JP6899609B1 - Biometric information calculation system and server - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a biometric information calculation system capable of improving accuracy when evaluating biometric information. SOLUTION: A first evaluation result including a first evaluation data and a first evaluation result by referring to a database and an acquisition means for acquiring the first evaluation data and the second evaluation data based on a user's pulse wave. , And a second evaluation result including a second biological information different from the first biological information for the second evaluation data, and a storage means for storing the first evaluation result and the second evaluation result, respectively. And. In the database, input data based on the learning pulse wave acquired in advance and a pair of reference data including biometric information associated with the input data are used as learning data, and classification information generated using a plurality of learning data is used. Is memorized. [Selection diagram] Fig. 1

Description

The present invention, the biological information calculation system, and relates to the server.

Conventionally, as a method for evaluating biological information such as the partial pressure of carbon dioxide in the blood (PaCO ₂ ) of a user, a method such as Patent Document 1 has been proposed.

In Patent Document 1, after constructing an average frame for each of at least two volume pulse waves having different light wavelengths, it is used to estimate the gain between the two average frames for those two volume pulse waves. Is possible and its gain value is used to estimate information for obtaining blood oxygen saturation, or other components present in the blood, such as hemoglobin, carbon dioxide or the like. It is disclosed that it may be used.

Special Table 2020-513876

Here, for example, when evaluating the biometric information of a user, it is desired to evaluate a plurality of biometric information at once. In this regard, the technique disclosed in Patent Document 1 is premised on the use of two volume pulse waves when measuring blood carbon dioxide saturation, hemoglobin, etc. in biological information. Therefore, the variation in data due to the measurement conditions of each volume pulse wave may greatly affect the estimation accuracy. Further, when estimating a plurality of biometric information using the disclosure technique of Patent Document 1, it is necessary to acquire a plurality of volumetric pulse waves according to the number and type of biometric information. Therefore, there is a concern that the estimation accuracy will decrease due to the variation of each volume pulse wave in proportion to the number of volume pulse waves required to estimate a plurality of biological information. Therefore, it is desired to improve the accuracy when evaluating biological information.

The present invention has been devised in view of the above problems, it is an object of the biological information arithmetic system capable of improving the accuracy in evaluating the biological information, and the server To provide.

Biological information arithmetic system according to the first invention, the user A biological information calculation system for evaluating the biological information, one corresponding to one of based rather velocity pulse wave and acceleration pulse wave in the pulse wave of the user Acquisition means for acquiring the first evaluation data and the second evaluation data by performing different types of processing on the pulse wave data, input data based on the learning pulse wave acquired in advance, and the input data. A pair of reference data including biometric information associated with is used as training data, and a database in which classification information generated by using the plurality of training data is stored and the database are referred to with respect to the first evaluation data. A generation means for generating a first evaluation result including the first biometric information and a second evaluation result including a second biometric information of a type different from the first biometric information with respect to the second evaluation data, and the first. The evaluation result and a storage means for storing the second evaluation result are provided , and the classification information includes a first classification information and a second classification information generated by using different types of the training data. The generation means generates the first evaluation result for the first evaluation data with reference to the first classification information, and generates the second evaluation result for the second evaluation data with reference to the second classification information. It is characterized by including producing.

In the first invention, the biometric information calculation system according to the second invention states that the first evaluation data and the second evaluation data correspond to either a velocity pulse wave or an acceleration pulse wave of the user. It is a feature.

The biometric information calculation system according to the third invention is a calibration model obtained by using PLS regression analysis using the input data as an explanatory variable and the reference data as an objective variable in the first invention. It is characterized by that.

In the first invention, the biometric information calculation system according to the fourth invention acquires additional information indicating the characteristics of the user, and based on the first evaluation result, the second evaluation result, and the additional information, the user It is characterized by being provided with a comprehensive evaluation means for generating a comprehensive evaluation result in which the characteristics are comprehensively evaluated.

In the first invention, the biometric information calculation system according to the fifth invention obtains additional information indicating the characteristics of the user, refers to the database, and is based on the first evaluation data and the additional information. , The first evaluation result is generated.

In the first invention, the biological information calculation system according to the sixth invention is the first biological information indicating a blood glucose level, and the second biological information is blood pressure, pulse rate, respiratory rate, characteristics of blood carbon dioxide, and the like. It is characterized by containing at least one of a lactic acid level and an oxygen saturation.

In the first invention, the biometric information calculation system according to the seventh invention refers to the second classification information , and based on the first evaluation result and the second evaluation data, the second evaluation result is obtained. It is characterized by including producing.

The biometric information calculation system according to the eighth invention is a comprehensive evaluation in which the characteristics of the user are comprehensively evaluated based on the first evaluation result and the second evaluation result stored by the storage means in the first invention. It is characterized by providing a comprehensive evaluation means for generating a result.

In the first invention, the biometric information calculation system according to the ninth invention obtains the first evaluation result and the determination result determined by the user with respect to the contents of the second evaluation result, and the storage means obtains the determination result. It is characterized by including storing the determination result, the first evaluation result, and the second evaluation result in association with each other.

Biometric information computing system according to a first 0 invention, in the ninth invention, the storage means has been the determination result stored in the first evaluation result, and based on the second evaluation result, and updates the classification information update It is characterized by providing means.

Biometric information computing system according to a first first invention, in the first invention, the storage means, the first evaluation result, and include storing the sensor data brute cord in the second evaluation result, the sensor data , It is used for extracting the pulse wave data, and is characterized by showing the characteristics of the pulse wave of the user.

In the first invention, the biometric information calculation system according to the first and second invention includes a server in which the database is stored and the first evaluation result and the second evaluation result are generated by the generation means, and the first evaluation result. , And a biometric information calculation device that receives and displays the second evaluation result from the server.

The server according to the first third invention, characterized in that the first evaluation result of the first invention and the second evaluation result is stored.

According to the first aspect of the invention - the first second invention, obtaining means, for one pulse wave data corresponding to one of the speed pulse wave rather based on the pulse wave of the user and the acceleration pulse wave, different types of treatment By carrying out the above , the first evaluation data and the second evaluation data are acquired. Further, the generation means produces a first evaluation result including the first biological information for the first evaluation data and a second evaluation result including a second biological information of a type different from the first biological information for the second evaluation data. Generate. That is, the first biometric information and the second biometric information are calculated based on the pulse wave of one user. Therefore, when calculating each biological information, it is possible to eliminate variations due to pulse wave measurement conditions. This makes it possible to improve the accuracy when evaluating biological information.

Further, according to the first invention to the twelfth invention, the generation means refers to the database and generates the first evaluation result and the second evaluation result. In addition, the database stores classification information generated using a plurality of learning data. Therefore, when generating each evaluation result, it is possible to generate each quantitative evaluation result based on the connection between the characteristics of the pulse wave that has been proven in the past and the biological information. This makes it possible to suppress variations in evaluation due to the subjectivity of the user or the like.

Further , according to the first to twelfth inventions , the classification information includes the first classification information and the second classification information generated by using different types of learning data. Therefore, it is possible to refer to the optimum classification information according to the type of each evaluation data and generate each evaluation result. This makes it possible to further improve the accuracy when evaluating biological information.

In particular, according to the third invention, the classification information is a calibration model obtained by using PLS regression analysis with the input data as the explanatory variable and the reference data as the objective variable. Therefore, the number of learning data can be significantly reduced and the calibration model can be easily updated as compared with the case where the classification information is calculated by using machine learning or the like. This makes it possible to facilitate the construction and update of the biometric information calculation system.

In particular, according to the fourth invention, the comprehensive evaluation means generates a comprehensive evaluation result in which the characteristics of the user are comprehensively evaluated based on the first evaluation result, the second evaluation result, and the additional information. Therefore, it is possible to realize an evaluation considering the characteristics of the user for each evaluation result. This makes it possible to generate an evaluation result suitable for each user.

In particular, according to the fifth invention, the generation means includes acquiring additional information and generating a first evaluation result based on the first evaluation data and the additional information. Therefore, in addition to the first evaluation data, it is possible to generate a multifaceted first evaluation result in consideration of the user's characteristics. This makes it possible to generate an evaluation of the user's biological information with higher accuracy.

In particular, according to the sixth invention, the first biological information indicates the blood glucose level, and the second biological information includes blood pressure, pulse rate, respiratory rate, characteristics of blood carbon dioxide concentration, lactate level, and oxygen saturation. Indicates at least one. Therefore, since an invasive measurement method is not required as compared with the conventional measurement method, each information can be easily acquired. This makes it possible to significantly reduce the load on the user.

In particular, according to the seventh invention, the generation means generates the second evaluation result based on the first evaluation result and the second evaluation data. Therefore, the second evaluation result based on the first evaluation result can be generated. This makes it possible to generate an evaluation of the user's biological information with higher accuracy.

In particular, according to the eighth invention, the calculation means generates a comprehensive evaluation result in which the characteristics of the user are comprehensively evaluated based on the first evaluation result and the second evaluation result. Therefore, it is possible to realize an evaluation considering the characteristics of the user for each evaluation result. This makes it possible to generate an evaluation result suitable for each user.

