glucose level information 200a in FIG. 3A, waveform data for a period from the time of breakfast on February 2, 2010 when the Western food C was ingested until the next action “underwater walking” is started is extracted. The In this way, the waveform data when the diet information “Western food C” is ingested by the control unit 110 is extracted from the blood glucose level information 200a, and the extracted waveform data is averaged as shown in FIG. One model waveform is generated (step S14).

The control unit 110 compares the blood glucose level change of each period of the first prediction curve shown in FIG. 10A with the blood glucose level change of the first model waveform shown in FIG. 10B, and deforms the first prediction curve. Then, the first prediction curve shown in FIG. 10C is generated (step S15).
A rise h11 in the blood glucose level during the rise period d2 of the first prediction curve shown in FIG. 10 (a) is compared with a raise h21 (C4-C1) in the first model waveform shown in FIG. 10 (b). To do. When the difference in the increase amount is equal to or greater than the threshold value, the control unit 110 adjusts the coefficient α in the first prediction algorithm so that the increase amount h11 in the first prediction curve becomes h21 (C4-C1).

  In addition, the control unit 110 continues the duration d31 (t2−) from the peak value to the time t2 within the threshold ΔCth range after the time t1 when the blood sugar level of the first model waveform in FIG. t1) is compared with the equilibrium period d3 in the first prediction curve. When the difference between the continuous period d31 and the equilibrium period d3 is equal to or greater than the threshold value, the control unit 110 sets the continuous period d31 as the equilibrium period d3.

  In addition, the control unit 110 reduces the blood sugar level decrease (h12) in the falling period d4 of the first prediction curve in FIG. 10A and the blood glucose level decrease h22 in the first model waveform in FIG. C4-C2). When the difference in the decrease amount is equal to or greater than the threshold, the control unit 110 adjusts the coefficient β in the first prediction algorithm so that the decrease amount h12 becomes the decrease amount h22 in the first model waveform.

  Moreover, if the control part 110 receives a user's calorie consumption for every fixed time from the activity amount measurement part 120 as a user's energy consumption information (step S16), it will use the received calorie consumption and the 2nd prediction algorithm. A second prediction curve shown in FIG. 11A is calculated (step S17). For example, when exercising “walking”, the user performs an operation of displaying an exercise information input screen on the display unit 150 via the operation unit 130 at the start of the exercise, and the exercise type is determined from the exercise type information 212. “3” is selected and input (step S18: YES).

  The control unit 110 extracts user information 200 corresponding to the exercise content “walking” of the input exercise type “3”. In the example of blood glucose level information 200a in FIG. 3A, waveform data for a period from the start of walking on February 1, 2010, when “walking” was performed until the next action “snack” is ingested. Is extracted. Thus, the second model waveform shown in FIG. 11B is obtained by extracting the waveform data when the “walking” is performed by the control unit 110 from the blood glucose level information 200a and averaging the extracted waveform data. Is generated (step S19).

The control unit 110 compares the blood sugar level change during the falling period e2 of the second prediction curve shown in FIG. 11A with the blood sugar level change of the second model waveform shown in FIG. It deform | transforms and produces | generates the 2nd prediction curve shown in FIG.11 (c) (step S20).
In the example of FIGS. 11A and 11B, the control unit 110 causes the blood sugar level decrease amount ΔC at the time T0 in the descending period e2 region of the second prediction curve and the blood glucose level decrease amount ΔC1 of the second model waveform. Are compared. The decrease amount ΔC1 of the second model waveform is a decrease amount per unit time when the blood glucose level decreases by h31 from the blood glucose level at the start of exercise during the time t1 to t2. When the difference in the amount of decrease per unit time is equal to or greater than the threshold, the control unit 110 adjusts the coefficient γ in the second prediction algorithm so that the amount of decrease ΔC in the second prediction curve becomes ΔC1.

  When there is no posture information, the control unit 110 converts the first prediction curve transformed according to the meal information every time the meal information is input by the user and the energy consumption information measured by the activity amount measurement unit 120. An image indicating the generated predicted blood glucose level curve is generated by sequentially generating a predicted blood glucose level curve obtained by integrating the second predicted curve generated based on the second predicted curve or the second predicted curve deformed according to the exercise information input by the user. The information is displayed on the display unit 150 (step S23).

  When posture information is input (step S21), as shown in FIG. 8, the coefficient γ of the second prediction algorithm is adjusted so that the blood glucose level falls at a slope S4 determined by the posture in the fall period e2. Then, the second prediction curve is deformed (step S22). And the prediction blood glucose level curve which integrated the deformed 2nd prediction curve and the 1st prediction curve is produced | generated one by one, and the image which shows the produced | generated prediction blood glucose level curve is displayed on the display part 150 (step S23).

