KR20170098025A - System and method for analyzing bio-signal based on big data - Google Patents

System and method for analyzing bio-signal based on big data Download PDF

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KR20170098025A
KR20170098025A KR1020160019883A KR20160019883A KR20170098025A KR 20170098025 A KR20170098025 A KR 20170098025A KR 1020160019883 A KR1020160019883 A KR 1020160019883A KR 20160019883 A KR20160019883 A KR 20160019883A KR 20170098025 A KR20170098025 A KR 20170098025A
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signal
bio
data
feature value
raw data
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KR101827087B1 (en
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김희철
주문일
고동희
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인제대학교 산학협력단
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    • G06F19/32—
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/0402—
    • A61B5/0452—
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • G06F17/30194—
    • G06F17/30318—
    • G06F17/30339—
    • G06F17/30592—

Abstract

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a system and a method for analyzing a bio-signal based on a big data, and more particularly to a system and method for analyzing a bio-signal based on a big data, Receiving unit; And partitioning the characteristic values of the bio-signal extracted from the raw data of the received bio-signal into separate directories for each partition of the data warehouse Hadoop distributed file system (HDFS); And searching and extracting the corresponding biometric signal feature value using a query language corresponding to the feature value condition requested by the transceiver unit and providing the extracted biometric signal feature value as a result of the biometric signal analysis through the web service of the transceiver unit And a service providing unit.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a bio-

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a bio-signal analysis system based on a big data, and more particularly to a bio-signal analysis system based on a big data, and more particularly, to raw data of a vast amount of bio-signals (e.g., electrocardiogram (ECG) And a big data-based bio-signal analysis system and method for analyzing and servicing big data of the bio-signal.

The wearable technology convergence industry has emerged as the core of strengthening competitiveness of IT and other industries as a fusion of human - technology combined human - centered emotion. As a result, the apparel sector is also attempting many new technologies into the realm of u-healthcare. In recent years, the demand for welfare has expanded, medical expenses have increased rapidly due to improvement of economic and living standards, population aging, and chronic degenerative diseases. As a result, the importance of u-healthcare centered on 'disease-centered treatment' and 'preventive and health-care centering' have increased, and u-healthcare has become more interested in managing individual health status anytime and anywhere.

According to this paradigm change, u-healthcare system should store data by using bio-signals and combine the stored data with technologies such as analysis and processing.

In recent years, wearable devices have emerged as a new growth engine as the growth of the smartphone market stagnated and the PC industry showed a stagnation. Wearable devices have evolved into integrated devices in the form of woven / apparel that can be directly attached to the skin, such as patches, in the form of accessories such as glasses, watches, bracelets and the like. In addition, wearable devices are being developed into various devices such as a living body implantable type in which they are directly implanted or taken into a human body. Therefore, as researches on wearable devices become more active, various healthcare products related to health are being developed.

However, in the case of a wearable device using the currently developed bio-signal, the bio-signal of the user obtained through the sensor attached to the device is measured to provide only simple services. Research on services that can prevent user 's health by collecting biometric data measured in wearable devices has not been done properly.

In some healthcare services, services are being used to detect abnormal signals in devices attached to the body of the elderly and suggest necessary measures. However, they are all services that can be backed up by data analysis technology that can quickly read human thoughts, movements, and vital signs.

Korea now has a very high potential for utilizing big data in the field of biohealth, such as world-class medical personnel, IT infrastructure and health insurance system for the general public. It is expected that the potential will be even greater if the data and social network service (SNS) data generated from wearable devices are integrated and utilized.

However, the bio-signals measured through the device are merely measured. Research on bio-signal analysis platform based on collecting, storing, analyzing and processing measured biological data and big data is not actively conducted.

Here, the biological signal refers to all information that can be measured through the device with respect to a signal generated in the human body. However, it is possible not only to measure the bio signal but also to check and manage the user's health condition through the measurement data collection. For example, an electrocardiogram (ECG) signal can be obtained from a heart of a person in a living body signal, and an acceleration (ACC) signal can be obtained from motion.

The collected biometric data can be classified into two types of data. First, it is a discontinuous data form with simple characteristic values such as body temperature, blood pressure, pulse, and oxygen saturation (SpO2). Second, it is continuous data such as electrocardiogram (ECG), acceleration (ACC), respiration and the like.

However, continuously input biological signals such as electrocardiogram and respiration are raw data which are not processed as atypical data, and their volume is large, and they should be classified into big data and processed. Consecutive data is huge and is not suitable for storage in a database. Non-sequential data also has simple feature values, but when measured with a short period of time, it also has a large amount of data and is not suitable for storing in a database.

