JP7083185B2 - Biological information calculation system - Google Patents

Description

The present invention relates to a biometric information calculation system.

Conventionally, in the invasive measurement method of collecting blood from a user, biological information may be directly measured from the user. For example, when measuring a blood glucose level as biological information, blood is collected by puncture and measured by a blood glucose level sensor such as an enzyme electrode method. Such an invasive measurement method for measuring biological information causes a burden on the user's mind and body in blood collection.

Therefore, a technique for non-invasive biometric information measurement is required in order to reduce the burden on the user's mind and body. According to this non-invasive biometric information measurement method, for example, from a waveform signal of a pulse wave measured by a strain sensor, a gyro sensor, or a photoelectric volume pulse wave meter, a user can use a predetermined correlation between the pulse wave and the biometric information. Technology for obtaining biometric information is attracting attention.

In addition, a biometric information estimation device and its method that acquire biometric information from the waveform signal of the pulse wave by a non-invasive method by utilizing the fact that the pulse wave has a correlation with various biometric information such as blood pressure level and blood pressure. It has been proposed (see, for example, Patent Document 1).

In Patent Document 1, a biological information estimation device that extracts a plurality of feature values from biological signals, determines a scale factor based on the feature values, and estimates accurate and stable biological information based on the scale factor and the feature values. The method is disclosed.

Japanese Unexamined Patent Publication No. 2019-141583

Here, in the waveform signal of the pulse wave, the numerical value of the biometric information that can be acquired may vary depending on the extraction conditions and the processing method. For example, when comparing the case where the pulse wave differentiated as the extraction condition is processed and the case where the pulse wave without differentiation is processed as the extraction condition, there is a concern that the numerical value of the biometric information that can be acquired may vary. To. That is, in order to acquire biometric information with sufficient accuracy by using the pulse wave waveform signal, the pulse wave waveform signal is polygonized from a plurality of biometric information output simultaneously through a plurality of types of extraction conditions and the like. It is necessary to evaluate it.

On the other hand, Patent Document 1 does not disclose that a plurality of biological information is simultaneously output for a waveform signal of one pulse wave through a plurality of types of extraction conditions and the like. For this reason, the disclosure technique of Patent Document 1 has a problem that sufficient accuracy cannot be obtained because the biometric information that can be acquired varies depending on the extraction conditions and the like.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object of the present invention is to provide a biometric information calculation system capable of obtaining highly accurate evaluation results by multifaceted evaluation. It is in.

The biological information calculation system according to the first invention refers to an acquisition means for acquiring a velocity pulse wave as a pulse wave signal and a first extraction condition, and obtains first evaluation data based on the pulse wave signal acquired by the acquisition means. With reference to the first extraction means to be extracted and the first processing condition associated with the first extraction condition, the first blood glucose level for the first evaluation data extracted by the first extraction means is acquired as the first data . The first data acquisition means, the second extraction means for extracting the second evaluation data based on the pulse wave signal acquired by the acquisition means with reference to the second extraction condition, and the second extraction means associated with the second extraction condition. The second data acquisition means for acquiring the second data consisting of biological information of a type different from the first data with respect to the second evaluation data extracted by the second extraction means with reference to the processing conditions is provided. The first evaluation data and the second evaluation data are characterized in that they are extracted based on the same pulse wave signal.

In the first invention, the biological information calculation system according to the second invention refers to the first extraction condition in which the first extraction means does not differentiate the pulse wave signal acquired by the acquisition means, and the second extraction means. Is characterized by referring to the second extraction condition for differentiating the pulse wave signal acquired by the acquisition means.

The biological information calculation system according to the third invention is constructed in the first invention or the second invention by the first data acquisition means based on the correlation between the first evaluation data acquired in advance and the measured value of the biological information. The first regression analysis means has a first regression analysis means for acquiring the first data for the first evaluation data extracted by the first extraction means by using the first calibration model, and the first regression analysis means is said to be the first. Using a calibration model, the first blood glucose level for the first evaluation data extracted by the first extraction means is acquired as the first data.

According to the first to third inventions, the biometric information calculation system of the present invention extracts the first evaluation data based on the pulse wave signal by the first extraction means with reference to the first extraction condition, and further first extracts. The first data for the first evaluation data is acquired by the first data acquisition means referring to the first processing condition associated with the condition. Further, the biometric information calculation system of the present invention extracts the second evaluation data based on the pulse wave signal by the second extraction means with reference to the second extraction condition, and further sets the second processing condition associated with the second extraction condition. The second data for the second evaluation data is acquired by the referenced second data acquisition means. As a result, it is possible to simultaneously acquire a plurality of biometric information to which different extraction methods and processing methods are applied from one input pulse wave signal. With this plurality of biometric information, it is possible to obtain highly accurate evaluation results by multifaceted evaluation of biometric information with one pulse wave signal.

In particular, according to the second invention, the biometric information calculation system of the present invention extracts the acceleration pulse wave as the first evaluation data by differentiating the pulse wave signal by the first extraction means. Further, the biometric information calculation system of the present invention extracts the velocity pulse wave as the second evaluation data by not differentiating the pulse wave signal by the second extraction means. With these, the acceleration pulse wave and the velocity pulse wave can be acquired from one pulse wave signal. Since the feature points obtained from the acceleration pulse wave and the velocity pulse wave are different, by acquiring the biological information from these data, it becomes possible to obtain a highly accurate evaluation result by more multifaceted evaluation of the biological information.

In particular, according to the third invention, the first regression analysis means uses the first calibration model to acquire the first blood glucose level for the first evaluation data extracted by the first extraction means as the first data. .. As a result, the velocity pulse wave with less erroneous detection can be used as the first evaluation data, and the blood glucose level can be calculated, so that more accurate evaluation results can be obtained.

FIG. 1 is a diagram showing a configuration example of a biometric information calculation system according to the present embodiment. FIG. 2 is a diagram showing a specific configuration example of the sensor in the present embodiment. FIG. 3 is a diagram showing an example of a sensor embedded in a wristband. FIG. 4 is a diagram showing a specific configuration example of the electronic device in the present embodiment. FIG. 5 is a diagram showing a sequence for realizing the biometric information calculation program of the electronic device in the present embodiment. FIG. 6 is a diagram showing a specific configuration example of the first extraction unit and the first data acquisition unit in the present embodiment. FIG. 7 is a diagram showing a specific configuration example of the second extraction unit and the second data acquisition unit in the present embodiment. FIG. 8 is a flowchart showing an example of the biometric information calculation system according to the present embodiment. FIG. 9 is a diagram showing an example of the classification pattern of the acceleration pulse wave in the present embodiment. FIG. 10 is a diagram showing an example of a classification pattern of velocity pulse waves in the present embodiment.

Hereinafter, the biological information calculation system 1 to which the present invention is applied will be described in detail with reference to the drawings.