In particular, according to the ninth invention, the storage means stores the determination result, the first evaluation result, and the second evaluation result in association with each other. Therefore, it is possible to easily compare each evaluation result with the determination result. This makes it possible to easily find a decrease in accuracy when generating an evaluation result.

In particular, according to the first 0 invention, updating means, the determination result, the first evaluation result, and based on the second evaluation result, and updates the classification information. Therefore, when the accuracy of each evaluation result is lowered, it can be easily improved. This makes it possible to maintain an improvement in accuracy when evaluating biological information.

In particular, according to the first invention, the storage means includes storing the first evaluation result and the sensor data associated with the second evaluation result. Therefore, learning data can be easily prepared when the classification information is updated or newly generated. This makes it possible to easily realize the maintenance of the biometric information calculation system.

In particular, according to the first second invention, the server first evaluation result by generation means, and generating a second evaluation result. Further, the biometric information calculation device receives the first evaluation result and the second evaluation result from the server and displays them. Therefore, it is possible to reduce the load on the biometric information calculation device when generating each evaluation result. This makes it possible to improve the convenience of the biometric information calculation device. Moreover, it is not necessary to save the database in the biometric information calculation device. This makes it possible to significantly reduce the data storage capacity of the biometric information arithmetic unit. Further, by storing the database in the server, it is possible to output the evaluation result generated by referring to one classification information to a plurality of biometric information arithmetic units. As a result, it is possible to reduce a huge amount of time and cost for updating the database for each biometric information arithmetic unit due to maintenance such as updating the database.

In particular, according to the first third invention, the server is able to store a first evaluation result improve the accuracy in evaluating the biological information, and the second evaluation result.

FIG. 1 is a schematic diagram showing an example of a biological information calculation system according to the first embodiment. FIG. 2 is a schematic diagram showing an example of the operation of the biological information calculation system according to the first embodiment. FIG. 3A is a schematic diagram showing an example of classification information, and FIG. 3B is a schematic diagram showing another example of classification information. 4 (a) to 4 (d) are schematic views showing an example of processing for sensor data. FIG. 5A is a schematic diagram showing an example of the configuration of the biological information arithmetic unit, and FIG. 5B is a schematic diagram showing an example of the function of the biological information arithmetic unit. 6 (a) and 6 (b) are schematic views showing an example of the sensor. FIG. 7 is a flowchart showing an example of the operation of the biometric information calculation system according to the first embodiment. FIG. 8 is a schematic diagram showing an example of the operation of the biometric information calculation system according to the second embodiment. FIG. 9 is a schematic diagram showing a modified example of the operation of the biological information calculation system according to the second embodiment. FIG. 10 is a schematic diagram showing an example of the operation of the biometric information calculation system according to the third embodiment. FIG. 11 is a schematic diagram showing an example of the operation of the biometric information calculation system according to the fourth embodiment. FIG. 12 is a schematic diagram showing a classification example of data corresponding to an acceleration pulse wave. FIG. 13 is a schematic diagram showing a classification example of data corresponding to a velocity pulse wave. FIG. 14 is a schematic view showing a first modification of the operation of the biological information calculation system according to the fourth embodiment. FIG. 15 is a schematic view showing a second modification of the operation of the biometric information calculation system according to the fourth embodiment. FIG. 16 is a schematic view showing a third modification of the operation of the biometric information calculation system according to the fourth embodiment. FIG. 17 is a schematic diagram showing an example of the operation of the biometric information calculation system according to the fifth embodiment. FIG. 18 is a schematic diagram showing an example of the operation of the biometric information calculation system according to the sixth embodiment.

Hereinafter, an example of the biometric information calculation system, the server, and the data structure according to the embodiment of the present invention will be described with reference to the drawings.

(First Embodiment: Biometric information calculation system 100)
FIG. 1 is a schematic diagram showing an example of the biometric information calculation system 100 according to the first embodiment.

The biometric information calculation system 100 is used to evaluate the biometric information of the user. The biometric information calculation system 100 can generate a plurality of evaluation results including different types of biometric information from a plurality of evaluation data based on a user's pulse wave, and store each evaluation result. That is, it is possible to generate an evaluation result including a plurality of types of biological information based on one pulse wave measured by the user. The "biological information" indicates, for example, blood characteristics that can be estimated from pulse waves, body characteristics, and the like.

As biological information, for example, the characteristics of blood carbon dioxide are used, and for example, blood glucose level, blood pressure, oxygen saturation, lactic acid level, pulse rate, respiratory rate, stress level, blood vessel age, degree of diabetes and the like are used. The "characteristics of carbon dioxide in blood" indicates the degree of carbon dioxide contained in the user's blood. As a feature of the blood carbon dioxide, for example, in addition to the value of the blood carbon dioxide partial pressure (PaCO ₂₎ is used, and the dissolved concentration in blood carbon dioxide, bicarbonate, bicarbonate contained in the blood _- the (HCO ³⁾ The concentration may be used, and a value considering the pH of blood may be used depending on the situation.

As shown in FIG. 1, the biometric information calculation system 100 may include a biometric information calculation device 1, for example, at least one of a sensor 5 and a server 4. The biological information arithmetic unit 1 is connected to the sensor 5 and the server 4 via, for example, a communication network 3.

In the biometric information calculation system 100, for example, as shown in FIG. 2A, the biometric information calculation device 1 acquires the sensor data generated by the sensor 5 or the like. The sensor data shows data including the characteristics of one user's pulse wave measured in a specific period. After that, the biometric information calculation device 1 performs preprocessing such as filtering on one acquired sensor data, and acquires a plurality of evaluation data (for example, first evaluation data and second evaluation data). That is, when the biometric information calculation device 1 acquires each evaluation data, different types of preprocessing are performed on one sensor data.

The biometric information calculation device 1 refers to a database and calculates different types of biometric information (for example, first biometric information and second biometric information) for each evaluation data. After that, the biometric information calculation device 1 generates a plurality of evaluation results (for example, a first evaluation result and a second evaluation result) including different types of biometric information. That is, the plurality of biological information is calculated based on the pulse wave of one user. Therefore, when calculating each biological information, it is possible to eliminate variations due to pulse wave measurement conditions. This makes it possible to improve the accuracy when evaluating biological information.

Here, the biometric information calculation device 1 refers to a database when calculating biometric information for each evaluation data. The database stores classification information generated using a plurality of learning data.

As shown in FIG. 3A, for example, the classification information includes a pair of input data based on the learning pulse wave acquired in the past and reference data including biometric information associated with the input data as learning data. It is generated using the training data of. Therefore, when calculating the biological information, it is possible to generate each quantitative evaluation result based on the connection between the characteristics of the pulse wave that has been proven in the past and the biological information. This makes it possible to suppress variations in evaluation due to the subjectivity of the user or the like.

The classification information may include a plurality of classification information (for example, first classification information and second classification information) generated by using different types of learning data, for example, as shown in FIG. 3 (b). In this case, each evaluation result can be generated by referring to the optimum classification information according to the type of each evaluation data.

The biological information arithmetic unit 1 stores the generated plurality of evaluation results in the server 4 or the like. As a result, secondary use of each evaluation result can be easily realized.

The biological information arithmetic unit 1 outputs, for example, each generated evaluation result to a display or the like. As a result, the user can grasp a plurality of generated evaluation results.

In the biometric information calculation system 100, a plurality of evaluation data may be acquired from, for example, a sensor 5. In this case, the preprocessing for acquiring a plurality of evaluation data from the sensor data is performed by the sensor 5 or the like.

<Sensor data>
The sensor data includes data indicating the characteristics of the user's pulse wave, and may include, for example, data (noise) indicating characteristics other than the pulse wave. The sensor data is data showing the amplitude with respect to the measurement time, and by performing filter processing according to the application and the generation conditions of the sensor data, data corresponding to the acceleration pulse wave, the velocity pulse wave, etc. is acquired from the sensor data. be able to.

The sensor data can be generated by a known sensor such as a strain sensor, a gyro sensor, a photoelectric volume pulse wave (PPG) sensor, and a pressure sensor. The sensor data may be, for example, an analog signal as well as a digital signal. The measurement time when generating the sensor data is, for example, the measurement time for 1 to 20 cycles of the pulse wave, and can be arbitrarily set according to the conditions such as the sensor data processing method and the data communication method. Can be done.

<Evaluation data>
The evaluation data indicates data for calculating biological information. The evaluation data shows, for example, data corresponding to an acceleration pulse wave based on a user's pulse wave, and shows an amplitude with respect to a specific period (for example, one period).

The evaluation data is acquired by processing (preprocessing) the sensor data by the biometric information calculation device 1 or the like. For example, as shown in FIGS. 4A to 4D, evaluation data can be obtained by performing a plurality of processes on the sensor data. Details of each process will be described later.

<Database>
The database is mainly used to generate evaluation results for evaluation data. In addition to storing one or more classification information, the database may store, for example, a plurality of learning data used for generating the classification information.