Therefore, huge amounts of data that are not suitable for storing in the database should be classified and processed as big data. The biological signal itself is unprocessed irregular data. The characteristic value should be extracted through the analysis algorithm with the raw data of the biological signal (Law Data) and processed into analytical data.

On the other hand, if you look at Big Data, you can see big names like Google, Amazon and Wal-mart. However, as the amount of data to collect, store, process, and analyze from the moment became enormous, traditional systems could not handle huge amounts of data properly. The need for a new level of data processing and approach was further compromised by the inability to maintain services that could satisfy customers. They referred to this method as Big Data.

Big data refers to large data that is large in size, short in generation period, and includes not only numerical data but also text and image data, compared with data generated in the past analog environment. However, just because the capacity is large is not called big data. This means when the three-dimensional characteristics of the volume of data, the velocity of production of data, and the varieties of form are all considered. In recent years, it has become common to define values or complexities, and as the five elements are satisfied, they are suitable for big data.

Big Tables, Cassandra, Data Warehousing and Analysis Appliances, Distributed Systems, Google File System, Hadoop, Hbase, and Data Warehouse technologies, including Big Data's large volumes of data, collection and retrieval of unstructured data, data preprocessing and analysis, MapReduce, and non-relational databases.

Korean Patent Laid-Open Publication No. 10-2015-0144562 (published Dec. 28, 2015)

Embodiments of the present invention define raw data of a vast amount of a living body signal (e.g., an electrocardiogram (ECG) signal, an acceleration signal, etc.) having a linear structure as big data, analyze the big data of the living body signal, To provide a big data-based bio-signal analysis system and method.

In the embodiments of the present invention, the characteristic values of the bio-signal extracted from the raw data of the bio-signal received through the web service are partitioned and stored in a partition for each partition of the data warehouse, The bio-signal characteristic value is extracted through the query language corresponding to the characteristic value condition and is provided as a result of the bio-signal analysis through the web service, so that only the file of the partitioned area can be retrieved, The present invention provides a system and method for analyzing bio-signals based on a large data.

According to a first aspect of the present invention, there is provided a bio-signal measuring apparatus for a user-wearing type, comprising: a transceiver for receiving raw data of bio-signals measured based on a big data through a web service; And partitioning the characteristic values of the bio-signal extracted from the raw data of the received bio-signal into separate directories for each partition of the data warehouse Hadoop distributed file system (HDFS); And searching and extracting the corresponding biometric signal feature value using a query language corresponding to the feature value condition requested by the transceiver unit and providing the extracted biometric signal feature value as a result of the biometric signal analysis through the web service of the transceiver unit A bio-signal analysis system based on a big data may be provided.

The transceiver can receive raw data of any one of an electrocardiogram signal, an acceleration signal, a breathing signal, and a blood coral saturation signal, which are atypical data based on big data.

The Hadoop distributed file system includes: a raw data storage unit for storing raw data of the received bio-signal to generate a repository for each bio-signal by analyzing raw data of the received bio-signal; A feature value extracting unit for extracting a feature value of a bio-signal by filtering raw data of the stored bio-signal; And a data warehouse having a data table divided into directories and partitioned, and partitioning the extracted feature values into separate directories for each partition of the data table.

Wherein the feature value extracting unit detects an R peak value among a P wave, a QRS wave, and a T wave when the raw data of the bio-signal is raw data of the electrocardiogram signal, (HRV), low frequency and high frequency band intensity (LF / HF) and standard deviation (SDNN: SDNN) of the predetermined normal RR interval during the recording time The feature value of at least one of the bio-signals can be extracted.

When the raw data of the bio-signal is raw data of the acceleration signal, the feature value extracting unit may extract a feature value of at least one of the exercise time, the calorie consumption, the movement distance, the number of steps and the behavior pattern.

Wherein the data warehouse stores a feature value of a divided bio-signal corresponding to a feature value condition of at least one of a user ID, a measurement year, a measurement month, a measurement date and a measurement time as a partition of the data warehouse corresponding to the feature value condition It can be stored separately in a separate directory.

The service provider can search for and extract the corresponding bio-signal feature value using the query language of the SQL-On-Hadoop corresponding to the requested feature value condition.

If the data warehouse is a Hive, the service provider may search for and extract a corresponding biometric signal feature value using a query language of HiveQL provided in the hive.