The biometric information calculation system 1 to which the present invention is applied is embodied by, for example, the configuration shown in FIG. The biometric information calculation system 1 includes an electronic device 2, a server 4 connected to the electronic device 2 via a public communication network 3, and a sensor 5.

The public communication network 3 is an Internet network or the like in which an electronic device 2, a server 4, and a sensor 5 are connected via a communication line. When the biometric information calculation system 1 is operated in a certain narrow area, the public communication network 3 may be configured by a LAN (Local Area Network). Further, the public communication network 3 may be configured by a so-called optical fiber communication network. Further, the public communication network 3 is not limited to the wired communication network, and may be realized by a wireless communication network.

The server 4 is constructed of a database in which information sent via the public communication network 3 is stored. Further, the server 4 transmits the accumulated information to the electronic device 2 via the public communication network 3 based on the request from the electronic device 2.

FIG. 2 shows a specific configuration example of the sensor 5. In the sensor 5, the acquisition unit 50, the communication I / F 51 (communication I / F 27), the memory 52, and the command unit 53 are connected by an internal bus 54, respectively. The sensor 5 is embedded in the wristband 55 as shown in FIG. 3, for example, and the wristband 55 is worn on the wrist. As a result, the sensor 5 can easily measure the pulse wave signal because the acquisition unit 50 comes into contact with the wrist pulse. Further, the sensor 5 may be embedded in clothes. Further, although the sensor 5 is described on the assumption that it can be attached to a human as a user, it may be used not only for humans but also for livestock such as pets, cows and pigs, or fish being cultivated. ..

The pulse wave signal indicates a digital signal or an analog signal corresponding to the user's velocity pulse wave. For example, the vertical axis can be plotted as a waveform on a plane graph with the amplitude of the velocity pulse wave and the horizontal axis with time, and the amplitude can be large. It can be treated as a signal that visualizes the change over time.

The acquisition unit 50 includes at least one measuring instrument for measuring a pulse wave signal from the user. For example, the acquisition unit 50 includes a strain sensor such as a fiber Bragg grating (FBG) sensor, a gyro sensor, one or more electrodes for measuring pulse wave signals, a photoelectric volume pulse wave (PPG) sensor, and a pressure sensor. A measuring instrument including at least one of a light source and a light detection module including a detector. The acquisition unit 50 can directly interface with the user through the measuring instrument to acquire the pulse wave signal. The acquisition unit 50 transmits the acquired pulse wave signal to the communication I / F 51 or the memory 52.

The communication I / F 51 transmits a pulse wave signal transmitted from the acquisition unit 50 via the public communication network 3 to the electronic device 2 or the server 4. Further, the communication I / F 51 is equipped with a line control circuit for connecting to the public communication network 3, a signal conversion circuit for performing data communication with the electronic device 2 and the server 4, and the like. The communication I / F 51 performs conversion processing on various instructions from the internal bus 54 and sends them to the public communication network 3 side, and when data from the public communication network 3 is received, the communication I / F 51 is predetermined. It is converted and transmitted to the internal bus 54.

The memory 52 stores the pulse wave signal transmitted from the acquisition unit 50. The memory 52 transmits the stored pulse wave signal to the communication I / F 51 by receiving a command from another terminal device connected via the public communication network 3.

The command unit 53 includes an operation button, a keyboard, and the like for inputting a command for acquiring a pulse wave signal to the acquisition unit 50. When the command unit 53 receives the command for acquiring the pulse wave signal, the command unit 53 notifies the acquisition unit 50 of the command. Upon receiving this notification, the acquisition unit 50 acquires the pulse wave signal.

The electronic device 2 is, for example, a personal computer (PC), a mobile phone, a smartphone, a tablet terminal, a wearable terminal, or the like, and is a device capable of communicating via a public communication network 3 at least based on a user's operation. The electronic device 2 may be a device that has a built-in sensor 5 and does not communicate with the sensor 5 via the public communication network 3. In the following example, a case where a PC is applied to the electronic device 2 will be described as an example.

FIG. 4 shows a specific configuration example of the electronic device 2. The electronic device 2 includes a ROM (Read Only Memory) 20, a RAM (Random Access Memory) 21, a CPU (Central Processing Unit) 22, an operation unit 23, an output I / F 16, a storage unit 24, and data. The input / output unit 25 and the communication I / F 27 are connected to the internal bus 26, respectively. Further, a display unit 28 is connected to the output I / F 16.

Further, in the electronic device 2, the CPU 22 acquires biometric information from the pulse wave signal by executing the biometric information calculation program stored in the storage unit 24 or the like with the RAM 21 as the working area.

The ROM 20 stores a program for controlling the hardware resources of the entire electronic device 2.

The RAM 21 is a work area used for storing and expanding data that temporarily stores various instructions when controlling the hardware resources of the entire electronic device 2.

The CPU 22 is a so-called central arithmetic unit for controlling each component mounted in the electronic device 2 by transmitting a control signal via the internal bus 26. Further, the CPU 22 transmits various control commands via the internal bus 26 according to the user's operation via the operation unit 23.

The operation unit 23 is composed of devices such as a mouse, a keyboard, a touch panel, and operation buttons for inputting various control commands. In the operation unit 23, information about the biometric information that the user actually wants to acquire is input, and an execution command for executing the biometric information calculation program is input from the user. When the above-mentioned execution command is input by the user, the operation unit 23 notifies the CPU 22 of this. Upon receiving this notification, the CPU 22 reads the biometric information calculation program from the storage unit 24 and executes it.

The output I / F 16 controls the display of various information. The output I / F 16 is configured by a graphic controller that creates a display image based on control by the CPU 22. The display unit 28 connected to the output I / F 16 is realized by, for example, a liquid crystal display (LCD) or the like.

The storage unit 24 is a recording medium typified by a hard disk or the like, and stores a biometric information acquisition program to be executed. When the storage unit 24 is composed of a hard disk, predetermined information is written to each address based on the control by the CPU 22, and is read out as needed. The storage unit 24 is read by the CPU 22 and outputs a biometric information acquisition program.

The communication I / F 27 is equipped with a line control circuit for connecting to the public communication network 3, a signal conversion circuit for performing data communication with other terminal devices, and the like. The communication I / F 27 performs conversion processing on various instructions from the internal bus 26 and sends them to the public communication network 3 side, and when data from the public communication network 3 is received, the communication I / F 27 is predetermined. It is converted and transmitted to the internal bus 26 or the CPU 22.

The data input / output unit 25 inputs data from the outside into the electronic device 2, or outputs the data generated in the electronic device 2 to the outside. The data input / output unit 25 is configured as an interface for inputting data to / from a USB (Universal Serial Bus) memory or a recording medium.

The biometric information calculation system 1 to which the present invention is applied will be executed via a biometric information calculation program installed in the electronic device 2. That is, the user operates the electronic device 2 or the sensor 5 and acquires biometric information from the pulse wave signal through the biometric information calculation program installed in the electronic device 2.