The classification information is, for example, a function showing the interphase relationship between the past evaluation data (input data) acquired in advance and the reference data including the biometric information. The classification information shows, for example, a calibration model generated based on the analysis result of analysis by regression analysis or the like with the input data as the explanatory variable and the reference data as the objective variable. The classification information can be updated periodically, for example, and may include a plurality of calibration models generated for each attribute information such as the user's gender, age, and exercise content.

As a method of regression analysis used when generating classification information, for example, PLS (Partial Least Squares) regression analysis and SIMCA (Soft Independent Modeling of Class Analogy) method for obtaining a principal component model by performing principal component analysis for each class are used. Regression analysis and the like can be used.

The classification information may include, for example, a trained model generated by machine learning using a plurality of training data. The trained model shows, for example, a neural network model such as CNN (Convolutional Neural Network) and SVM (Support vector machine). Further, as machine learning, for example, deep learning can be used.

As the input data, the same type of data as the evaluation data is used, and for example, past evaluation data in which the corresponding biometric information is clarified is shown. For example, the subject is made to wear a sensor 5 or the like to generate sensor data (learning sensor data) showing the characteristics of the learning pulse wave. Then, the input data can be acquired by performing the processing on the learning sensor data. The input data may be acquired from the user of the biometric information calculation system 100, or may be acquired from a user other than the user, for example. That is, the above-mentioned subject may be a user of the biometric information calculation system 100, may be a target other than the user, and may be a specific or unspecified majority.

It is preferable that the input data is acquired according to the same contents as, for example, the type of the sensor 5 used when acquiring the evaluation data, the generation condition of the sensor data, and the processing condition for the sensor data. For example, by unifying the above three contents, it is possible to dramatically improve the accuracy when generating biometric information.

The reference data includes the biological information of the subject measured by using a measuring device or the like. For example, when a subject is equipped with a sensor 5 or the like to generate learning sensor data, reference data associated with the input data is obtained by measuring biological information such as the characteristics of carbon dioxide in the blood of the subject. Can be obtained. In this case, the timing of measuring the biological information is preferably the same as the timing of generating the learning sensor data, but it may be, for example, about 1 to 10 minutes.

The reference data is measured using a known measuring device. For example, when measuring the characteristics of blood carbon dioxide concentration, a device such as a percutaneous blood gas monitor TCM5 (manufactured by Radiometer Basel) is used as the measuring device. For example, when measuring the amount of lactate in blood, a known device such as Lactate Pro 2 (manufactured by ARKRAY, Inc.) is used as the measuring device. For example, when measuring oxygen saturation, a known device such as PULSOX-Neo (manufactured by Konica Minolta Co., Ltd.) is used as the measuring device.

<Biological information>
The biometric information calculated by the biometric information calculation system 100 is calculated as the same type of data as the reference data. The biometric information is calculated as the same or similar data as the reference data with reference to the classification information. The biometric information calculation system 100 acquires, for example, a plurality of evaluation data in an arbitrary time series, and generates a plurality of biometric information for each evaluation data. Further, in the biometric information calculation system 100, for example, a plurality of evaluation data may be acquired at arbitrary timings, and a plurality of biometric information may be calculated for each evaluation data.

<Evaluation result>
The evaluation result includes the biometric information of the user calculated by the biometric information calculation device 1. In addition to biological information, the evaluation result may include, for example, a comparison result with a preset threshold value. The evaluation result may include, for example, a value derived based on a plurality of biological information along the time series. The evaluation results include information indicating the user's health condition, advice for maintaining health, and evaluation of the tendency of athletic ability, such as "health", "needs exercise", "anaerobic exercise", etc. May be. As a result, it is possible to easily grasp the change over time for each of a plurality of biological information. For example, by outputting the evaluation result, the biometric information of the user can be grasped.

<Biological information arithmetic unit 1>
The biological information arithmetic unit 1 indicates an electronic device such as a personal computer (PC), a mobile phone, a smartphone, a tablet terminal, or a wearable terminal, and is an electronic device capable of communicating via a communication network 3 based on a user's operation, for example. Indicates the device. The biometric information calculation device 1 may include a sensor 5. Hereinafter, an example in which a PC is used as the biometric information calculation device 1 will be described.

FIG. 5A is a schematic diagram showing an example of the configuration of the biological information arithmetic unit 1, and FIG. 5B is a schematic diagram showing an example of the function of the biological information arithmetic unit 1.

As shown in FIG. 5A, for example, the biological information arithmetic unit 1 includes a housing 10, a CPU (Central Processing Unit) 101, a ROM (Read Only Memory) 102, a RAM (Random Access Memory) 103, and the like. A storage unit 104 and an I / F 105-107 are provided. Each configuration 101-107 is connected by an internal bus 110.

The CPU 101 controls the entire biometric information arithmetic unit 1. The ROM 102 stores the operation code of the CPU 101. The RAM 103 is a work area used during the operation of the CPU 101. The storage unit 104 stores various information such as a database and evaluation data. As the storage unit 104, for example, in addition to an HDD (Hard Disk Drive), a data storage device such as an SSD (Solid State Drive) is used. For example, the biometric information calculation device 1 may have a GPU (Graphics Processing Unit) (not shown).

The I / F 105 is an interface for transmitting and receiving various information to and from the server 4, the sensor 5, and the like as needed via the communication network 3. The I / F 106 is an interface for transmitting and receiving information to and from the input unit 108. For example, a keyboard is used as the input unit 108, and a user or the like of the biometric information calculation device 1 inputs various information, a control command of the biometric information calculation device 1, or the like via the input unit 108. The I / F 107 is an interface for transmitting and receiving various information to and from the display unit 109. The display unit 109 displays various information stored in the storage unit 104, evaluation results, and the like. A display is used as the display unit 109, and for example, in the case of a touch panel type, it is provided integrally with the input unit 108.

FIG. 5B is a schematic diagram showing an example of the function of the biological information arithmetic unit 1. The biological information calculation device 1 includes an acquisition unit 11, a generation unit 12, an output unit 13, and a storage unit 14, and may include, for example, a learning unit 15. Each function shown in FIG. 5B is realized by the CPU 101 executing a program stored in the storage unit 104 or the like using the RAM 103 as a work area.

<Acquisition unit 11>
The acquisition unit 11 acquires a plurality of evaluation data based on the pulse wave of the user. The acquisition unit 11 acquires the evaluation data by, for example, acquiring the sensor data from the sensor 5 or the like and then performing processing on the sensor data. The acquisition unit 11 acquires a plurality of evaluation data for one sensor data by performing different types of processing.

As shown in FIG. 4A, for example, the acquisition unit 11 performs a filtering process (filter process) on the acquired sensor data. In the filtering process, for example, a bandpass filter of 0.5 to 5.0 Hz is used. As a result, the acquisition unit 11 extracts data (pulse wave data) corresponding to the user's pulse wave. The pulse wave data indicates, for example, data corresponding to a velocity pulse wave. The pulse wave data may indicate data corresponding to, for example, an acceleration pulse wave or a volumetric pulse wave, and can be arbitrarily set according to the type and application of the sensor. Further, the filter range of the bandpass filter can be arbitrarily set according to the intended use.

The acquisition unit 11 performs differential processing on, for example, pulse wave data. For example, when the differential processing is performed on the pulse wave data corresponding to the velocity pulse wave, the acquisition unit 11 acquires the data (differential data) corresponding to the acceleration pulse wave. In the differentiation process, the differentiation may be performed twice in addition to the one-time differentiation.

The acquisition unit 11 performs division processing on, for example, the differential data. In the division process, for example, the differential data corresponding to the acceleration pulse wave of a plurality of cycles is divided into the data (divided data) corresponding to the acceleration pulse wave of each cycle. Therefore, the acquisition unit 11 can acquire a plurality of divided data by, for example, performing differential processing on one differential data. In the division process, the differential data can be divided into arbitrary cycles (for example, positive multiples of the cycles) depending on the application.

For example, in the division process, the amount of data in each of the divided data may be different. In this case, the acquisition unit 11 may specify the divided data having the smallest amount of data and reduce (trim) the amount of data with respect to the other divided data. As a result, the amount of data in each divided data can be unified, and the data in each divided data can be easily compared.

In addition to the above, for example, standardization processing may be performed on values corresponding to the time axis of the divided data. In the standardization process, for example, standardization is carried out in which the minimum value corresponding to the time axis is set to 0 and the maximum value is set to 1. This facilitates the comparison of the data in each divided data.

The acquisition unit 11 may calculate the average of a plurality of divided data for which the amount of data has been reduced or standardized, and use the divided data as the divided data.

The acquisition unit 11 performs standardization processing on the divided data. In the standardization process, standardized data (normalized data) is generated for the values corresponding to the amplitude. In the normalization process, for example, standardization is performed in which the minimum value of the amplitude is set to 0 and the maximum value of the amplitude is set to 1. The acquisition unit 11 acquires, for example, standardized data as evaluation data (for example, evaluation data A). In this case, as the evaluation data A, data corresponding to the acceleration pulse wave of the user is obtained.