  As described above, in this embodiment, by using the user information 200 including the time series data of the past user's blood glucose level and action content, the change tendency of the blood glucose level with respect to the meal content taken by the user and the exercise performed by the user is shown. The analysis result can be reflected in a blood glucose level prediction algorithm (first prediction algorithm, second prediction algorithm) defined in advance. Therefore, the blood sugar level can be predicted according to characteristics such as the user's constitution without increasing the number of times the blood sugar level is actually measured, and the prediction accuracy of the blood sugar level can be improved.

  Also, by incorporating posture information, it is possible to detect a change in energy consumption due to a difference in posture, and thus it is possible to predict a blood glucose level with high accuracy.

In addition, this invention is not limited to embodiment mentioned above, You may implement as deform | transforming as follows. Further, the following modifications may be combined.
(Modification 1)

The blood glucose level prediction apparatus 10 is not limited to the configuration of the embodiment described above, and may be configured as shown in FIGS.
FIG. 12 is a configuration explanatory diagram illustrating a configuration of the blood sugar level prediction apparatus according to the first modification.
FIG. 13 is a diagram illustrating an example of blood glucose level information in the user information according to the first modification. The blood glucose level prediction device 11 is different from the configuration of the blood glucose level prediction device 10 according to the above-described embodiment (see FIG. 1) in that it includes a pulse measurement unit 170 that measures a pulse as the biological information of the user. As shown in FIG. 13, the blood glucose level information 201a according to the present modification stores time series data 42 of the pulse measured in the past of the user, as with the blood glucose level.

  The pulse measurement unit 170 includes an infrared light irradiation unit that irradiates infrared light. The infrared light irradiation unit irradiates the user's blood vessel with infrared light, the intensity of the infrared light reflected by the blood vessel, or the infrared light transmitted through the blood vessel. The intensity of light is detected by an image sensor such as a CCD. The pulse measuring unit 170 converts the output signal from the image sensor into a pulse using an arithmetic expression for calculating a predefined pulse. In Modification 1, the infrared light irradiation unit of the pulse measurement unit 170 is in contact with the user's wrist. In the first modification, an example in which the pulse measurement unit 170 is built in the blood sugar level prediction apparatus 11 will be described, but it may be provided outside the blood sugar level prediction apparatus 11. In this case, it is configured to perform wired or wireless communication with the blood sugar level predicting device 11 and the pulse measuring device having the pulse measuring unit 170, and pulse data is transmitted from the pulse measuring device to the blood sugar level predicting device 11. To send.

  FIG. 14 is a functional configuration diagram centering on the control unit of the blood sugar level predicting apparatus according to the first modification. In the first modification, the analysis unit 114 analyzes the change tendency of the pulse after ingesting a meal according to the content of the meal from the pulse data of the user information 200, and is measured by the pulse measurement unit 170 based on the analysis result. Predict changes in pulse data. The generation unit 115 adjusts the slope S1 of the first prediction curve during the rising period d2 according to the prediction result of the pulse data in the analysis unit 114 (see FIG. 15).

A specific example will be described with reference to FIGS.
FIGS. 15A and 15B show a specific example according to the first modification, in which FIG. 15A shows a change in pulse, and FIG. 15B shows an example in which the first prediction curve is changed based on the change in pulse. Specifically, when a certain meal menu is input via the operation unit 130, the analysis unit 114 obtains pulse data from the blood glucose level information 201a in a period from when the meal menu is ingested until the next action is performed. Extract.

  When the analysis unit 114 obtains, for example, a waveform in which the pulse changes with a transition as indicated by the solid line 51 in FIG. 15A by averaging the extracted pulse data, the pulse measurement unit 170 performs the meal menu. When the pulse measured at the time of ingestion is p1, it is predicted that the measured pulse p1 will change with the same inclination as the solid line 51, as shown by the broken line 52.

  When the amount of change between the pulse p1 at the time of taking a meal and the pulse p2 predicted by the analysis unit 114 is equal to or greater than a predetermined threshold, the generation unit 115 performs the first operation as illustrated in FIG. In the rising period d2 of the prediction curve, the coefficient α in the first prediction algorithm is adjusted so that the blood glucose level rises with a slope S12 determined according to the difference between the change amount and the threshold value.

  In the first modification, the analysis unit 114 predicts changes in measured biological information based on the past user's pulse data, and the generation unit 115 determines the coefficient α of the first prediction algorithm according to the prediction result. This is an example of adjusting. However, the generation unit 115 may modify the parameters in the first prediction algorithm other than the coefficient α or the parameters in the second prediction algorithm according to the prediction result of the pulse data. Further, the generation unit 115 may change the parameters of the first prediction algorithm and the second prediction algorithm according to the change tendency of the pulse of the user and the change tendency of the blood sugar level analyzed by the analysis unit 114. .