FIG. 5 shows a sequence for realizing the biometric information calculation program of the electronic device 2. The electronic device 2 includes a pulse wave signal acquisition unit 60, a first extraction unit 61 and a second extraction unit 63 connected to the pulse wave signal acquisition unit 60, and a first data acquisition unit connected to the first extraction unit 61. 62, a second data acquisition unit 64 connected to the second extraction unit 63, and an optimum blood glucose level calculation unit 65 connected to the first data acquisition unit 62 and the second data acquisition unit 64. ..

The pulse wave signal acquisition unit 60 acquires a pulse wave signal transmitted from the sensor 5, the server 4, and other electronic devices via the public communication network 3. The pulse wave signal acquisition unit 60 outputs the acquired pulse wave signal to the first extraction unit 61 and the second extraction unit 63.

The first extraction unit 61 refers to the first extraction condition and extracts the first evaluation data based on the pulse wave signal input from the pulse wave signal acquisition unit 60. The first extraction unit 61 outputs the extracted first evaluation data to the first data acquisition unit 62.

The first data acquisition unit 62 refers to the first processing condition associated with the first extraction condition, processes the first evaluation data input from the first extraction unit 61, and acquires the first data. The first data acquisition unit 62 outputs the acquired first data to the optimum blood glucose level calculation unit 65.

The second extraction unit 63 refers to the second extraction condition and extracts the second evaluation data based on the pulse wave signal input from the pulse wave signal acquisition unit 60. The second extraction unit 63 outputs the extracted second evaluation data to the second data acquisition unit 64.

The second data acquisition unit 64 refers to the second processing condition associated with the second extraction condition, processes the second evaluation data input from the second extraction unit 63, and acquires the second data. The second data acquisition unit outputs the acquired second data to the optimum blood glucose level calculation unit 65.

The optimum blood glucose level calculation unit 65 calculates biometric information having an optimum value based on the first data input from the first data acquisition unit 62 and the second data input from the second data acquisition unit 64.

Further, the optimum blood glucose level calculation unit 65 is not necessarily provided in the electronic device 2, and may output the first data and the second data as biological information.

FIG. 6 shows a specific configuration example of the first extraction unit 61 and the first data acquisition unit 62. The first extraction unit 61 includes a filter processing unit 610, a differentiation unit 611 connected to the filter processing unit 610, a peak position calculation unit 614 connected to the filter processing unit 610, and a division unit connected to the differentiation unit 611. 612, a normalization unit 613 connected to the division unit 612, a peak interval average calculation unit 615 connected to the peak position calculation unit 614, a peak interval plot unit 616 connected to the peak position calculation unit 614, and a peak. It includes a Fourier transform unit 617 connected to the position calculation unit 614 and a maximum frequency detection unit 618 connected to the Fourier transform unit 617.

The filter processing unit 610 performs filtering processing on the acquired pulse wave signal. The filter processing unit 610 uses, for example, a bandpass filter of 0.5 to 5 Hz for filtering, but the present invention is not limited to this. Further, the filter processing unit 610 refers to the first extraction condition and determines an extraction method for extracting the first evaluation data from the acquired pulse wave signal. The filter processing unit 610 outputs the filtered pulse wave signal to at least one of the differentiation unit 611, the peak position calculation unit 614, and the Fourier transform unit 617 based on the determined extraction method.

The first extraction unit 61 does not necessarily use all of the extraction methods provided in the first extraction unit 61 in order to acquire one first evaluation data, and at least one determined by the filtering unit 610. The first evaluation data is extracted from the pulse wave signal by the extraction method.

The differentiation unit 611 differentiates the pulse wave signal input from the filter processing unit 610. When the filter processing unit 610 determines that the differentiation processing is necessary, the differentiation unit 611 performs the differentiation processing on the input pulse wave signal. The differentiation unit 611 outputs the processed pulse wave signal to the division unit 612.

The division unit 612 divides each of the plurality of waveform signals input from the differentiation unit 611 into divided waveform data for an integer period. In the present embodiment, the division unit 612 has one integer cycle, but may have a plurality of cycles. The division unit 612 outputs the division waveform data to the normalization unit 613.

The standardization unit 613 standardizes in order to unify the time width of the plurality of divided waveform signals input from the divided unit 612, acquires an average waveform signal that is the average of the plurality of divided waveform signals, and averages the waveform signal. The maximum value of the amplitude of is 1 and the minimum value is 0. The normalized unit 613 outputs the normalized average waveform signal to the regression analysis unit 620.

The normalization unit 613 may trim a plurality of division waveform signals input from the division unit 612 with a constant time width or a constant sampling number in order to unify the time width of the division waveform signal. In the standardization unit 613, the processing method for unifying the time width is determined by the filter processing unit 610.

The normalization unit 613 requires a plurality of divided waveform signals when acquiring the average waveform signal, and the number of required divided waveform signals is determined by the filter processing unit 610.

The peak position calculation unit 614 calculates the peak position of the pulse wave signal input from the filter processing unit 610 and the peak interval, which is the distance between adjacent peaks. The peak position calculation unit 614 outputs the calculated peak interval to at least one of the peak interval average calculation unit 615, the peak interval plot unit 616, and the Fourier transform unit 617 based on the extraction method.

The peak interval average calculation unit 615 calculates the average of the peak intervals input from the peak position calculation unit 614, divides the average value of the peak intervals by the sampling rate of the measuring instrument, and converts it into the number of seconds. The peak interval average calculation unit 615 outputs the average value converted into the number of seconds to the pulse processing unit 621.

The peak interval plotting unit 616 plots a graph in which the peak interval input from the peak position calculation unit 614 is used as the horizontal axis and the peak interval next to the above-mentioned peak interval is used as the vertical axis. To get. The peak interval plotting unit 616 outputs a Poincare plot of the peak interval of the pulse wave to the stress plot processing unit 622.

When the peak interval is input from the peak position calculation unit 614, the Fourier transform unit 617 Fourier transforms the data obtained by converting the peak interval into time series data. Further, the Fourier transform unit 617 may perform the Fourier transform by inputting the pulse wave signal processed by the filter processing unit 610. The Fourier transform unit 617 outputs the Fourier transformed signal to at least one of the maximum frequency detection unit 618 and the stress Fourier processing unit 623 based on the extraction method.

The maximum frequency detection unit 618 detects the maximum frequency, which is the frequency showing the maximum value between 0.15 and 0.35 Hz for the signal input from the Fourier transform unit 617. The maximum frequency detection unit 618 outputs the detected maximum frequency to the respiratory rate processing unit 624.

The first data acquisition unit 62 includes a regression analysis unit 620 connected to the standardization unit 613, a pulse processing unit 621 connected to the peak interval average calculation unit 615, and a stress plot process connected to the peak interval plotting unit 616. It includes a unit 622, a stress Fourier processing unit 623 connected to the Fourier transform unit 617, and a respiratory rate processing unit 624 connected to the maximum frequency detection unit 618.