In addition to sequentially performing each of the above-mentioned processes, the acquisition unit 11 does not have to perform the differential process, for example, as shown in FIG. 4 (b). In this case, as evaluation data (for example, evaluation data B), data corresponding to the velocity pulse wave of the user can be obtained.

Further, the acquisition unit 11 may perform only a part of each of the above-mentioned processes, for example. In this case, the acquisition unit 11 may acquire any of pulse wave data, differential data, divided data, trimmed divided data, and divided data in which values corresponding to the time axis are standardized as evaluation data. , Can be set arbitrarily according to the application.

As shown in FIG. 4C, for example, the acquisition unit 11 may acquire evaluation data C suitable for calculating the pulse rate as biological information.

In this case, the acquisition unit 11 performs the above-mentioned filter processing on the sensor data and extracts the pulse wave data. Then, the acquisition unit 11 performs a peak position calculation process on the pulse wave data. In the peak position calculation process, a plurality of peaks (maximum amplitude values) included in the pulse wave data are detected, and the sampling order (corresponding to the time from the start of measurement) is specified. As a result, the acquisition unit 11 acquires the peak position data included in the pulse wave data.

After that, the acquisition unit 11 performs a peak interval average calculation process on the peak position data. In the peak interval average calculation process, the interval between peaks included in the peak position data (difference in the order in which adjacent peaks are sampled) is calculated, and for example, the average value of the peak intervals is calculated. After that, the acquisition unit 11 divides the peak interval or the average value of the peak intervals by the sampling rate of the sensor data, and acquires data indicating the peak interval corresponding to the number of seconds as evaluation data (for example, evaluation data C).

As shown in FIG. 4D, for example, the acquisition unit 11 may acquire evaluation data D suitable for calculating the respiratory rate as biological information.

In this case, the acquisition unit 11 performs the above-mentioned filter processing on the sensor data and extracts the pulse wave data. After that, the acquisition unit 11 performs a Fourier transform process on the pulse wave data. In the Fourier transform process, for example, pulse wave data indicating sampling time vs. amplitude is converted into frequency data indicating frequency vs. intensity. As a result, the acquisition unit 11 acquires frequency data for the pulse wave data.

After that, the acquisition unit 11 performs the maximum frequency detection process on the frequency data. In the maximum frequency detection process, the frequency having the maximum intensity between 0.15 and 0.35 Hz is specified in the frequency data. As a result, the acquisition unit 11 acquires the value of the specified frequency as the evaluation data D.

<Generator 12>
The generation unit 12 refers to the database and generates an evaluation result for the evaluation data. The generation unit 12 refers to, for example, the classification information stored in the database, calculates the biometric information for the evaluation data, and generates it as the evaluation result. The generation unit 12 generates a plurality of evaluation results for each of a plurality of different evaluation data.

For example, the generation unit 12 may generate a plurality of evaluation data with reference to the same classification information for a plurality of evaluation data. In this case, for example, by acquiring a plurality of evaluation data using different types of pretreatment, different types of biometric information can be calculated even if the same classification information is used. Therefore, it is not necessary to generate classification information for each evaluation data, and the data capacity of the database can be suppressed.

For example, the generation unit 12 may calculate a plurality of biological information with reference to different classification information for a plurality of evaluation data. In this case, each evaluation result can be generated by referring to the optimum classification information according to the type of each evaluation data.

The generation unit 12 may generate an evaluation result in which the biometric information is converted into a format that can be understood by the user by using, for example, a display format stored in advance in the storage unit 104 or the like.

<Output unit 13>
The output unit 13 outputs a plurality of evaluation results. The output unit 13 may output a plurality of evaluation results to the display unit 109, or may output a plurality of evaluation results to, for example, a sensor 5.

<Memory unit 14>
The storage unit 14 retrieves various data such as a database stored in the storage unit 104 as needed. The storage unit 14 stores various data acquired or generated by the configurations 11 to 13 and 15 in the storage unit 104 as needed.

The storage unit 14 may store, for example, sensor data associated with a plurality of evaluation results in the storage unit 104. For example, the storage unit 14 may also store a plurality of evaluation data generated using the sensor data in the storage unit 104.

<Learning Department 15>
The learning unit 15 generates classification information using, for example, a plurality of learning data. The learning unit 15 may acquire new learning data, for example, and update the existing classification information.

<Communication network 3>
The communication network 3 is a known Internet network or the like in which the biometric information arithmetic unit 1, the server 4, and the sensor 5 are connected via a communication line. When the biometric information calculation system 100 is operated in a certain narrow area, the communication network 3 may be configured by a LAN (Local Area Network) or the like. Further, the communication network 3 may be composed of a so-called optical fiber communication network. Further, the communication network 3 is not limited to the wired communication network, but may be realized by a wireless communication network, and can be arbitrarily set according to the application.

<Server 4>
The server 4 stores and stores various information such as evaluation results sent via the communication network 3. The server 4 transmits the information accumulated via the communication network 3 to the biometric information arithmetic unit 1 based on the request from the biometric information arithmetic unit 1.

The server 4 may be connected to, for example, a plurality of biometric information arithmetic units 1, acquire various information such as evaluation results from each biometric information arithmetic unit 1, and store them collectively. The server 4 may have at least some of the functions of the biometric information arithmetic unit 1 described above. Further, the server 4 may store a database or the like stored in the biometric information arithmetic unit 1 described above.

<Sensor 5>
The sensor 5 generates sensor data. The sensor 5 includes a detection unit 6, for example, as shown in FIG. 6A. The sensor 5 is mounted at a position where the pulse wave of the user can be detected via the detection unit 6, and is fixed to, for example, a wristband 55.

As the detection unit 6, a known detection device capable of detecting the pulse wave of the user is used. The detector 6 includes, for example, a strain sensor such as a fiber bragg grating (FBG) sensor, a gyro sensor, one or more electrodes for pulse wave signal measurement, a photoelectric volume pulse wave (PPG) sensor, a pressure sensor, and an optical detection module. At least one of the above is used. A plurality of detection units 6 may be arranged, for example.

The sensor 5 may be embedded in clothing. Further, the user who wears the sensor 5 may target not only humans but also pets such as dogs and cats, and may target livestock such as cows and pigs, and aquaculture of fish and the like.

As shown in FIG. 6B, for example, the sensor 5 includes an acquisition unit 50, a communication I / F 51, a memory 52, and a command unit 53, and each configuration is connected by an internal bus 54.

The acquisition unit 50 measures the pulse wave of the user via the detection unit 6 and generates sensor data. The acquisition unit 50 transmits, for example, the generated sensor data to the communication I / F 51 or the memory 52.

The communication I / F 51 transmits various data such as sensor data to the biometric information calculation device 1 and the server 4 via the communication network 3. Further, the communication I / F 51 is equipped with a line control circuit for connecting to the communication network 3, a signal conversion circuit for performing data communication with the biometric information arithmetic unit 1 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 communication network 3 side, and when data from the communication network 3 is received, the communication I / F 51 performs a predetermined conversion processing. Is applied and transmitted to the internal bus 54.

The memory 52 stores various data such as sensor data transmitted from the acquisition unit 50. The memory 52 transmits various data such as stored sensor data to the communication I / F 51 by receiving a command from another terminal device connected via the communication network 3, for example.

The instruction unit 53 includes an operation button, a keyboard, and the like for acquiring sensor data, and includes, for example, a processor such as a CPU. When the command unit 53 receives the command for acquiring the sensor data, the command unit 53 notifies the acquisition unit 50 of this. Upon receiving this notification, the acquisition unit 50 acquires the sensor data. The command unit 53 may perform a process for acquiring a plurality of evaluation data from the sensor data, for example, as shown in FIGS. 4A to 4D.

Here, a case where an FBG sensor is used will be described as an example of acquiring sensor data.

The FBG sensor has a diffraction grating structure formed in one optical fiber at predetermined intervals. The FBG sensor is characterized in that, for example, the length of the sensor portion is 10 mm, the wavelength resolution is ± 0.1 pm, the wavelength range is 1550 ± 0.5 nm, the fiber diameter is 145 μm, and the core diameter is 10.5 μm. Using the FBG sensor as the detection unit 6 described above, measurement can be performed in a state where the FBG sensor is in contact with the user's skin.

For example, as a light source used for an optical fiber, an ASE (Amplified Spontaneous Emission) light source having a wavelength range of 1525-1570 nm is 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 photodetector. The Mach-Zehnder interferometer is for splitting into two optical paths having different optical paths by a beam splitter and superimposing them on one another by a beam splitter to generate interferometric light. In order to make an optical path difference, for example, the length of one optical fiber may be increased. Since coherent light produces interference fringes according to the optical path difference, it is possible to detect a change in distortion generated in the FBG sensor, that is, a pulse wave by measuring the pattern of the interference fringes. The acquisition unit 50 generates sensor data based on the detected pulse wave. As a result, sensor data is acquired.