In this way, by reflecting not only the blood glucose level change trend based on the user's past diet and exercise but also the past biological information change trend in the blood glucose level prediction algorithm, the user's constitution etc. The blood glucose level in consideration of the characteristics is predicted, and the prediction accuracy of the blood glucose level can be improved.
In the first modification, the pulse is exemplified as the biological information. For example, the pulse wave, the pulse wave R-R interval heartbeat, the body temperature, the electrocardiogram, the blood pressure, the sleep time, the bioelectrical impedance, and the like are detected or externally wired or You may make it acquire by radio | wireless communication.
(Modification 2)

  Subsequently, Modification 2 will be described. In the first modification described above, an example in which a change in the user's biometric information is used for the deformation of the first prediction curve has been described. For example, the analysis unit 114 may extract the user information 200 during a period in which the blood glucose level prediction apparatus 10 includes a state similar to the state of the user's biological information measured or acquired from the outside.

  Further, blood glucose level data input by the user may be used as the extraction condition of the user information 200. For example, the analysis unit 114 may extract the user information 200 when the blood glucose level before breakfast input by the user is the same, or the user when the blood glucose level data and the biological information are the same. Information 200 may be extracted. In short, it is only necessary to set the extraction conditions so as to extract past blood glucose levels and behavior information in a state similar to the state (physical condition) of the user at the time of prediction.

The blood glucose level data, meal information, exercise information, and acquired biological information input by the user are configured to be stored in the storage unit 140 by the control unit 110 as user information 200 relating to time-series changes in the user's blood glucose level. May be.
(Modification 3)

Next, Modification 3 will be described. In the above-described embodiment, the blood sugar level prediction curve is generated using a pre-defined blood sugar level prediction algorithm. However, the method is estimated using a method such as nonlinear regression analysis or time series analysis. The blood glucose level may be predicted by applying data such as meal information and exercise information input by the user to the model.
(Modification 4)

Next, Modification 4 will be described. In the embodiment described above, the user information 200 is an example in which a blood glucose level and an action history measured during the past education hospitalization period of the user are stored. It may be the data. For example, it may be time series data of a user's blood glucose level and behavior history measured at home, or time series data of blood glucose level and behavior history of another diabetic patient having the same characteristics (such as medical condition and constitution) as the user. May be.
(Modification 5)

Next, Modification 5 will be described. In the above-described embodiment, the user's calorie consumption is sequentially measured by the activity amount measurement unit 120. However, when the pulse measurement unit 170 is provided as in the modification example (1), the consumption energy acquisition unit 112 is Alternatively, the measurement result of the pulse measuring unit 170 may be converted into calorie consumption using a pre-defined arithmetic expression for converting the pulse data into calorie consumption.
(Modification 6)
Next, Modification 6 will be described. In the above-described embodiment, the example in which the intake energy information is acquired by sequentially inputting the meal information by the user has been described, but the method of acquiring the intake energy information is not limited to this. For example, in the intake energy acquisition unit 111, image data obtained by photographing meal contents and photographing data including the photographing time are acquired from outside by wired or wireless communication, and the image data of the obtained photographing data is subjected to image analysis and defined in advance. If the image data corresponding to the meal content is included, the calorie intake corresponding to the meal content may be used as the intake energy information.
(Modification 7)

  Next, Modification 7 will be described. In the above-described embodiment, the intake energy information is input when the user ingests a meal, and the consumed energy information is an example that is measured by the activity amount measurement unit 120 at regular intervals. The timing at which the consumed energy acquisition unit 112 acquires the intake energy information and the consumed energy information is not limited to this. For example, you may be allowed to receive input from the user by combining exercise information and ingested meal information performed in the past from the current time with time information, or combining exercise information scheduled to be performed in the future and meal information scheduled to be ingested with time information. It is also possible to accept input from the user.

  When past exercise information or meal information is acquired by the intake energy acquisition unit 111 or the consumed energy acquisition unit 112, the first prediction curve is traced back to the past time point based on the meal information and exercise information. In addition, the second prediction curve may be recalculated, and the change tendency of the blood sugar level may be reanalyzed based on the meal information and the exercise information to regenerate the predicted blood sugar level curve.

