JP7083193B1 - Biological information calculation system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a biometric information calculation system capable of generating a user's blood lactate level with high accuracy. SOLUTION: This is a biological information calculation system that generates a user's blood lactic acid level, and is an acquisition means for acquiring evaluation data based on the user's pulse wave, and input data based on a learning pulse wave acquired in advance. And the database in which the classification information generated by using the plurality of the training data is stored as the pair of reference data including the blood lactic acid value associated with the input data as the training data, and the database are referred to. A biological information calculation system comprising: a generation means for generating an evaluation result including the blood lactic acid value with respect to the evaluation data. [Selection diagram] Fig. 2

Description

The present invention relates to a biometric information calculation system that generates a user's blood lactate level.

Conventionally, as a method for generating a blood lactate level of a user, for example, a method as in Patent Document 1 has been proposed.

In the exercise effect determination method disclosed in Patent Document 1, the user's pulse wave signal and body motion signal during a predetermined exercise are measured, and the exercise pulse based on the pulse wave signal and the body motion signal. The effect that a predetermined exercise contributes to the physical strength of the user based on the calculation of the number, the lactic acid value information indicating the relationship between the user's pulse rate and the blood lactic acid level acquired in advance, and the pulse rate during exercise. It is characterized by including obtaining an exercise effect determination result for determining the degree and notifying the user of the determination result.

Japanese Unexamined Patent Publication No. 2016-195661

Here, when the exercise intensity is high when evaluating the blood lactate level of the user, the pulse rate of the user performing the exercise tends to change abruptly, whereas the blood lactate level tends to change slowly. From this, since there is a time lag in the relationship between the pulse rate and the blood lactate level, the relationship between the pulse rate and the blood lactate level may not be uniquely determined. Therefore, in the prior art as in Patent Document 1, it is difficult to specify the blood lactate level suitable for the pulse rate, and there is a concern that the blood lactate level of the user cannot be generated with high accuracy.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object thereof is to provide a biometric information calculation system capable of generating a user's blood lactate level with high accuracy. There is something in it.

The biometric information calculation system according to the first invention is a biometric information calculation system that generates a blood lactic acid level of a user, and is either a volume pulse wave, a velocity pulse wave, or an acceleration pulse wave based on the pulse wave of the user. For learning, a pair of acquisition means for acquiring the corresponding evaluation data, input data of the same type as the evaluation data based on the previously acquired learning pulse wave, and reference data including the blood lactic acid value associated with the input data. As data, a database in which classification information generated using the plurality of learning data is stored, a generation means for generating an evaluation result including the blood lactic acid value for the evaluation data by referring to the database, and a generation means. It is characterized by having.

The biometric information calculation system according to the second 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 third invention acquires additional information indicating the characteristics of the user, and based on the evaluation result and the additional information, evaluates the motor ability of the user as a comprehensive evaluation result. It is characterized by having a comprehensive evaluation means for generating.

In the third invention, the biometric information calculation system according to the fourth aspect of the invention includes that the acquisition means acquires additional data showing characteristics different from the evaluation data based on the pulse wave, and the generation means includes the acquisition of additional data. It is characterized by including generating the additional information based on the additional data.

The biological information calculation system according to the fifth invention, in the third invention or the fourth invention, the additional information is at least the stress level, the pulse rate, the respiratory rate, the blood glucose level, the blood pressure, the characteristics of blood carbon dioxide, and the amount of exercise. It is characterized by showing either one.

In the first invention, the biometric information calculation system according to the sixth aspect of the invention includes that the acquisition means acquires additional data showing characteristics different from the evaluation data based on the pulse wave, and the generation means includes the acquisition of additional data. It is characterized by referring to the database and generating additional information indicating the characteristics of the user with respect to the additional data.

In the first invention, the biometric information calculation system according to the seventh invention acquires the additional information indicating the characteristics of the user, and generates the evaluation result based on the plurality of the evaluation data and the additional information. It is characterized by including doing.

In the first invention, the biometric information calculation system according to the eighth invention includes that the acquisition means acquires preliminary evaluation data different from the evaluation data based on the pulse wave, and the classification information is different from each other. A selection means that includes a plurality of attribute-specific classification information calculated using the training data, the generation means refers to the preliminary evaluation data, and selects the first classification information from the plurality of attribute-specific classification information. And the attribute-specific generation means for generating the evaluation result for the evaluation data with reference to the first classification information.