The optical fiber sensor system that detects the distortion amount of the FBG sensor and detects the waveform of the pulse wave includes a wide band ASE light source, a circulator, a Mach zender interferometer, and a beam splitter in addition to the light source incident on the FBG sensor. It includes an optical system such as, a light receiving sensor provided in an optical detector, and an analysis method for analyzing a 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 analysis methods such as a detection method can be adopted.

<Data structure>
For example, a data structure including a plurality of evaluation results (for example, a first evaluation result and a second evaluation result) generated by the biometric information calculation system 100 described above is stored in the server 4 or the storage unit 104. The data structure is used in the biometric information calculation device 1 (a computer including a display unit 109, a CPU 101 (control unit), and a storage unit 104) described above. The data structure including a plurality of evaluation results is used, for example, when the generation unit 12 controlled by the CPU 101 generates a comprehensive evaluation result based on each evaluation result. The results of the comprehensive evaluation will be described later.

(First Embodiment: Operation of biometric information calculation system 100)
Next, an example of the operation of the biometric information calculation system 100 in this embodiment will be described. FIG. 7 is a flowchart showing an example of the operation of the biometric information calculation system 100 in the present embodiment.

The biometric information calculation system 100 is executed, for example, via a biometric information calculation program installed in the biometric information calculation device 1. That is, the user operates the biometric information calculation device 1 or the sensor 5 and acquires a plurality of evaluation results including the user's biometric information from the sensor data through the biometric information calculation program installed in the biometric information calculation device 1. Can be done.

The operation of the biological information calculation system 100 includes an acquisition step S110, a generation step S120, and a storage step S140, and may include, for example, an output step S130.

<Acquisition step S110>
The acquisition step S110 acquires a plurality of evaluation data based on the pulse wave of the user. For example, the acquisition unit 50 of the sensor 5 measures the pulse wave of the user via the detection unit 6 and generates sensor data. The acquisition unit 50 transmits the sensor data to the biometric information calculation device 1 via the communication I / F 51 and the communication network 3. The acquisition unit 11 of the biological information calculation device 1 receives the sensor data from the sensor 5.

The acquisition unit 11 performs, for example, the processes shown in FIGS. 4 (a) and 4 (b) on the sensor data, and acquires the first evaluation data and the second evaluation data. The acquisition unit 11 stores each acquired evaluation data in the storage unit 104, for example, via the storage unit 14. Conditions such as the frequency with which the acquisition unit 11 acquires sensor data from the sensor 5 can be arbitrarily set according to the application. For example, the acquisition unit 11 acquires each evaluation data at a preset cycle.

<Generation step S120>
Next, the generation step S120 refers to the database and generates a plurality of evaluation results including biometric information for each evaluation data. For example, the generation unit 12 refers to the classification information, calculates the value of the blood carbon dioxide partial pressure with respect to the first evaluation data as the first biological information, and calculates the blood glucose level with respect to the second evaluation data as the second biological information. The generation unit 12 generates a first evaluation result including the first biological information and a second evaluation result including the second biological information.

For example, the generation unit 12 refers to the first classification information to generate the first evaluation result for the first evaluation data, and refers to the second classification information to generate the second evaluation result for the second evaluation data. At this time, the first classification information and the second classification information are included in the classification information and are generated using different types of learning data.

The generation unit 12 stores each generated evaluation result in the storage unit 104, for example, via the storage unit 14. In addition to showing a specific value as each evaluation result, for example, an error range (for example, "○○ ± 2 mmHg") may be calculated.

For example, when calculating the pulse rate as biological information, for example, the evaluation data C shown in FIG. 4C is used as the evaluation data, and the generation unit 12 refers to the pulse rate classification information included in the classification information. The pulse rate classification information shows, for example, a function of dividing 60 [seconds] by the peak interval. Therefore, the generation unit 12 can calculate, for example, the pulse rate (= 71 [bpm]) for the evaluation data C (peak interval = 0.85 [seconds]). As a result, the generation unit 12 can generate an evaluation result including biological information indicating the pulse rate.

For example, when calculating the respiratory rate as biological information, for example, the evaluation data D shown in FIG. 4D is used as the evaluation data, and the generation unit 12 refers to the respiratory rate classification information included in the classification information. The respiratory rate classification information indicates, for example, a function of multiplying the specified frequency by 60 [seconds]. Therefore, the generation unit 12 can calculate, for example, the respiratory rate (= 13.5 [bpm]) for the evaluation data D (specified frequency = 0.225 Hz). As a result, the generation unit 12 can generate an evaluation result including biological information indicating the respiratory rate.

<Output step S130>
Next, for example, the output step S130 may output a plurality of evaluation results. For example, the output unit 13 outputs the first evaluation result and the second evaluation result to the display unit 109.

<Save step S140>
Next, the storage step S140 stores the first evaluation result and the second evaluation result. For example, the storage unit 14 stores the first evaluation result and the second evaluation result in the storage unit 104. For example, the output unit 13 may output the first evaluation result and the second evaluation result to the server 4 via the communication network 3 and store them. For example, the storage step S140 may be performed before the output step S130.

In the saving step S140, for example, sensor data associated with a plurality of evaluation results may be saved. In this case, the sensor data is used to generate a plurality of evaluation data and show the characteristics of the user's pulse wave.

As a result, the operation of the biometric information calculation system 100 is completed. The frequency and order of performing each step can be arbitrarily set according to the application.

In the biometric information calculation system 100, for example, the above-mentioned steps S110 and S120 may be performed by the biometric information calculation device 1, or at least a part thereof may be performed by the server 4. In this case, each of the steps S110 and S120 described above includes a process for the server 4 to send and receive various information via the communication network 3. Communication between the biometric information arithmetic unit 1 and the server 4 can be realized by using a known technique.

For example, in the acquisition step S110, the server 4 may acquire a plurality of evaluation data. In this case, the acquisition unit included in the server 4 acquires a plurality of evaluation data transmitted from the biometric information arithmetic unit 1 via the communication network 3.

The acquisition unit included in the server 4 may acquire a plurality of evaluation data by, for example, acquiring sensor data transmitted from the biometric information calculation device 1 or the sensor 5 and performing the above-mentioned preprocessing. In this case, it is possible to reduce the load of performing the above-mentioned preprocessing in the biometric information arithmetic unit 1.

For example, in the generation step S120, the server 4 may generate a plurality of evaluation results for each evaluation data. In this case, the generation unit included in the server 4 refers to the database stored in the server 4 and generates a plurality of evaluation results. In this case, it is possible to reduce the load of performing the above-mentioned processing for generating a plurality of evaluation results in the biometric information calculation device 1.

When the generation step S120 is performed on the server 4, in the output step S130, for example, the output unit included in the server 4 transmits a plurality of evaluation results to the biometric information calculation device 1 and the like via the communication network 3. In this case, the output unit 13 of the biometric information calculation device 1 outputs a plurality of received evaluation results to the display unit 109.

As described above, each of the steps S110 to S140 in the biometric information calculation system 100 can be performed by either the biometric information calculation device 1 or the server 4. In particular, by performing the acquisition step S110 and the generation step S120 on the server 4, it is possible to reduce the load on the biometric information calculation device 1. Since the same applies to each embodiment described later, the description thereof will be omitted.

According to the present embodiment, the acquisition unit 11 acquires the first evaluation data and the second evaluation data based on the pulse wave of the user. In addition, the generation unit 12 obtains the first evaluation result including the first biometric information for the first evaluation data and the second evaluation result including the second biometric information of a type different from the first biometric information for the second evaluation data. Generate each. That is, the first biometric information and the second biometric information are calculated based on the pulse wave of one user. Therefore, when calculating each biological information, it is possible to eliminate variations due to pulse wave measurement conditions. This makes it possible to improve the accuracy when evaluating biological information.

Further, according to the present embodiment, the generation unit 12 refers to the database and generates the first evaluation result and the second evaluation result. In addition, the database stores classification information calculated using a plurality of learning data. Therefore, when generating each evaluation result, it is possible to generate each quantitative evaluation result based on the connection between the characteristics of the pulse wave that has been proven in the past and the biological information. This makes it possible to suppress variations in evaluation due to the subjectivity of the user or the like.

Further, according to the present embodiment, the classification information includes the first classification information and the second classification information generated by using different types of learning data. Therefore, it is possible to refer to the optimum classification information according to the type of each evaluation data and generate each evaluation result. This makes it possible to further improve the accuracy when evaluating biological information.

Further, according to the present embodiment, the classification information is a calibration model obtained by using PLS regression analysis with the input data as the explanatory variable and the reference data as the objective variable. Therefore, the number of learning data can be significantly reduced and the calibration model can be easily updated as compared with the case where the classification information is calculated by using machine learning or the like. This makes it possible to facilitate the construction and update of the biometric information calculation system 100.

Further, according to the present embodiment, the first biological information indicates the blood glucose level, and the second biological information includes blood pressure, pulse rate, respiratory rate, characteristics of blood carbon dioxide concentration, lactate level, and oxygen saturation. Indicates at least one. Therefore, since an invasive measurement method is not required as compared with the conventional measurement method, each information can be easily acquired. This makes it possible to significantly reduce the load on the user.