The first data acquisition unit 62 uses, for example, as a processing method, a calibration model constructed based on the correlation between the actually measured value of the biological information and the pulse wave signal acquired in advance, and is the first from the first evaluation data. Get the data.

The regression analysis unit 620 uses, for example, blood glucose level, blood pressure, blood oxygen saturation, and blood carbon dioxide as the first data based on the calibration model from the standardized average waveform signal input from the standardization unit 613. Get the concentration etc.

The regression analysis unit 620 can also acquire the first data by storing it in the first data acquisition unit 62 by using a calibration model that has been constructed in advance and can be used for general purposes.

The pulse processing unit 621 divides the average value of the peak intervals input from the peak interval average calculation unit 615 by the sampling rate and converts it into the number of seconds. The pulse processing unit 621 divides 60 seconds by the calculated number of seconds, calculates the pulse rate per minute (bpm), and acquires the pulse rate as the first data.

The stress plot processing unit 622 acquires, for example, the stress degree as the first data from the Poincare plot input from the peak interval plot unit 616. The stress plot processing unit 622 calculates the variance value of the Poincare plot and estimates the stress degree from the magnitude of the variance value.

The stress Fourier processing unit 623 acquires, for example, the degree of stress as the first data from the integral ratio of the signal input from the Fourier transform unit 617. As a specific method, the stress Fourier processing unit 623 calculates PSD (power spectral density) from, for example, Fourier-transformed time-series data of peak intervals using an autoregressive model, and has a power spectrum of 0. The region from 0.5 Hz to 0.15 Hz is defined as high frequency LF (Low Frequency), and the region from 0.15 Hz to 0.40 Hz is the stress level by the ratio of the integrated values obtained by summing the intensities of low frequency HF (Hi Frequency). decide.

The respiratory rate processing unit 624 takes 60 seconds to convert the frequency indicating the maximum value input from the maximum frequency detection unit 618 into the respiratory rate (bpm) per minute, and acquires the respiratory rate as the first data.

FIG. 7 shows a specific configuration example of the second extraction unit 63 and the second data acquisition unit 64. The second extraction unit 63 includes a filter processing unit 630, a differentiation unit 631 connected to the filter processing unit 630, a peak position calculation unit 634 connected to the filter processing unit 630, and a division unit connected to the differentiation unit 631. 632, a standardization unit 633 connected to the division unit 632, a peak interval average calculation unit 635 connected to the peak position calculation unit 634, a peak interval plot unit 636 connected to the peak position calculation unit 634, and a peak. It includes a Fourier transform unit 637 connected to the position calculation unit 634 and a maximum frequency detection unit 638 connected to the Fourier transform unit 637. The second extraction unit 63 is configured to be the same as the first extraction unit 61, but when the first extraction unit 61 refers to the first extraction condition, the second extraction unit 63 is the case. The second extraction condition is referred to instead of the first extraction condition.

The filter processing unit 630 performs filtering processing on the acquired pulse wave signal. The filter processing unit 630 uses, for example, a bandpass filter of 0.5 to 5 Hz for filtering, but the present invention is not limited to this. Further, the filter processing unit 630 refers to the second extraction condition and determines a method for extracting the first evaluation data from the acquired pulse wave signal. The filter processing unit 630 outputs the filtered pulse wave signal to at least one of the differentiation unit 631, the peak position calculation unit 634, and the Fourier transform unit 637 based on the determined extraction method.

The second extraction unit 63 does not necessarily use all the extraction methods provided in the second extraction unit 63 in order to acquire one second evaluation data, and the second extraction unit 63 does not necessarily use all the extraction methods provided in the second extraction unit 63, but at least one extraction determined by the filter processing unit 630. The second evaluation data is extracted from the pulse wave signal by the method.

The differentiation unit 631 differentiates the pulse wave signal input from the filter processing unit 630. When the filter processing unit 630 determines that the differentiation processing is necessary, the differentiation unit 631 performs the differentiation processing on the input pulse wave signal. The differentiation unit 631 outputs the differentially processed pulse wave signal to the division unit 632.

The division unit 632 divides each of the plurality of waveform signals input from the differentiation unit 631 into divided waveform data for an integer period. In the present embodiment, the division unit 632 has one integer cycle, but may have a plurality of cycles. The division unit 632 outputs the division waveform data to the normalization unit 633.

The standardization unit 633 standardizes in order to unify the time width of the plurality of divided waveform signals input from the divided unit 632, acquires an average waveform signal that is the average of the plurality of divided waveform signals, and averages the waveform signal. The maximum value of the amplitude of is 1 and the minimum value is 0. The normalized unit 633 outputs the normalized average waveform signal to the regression analysis unit 640.

The normalization unit 633 may trim a plurality of divided waveform signals input from the divided unit 632 with a fixed time width or a fixed number of samplings in order to unify the time width of the divided waveform signal. In the standardization unit 633, the processing method for unifying the time width is determined by the filter processing unit 630.

The normalization unit 633 requires a plurality of divided waveform signals when acquiring the average waveform signal, and the number of required divided waveform signals is determined by the filter processing unit 630.

The peak position calculation unit 634 calculates the peak position of the pulse wave signal input from the filter processing unit 630 and the peak interval which is the distance between adjacent peaks. The peak position calculation unit 634 outputs the calculated peak interval to at least one of the peak interval average calculation unit 635, the peak interval plot unit 636, and the Fourier transform unit 637 based on the extraction method.

The peak interval average calculation unit 635 calculates the average of the peak intervals input from the peak position calculation unit 634, divides the average value of the peak intervals by the sampling rate of the measuring instrument, and converts it into the number of seconds. The peak interval average calculation unit 635 outputs the average value converted into the number of seconds to the pulse processing unit 641.

The peak interval plotting unit 636 plots a graph with the peak interval input from the peak position calculation unit 634 as the horizontal axis and the peak interval next to the above-mentioned peak interval as the vertical axis, and Poincare plot of the peak interval of the pulse wave. To get. The peak interval plotting unit 636 outputs the Poincare plot of the peak interval of the pulse wave to the stress plot processing unit 642.

When the peak interval is input from the peak position calculation unit 634, the Fourier transform unit 637 Fourier transforms the data obtained by converting the peak interval into time series data. Further, the Fourier transform unit 637 may perform the Fourier transform by inputting the pulse wave signal processed by the filter processing unit 630. The Fourier transform unit 637 outputs the Fourier transformed signal to at least one of the maximum frequency detection unit 638 and the stress Fourier processing unit 643 based on the extraction method.

The maximum frequency detection unit 638 detects the maximum frequency, which is the frequency showing the maximum value between 0.15 and 0.35 Hz for the signal input from the Fourier transform unit 637. The maximum frequency detection unit 638 outputs the detected maximum frequency to the respiratory rate processing unit 644.