In the eighth aspect of the invention, the biometric information calculation system according to the ninth invention acquires data corresponding to the velocity pulse wave based on the pulse wave as the evaluation data, and the acceleration pulse wave based on the pulse wave. It is characterized by including acquiring the data corresponding to the above as the preliminary evaluation data.

According to the first to ninth inventions, the generation means refers to a database and generates an evaluation result including a blood lactate level with respect to the evaluation data. Therefore, the evaluation result can be generated from the evaluation data acquired based on the pulse wave directly affected by the blood lactate level. This makes it possible to generate the blood lactate level of the user with high accuracy.

Further, according to the first to ninth inventions, the generation means refers to the database and generates the evaluation result for the evaluation data. In addition, the database stores classification information generated using a plurality of learning data. Therefore, when generating the evaluation result, it is possible to include the quantitative blood lactate level based on the data which has been proven in the past. This makes it possible to improve the accuracy when generating the evaluation result.

In particular, according to the second 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 third invention, the comprehensive evaluation means generates a comprehensive evaluation result for evaluating the motor ability of the user based on the evaluation result and additional information. Therefore, in addition to the evaluation result, it is possible to realize a comprehensive evaluation considering the characteristics of the user. This makes it possible to generate information on the user's athletic ability with high accuracy.

In particular, according to the fourth invention, the acquisition means acquires additional data showing features different from the evaluation data based on the pulse wave. Further, the generation means generates additional information based on the additional data. That is, a comprehensive evaluation result is generated from the evaluation result and additional information generated based on one pulse wave. Therefore, by using a plurality of types of information based on the same parameter, it is possible to realize a comprehensive evaluation from various viewpoints. This makes it possible to generate information on the user's athletic ability with higher accuracy.

In particular, according to the fifth invention, the additional information indicates at least one of stress level, pulse rate, respiratory rate, blood glucose level, blood pressure, characteristics of blood carbon dioxide, and amount of exercise. Therefore, it is possible to realize a comprehensive evaluation based on a multifaceted viewpoint by using a parameter that correlates with the user's athletic ability. This makes it possible to generate information on the user's athletic ability with higher accuracy.

In particular, according to the sixth invention, the generation means refers to the database and generates additional information indicating the characteristics of the user with respect to the additional data. Therefore, it is possible to use the additional information generated from a viewpoint different from the evaluation result, and it is possible to realize a multifaceted evaluation. This makes it possible to realize an appropriate evaluation according to the user's request.

In particular, according to the seventh invention, the evaluation means includes acquiring additional information and generating an evaluation result based on a plurality of evaluation data and the additional information. Therefore, in addition to a plurality of evaluation data, it is possible to generate a multifaceted evaluation result in consideration of the characteristics of the user. This makes it possible to generate the blood lactate level of the user with higher accuracy.

In particular, according to the eighth invention, the generation means refers to the preliminary evaluation data and selects the first classification information, and the first classification information is referred to, and the generation by attribute that generates the evaluation result for the evaluation data. Including information. Therefore, it is possible to generate the evaluation result for the evaluation data after selecting the optimum attribute classification information for the characteristics of the pulse wave. This makes it possible to further improve the evaluation accuracy.

In particular, according to the ninth invention, the acquisition means acquires the data corresponding to the velocity pulse wave based on the pulse wave as evaluation data, and acquires the data corresponding to the acceleration pulse wave based on the pulse wave as preliminary evaluation data. do. Therefore, the attribute classification information can be selected by using the acceleration pulse wave, which is easier to classify the characteristics of the pulse wave than the velocity pulse wave. Further, the evaluation result can be generated by using the velocity pulse wave which is easier to calculate the blood lactate level than the acceleration pulse wave. This makes it possible to further improve the evaluation accuracy.