Further, according to the present embodiment, the storage unit 14 or the server 4 includes storing the first evaluation result and the sensor data associated with the second evaluation result. Therefore, learning data can be easily prepared when the classification information is updated or newly generated. This makes it possible to easily realize the maintenance of the biometric information calculation system 100.

Further, according to the present embodiment, the server 4 generates the first evaluation result and the second evaluation result by the generation unit included in the server 4. Further, the biometric information calculation device 1 receives the first evaluation result and the second evaluation result from the server 4 and displays them. Therefore, it is possible to reduce the load on the biometric information calculation device 1 when generating each evaluation result. This makes it possible to improve the convenience of the biometric information calculation device 1. Further, it is not necessary to save the database in the biological information arithmetic unit 1. This makes it possible to significantly reduce the data storage capacity of the biometric information calculation device 1. Further, by storing the database in the server 4, it is possible to output the evaluation result generated by referring to one classification information to the plurality of biometric information arithmetic units 1. As a result, it is possible to reduce a huge amount of time and cost for updating the database for each biometric information arithmetic unit 1 due to maintenance such as updating the database.

Further, according to the present embodiment, the server 4 can store the first evaluation result and the second evaluation result for improving the accuracy when evaluating the biological information.

Further, according to the present embodiment, the data structure includes the first evaluation result and the second evaluation result for improving the accuracy when evaluating the biological information, and can be used when generating the comprehensive evaluation result. It becomes.

(Second Embodiment: Biological Information Calculation System 100)
Next, an example of the biometric information calculation system 100 according to the second embodiment will be described. The difference between the above-described embodiment and the second embodiment is that additional information is used. The description of the same contents as those of the above-described embodiment will be omitted.

The biometric information calculation system 100 in the present embodiment includes, for example, comprehensive evaluation step S150. In the biometric information calculation system 100, for example, the comprehensive evaluation step S150 is performed after the above-mentioned generation step S120, and the storage step S140 is performed after the comprehensive evaluation step S150.

The comprehensive evaluation step S150 acquires additional information, for example, as shown in FIG. 8, and generates a comprehensive evaluation result based on a plurality of evaluation results (for example, the first evaluation result and the second evaluation result) and the additional information. .. The comprehensive evaluation step S150 can be executed by, for example, the comprehensive evaluation unit included in the generation unit 12.

The additional information indicates the characteristics of the user, for example, information different from the biometric information described above. In this case, as additional information, for example, attribute information such as the user's gender and age may be used, and information specifying the user's health condition such as a diagnosis result and the amount of exercise may be used. The additional information is input by the user via, for example, the input unit 108 or the like, and is acquired by the comprehensive acquisition unit or the like.

The comprehensive evaluation result shows the result of comprehensively evaluating the characteristics of the user. The comprehensive evaluation result shows the result of performing correction processing or the like based on the additional information on the biological information included in each evaluation result. For example, when the user's age is used as additional information, a comparison result between a preset reference value for each age group and each evaluation result is generated as a comprehensive evaluation result.

In addition to the above, as a comprehensive evaluation result, character strings representing characteristics for each user such as "high blood sugar level", "high blood pressure", "high exercise ability", "it is better to suppress the amount of exercise" are used. In addition, for example, a difference from an arbitrary reference value or a numerical value such as a deviation value may be used.

In addition, the comprehensive evaluation result shows, for example, estimated insurance information including insurance premiums. The estimated insurance information may include, for example, a value indicating an insurance premium estimated based on each evaluation result, or may include, for example, a character string indicating an insurance type or the like. The estimated premium is calculated based on, for example, actuarial science.

The comprehensive evaluation unit refers to a user-recognizable data format stored in advance in, for example, a storage unit 104 or the like, and generates a comprehensive evaluation result. The comprehensive evaluation unit may refer to, for example, a post-processing database and generate a comprehensive evaluation result suitable for a plurality of evaluation results and additional information. The post-processing database is stored in, for example, the storage unit 104.

The post-processing database may store post-processing classification information for generating a plurality of evaluation results and comprehensive evaluation results for additional information, as in the database described above, for example. In addition to storing one or more post-processing classification information, the post-processing database may store, for example, a plurality of post-processing learning data used for generating the post-processing classification information.

The post-processing classification information is, for example, the correlation between a plurality of past evaluation results and past additional information (post-processing input data) acquired in advance and post-processing reference data associated with the post-processing input data. It is a function to show. The post-processing reference data shows the result of comprehensively evaluating the characteristics of the user. The post-processing classification information is generated by using a plurality of post-processing learning data with the post-processing input data and the post-processing reference data as a pair of post-processing learning data.

The post-processing classification information shows, for example, a calibration model generated based on the analysis results obtained by analyzing the post-processing input data as an explanatory variable and the post-processing reference data as an objective variable by the regression analysis described above. .. The post-processing classification information can be, for example, periodically updated the calibration model (post-processing calibration model), or may be generated for each additional information, for example. Note that the post-processing classification information may include a trained model (post-processing trained model) generated by machine learning using, for example, a plurality of post-processing learning data, similarly to the above-mentioned classification information. ..

The storage step S140 stores the comprehensive evaluation result. In the storage step S140, the comprehensive evaluation result is stored in at least one of the storage unit 104 and the server 4 by executing the same process as that of the above-described embodiment.

According to the present embodiment, in addition to the effects of the above-described embodiment, the comprehensive evaluation unit comprehensively evaluates the characteristics of the user based on the first evaluation result, the second evaluation result, and additional information. Generate. Therefore, it is possible to realize an evaluation considering the characteristics of the user for each evaluation result. This makes it possible to generate an evaluation result suitable for each user.

(Second Embodiment: Modification Example of Biological Information Calculation System 100)
Next, a modified example of the biometric information calculation system 100 in the second embodiment will be described. The difference between the second embodiment described above and the modified example is that the additional information described above is acquired in the generation step S120. The description of the same contents as those of the above-described embodiment will be omitted.

In this modification, for example, as shown in FIG. 9, the generation step S120 includes acquiring additional information and generating a first evaluation result based on the first evaluation data and the additional information. The additional information is the same as the above-mentioned contents, for example, the user inputs the additional information via the input unit 108 or the like, and the generation unit 12 or the like acquires the additional information.

The generation unit 12 may determine the calculation method for the first evaluation data, for example, according to the content of the additional information. In this case, the classification information includes functions and the like that differ depending on the type of additional information. The generation unit 12 may generate the first evaluation result based on the information obtained by combining the first evaluation data and the additional information, for example.

According to this modification, the generation unit 12 includes acquiring additional information and generating a first evaluation result based on the first evaluation data and the additional information. Therefore, in addition to the first evaluation data, it is possible to generate a multifaceted first evaluation result in consideration of the user's characteristics. This makes it possible to generate an evaluation of the user's biological information with higher accuracy.

(Third Embodiment: biometric information calculation system 100)
Next, an example of the biometric information calculation system 100 according to the third embodiment will be described. The difference between the above-described embodiment and the third embodiment is that the first evaluation result is used in addition to the second evaluation data when the second evaluation result is generated. The description of the same contents as those of the above-described embodiment will be omitted.

In the biometric information calculation system 100 of the present embodiment, for example, as shown in FIG. 10, the generation step S120 refers to a database and generates a second evaluation result based on the first evaluation result and the second evaluation data. Including. For example, the generation unit 12 generates the second evaluation result after generating the first evaluation result.

The generation unit 12 may determine the calculation method for the second evaluation data, for example, according to the content of the first evaluation result. In this case, the classification information includes functions and the like that differ depending on the characteristics of the content of the first evaluation result. The generation unit 12 may generate the second evaluation result based on the information obtained by combining the first evaluation result and the second evaluation data, for example.

According to the present embodiment, in addition to the effects of the above-described embodiment, the generation unit 12 generates a second evaluation result based on the first evaluation result and the second evaluation data. Therefore, the second evaluation result based on the first evaluation result can be generated. This makes it possible to generate an evaluation of the user's biological information with higher accuracy.

(Fourth Embodiment: Biometric information calculation system 100)
Next, an example of the biometric information calculation system 100 according to the fourth embodiment will be described. The difference between the above-described embodiment and the fourth embodiment is that the attribute-based classification information suitable for the evaluation data is selected from the plurality of attribute-based classification information included in the classification information. The description of the same contents as those of the above-described embodiment will be omitted.

In the biometric information calculation device 1 of the present embodiment, as shown in FIG. 11, for example, the generation step S120 includes the selection step S121 and the attribute-specific generation step S122. In FIG. 11, the description of the contents of the second evaluation data and the second evaluation result is omitted.

The selection step S121 refers to the preliminary evaluation data and selects specific attribute-based classification information (for example, first classification information) from the plurality of attribute-based classification information. The selection step S121 can be executed, for example, by the selection unit included in the generation unit 12. The preliminary evaluation data shows different characteristics from the first evaluation data, for example, the same characteristics as the second data.