The second data acquisition unit 64 is the stress level from the graph plotted by the regression analysis unit 640 connected to the standardization unit 633, the pulse processing unit 641 connected to the peak interval average calculation unit 635, and the peak interval plotting unit 636. The stress plot processing unit 642, the stress Fourier processing unit 643 connected to the Fourier transform unit 637, and the respiratory rate processing unit 644 connected to the maximum frequency detection unit 638 are provided.

The second data acquisition unit 64 refers to the second processing condition associated with the second extraction condition, processes the second evaluation data extracted by the second extraction unit 63, and acquires the second data.

The second data acquisition unit 64 is constructed using, for example, as a processing method, a calibration model constructed based on the correlation between the measured value of the biological information and the pulse wave signal acquired in advance, and is constructed from the second evaluation data. Acquire the second data.

The regression analysis unit 640 uses, for example, blood glucose level, blood pressure, blood oxygen saturation, and blood carbon dioxide as second data based on the calibration model from the standardized average waveform signal input from the standardization unit 633. Get the concentration etc.

The regression analysis unit 640 can also acquire the second data by storing the calibration model that has been constructed in advance and can be used for general purposes as general-purpose data in the second data acquisition unit 64.

The pulse processing unit 641 divides the average value of the peak intervals input from the peak interval average calculation unit 635 by the sampling rate and converts it into the number of seconds. The pulse processing unit 641 divides 60 seconds by the calculated number of seconds, calculates the pulse rate per minute (bpm), and acquires the pulse rate as the second data.

The stress plot processing unit 642 acquires, for example, the stress degree as second data from the Poincare plot input from the peak interval plot unit 636. The stress plot processing unit 642 calculates the variance value of the Poincare plot and estimates the stress degree from the magnitude of the variance value.

The stress Fourier processing unit 643 acquires, for example, the degree of stress as second data from the integral ratio of the signal input from the Fourier transform unit 637.

The respiratory rate processing unit 644 takes 60 seconds to convert the frequency indicating the maximum value input from the maximum frequency detection unit 638 into the respiratory rate (bpm) per minute, and acquires the respiratory rate as the second data.

The first data and the second data acquired by the biological information calculation system 1 include, for example, blood pressure, blood glucose level, blood oxygen saturation, blood carbon dioxide concentration, pulse rate, respiratory rate, stress level, blood vessel age, and diabetes. Include at least one of the degree of.

Next, an example of the biometric information calculation system 1 in the present embodiment will be described. FIG. 8 is a flowchart showing an example of the biological information calculation system 1 in the present embodiment.

First, in the pulse wave signal acquisition step S10, the biometric information calculation system 1 acquires the pulse wave signal by the acquisition unit 50 and outputs the pulse wave signal to the communication I / F 51 via the internal bus 54. Further, at this time, instead of the pulse wave signal acquired by the acquisition unit 50, the biometric information calculation system 1 transmits the pulse wave signal recorded in the memory 52 to the communication I / F 51 via the internal bus 54. A signal may be output.

As a specific acquisition method of the pulse wave signal, a case of measuring using an FBG sensor will be described as an example.

The FBG sensor has a diffraction grating structure formed in one optical fiber at predetermined intervals. In the present embodiment, the FBG sensor has a sensor portion length of 10 mm, a wavelength resolution of ± 0.1 pm, and a wavelength range of 1550 ± 0.5 nm, and has a fiber diameter of 145 μm and a core diameter of 10.5 μm. However, these settings are examples of FBG sensors, and are not limited to this in the present invention. This sensor is attached to the wrist, and the measurement is performed with the FBG sensor in contact with the skin.

As the light source used for the optical fiber, an ASE (Amplified Spontaneous Emission) light source having a wavelength range of 1525 to 1570 nm may be used. The light emitted from the light source is incident on the FBG sensor via the circulator. The reflected light from the FBG sensor is guided to the Mach-Zehnder interferometer via the circulator, and the output light from the Mach-Zehnder interferometer is detected by the optical detector. The Mach-Zehnder interferometer is for splitting into two optical paths having optical path differences by a beam splitter and superimposing them on one again by a beam splitter to generate interferometric light. In this embodiment, the length of one of the optical fibers is increased in order to make an optical path difference. Since coherent light produces interference fringes according to the optical path difference, it is possible to detect a change in strain generated in the FBG sensor, that is, a pulse wave, by measuring the pattern of the interference fringes.

The optical fiber sensor system, which is a system that detects the amount of distortion of the FBG sensor and detects the waveform of the pulse wave, has a means for using a wide band ASE light source, a circulator, and Mach Zender interference in addition to the light source incident on the FBG sensor. It includes an optical system such as a meter and a beam splitter, a light receiving sensor provided in the optical detector, and an analysis means for analyzing the wavelength shift amount. The optical fiber sensor system can be used by selecting a light source or band light according to the characteristics of the FBG sensor to be used, and various methods can be adopted as analysis means such as a detection method. Further, the present invention is not limited to the functions and methods of the optical fiber sensor system.

By these methods, the acquisition unit 50 acquires the pulse wave signal and outputs the pulse wave signal to the communication I / F 51 via the internal bus 54.

Next, the communication I / F 51 to which the pulse wave signal is input from the acquisition unit 50 transmits the pulse wave signal to the pulse wave signal acquisition unit 60 via the public communication network 3. Further, at this time, instead of the pulse wave signal acquired by the acquisition unit 50, the pulse wave signal stored in the server 4 may be transmitted to the pulse wave signal acquisition unit 60.

Next, the pulse wave signal acquisition unit 60, which has transmitted the pulse wave signal via the public communication network 3, outputs the pulse wave signal to the first extraction unit 61 and the second extraction unit 63.

Next, in the first extraction condition determination step S11, the first extraction unit 61 inputs the pulse wave signal input from the pulse wave signal acquisition unit 60 to the filter processing unit 610. After that, the filter processing unit 610 determines the extraction method to be applied to the input pulse wave signal with reference to the first extraction condition.

The filter processing unit 610 determines the extraction method applied to the pulse wave signal by the first extraction unit 61 according to the state of the acquired pulse wave signal, the biological information to be acquired, and external factors from the first extraction conditions. External factors include, for example, user's age, gender, medical history, lifestyle, medication information, degree of arteriosclerosis, health status, genetic information, and other user information, and environmental information such as temperature and humidity. Include at least one of such. For example, when it is desired to acquire the blood glucose level as the biological information to be acquired, the filter processing unit 610 first extracts the pulse wave signal so that it is output to the differentiation unit 611 after being processed by the filter processing unit 610. Give an order to unit 61. The first extraction condition is a data group including a list of extraction methods for extracting the first evaluation data from the pulse wave signal. The data group of the first extraction condition may include a plurality of extraction methods, or may include a single predetermined extraction method.