FIG. 1 is a schematic diagram showing an example of a biometric information calculation system according to the first embodiment. 2 (a) and 2 (b) are schematic views showing an example of the operation of the biometric information calculation system in the first embodiment. FIG. 3A is a schematic diagram showing an example of classification information, and FIGS. 3B and 3C are schematic diagrams showing an example of processing for sensor data. FIG. 4A is a schematic diagram showing an example of the configuration of the biometric information arithmetic unit, and FIG. 4B is a schematic diagram showing an example of the function of the biometric information arithmetic unit. 5 (a) and 5 (b) are schematic views showing an example of a sensor. FIG. 6 is a flowchart showing an example of the operation of the biometric information calculation system according to the first embodiment. FIG. 7 is a schematic diagram showing an example of the operation of the biometric information calculation system in the second embodiment. FIG. 8 is a schematic diagram showing an example of the operation of the biometric information calculation system according to the third embodiment. 9 (a) and 9 (b) are schematic views showing an example of processing for calculating additional information with respect to the sensor data. FIG. 10 is a schematic diagram showing a modified example of the operation of the biological 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 an example of the operation of the biometric information calculation system according to the fifth embodiment. FIG. 13 is a schematic diagram showing a classification example of data corresponding to an acceleration pulse wave. FIG. 14 is a schematic diagram showing a classification example of data corresponding to a velocity pulse wave.

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

(First Embodiment: Biological 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 generate the blood lactate level of the user. The blood lactic acid level of the user indicates, for example, the concentration of lactic acid contained in the blood of the user, the tendency of the amount of lactic acid in the blood, the degree of deviation from a specific reference value, and the like.

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

The biological information calculation system 100 generates an evaluation result including a blood lactate level from the evaluation data based on the pulse wave of the user.

In the biometric information calculation system 100, for example, as shown in FIG. 2A, the biometric information calculation device 1 acquires sensor data generated by a sensor 5 or the like. After that, the biometric information calculation device 1 performs preprocessing such as filter processing on the acquired sensor data, and acquires evaluation data.

The biological information arithmetic unit 1 generates an evaluation result for the evaluation data. Therefore, the evaluation result can be generated from the evaluation data acquired based on the pulse wave in which the characteristics of the blood lactate level directly affect. This makes it possible to generate the blood lactate level of the user with high accuracy.

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

As the classification information, for example, as shown in FIG. 3A, a pair of input data based on the learning pulse wave acquired in the past and reference data including the blood lactic acid value associated with the input data is used as the learning data. , Generated using multiple learning data. Therefore, when generating the evaluation result, it is possible to include the quantitative blood lactate level based on the input data and the output data which have been proven in the past. This makes it possible to improve the accuracy when generating the evaluation result.

The biometric information calculation device 1 outputs, for example, the generated evaluation result to a display or the like. The evaluation result includes the blood lactate level.

In the biometric information calculation system 100, evaluation data may be acquired from the sensor 5 or the like, for example, as shown in FIG. 2B. In this case, the preprocessing for acquiring the 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 may 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 shows the data for generating the evaluation result. 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 cycle (for example, one cycle).

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. 3 (b) and 3 (c), 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 correlation between the past evaluation data (input data) acquired in advance and the reference data including the blood lactate level. The classification information shows, for example, a calibration model generated based on the analysis result analyzed by regression analysis or the like with the input data as an explanatory variable and the reference data as an objective variable. The classification information can be, for example, periodically updated in the calibration model, or may be generated according to 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. As the trained model, for example, a neural network model such as CNN (Convolutional Neural Network) is shown, and an SVM (Support vector machine) or the like is shown. Further, as machine learning, for example, deep learning can be used.

As the input data, data of the same type as the evaluation data is used, and for example, past evaluation data in which the corresponding evaluation result is clarified is shown. For example, the subject is fitted with 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 or the like used when acquiring the evaluation data, the sensor data generation conditions, and the processing conditions for the sensor data. For example, by unifying the above three contents, it is possible to dramatically improve the accuracy when generating the evaluation result.

The reference data includes the blood lactate level of the subject as measured by using a measuring device or the like. For example, when a subject is fitted with a sensor 5 or the like to generate learning sensor data, the reference data associated with the input data can be acquired by measuring the blood lactate level of the subject. In this case, the timing of measuring the blood lactate level 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 "blood lactate level" indicates a measurable concentration or ratio of lactate in the blood, and indicates, for example, the amount of lactate in the blood.

The reference data is measured using a known measuring device. For example, when measuring the blood lactate level, 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.