According to a second aspect of the present invention, there is provided a bio-signal measuring apparatus comprising: a raw data receiving step of receiving, through a web service, raw data of a bio-signal measured based on a big data in a user- A bio-signal storage step of partitioning the feature value of the bio-signal extracted from the raw data of the received bio-signal into a partition for each partition of the data warehouse and storing the partition; And a service providing step of searching for and extracting the corresponding biometric signal feature value using a query language corresponding to the requested characteristic value condition and providing the extracted biometric signal characteristic value as a result of a biometric signal analysis through a web service A big data-based bio-signal analysis method can be provided.

The raw data receiving step may receive raw data of at least one of an ECG signal, an acceleration signal, a breathing signal, and a blood coral saturation signal, which are atypical data based on a big data.

The bio-signal storing step may include storing raw data of the received bio-signal in a repository for each bio-signal by analyzing raw data of the received bio-signal to generate a repository for each bio-signal, Extracting a characteristic value of a biological signal by filtering raw data of the stored biological signal; And dividing the extracted feature value into a directory for each partition of the data table, and storing the separated feature value.

Wherein the extracting of the feature value of the bio-signal includes detecting an R peak value among the P wave, the QRS wave, and the T wave when the raw data of the bio-signal is the raw data of the electrocardiogram signal, Extracts feature values of at least one of the bio signal from the HRV, the low frequency and high frequency band intensity (LF / HF) and the standard deviation (SDNN) of the predetermined normal RR interval during a predetermined recording time can do.

Wherein the step of extracting the feature value of the bio-signal includes a step of, when the raw data of the bio-signal is the raw data of the acceleration signal, calculating a feature value of at least one of the exercise time, the calorie consumption, the movement distance, Can be extracted.

Wherein the step of separately storing the biometric signal in the partitioned directory includes a step of storing the feature value of the divided bio-signal corresponding to the at least one feature value condition among the user ID, the measurement year, the measurement month, the measurement date, Can be separately stored in a directory for each partition of the data warehouse.

The service providing step may retrieve and extract the corresponding bio-signal feature value using a query language of the SQL-On-Hadoop corresponding to the requested feature value condition.

In the case where the data warehouse is a Hive, the service providing step may search for and extract a corresponding bio-signal feature value using a query language of HiveQL provided in the hive.

Embodiments of the present invention define raw data of a vast amount of a living body signal (e.g., an electrocardiogram (ECG) signal, an acceleration signal, etc.) having a linear structure as big data, analyze the big data of the living body signal, .

In the embodiments of the present invention, the characteristic values of the bio-signal extracted from the raw data of the bio-signal received through the web service are partitioned and stored in a partition for each partition of the data warehouse, The bio-signal characteristic value is extracted through the query language corresponding to the characteristic value condition and is provided as a result of the bio-signal analysis through the web service, so that only the file of the partitioned area can be retrieved, have.