Next, in the first extraction step S12, the first extraction unit 61 extracts the first evaluation data from the pulse wave signal input from the pulse wave signal acquisition unit 60. The first evaluation data is waveform data extracted from the pulse wave signal by the first extraction unit 61 in order to acquire biometric information by the first data acquisition unit 62. The first evaluation data is waveform data in which, for example, a pulse wave signal is processed into a waveform for one cycle or the like by at least one of filtering processing, differentiation processing, normalization processing, and averaging processing. In this embodiment, a biological information calculation system 1 for acquiring a blood glucose level will be described as an example.

First, the filter processing unit 610 performs filtering processing on the pulse wave signal input from the pulse wave signal acquisition unit 60, and then, for example, when acquiring the blood glucose level as biological information, the extraction determined in the first extraction condition determination step S11. Based on the method, the pulse wave signal is output to the differential unit 611.

Next, the differentiation unit 611 determines whether or not to differentiate the pulse wave signal input from the filter processing unit 610 based on the extraction method determined in the first extraction condition determination step S11, and performs processing. After that, the pulse wave signal is output to the division unit 612.

The reason why the differentiation unit 611 determines whether or not to differentiate the pulse wave signal based on the extraction method determined in the first extraction condition determination step S11 is the first evaluation obtained by whether or not the pulse wave signal is differentiated. This is because there is a difference in the characteristics of the data, and the first evaluation data suitable for the first data to be acquired is obtained. Further, "differentiating the pulse wave signal" means extracting the pulse wave signal as an acceleration pulse wave, and "not differentiating the pulse wave signal" means extracting the pulse wave signal as a velocity pulse wave. be.

Next, the division unit 612 divides each of the plurality of waveform signals input from the differentiation unit 611 into divided waveform data for one cycle in order to average them. After that, the division unit 612 outputs the division waveform data to the normalization unit 613.

The standardization unit 613 standardizes the horizontal axis in order to unify the time width of the plurality of divided waveform signals input from the divided unit 612, and acquires an average waveform signal that is the average of the plurality of divided waveform signals. The vertical axis is standardized with the maximum value of the average waveform signal set to 1 and the minimum value set to 0. At this time, the first extraction unit 61 acquires the average waveform signal as the first evaluation data. After that, the standardized unit 613 outputs the standardized average waveform signal to the first data acquisition unit 62.

The reason why the normalization process on the horizontal axis is performed in the normalization section 613 to unify the time width is that a large difference appears on the end side of the pulse wave, so this part is deleted and the main body part of the pulse wave is analyzed. This is because. The reason for standardizing the vertical axis with the maximum value of the average waveform signal set to 1 and the minimum value set to 0 is that the pressing pressure varies when the FBG sensor is attached to the measurement site and the position of the FBG sensor shifts during measurement. This is to average the variation of the measurement data due to the measurement, suppress the noise caused by the variation at the time of measurement, and improve the accuracy of the correlation between the pulse wave signal and the measured value of the biological information.

Next, the biometric information calculation system 1 proceeds to the first data acquisition step S13, refers to the first processing condition associated with the first extraction condition, and first uses the first evaluation data input from the first extraction unit 61. The data acquisition unit 62 processes and acquires the first data.

The first processing condition is a data group including the method associated with the first extraction condition of the processing performed by the first data acquisition unit 62 in the first evaluation data input from the first extraction unit 61. The first data acquisition unit 62 determines the processing method from the first processing conditions. The data group of the first processing condition may include a plurality of processing methods, or may include a single predetermined processing method.

For example, when the average waveform signal acquired by the above-mentioned extraction method is processed by the first data acquisition unit 62 as the first evaluation data, the first data acquisition unit 62 outputs the average waveform signal to the regression analysis unit 620. Further, the regression analysis unit 620 determines the processing method to be applied to the average waveform signal.

The regression analysis unit 620, in which the average waveform signal is input from the first extraction unit 61, acquires, for example, the blood glucose level from the average waveform signal by using a calibration model showing the correlation between the measured value and the pulse wave signal, and the first Output as data.

The calibration model is constructed based on the analysis result, for example, by performing regression analysis using the measured average waveform pulse wave measured in advance as the explanatory variable and the measured value of the biological information as the objective variable. As the calibration model, a calibration model that has been constructed in advance and can be used for general purposes can be stored in the storage unit 24, and the first data can be measured. Building a calibration model may be necessary, for example, when calibrating on a regular basis or when rebuilding when the user changes.

As a method of regression analysis for constructing a calibration model, for example, PLS regression analysis, regression analysis using the SIMCA (Soft Independent Modeling of Class Analogy) method for obtaining a calibration model as a principal component model by performing principal component analysis for each class. , Includes at least one method, such as a regression analysis method using a neural network using AI.

Further, when estimating biological information in which it is difficult to observe an abnormal value such as a blood carbon dioxide concentration and it is difficult to collect abnormal value data, the regression analysis unit 620 has an average waveform input from the first extraction unit 61. Outliers of biometric information may be estimated from the degree of discrepancy between the signal and the calibration model.

The regression analysis unit 620 has a plurality of calibration models, and which calibration model is used for the input average waveform data is determined by the first data acquisition unit 62 with reference to the first processing condition. Determined by. For example, when the blood glucose level is selected as the biological information to be acquired by the filter processing unit 610, the first data acquisition unit 62 outputs the average waveform signal input from the first extraction unit 61 to the regression analysis unit 620. In the regression analysis unit 620, the waveform data of the pulse wave measured in advance is used as an explanatory variable, the measured value of the blood glucose level is used as the objective variable, and the analysis is performed by regression analysis, and the calibration model constructed based on the analysis result is used. , Determines to acquire the first data from the input average waveform data.

Further, for example, when the filter processing unit 610 determines the signal extraction condition as an external factor based on the age of the user, the first data acquisition unit 62 uses the first processing condition associated with the first extraction condition. By referring to, the average waveform signal input from the first extraction unit 61 is output to the regression analysis unit 620, and the waveform data of the pulse wave measured in advance by the regression analysis unit 620 is used as an explanatory variable, and the age of the user is used. Using the measured value of the user's blood glucose level close to that as the objective variable, analyze it by regression analysis, and use the calibration model constructed based on the analysis result to acquire the first data from the input average waveform data. decide. These make it possible to determine a processing method suitable for the extraction method.

Next, in the second extraction condition determination step S14, the second extraction unit 63 inputs the pulse wave signal input from the pulse wave signal acquisition unit 60 to the filter processing unit 630. After that, the filter processing unit 630 determines the extraction method to be applied to the input pulse wave signal with reference to the second extraction condition.

The second extraction condition is a data group including a list of extraction methods for extracting the second evaluation data from the pulse wave signal. The data group of the second extraction condition may include a plurality of extraction methods, or may include a single predetermined extraction method. Further, the second extraction condition may be the same as the first extraction condition.