<Evaluation result>
The evaluation result is generated as the same kind of data as the reference data and includes the blood lactate level. The evaluation result refers to the classification information and is generated as the same or similar data as the reference data.

In the biometric information calculation system 100, for example, a plurality of evaluation data are acquired along an arbitrary time series, and a plurality of evaluation results for each evaluation data are generated. Further, in the biological information calculation system 100, a plurality of evaluation data may be acquired at arbitrary timings such as when the amount of exercise changes, and a plurality of evaluation results for each evaluation data may be generated. In this case, for example, at least one of LT (Lactate Threshold) and OBLA (Onset of Blood Lactate Accumulation) may be included as the evaluation result.

<Biological information arithmetic unit 1>
The biometric information calculation device 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, for example, a user's operation. Indicates the device. The biometric information calculation device 1 may include a sensor 5. Hereinafter, an example of a case where a PC is used as the biometric information calculation device 1 will be described.

FIG. 4A is a schematic diagram showing an example of the configuration of the biological information calculation device 1, and FIG. 4B is a schematic diagram showing an example of the function of the biological information calculation device 1.

As shown in FIG. 4A, for example, the biometric 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. It includes a storage unit 104 and I / F 105 to 107. Each configuration 101 to 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 when the CPU 101 operates. 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 / receiving information to / from the input unit 108. For example, a keyboard is used as the input unit 108, and the 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. 4B is a schematic diagram showing an example of the function of the biometric 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. 4B is realized by the CPU 101 executing a program stored in the storage unit 104 or the like with the RAM 103 as a work area.

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

The acquisition unit 11 performs a filtering process (filter process) on the acquired sensor data, for example, as shown in FIG. 3 (b). 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 shows, for example, data corresponding to a velocity pulse wave. The pulse wave data may show 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 intended use.

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 data amount 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 the values corresponding to the time axis of the divided data. In the normalization process, for example, standardization is carried out with the minimum value of the value corresponding to the time axis set to 0 and the maximum value set to 1. This facilitates the comparison of the data in each divided data.

The acquisition unit 11 may calculate, for example, an 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 amplitudes. In the normalization process, for example, standardization is carried out with the minimum value of the amplitude being 0 and the maximum value of the amplitude being 1. The acquisition unit 11 acquires, for example, standardized data as evaluation data. In this case, as evaluation data, data corresponding to the acceleration pulse wave of the user can be 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. 3 (c). In this case, as evaluation data, data corresponding to the velocity pulse wave of the user can be obtained.

Further, the acquisition unit 11 may perform, for example, only a part of each of the above-mentioned processes. 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 the values corresponding to the time axis are standardized as evaluation data. , Can be set arbitrarily according to the application.

The acquisition unit 11 may acquire information to be evaluated, such as user characteristics, exercise content, and competition items, which is input by the user via the input unit 108 or the like, and may be included in the evaluation data. The information to be evaluated may be used, for example, when generating an evaluation result.

<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 blood lactate level for the evaluation data, and generates it as an evaluation result. The generation unit 12 generates a plurality of evaluation results for different evaluation data.

<Output unit 13>
The output unit 13 outputs the evaluation result. In addition to outputting the evaluation result to the display unit 109, the output unit 13 may output the evaluation result 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.

<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 configured by 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.

The server 4 stores information 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.

<Sensor 5>
The sensor 5 generates sensor data. The sensor 5 includes a detection unit 6, for example, as shown in FIG. 5A. The sensor 5 is mounted at a position where the user's pulse wave 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. As the detection unit 6, for example, a strain sensor such as a fiber Bragg grating (FBG) sensor, a gyro sensor, one or more electrodes for measuring pulse wave signals, a photoelectric volume pulse wave (PPG) sensor, 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. 5B, 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. And send it 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 evaluation data from the sensor data, for example, as shown in FIGS. 3 (b) and 3 (c).

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 above-mentioned detection unit 6, measurement can be performed in a state of being 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 to 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 optical detector. The Mach-Zehnder interferometer is for splitting into two optical paths having optical path differences by a beam splitter and superimposing them on one again by a beam splitter to generate interferometric light. For example, the length of one optical fiber may be increased in order to make an optical path difference. Since coherent light produces interference fringes according to the optical path difference, it is possible to detect a change in strain generated in the FBG sensor, that is, a pulse wave, by measuring the pattern of the interference fringes. The 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 included in a light 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.