The attribute-based generation step S122 refers to the selected first classification information, calculates the first biological information (for example, the value of blood carbon dioxide partial pressure) with respect to the first evaluation data, and generates the first evaluation result. The attribute-based generation step S122 can be executed, for example, by the attribute-based generation unit included in the generation unit 12.

The plurality of attribute-based classification information is calculated using different learning data. For example, when data corresponding to the acceleration pulse wave of the subject is used as the input data of the learning data, for example, input data is prepared for each of the seven types (A to G) as shown in FIG. 12, and seven types of attributes are prepared. Generate classification information.

When such a plurality of attribute-based classification information is stored in the database, for example, the acquisition unit 11 acquires the evaluation data corresponding to the acceleration pulse wave of the user and the preliminary evaluation data. Then, the generation unit 12 refers to the preliminary evaluation data and selects the first classification information. After that, the generation unit 12 refers to the first classification information and generates the first evaluation result for the first evaluation data. Therefore, it is possible to select the most suitable classification information for the user from each attribute classification information.

For example, when data corresponding to the velocity pulse wave of the subject is used as the input data of the learning data, the input data is prepared for each of the two types (group 1 and group 2) as shown in FIG. 13, for example. Two types of attribute classification information may be generated.

Here, while the data corresponding to the acceleration pulse wave shown in FIG. 12 can be easily classified in detail based on the characteristics, there is a concern that the accuracy may decrease due to erroneous detection of peaks when calculating biological information. Further, the data corresponding to the velocity pulse wave shown in FIG. 13 is more difficult to classify in detail based on the characteristics than the data corresponding to the acceleration pulse wave, but the biometric information is high because there are few false detections of peaks and the like. It can be calculated with accuracy.

Based on the above, the plurality of attribute classification information includes data corresponding to the acceleration pulse wave as shown in FIG. 12, for example, as selection data used for selecting specific classification information, and when generating the attribute classification information. Data corresponding to the velocity pulse wave may be used as the training data of.

In this case, in the acquisition step S110, for example, the acquisition unit 11 acquires data corresponding to the velocity pulse wave as the first evaluation data from the sensor data based on the user's pulse wave. Further, the acquisition unit 11 acquires data corresponding to the acceleration pulse wave as preliminary evaluation data from the sensor data.

Next, as the selection step S121, for example, the generation unit 12 refers to the preliminary evaluation data, and among the plurality of selection data including the data corresponding to the acceleration pulse wave, the selection data most similar to the preliminary evaluation data (the first). 1) is specified, and the first classification information associated with the first selection data is selected. Then, as the attribute-specific generation step S122, the generation unit 12 refers to the first classification information and generates the first evaluation result for the first evaluation data. This makes it possible to further improve the evaluation accuracy.

Here, an example of data used for the above-mentioned selection data and the like will be described.

For example, as shown in FIG. 12, the acceleration pulse wave has inflection points a to e. For example, the maximum peak in the acceleration pulse wave is set to point a, each inflection point is set to point b, c, d, and e in order from point a, point a is set to 1, and the minimum value is point b or d. When standardization is performed with the points set to 0, the acceleration pulse wave can be classified into 7 patterns by using a method of classifying by the value of each inflection point and the magnitude relation of the difference. 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 classified as A, otherwise it is classified as B. Next, when the value of the inflection point is b≈d, it is classified into 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, when 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.

For example, the generation unit 12 determines which pattern of FIG. 12, for example, the preliminary evaluation data applies to, and specifies the first selection data. For example, if the inflection point b of the input preliminary evaluation data is smaller than the inflection point d and the inflection point c ≧ 0.5, the pattern A is set as the first selection data. As a result, the biological information can be calculated accurately by referring to the classification information suitable for the characteristics of the first evaluation data.

According to the present embodiment, in addition to the effects of the above-described embodiment, the generation unit 12 refers to the preliminary evaluation data, refers to the selection unit that selects the first classification information, and refers to the first classification information, and makes the first evaluation. Includes an attribute-based generation unit that generates the first evaluation result for the data. Therefore, it is possible to generate the first evaluation result for the first evaluation data after selecting the most suitable first classification information for the characteristics of the pulse wave. This makes it possible to further improve the evaluation accuracy.

Further, according to the present embodiment, the acquisition unit 11 acquires data corresponding to the velocity pulse wave based on the pulse wave as the first evaluation data. In addition, the acquisition unit 11 acquires data corresponding to the acceleration pulse wave based on the pulse wave as preliminary evaluation data. Therefore, the attribute classification information can be selected by using the acceleration pulse wave, which makes it easier to classify the characteristics of the pulse wave than the velocity pulse wave. In addition, the first evaluation result can be generated by using the velocity pulse wave, which is easier to calculate the biological information than the acceleration pulse wave. This makes it possible to further improve the evaluation accuracy.

(Fourth Embodiment: First modification of biometric information calculation system 100)
Next, a first modification of the biometric information calculation system 100 according to the fourth embodiment will be described. The difference between the above-mentioned example of the fourth embodiment and the first modification is that the classification information is selected using the evaluation result. The description of the same contents as those of the above-described embodiment will be omitted.

In this modification, for example, as shown in FIG. 14, the generation step S120 selects the first classification information based on the second evaluation result from the classification information, refers to the first classification information, and refers to the first evaluation data. Generate the first evaluation result. For example, the generation unit 12 refers to the second evaluation result and selects specific attribute-based classification information from the plurality of attribute-based classification information in the same manner as in the selection step S121 described above.

The generation unit 12 selects the first classification information based on, for example, the value of the biological information included in the second evaluation result. At this time, values for selection are preset in the plurality of attribute information.

Similar to the attribute-specific generation step S122 described above, the generation unit 12 refers to the selected first classification information, calculates the first biometric information for the first evaluation data, and generates the first evaluation result.

According to this modification, the generation unit 12 selects the first classification information based on the second evaluation result, refers to the first classification information, and generates the first evaluation result for the first evaluation data. Therefore, it is possible to generate the first evaluation result for the first evaluation data after selecting the optimum first classification information according to the second evaluation result. This makes it possible to further improve the evaluation accuracy.

(Fourth Embodiment: Second modification of biometric information calculation system 100)
Next, a second modification of the biometric information calculation system 100 according to the fourth embodiment will be described. The difference between the above-mentioned example of the fourth embodiment and the second modification is that the first classification information is selected based on the characteristics of the pulse wave. The description of the same contents as those of the above-described embodiment will be omitted.

In this modification, for example, as shown in FIG. 15, in the generation step S120, the first classification information is selected based on the characteristics of the pulse wave (for example, sensor data) from the classification information, and the first classification information is referred to. Generate the first evaluation result for the first evaluation data. For example, the generation unit 12 refers to the sensor data and selects specific attribute-based classification information from the plurality of attribute-based classification information in the same manner as in the selection step S121 described above.

As the "characteristics of the pulse wave", for example, data after performing at least a part of each process as shown in FIGS. 4 (a) to 4 (d) on the sensor data may be used. .. In particular, by using the pulse wave data after filtering the sensor data as a feature of the pulse wave, it is possible to improve the accuracy when selecting specific attribute classification information.

The generation unit 12 compares, for example, the above-mentioned selection data with the characteristics of the pulse wave, and selects the first classification information. The generation unit 12 may select the first classification information based on, for example, the full width at half maximum and the relative intensity of the peak included in the sensor data or the like. At this time, values for selection are preset in the plurality of attribute information.

Similar to the attribute-specific generation step S122 described above, the generation unit 12 refers to the selected first classification information, calculates the first biometric information for the first evaluation data, and generates the first evaluation result.

According to this modification, the generation unit 12 selects the first classification information based on the characteristics of the pulse wave, refers to the first classification information, and generates the first evaluation result for the first evaluation data. Therefore, it is possible to generate the first evaluation result for the first evaluation data after selecting the optimum first classification information according to the characteristics of the pulse wave. This makes it possible to further improve the evaluation accuracy.

(Fourth Embodiment: Third modification of the biological information calculation system 100)
Next, a third modification of the biometric information calculation system 100 according to the fourth embodiment will be described. The difference between the above-mentioned example of the fourth embodiment and the third modification is that the first classification information is selected based on the additional information. The description of the same contents as those of the above-described embodiment will be omitted.

In this modification, for example, as shown in FIG. 16, in the generation step S120, the first classification information is selected based on the additional information from the classification information, the first classification information is referred to, and the first evaluation data is first. Generate evaluation results. For example, the generation unit 12 refers to the additional information and selects specific attribute-based classification information from the plurality of attribute-based classification information in the same manner as in the selection step S121 described above. The additional information is the same as the above-mentioned additional information.

The generation unit 12 may select the first classification information based on, for example, attribute information such as age and gender included in the additional information. At this time, attribute information and the like for selection are set in advance in the plurality of attribute information.

Similar to the attribute-specific generation step S122 described above, the generation unit 12 refers to the selected first classification information, calculates the first biometric information for the first evaluation data, and generates the first evaluation result.