The filter processing unit 630 determines the extraction method applied to the pulse wave signal by the second extraction unit 63 according to the state of the acquired pulse wave signal, the biological information to be acquired, and an external factor from the second extraction conditions. The external factor includes, for example, at least one of the user's age, gender, medical history, lifestyle, health condition, user's information such as genetic information, and environmental information such as temperature and humidity. Further, the filter processing unit 630 may determine the extraction method to be applied to the pulse wave signal by the second extraction unit 63 according to the content of the first data acquired by the first data acquisition unit 62. For example, when the accuracy of the blood glucose level of the first data acquired by the first data acquisition unit 62 is low, the filter processing unit 630 determines an extraction method not performed by the first extraction unit 61. Further, for example, when the blood glucose level is acquired as the first data, the filter processing unit 630 is the standardization unit 633 in order to acquire the blood pressure as the second data, and is constant in order to unify the time width of the divided waveform signal. The extraction condition for trimming may be determined by the number of samplings. As a result, it becomes possible to acquire the second data according to the change and the feature of the first data, and it is possible to obtain a highly accurate evaluation result. Further, the filter processing unit 630 may determine the processing method to be applied to the second evaluation data by the second data acquisition unit 64 according to the content of the first data acquired by the first data acquisition unit 62. As a specific method, the method similar to the method in which the second data acquisition unit 64, which will be described later, determines the processing method to be applied to the second evaluation data based on the content of the first data acquired by the first data acquisition unit 62. good.

Further, the filter processing unit 630 may determine the extraction method to be applied to the pulse wave signal by the second extraction unit 63 according to the extraction method of the first evaluation data acquired by the first data acquisition unit 62.
For example, when the differentiation unit 611 performs the differentiation when the first evaluation data is acquired, the filter processing unit 630 determines that the differentiation unit 631 does not perform the differentiation. As a result, it becomes possible to acquire the optimum second data as the first data changes.

Next, in the second extraction step S15, the second extraction unit 63 extracts the second evaluation data from the pulse wave signal input from the pulse wave signal acquisition unit 60. The second evaluation data is waveform data extracted from the pulse wave signal by the second extraction unit 63 for processing by the second data acquisition unit 64. The second evaluation data is waveform data in which, for example, a pulse wave signal is processed into a waveform for one cycle or the like by at least one of filtering processing, differentiation processing, normalization processing, and averaging processing.

First, the filter processing unit 630 performs filtering processing on the pulse wave signal input from the pulse wave signal acquisition unit 60, and then, for example, when acquiring the blood glucose level as biological information, the extraction determined in the second extraction condition determination step S14. Based on the method, the pulse wave signal is output to the differential unit 631.

Next, the differentiation unit 631 determines whether or not to differentiate the pulse wave signal input from the filter processing unit 630 based on the extraction method determined in the second extraction condition determination step S14, and performs processing. After that, the pulse wave signal is output to the division unit 632.

Next, the division unit 632 divides each of the plurality of waveform signals input from the differentiation unit 631 into divided waveform data for one cycle. After that, the division unit 632 outputs the division waveform data to the normalization unit 633.

The standardization unit 633 standardizes in order to unify the time width of the plurality of divided waveform signals input from the divided unit 632, acquires an average waveform signal that is the average of the plurality of divided waveform signals, and averages the waveform signal. Standardization is performed with the maximum value of 1 being 1 and the minimum value being 0. At this time, the second extraction unit 63 acquires the average waveform signal as the second evaluation data. After that, the standardized unit 633 outputs the standardized average waveform signal to the second data acquisition unit 64.

Next, the biometric information calculation system 1 proceeds to the second data acquisition step S16, refers to the second processing condition associated with the second extraction condition, and uses the second evaluation data input from the second extraction unit 63 as the second evaluation data. The data acquisition unit 64 processes and acquires the second data.

The second processing condition is a data group including the method associated with the second extraction condition of the processing performed by the second data acquisition unit 64 in the second evaluation data input from the second extraction unit 63. The second data acquisition unit 64 determines the processing method from the second processing conditions. The data group of the second processing condition may include a plurality of processing methods, or may include a single predetermined processing method. Further, the second processing condition may be the same as the first processing condition.

Further, the second data acquisition unit 64 may determine the processing method of the second evaluation data to be applied by the second data acquisition unit 64 according to the processing method of the first data. For example, when a processing method for acquiring the first data from the average waveform signal is applied using a calibration model showing the correlation between the measured value measured from a young user and the pulse wave signal, the second data acquisition unit 64 May acquire second data from the average waveform signal using a calibration model that is more suitable for the user, depending on the processing method described above. This makes it possible to acquire a plurality of biometric information acquired by different processing methods, and enables more multifaceted and highly accurate evaluation.

Further, the second data acquisition unit 64 may refer to the second processing condition and determine a processing method corresponding to the content of the first data. For example, when it is found from the first data that the user tends to have low blood glucose, the second data acquisition unit 64 uses the low blood glucose user as a method of processing the second evaluation data performed by the second data acquisition unit 64. The processing method for acquiring the blood glucose level may be determined by using a calibration model showing the correlation between the measured blood glucose level and the pulse wave signal. Since it is assumed that there is a large error in the blood glucose level that can be obtained, it is classified into hypoglycemic zone, normal blood glucose zone, hyperglycemic zone, and ultrahyperglycemic zone from the contents of the first data, and according to the blood glucose band. By using the calibration model, it is possible to greatly improve the accuracy of the blood glucose level acquired as the second data. Further, it may be determined from the content of the first data whether or not the user has diabetes, and the processing method of the second evaluation data to be applied by the second data acquisition unit 64 may be determined according to the result. As a result, more accurate evaluation becomes possible by deciding the processing of the second evaluation data according to the change of the first data.

The processing method can also be classified according to the content of the calibration model used in the processing of acquiring the first data from the average waveform signal, for example, in the regression analysis unit 620. For example, a processing method for acquiring the first data from the average waveform signal using a calibration model showing the correlation between the measured value measured from a young user and the pulse wave signal, and the measured value measured from a user in another age group. The processing methods for acquiring the first data from the average waveform signal using the calibration model showing the correlation between the pulse wave signal and the pulse wave signal are different processing methods.

Further, the second data acquisition unit 64 refers to the classification pattern of the first evaluation data, sets the result of classifying the first evaluation data as the first data, and refers to the above-mentioned first data to acquire the second data. You may decide the processing method of the 2nd evaluation data to apply in part 64. This enables highly accurate evaluation using a velocity pulse wave that can suppress erroneous detection after classifying the characteristics of the signal using, for example, an acceleration pulse wave that is easy to classify. This makes it possible to follow changes in the first data and determine processing of the second evaluation data that is more suitable for the user.

The classification pattern is a classification table for classifying signals into two or more groups according to the characteristics of the waveform of the signal.