(First Embodiment: Operation of biometric information calculation system 100)
Next, an example of the operation of the biometric information calculation system 100 in the present embodiment will be described. FIG. 6 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 obtains an evaluation result indicating the user's blood lactate level from the sensor data through the biometric information calculation program installed in the biometric information calculation device 1. can do.

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

<Acquisition step S110>
The acquisition step S110 acquires evaluation data based on the user's pulse wave. 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 biometric information calculation device 1 receives the sensor data from the sensor 5.

For example, the acquisition unit 11 performs the process shown in FIG. 3B on the sensor data and acquires the evaluation data. The acquisition unit 11 stores the 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 evaluation data at a preset cycle. In this case, since the arithmetic processing when generating the evaluation result can be simplified, the processing speed can be improved.

<Generation step S120>
Next, the generation step S120 refers to the database and generates an evaluation result including the blood lactate level for the evaluation data. For example, the generation unit 12 refers to the classification information and calculates the blood lactate level with respect to the evaluation data. The generation unit 12 generates an evaluation result including the calculated value. The generation unit 12 stores the generated evaluation result in the storage unit 104, for example, via the storage unit 14. In addition to showing a specific value as the blood lactate value, for example, an error range (for example, "○○ ± 2 mmol / L" or the like) may be calculated.

<Output step S130>
Next, for example, the output step S130 may output the evaluation result. For example, the output unit 13 outputs the evaluation result to the display unit 109.

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

According to the present embodiment, the generation unit 12 generates an evaluation result including a blood lactate level with respect to the evaluation data. Therefore, by using the evaluation data, for example, the blood lactate level can be specified by using the peak shape of the amplitude based on the pulse wave, the relative intensity, etc., so that the blood lactate level can be specified by using the pulse rate showing only a specific value. The evaluation result can be generated from the evaluation data acquired based on the pulse wave directly affected by the blood lactate level, which has more features than the case of specifying. This makes it possible to generate the blood lactate level of the user with high accuracy.

Further, according to the present embodiment, the generation unit 12 refers to the database and generates the evaluation result for the evaluation data. In addition, the database stores classification information calculated using a plurality of learning data. Therefore, when generating the evaluation result, it is possible to include the quantitative blood lactate level based on the data which has been proven in the past. This makes it possible to improve the accuracy when generating the evaluation result.

Further, according to the present embodiment, the classification information is a calibration model obtained by using PLS regression analysis using input data as an explanatory variable and reference data as an 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.

(Second embodiment: biometric information calculation system 100)
Next, an example of the biometric information calculation system 100 in the second embodiment will be described. The difference between the above-described embodiment and the second embodiment is that additional information is used and that the comprehensive evaluation step S140 is provided. 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, the comprehensive evaluation step S140 is performed after the above-mentioned generation step S120, and the output step S130 is performed after the comprehensive evaluation step S140.

Comprehensive evaluation step S140 acquires additional information, for example, as shown in FIG. 7, and generates a comprehensive evaluation result based on the evaluation result and the additional information. The comprehensive evaluation step S140 can be executed by, for example, the comprehensive evaluation unit included in the biometric information calculation device 1.

Additional information indicates a user's characteristics and includes, for example, at least one of the user's stress level, pulse rate, respiratory rate, blood glucose level, blood pressure, blood carbon dioxide characteristics, and amount of exercise.

The momentum may be acquired, for example, based on the acceleration acquired from an accelerometer attached to the user during exercise. Further, for the momentum, for example, when a 3-axis acceleration sensor is used, the acceleration value for each xyz axis is obtained, so that the processing for obtaining one acceleration from the three acceleration values, the noise reduction processing, and the moving average are accompanied by the processing. The processing for obtaining the above processing may be performed, or the information after the processing may be acquired.

As the additional information, for example, data measured by using a known measurement method is used, and the data format is arbitrary. Further, the timing of acquiring the additional information may be the same as the timing of measuring the pulse wave, or may be any timing depending on the application.