In addition, when future exercise information and meal information are acquired by the intake energy acquisition unit 111 and the consumed energy acquisition unit 112, the calculation unit 113 performs a process based on the future exercise information and meal information, as in the embodiment. The 1 prediction curve and the 2nd prediction curve are calculated, and the production | generation part 115 produces | generates a prediction blood glucose level curve (A). In addition, as the predicted blood glucose level curve (B) between the current time point and the future time point, for example, the generation unit 115 generates a predicted blood sugar level curve obtained by averaging the past predicted blood sugar level curves from the current time point, You may make it connect a value curve, a prediction blood glucose level curve (B), and a prediction blood glucose level curve (A) sequentially.
(Modification 8)

Next, Modification 8 will be described. In the above-described embodiment, the image indicating the predicted blood glucose level curve is displayed on the display unit 150, but as the information based on the predicted blood glucose level curve, for example, the current predicted blood glucose level based on the predicted blood glucose level curve is previously stored. If the target value is exceeded, you may be informed that there is a lot of energy intake or low energy consumption, and the amount of exercise and dietary restrictions required for the predicted blood glucose level to reach the target value. You may make it alert | report information. Moreover, you may alert | report by displaying on the display part 150 the image which shows the state of the user estimated beforehand set according to the prediction blood glucose level shown with a prediction blood glucose level curve.
(Modification 9)

Next, Modification 9 will be described. In the above-described embodiment, an example of the first prediction algorithm using calorie intake has been described. However, a prediction algorithm using a sugar mass, a GI value, a lipid amount, or the like included in the meal content may be applied. In this case, a table in which components such as a sugar mass, a GI value, and a lipid amount corresponding to meal contents are defined in advance may be stored in the storage unit 140, or meal information is input by the user. At this time, the intake energy acquisition unit 111 may be configured to acquire component data corresponding to the meal information from the outside by wired or wireless communication.
(Modification 10)

Next, Modification 10 will be described. In the above-described embodiment, the example in which the patient inputs the exercise type via the operation unit 130 is used. For example, the blood glucose level prediction apparatus determines the exercise type by using the activity amount measuring unit alone or the acceleration sensor. May be.
(Embodiment 2)

  Subsequently, a blood sugar level prediction apparatus according to the second embodiment will be described. The blood glucose level prediction apparatus 10 according to the first embodiment described above includes the activity amount measuring unit 120 and the posture measuring unit 180 separately for the energy consumption information, whereas the second embodiment has the activity amount measuring unit 120 and the posture. It is characterized by being performed by an activity meter including the measuring unit 180. Although not shown, the description will be given with reference to FIG.

  The activity measuring unit 120 has a three-axis acceleration sensor, and the posture measuring unit 180 has a GPS sensor and a barometric sensor. Here, in this embodiment, it is comprised as an active mass meter containing these 3-axis acceleration sensors, a GPS sensor, and an atmospheric pressure sensor. Therefore, by inputting consumption energy information to the consumption energy acquisition unit 112 with the three-axis acceleration sensor, the GPS sensor, and the atmospheric pressure sensor, the calculation unit 113 can create a second prediction curve including posture information. .

  DESCRIPTION OF SYMBOLS 10 ... Blood glucose level prediction apparatus, 110 ... Control part, 111 ... Intake energy acquisition part, 112 ... Consumption energy acquisition part, 113 ... Calculation part, 114 ... Analysis part, 115 ... Generation part, 120 ... Activity amount measurement part, 130 ... Operation unit, 150 ... display unit, 180 ... posture measurement unit.

Claims (5)

An ingestion energy acquisition unit that acquires ingestion energy information ingested by a user who is predicting blood glucose levels;
An energy consumption acquisition unit for acquiring energy consumption information consumed by the user;
An attitude measurement unit for acquiring the user's attitude information;
A calculation unit that creates a first prediction curve of blood sugar level using the first prediction algorithm for the intake energy information, and creates a second prediction curve of blood sugar level using the second prediction algorithm for the consumed energy information; ,
Obtaining user information including blood glucose level information that changes based on the user's meal information and behavior information, and creating a first model waveform and a second model waveform;
A generator that creates a predictive blood sugar level curve by integrating the first predictive curve, the first model waveform, the second predictive curve deformed based on the posture information, and the second model waveform, respectively;
A display unit for displaying at least the predictive blood sugar level curve;
A blood glucose level predicting apparatus comprising: In the blood glucose level prediction apparatus according to claim 1,
The blood sugar level predicting apparatus, wherein the posture measuring unit includes an altitude sensor that detects the posture of the user. In the blood glucose level prediction apparatus according to claim 1,
The blood glucose level prediction apparatus, wherein the posture measurement unit includes an altitude sensor and a GPS sensor that detects position information of the user. In the blood glucose level prediction apparatus according to claim 2 or claim 3,
The blood glucose level prediction apparatus, wherein the altitude sensor is a barometric pressure sensor. In the blood sugar level prediction apparatus according to any one of claims 1 to 4,
The blood sugar level predicting apparatus, wherein the energy consumption information is measured by an activity meter including an activity amount measuring unit and the posture measuring unit.

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