FIG. 9 is an example of the classification pattern of the acceleration pulse wave. In FIG. 9, inflection points a to e exist in the acceleration pulse wave, the maximum peak in the acceleration pulse wave is point a, and each inflection point is point b, point c, point d in order from point a. Normalization is performed with point e, point a as 1, and the minimum value b or d as 0, and the pulse wave is classified according to the value of each inflection point and the magnitude relationship of the difference. It is a figure classified into 7 patterns. First, when the value of the inflection point is b <d, it is classified into pattern A or B. If b <d and c ≧ 0.5, it is further classified as A, and if not, it is classified as B. Next, when the value of the inflection point is b≈d, it is classified into the pattern C or D. If b≈d and c≈0, it is classified into pattern D, and if not, it is classified into pattern C. Finally, if b> d, it can be classified into any of patterns E, F, and G. If b> d and b <c, it is classified into pattern E, if b≈c, it is classified into pattern F, and if b> c, it is classified into pattern G.

When the first evaluation data based on the acceleration pulse wave obtained by differentiating the pulse wave signal by the differentiation unit 611 is input, the first data acquisition unit 62 determines which pattern of FIG. 9 the first evaluation data applies to, for example. Judge and determine the classification pattern. For example, if the inflection point b of the input first evaluation data is smaller than the inflection point d and the inflection point c ≧ 0.5, the pattern A is used as the classification pattern of the first evaluation data.

Since the suitable calibration model differs depending on the classification of the acceleration pulse wave, the second data acquisition unit 64 refers to the classification pattern of the first evaluation data and uses the result of classifying the first evaluation data as the first data. By referring to the first data described above and determining the method of processing the second evaluation data to be applied by the second data acquisition unit 64, it is possible to realize a processing method suitable for each user. For example, when the result of the first evaluation data being classified into the pattern A is used as the first data, the second data acquisition unit 64 explains the waveform data of the pulse wave pattern A measured in advance by the regression analysis unit 640. It is decided to acquire the second data from the input average waveform data using the calibration model constructed based on the analysis result by performing regression analysis using the measured value of biological information as a variable and the objective variable. do.

FIG. 10 is an example of a classification pattern of velocity pulse waves. In FIG. 10, normalization is performed with the maximum peak in the velocity pulse wave being 1 and the minimum value being 0, and the pattern in which the peak is seen other than the maximum peak is group 1, and the peak is seen in other than the maximum peak. This is a classification pattern in which the patterns that were not obtained are group 2. By using this as described above, the first data acquisition unit 62 determines the classification pattern even when the first evaluation data based on the velocity pulse wave that does not differentiate the pulse wave signal is input by the differentiation unit 611. Can be done.

Next, in the optimum blood glucose level calculation step S17, the first data acquired by the first data acquisition unit 62 and the second data acquired by the second data acquisition unit 64 are input to the optimum blood glucose level calculation unit 65, and the first Optimal biometric information is obtained from the data and the second data. For example, the blood glucose level acquired as the first data is defined as the first blood glucose level, the blood glucose level acquired as the second data is defined as the second blood glucose level, and the optimum blood glucose level is calculated from the first blood glucose level and the second blood glucose level. Regarding the method of calculating the biometric information that is the optimum value, for example, the first data and the second data are weighted according to the measurement accuracy of each, and the optimum value is obtained from a plurality of first data and a plurality of second data based on the weighting. You may calculate the biological information that becomes. Further, as another example, using the blood glucose level acquired by another sensor or the like as a reference value, a plot graph of the reference value and the first data and the second data is created, and of the two plot graphs, the one that is better on the error grid. A method of outputting data indicating a value, a method of acquiring a plurality of first data and a second data, and outputting data with a small variation, and a method of outputting the first data and the second data within a preset allowable range. There is a method of evaluating whether it is included in the data and outputting the included data.

1 Biometric information calculation system 2 Electronic equipment 3 Public communication network 4 Server 5 Sensor 16 Output I / F
20 ROM
21 RAM
22 CPU
23 Operation unit 24 Storage unit 25 Data input / output unit 26 Internal bus 27 Communication I / F
28 Display unit 50 Acquisition unit 51 Communication I / F
52 Memory 53 Command unit 54 Internal bus 55 Wristband 60 Pulse wave signal acquisition unit 61 First extraction unit 62 First data acquisition unit 63 Second extraction unit 64 Second data acquisition unit 65 Optimal blood glucose level calculation unit 610 Filter processing unit 611 Differentiation unit 612 Division unit 613 Standardization unit 614 Peak position calculation unit 615 Peak interval average calculation unit 616 Peak interval plotting unit 617 Fourier transform unit 618 Maximum frequency detection unit 620 Regression analysis unit 621 Pulse processing unit 622 Stress plot processing unit 623 Stress Fourier Processing unit 624 Respiratory rate processing unit 630 Filter processing unit 631 Differentiation unit 632 Division unit 633 Standardization unit 634 Peak position calculation unit 635 Peak interval average calculation unit 636 Peak interval plotting unit 637 Fourier transform unit 638 Maximum frequency detection unit 640 Regression analysis unit 641 Pulse processing unit 642 Stress plot processing unit 643 Stress Fourier processing unit 644 Respiratory frequency processing unit S10 Pulse wave signal acquisition step S11 First extraction condition determination step S12 First extraction step S13 First data acquisition step S14 Second extraction condition determination step S15 Second extraction step S16 Second data acquisition step S17 Optimal blood glucose level calculation step

Claims (3)

An acquisition means for acquiring a velocity pulse wave as a pulse wave signal,
With reference to the first extraction condition, the first extraction means for extracting the first evaluation data based on the pulse wave signal acquired by the acquisition means, and the first extraction means.
A first data acquisition means for acquiring the first blood glucose level for the first evaluation data extracted by the first extraction means as the first data with reference to the first processing condition associated with the first extraction condition.
With reference to the second extraction condition, the second extraction means for extracting the second evaluation data based on the pulse wave signal acquired by the acquisition means, and the second extraction means.
With reference to the second processing condition associated with the second extraction condition, the second data consisting of biological information of a type different from the first data is acquired with respect to the second evaluation data extracted by the second extraction means. 2 Data acquisition means and
Equipped with
A biological information calculation system characterized in that the first evaluation data and the second evaluation data are extracted based on the same pulse wave signal. The first extraction means refers to the first extraction condition that does not differentiate the pulse wave signal acquired by the acquisition means.
The biometric information calculation system according to claim 1, wherein the second extraction means refers to the second extraction condition for differentiating the pulse wave signal acquired by the acquisition means. The first data acquisition means is the first extracted by the first extraction means using the first calibration model constructed based on the correlation between the first evaluation data acquired in advance and the measured value of the biological information. 1 Has a first regression analysis means for acquiring the first data for the evaluation data,
The first regression analysis means is characterized in that the first blood glucose level for the first evaluation data extracted by the first extraction means is acquired as the first data by using the first calibration model. Item 2. The biometric information calculation system according to Item 1 or 2.

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