The additional information may include, for example, attribute information such as the gender and age of the user, as well as information related to exercise at the time of evaluation such as the content of exercise performed by the user and the sport of competition. 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 evaluating the athletic ability of the user. As a comprehensive evaluation result, a character string indicating the degree of athletic ability for each user such as "high athletic ability" and "low athletic ability" is used, and for example, a difference from an arbitrary reference value, a deviation value, etc. Numerical values may be used.

The comprehensive evaluation result includes information quantifying the tendency of the user's athletic ability, and may include a threshold value different for each user such as an anaerobic work threshold value. The comprehensive evaluation result may include information indicating an evaluation of the tendency of the user's athletic ability, such as "during aerobic exercise" and "during anaerobic exercise". Further, at least one of LT and OBLA may be included as the comprehensive evaluation result.

In the comprehensive evaluation step S140, for example, the comprehensive evaluation unit refers to a preset threshold value and generates a result of comparing the evaluation result and additional information with the threshold value as a comprehensive evaluation result. For example, the comprehensive evaluation unit generates a comprehensive evaluation result indicating that the athletic ability is high when the evaluation result and the additional information are lower than the threshold value, and when the evaluation result and the additional information are higher than the threshold value, the athletic ability is low. Generate a comprehensive evaluation result showing.

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

In the post-processing database, for example, the post-processing classification information for generating the evaluation result and the comprehensive evaluation result for the additional information may be stored in the same manner as the above-mentioned database. 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 post-processing classification information.

The post-processing classification information is, for example, a function showing the correlation between the past evaluation results and additional information (post-processing input data) acquired in advance and the post-processing reference data associated with the post-processing input data. .. The post-processing reference data shows the evaluation result of the user's athletic ability. 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. ..

According to the present embodiment, in addition to the effects of the above-described embodiment, the comprehensive evaluation unit generates a comprehensive evaluation result that evaluates the motor ability of the user based on the evaluation result and additional information. Therefore, in addition to the evaluation result, it is possible to realize a comprehensive evaluation considering the characteristics of the user. This makes it possible to generate information on the user's athletic ability 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 additional data for generating additional information is acquired. 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, the acquisition step S110 includes acquiring additional data. Further, in the biometric information calculation system 100 in the present embodiment, the generation step S120 includes generating additional information based on the additional data.

As shown in FIG. 8, for example, the biometric information arithmetic unit 1 in the present embodiment performs two types of processing on one sensor data generated based on a pulse wave. As a result, for example, the acquisition unit 11 acquires the evaluation data and additional data showing features different from the evaluation data based on the same pulse wave. In addition, a plurality of additional data may be acquired.

For example, the generation unit 12 generates additional information based on the additional data. In the present embodiment, as additional information, for example, at least one of a pulse rate and a respiratory rate can be calculated. In this case, for example, as shown in FIGS. 9A and 9B, the content of the preprocessing performed in the acquisition step S110 and the content of the database referred to in the generation step S120 may be different. Hereinafter, an example of calculating the pulse rate and the respiratory rate as additional information will be described.

<Calculation of pulse rate>
For example, when calculating the pulse rate, as shown in FIG. 9A, 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 additional data.

After that, the generation unit 12 refers to the database and calculates the pulse rate for the additional data. At this time, the generation unit 12 refers to the pulse rate classification information stored in the database. The pulse rate classification information indicates, 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 additional data (peak interval = 0.85 [seconds]). As a result, the generation unit 12 can generate additional information including the pulse rate.

<Calculation of respiratory rate>
For example, when calculating the respiratory rate, as shown in FIG. 9B, 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 additional data.

After that, the generation unit 12 refers to the database and calculates the respiratory rate for the additional data. At this time, the generation unit 12 refers to the respiratory rate classification information stored in the database. 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 additional data (specified frequency = 0.225 Hz). As a result, the generation unit 12 can generate additional information including, for example, the respiratory rate.

As described above, in the biometric information calculation device 1, the preprocessing for the sensor data can be set according to the content of the biometric information included in the additional information, and the content of the database to be referred to can be arbitrarily set.