According to this modification, the generation unit 12 selects the first classification information based on the additional information, refers to the first classification information, and generates the first evaluation result for the first evaluation data. Therefore, it is possible to generate the first evaluation result for the first evaluation data after selecting the optimum first classification information according to the characteristics of the additional information. This makes it possible to further improve the evaluation accuracy.

(Fifth Embodiment: Biometric information calculation system 100)
Next, an example of the biometric information calculation system 100 according to the fifth embodiment will be described. The difference between the above-described embodiment and the fifth embodiment is that the calculation step S160 is provided. The description of the same contents as those of the above-described embodiment will be omitted.

The biological information calculation system 100 in the present embodiment includes, for example, calculation step S160. In the biological information calculation system 100, for example, the calculation step S160 is performed after the storage step S140 described above.

In the calculation step S160, for example, as shown in FIG. 17, the characteristics of the user are comprehensively characterized based on a plurality of evaluation results (for example, the first evaluation result and the second evaluation result) stored in the server 4 or the storage unit 104. Generate the evaluated comprehensive evaluation result. The calculation step S160 can be executed by, for example, the comprehensive evaluation unit included in the generation unit 12 of the biometric information calculation device 1, or may be executed by, for example, the comprehensive evaluation unit included in the server 4.

The comprehensive evaluation result is the same as that of the above-described embodiment, and will be described below as an estimated insurance information including insurance premiums as an example.

The comprehensive evaluation unit may generate estimated insurance information by referring to a user-recognizable data format stored in advance in, for example, a storage unit 104 or the like. The comprehensive evaluation unit may refer to a database, for example, and generate appropriate estimated insurance information for a plurality of evaluation results.

Similar to the database described above, the database may store insurance classification information for generating estimated insurance information for a plurality of evaluation results. In addition to storing one or more insurance classification information, the database may store, for example, a plurality of insurance learning data used for generating the insurance classification information.

The insurance classification information is, for example, a function showing the correlation between a plurality of past evaluation results (insurance input data) acquired in advance and insurance reference data associated with the insurance input data. The insurance reference data includes premiums for past proven health input data. The insurance classification information is generated by using a plurality of insurance learning data with the insurance input data and the insurance reference data as a pair of insurance learning data.

The insurance classification information includes, for example, an insurance input data as an explanatory variable, an insurance reference data as an objective variable, an analysis by the regression analysis described above, and a calibration model generated based on the analysis result. The insurance classification information can be updated, for example, on a calibration model (insurance calibration model) on a regular basis. Note that the insurance classification information may include a trained model (insurance trained model) generated by machine learning using, for example, a plurality of insurance learning data, similarly to the above-mentioned classification information.

According to the present embodiment, in addition to the effects of the above-described embodiment, the comprehensive evaluation unit generates a comprehensive evaluation result based on the first evaluation result and the second evaluation result. Therefore, it is possible to realize an evaluation considering the characteristics of the user for each evaluation result. This makes it possible to generate an evaluation result suitable for each user. In particular, when the estimated insurance information is used as the comprehensive evaluation result, it is possible to suppress variations in the estimated insurance premiums and the like due to the subjectivity of the user and the like.

(Sixth Embodiment: Biometric information calculation system 100)
Next, an example of the biometric information calculation system 100 according to the sixth embodiment will be described. The difference between the above-described embodiment and the sixth embodiment is that the determination result is used. The description of the same contents as those of the above-described embodiment will be omitted.

In the biometric information calculation system 100 of the present embodiment, as shown in FIG. 18, for example, in the storage step S140, the user determines the contents of a plurality of evaluation results (for example, the first evaluation result and the second evaluation result). The judgment result is acquired, and the judgment result and a plurality of evaluation results are linked and saved. The storage step S140 can be executed, for example, by the storage unit 14 or the server 4.

The determination result is obtained by, for example, the user inputting the result of comparing the output plurality of evaluation results with the biological information measured by using a known measuring device via the input unit 108 or the like. Can be done.

The biometric information calculation system 100 in the present embodiment may include, for example, update step S170. In this case, the update step S170 can be executed by, for example, the learning unit 15, or may be executed by, for example, the server 4.

The learning unit 15 updates the classification information based on the determination result saved in the saving step S140 and the plurality of evaluation results. The learning unit 15 updates the classification information using, for example, a known technique.

According to the present embodiment, in addition to the effects of the above-described embodiment, the storage unit 14 or the server 4 stores the determination result, the first evaluation result, and the second evaluation result in association with each other. Therefore, it is possible to easily compare each evaluation result with the determination result.

Further, according to the present embodiment, the learning unit 15 updates the classification information based on the determination result, the first evaluation result, and the second evaluation result. Therefore, when the accuracy of each evaluation result is lowered, it can be easily improved. This makes it possible to maintain an improvement in accuracy when evaluating biological information.

Although embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. Such a novel embodiment can be implemented in various other embodiments, and various omissions, replacements, and changes can be made without departing from the gist of the invention. This embodiment and its modifications are included in the scope and gist of the invention, and are also included in the scope of the invention described in the claims and the equivalent scope thereof.

1: Biological information arithmetic unit 3: Communication network 4: Server 5: Sensor 6: Detection unit 10: Housing 11: Acquisition unit 12: Generation unit 13: Output unit 14: Storage unit 15: Learning unit 50: Acquisition unit 51: Communication I / F
52: Memory 53: Instruction unit 54: Internal bus 55: Wristband 100: Biometric information calculation system 101: CPU
102: ROM
103: RAM
104: Preservation unit 105: I / F
106: I / F
107: I / F
108: Input unit 109: Display unit 110: Internal bus S110: Acquisition step S120: Generation step S130: Output step S140: Storage step S150: Comprehensive evaluation step S160: Calculation step S170: Update step

Claims (13)

A biometric information calculation system that evaluates a user's biometric information.
For one pulse wave data corresponding to one of said speed pulse wave rather based on the pulse wave of the user and the acceleration pulse wave, by implementing different types of processing, respectively, the first evaluation data and the second evaluation data And the acquisition method to acquire
A pair of input data based on a learning pulse wave acquired in advance and reference data including biometric information associated with the input data is used as learning data, and classification information generated by using a plurality of the learning data is stored. Database and
Refer to the database and
The first evaluation result including the first biometric information for the first evaluation data, and the second evaluation result including the second biometric information of a type different from the first biometric information for the second evaluation data.
And the generation means to generate each
A storage means for storing the first evaluation result and the second evaluation result, and
Equipped with a,
The classification information includes a first classification information and a second classification information generated by using different types of the learning data.
The generation means
The first evaluation result for the first evaluation data is generated with reference to the first classification information.
To generate the second evaluation result for the second evaluation data with reference to the second classification information.
A biometric information calculation system characterized by including. The biometric information calculation system according to claim 1, wherein the first evaluation data and the second evaluation data are data corresponding to either a velocity pulse wave or an acceleration pulse wave of the user. The biometric information calculation system according to claim 1, wherein the classification information is a calibration model obtained by using PLS regression analysis using the input data as an explanatory variable and the reference data as an objective variable. Comprehensive evaluation means that acquires additional information indicating the characteristics of the user and generates a comprehensive evaluation result that comprehensively evaluates the characteristics of the user based on the first evaluation result, the second evaluation result, and the additional information. The biometric information calculation system according to claim 1, wherein the biometric information calculation system is provided. The generation means
Acquire additional information indicating the characteristics of the user,
The biometric information calculation system according to claim 1, further comprising generating the first evaluation result based on the first evaluation data and the additional information with reference to the database. The first biological information indicates a blood glucose level and
The biometric information calculation system according to claim 1, wherein the second biometric information includes at least one of blood pressure, pulse rate, respiratory rate, characteristics of blood carbon dioxide, lactic acid level, and oxygen saturation. The living body according to claim 1, wherein the generation means includes generating the second evaluation result based on the first evaluation result and the second evaluation data with reference to the second classification information. Information calculation system. Claim 1 is provided with a calculation means for generating a comprehensive evaluation result in which the characteristics of the user are comprehensively evaluated based on the first evaluation result and the second evaluation result stored by the storage means. The described biometric information calculation system. The storage means
The determination result determined by the user with respect to the contents of the first evaluation result and the second evaluation result is acquired, and the determination result is obtained.
The biometric information calculation system according to claim 1, further comprising storing the determination result, the first evaluation result, and the second evaluation result in association with each other. The biometric information calculation system according to claim 9 , further comprising an updating means for updating the classification information based on the determination result, the first evaluation result, and the second evaluation result stored in the storage means. .. The storage means includes storing the first evaluation result and the sensor data associated with the second evaluation result.
The biometric information calculation system according to claim 1, wherein the sensor data is used for extracting the pulse wave data and shows the characteristics of the pulse wave of the user. A server in which the database is stored and the first evaluation result and the second evaluation result are generated by the generation means.
A biometric information calculation device that receives and displays the first evaluation result and the second evaluation result from the server.
The biometric information calculation system according to claim 1, wherein the biometric information calculation system is provided. A server characterized in that the first evaluation result according to claim 1 and the second evaluation result are stored.

Images