In addition to the above-mentioned pulse rate and respiratory rate, information that can be calculated based on pulse waves, such as blood glucose level, blood pressure, blood vessel age, degree of diabetes, and characteristics of blood carbon dioxide, is used as biological information. be able to. The "characteristics of carbon dioxide in blood" indicates the degree of carbon dioxide contained in blood. As the characteristics of blood carbon dioxide, for example, the value of blood carbon dioxide partial pressure (PaCO ₂ ) is used, the dissolved concentration of blood carbon dioxide, and the bicarbonate / bipolar (HCO _3- ⁾ contained in blood. The concentration may be used, or a value considering the pH of blood may be used depending on the situation.

Further, the classification information (preliminary classification information) used for the calculation may be stored in the database in advance according to the content of the biological information to be calculated. In this case, a plurality of pairs of preliminary input data indicating the above-mentioned learning pulse wave and preliminary reference data including biometric information associated with the preliminary input data are prepared as preliminary learning data. Then, for example, the learning unit 15 generates preliminary classification information using a plurality of preliminary learning data and stores it in a database.

The biometric information included in the preliminary reference data can be acquired by measuring the subject using a measuring device for measuring the necessary biometric information, as in the case of the reference data described above. .. Further, as the method for learning the preliminary classification information, the same method as the above-mentioned classification information can be used.

The biometric information calculation system 100 in the present embodiment may output the generated additional information, for example, as shown in FIG. In this case, the additional information generated by referring to the information different from the evaluation result can be used, and the user or the like can make a multifaceted judgment. This makes it possible to easily realize an appropriate evaluation according to the user's request.

According to the present embodiment, in addition to the effects of the above-described embodiment, the acquisition unit 11 acquires additional data showing features different from the evaluation data based on the pulse wave. Further, the generation unit 12 generates additional information based on the additional data. That is, a comprehensive evaluation result is generated from the evaluation result and additional information generated based on one pulse wave. Therefore, by using a plurality of types of information based on the same parameter, it is possible to realize a comprehensive evaluation from various viewpoints. This makes it possible to generate information on the user's athletic ability 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 above-mentioned additional information is acquired in the generation step S120. 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. 11, the generation step S120 includes acquiring additional information and generating an evaluation result based on the evaluation data and the additional information.

The additional information is the same as the above-mentioned content, 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 evaluation data, for example, according to the content of the additional information. In this case, a function or the like that differs depending on the type of additional information is stored in the storage unit 104 or the evaluation database. The generation unit 12 may generate an evaluation result based on, for example, information that is a combination of evaluation data and additional information.

According to the present embodiment, in addition to the effects of the above-described embodiment, the generation unit 12 includes acquiring additional information and generating an evaluation result based on the evaluation data and the additional information. Therefore, in addition to the evaluation data, it is possible to generate a multifaceted evaluation result in consideration of the characteristics of the user. This makes it possible to generate information on the user's athletic ability with higher accuracy.

(Fifth Embodiment: Biological 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 first embodiment is that the classification information suitable for the evaluation data is selected from the plurality of 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, for example, as shown in FIG. 12, the generation step S120 includes a selection step S121 and an attribute-specific generation step S122.

The selection step S121 refers to the preliminary evaluation data and selects specific 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 characteristics different from the evaluation data, for example, the same characteristics as the above-mentioned additional data. The preliminary evaluation data may be used for generating additional information, for example, similar to the additional data described above.

In the attribute-based generation step S122, the biological information calculation device 1 refers to the selected first classification information, calculates the blood lactate value for the evaluation data, and generates the 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, input data is prepared for each of the seven types (A to G) as shown in FIG. 13, 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 an evaluation result for the 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. 14, for example. Two types of attribute classification information may be generated.

Here, while the data corresponding to the acceleration pulse wave shown in FIG. 13 can be easily classified in detail based on the characteristics, when calculating the blood lactate level, there is a concern that the accuracy may decrease due to erroneous detection of a peak or the like. Be done. Further, the data corresponding to the velocity pulse wave shown in FIG. 14 is more difficult to classify in detail based on the characteristics than the data corresponding to the acceleration pulse wave, but the blood lactate level is less likely to be erroneously detected at the peak. Can be calculated with high accuracy.

Based on the above, the plurality of attribute classification information includes, for example, data corresponding to an acceleration pulse wave as shown in FIG. 13 as selection data used for selecting specific classification information, and when generating attribute classification information. Data corresponding to the velocity pulse wave may be used as the training data of.

In this case, as the acquisition step S110, for example, the acquisition unit 11 acquires data corresponding to the velocity pulse wave as 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 from the sensor data as preliminary evaluation 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) (1 selection data) is specified, and the first classification information associated with the first selection data is selected. Then, as the attribute-based generation step S122, the generation unit 12 refers to the first classification information and generates an evaluation result for the evaluation data. This makes it possible to further improve the evaluation accuracy.

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

For example, as shown in FIG. 13, there are inflection points a to e in the acceleration pulse wave. 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 the 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 further classified as A, and if not, it is classified as B. Next, when the value of the inflection point is b≈d, it is classified into the pattern C or D. If b≈d and c≈0, it is classified into pattern D, and if not, it is classified into pattern C. Finally, if b> d, it can be classified into any of patterns E, F, and G. If b> d and b <c, it is classified into pattern E, if b≈c, it is classified into pattern F, and if b> c, it is classified into pattern G.

For example, the generation unit 12 determines which pattern in FIG. 13 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 used as the first selection data. This makes it possible to accurately calculate the blood lactate level by referring to the classification information suitable for the characteristics of the evaluation data.

According to the present embodiment, in addition to the effect of the above-described embodiment, the generation unit 12 refers to the preliminary evaluation data and refers to the selection unit for selecting the first classification information and the first classification information with respect to the evaluation data. Includes an attribute-based generator that generates evaluation results. Therefore, it is possible to generate the evaluation result for the evaluation data after selecting the optimum attribute 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 evaluation data. Further, 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 is easier to classify the characteristics of the pulse wave than the velocity pulse wave. Further, the evaluation result can be generated by using the velocity pulse wave which is easier to calculate the blood lactate level than the acceleration pulse wave. This makes it possible to further improve the evaluation accuracy.

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: Storage 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: Comprehensive evaluation step

Claims (9)

It is a biometric information calculation system that generates the blood lactate level of the user.
An acquisition means for acquiring evaluation data corresponding to any one of a volumetric pulse wave, a velocity pulse wave, and an acceleration pulse wave based on the pulse wave of the user, and an acquisition means.
A pair of input data of the same type as the evaluation data based on the learning pulse wave acquired in advance and reference data including the blood lactic acid value associated with the input data is used as learning data, and a plurality of the learning data are used. A database that stores the classification information generated by
With reference to the database, a generation means for generating an evaluation result including the blood lactate level for the evaluation data, and
A biometric information calculation system characterized by being equipped with. 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. The first aspect of claim 1, wherein an additional evaluation means for acquiring additional information indicating the characteristics of the user and generating a comprehensive evaluation result for evaluating the motor ability of the user based on the evaluation result and the additional information is provided. Biometric information calculation system. The acquisition means includes acquiring additional data showing characteristics different from the evaluation data based on the pulse wave.
The biometric information calculation system according to claim 3, wherein the generation means includes generating the additional information based on the additional data. The biometric information calculation system according to claim 3 or 4, wherein the additional information indicates at least one of a stress level, a pulse rate, a respiratory rate, a blood glucose level, a blood pressure, a characteristic of blood carbon dioxide, and an amount of exercise. .. The acquisition means includes acquiring additional data showing characteristics different from the evaluation data based on the pulse wave.
The biometric information calculation system according to claim 1, wherein the generation means includes referring to the database and generating additional information indicating the characteristics of the user with respect to the additional data. The generation means is
Acquire additional information indicating the characteristics of the user,
The biometric information calculation system according to claim 1, further comprising generating the evaluation result based on the plurality of the evaluation data and the additional information. The acquisition means includes acquiring preliminary evaluation data different from the evaluation data based on the pulse wave.
The classification information includes a plurality of attribute-specific classification information calculated using the different learning data.
The generation means is
With reference to the preliminary evaluation data, a selection means for selecting the first classification information from the plurality of attribute-specific classification information, and
The biometric information calculation system according to claim 1, further comprising an attribute-based generation means for generating the evaluation result for the evaluation data with reference to the first classification information. The acquisition means
Data corresponding to the velocity pulse wave based on the pulse wave is acquired as the evaluation data, and the data is obtained.
The biometric information calculation system according to claim 8, further comprising acquiring data corresponding to the acceleration pulse wave based on the pulse wave as the preliminary evaluation data.

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