1 is a block diagram of a big data-based bio-signal analysis system according to an embodiment of the present invention.
2 is an explanatory diagram of various bio-signals generated from a human body applied to the embodiment of the present invention.
3 is an explanatory view of a smart garment applied to a bio-signal measuring device according to an embodiment of the present invention.
4 is an explanatory diagram of a smart clothing system applied to the embodiment of the present invention.
5 is an exemplary view of a continuously incoming electrocardiogram signal according to an embodiment of the present invention.
6 is an explanatory diagram of a web service applied to the embodiment of the present invention.
7 is an exemplary view of a SOAP message of a web service applied to an embodiment of the present invention.
8 is a configuration diagram of a hive system in the Hadoop distributed file system according to the embodiment of the present invention.
9 is a diagram illustrating an example of a file name and a storage format of raw data of a bio-signal according to an embodiment of the present invention.
10 is an exemplary view of an R peak value detected from an electrocardiogram signal according to an embodiment of the present invention.
11 is an explanatory diagram of characteristic values extractable from electrocardiogram data according to an embodiment of the present invention.
12 is an explanatory diagram of a process of creating a partitioned management table according to an embodiment of the present invention.
13 is an explanatory diagram of a process of creating a partitioned management table according to an embodiment of the present invention.
FIG. 14 is an explanatory diagram of a query processing process for extracting a feature value necessary for providing an analysis service according to the embodiment of the present invention.
15 is an exemplary view of information stored in a SOAP message according to an embodiment of the present invention;
16 is an exemplary view of a source code for storing an electrocardiogram signal according to an embodiment of the present invention.
17 is an explanatory diagram of a feedback process of feature values of an input bio-signal according to an embodiment of the present invention.
18 is an explanatory diagram of a main signal of an electrocardiogram signal according to an embodiment of the present invention.
19 and 20 are diagrams illustrating an example of a path where the raw data of the electrocardiogram signal is stored in the HDFS and raw data of the electrocardiogram signal in the bio-signal analysis system according to the embodiment of the present invention.
21 is an exemplary diagram of a feature value extraction source code of an electrocardiogram signal according to an embodiment of the present invention.
22 is an exemplary diagram of method source code for extracting feature values in an HRV class according to an embodiment of the present invention.
23 is an exemplary view of a feature value storage source code extracted from an electrocardiogram signal according to an embodiment of the present invention.
24 is an exemplary diagram of source code for loading a file storing a feature value according to an embodiment of the present invention into a hive.
25 is an exemplary view of a file loaded into a hive according to an embodiment of the present disclosure;
FIG. 26 is an exemplary diagram of a feature value stored in a hive according to an embodiment of the present invention. FIG.
Figure 27 is an illustration of an SOAP message requested for an autonomic nervous system service according to an embodiment of the present disclosure;
28 is an exemplary diagram of HiveQL used for extracting feature values of an autonomic nervous system service according to an embodiment of the present invention.
29 is an illustration of HiveQL for a momentum management service according to an embodiment of the present disclosure.
30 is an exemplary diagram of HiveQuels for a user behavior pattern analysis service according to an embodiment of the present invention.
31 is an explanatory diagram of the number of numbers corresponding to each action element of the user according to the embodiment of the present invention.
32 is an exemplary view of a soap message in which an autonomic nervous system management result according to an embodiment of the present invention is received.
33 is an exemplary view of a soap message in which a momentum management result value according to an embodiment of the present invention is received.
34 is an explanatory diagram of an autonomic nervous system management service and a detailed view screen according to the embodiment of the present invention.
35 is an explanatory diagram of a momentum management service and a detailed view screen according to an embodiment of the present invention.
36 is an exemplary diagram of a SOAP message received as a result of a user behavior pattern according to an embodiment of the present disclosure;
37 is an explanatory diagram of a user behavior pattern analysis service and a detailed view screen according to an embodiment of the present invention.
38 is a flowchart illustrating a method of analyzing a big data based bio signal according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. Will be described in detail with reference to the portions necessary for understanding the operation and operation according to the present specification. In describing the embodiments of the present invention, description of technical contents which are well known in the technical field to which the present invention belongs and which are not directly related to the present specification will be omitted. This is for the sake of clarity without omitting the unnecessary explanation and without giving the gist of the present invention.

In describing the components of the present specification, the same reference numerals may be given to components having the same name, and the same reference numerals may be given to different drawings. However, even in such a case, it does not mean that the corresponding component has different functions according to the embodiment, or does not mean that it has the same function in different embodiments, and the function of each component is different from that of the corresponding embodiment Based on the description of each component in FIG.

1 is a block diagram of a big data-based bio-signal analysis system according to an embodiment of the present invention.

1, a big data-based bio-signal analysis system 100 according to an embodiment of the present invention includes a transceiver 110, a Hadoop distributed file system (HDFS) 120, (130). Here, the bio-signal measuring device may be included in the user terminal 101. Alternatively, the bio-signal measuring device may be worn by the user separately from the measuring device of the user-wearing type, not inside the user terminal 101.

Hereinafter, the concrete configuration and operation of each component of the big data-based bio-signal analysis system 100 of FIG. 1 according to the embodiment of the present invention will be described.

The transceiving unit 110 receives raw data of bio-signals measured on the basis of the big data from the user terminal 101 or the user-wearing type bio-signal measuring instrument through the web service. That is, the bio-signal measuring instrument transmits the measured bio-signal raw data to the transmitting and receiving unit 110 through the network. The transmitted bio-signal raw data has a linear structure. For example, if the sampling frequency is 200 Hz, 200 data are obtained per second. That is, when the living body signal is an electrocardiogram signal, 60,000 (200 hz x 60 sec x 5 min) data is accumulated when the electrocardiogram data is measured for about 5 minutes. The ECG signal produced by one person for 24 hours is 17,280,000 (200hz × 60sec × 60min × 24hour). Its size is about 100MB. Therefore, a huge amount of electrocardiogram (ECG) data accumulated during long-term data collection should be classified and processed as big data. For example, the transceiver 110 can receive raw data of a bio-signal, which is atypical data based on big data, such as an electrocardiogram signal, an acceleration signal, a respiration signal, and a blood coral saturation signal.

The Hadoop distributed file system 120 includes a data warehouse 123 having a data table divided into partitions-specific directories. The transceiving unit 110 partitions the characteristic values of the bio-signal extracted from the raw data of the bio-signal received by the transceiving unit 110 into separate directories of the data warehouse 123 and stores the separated values.

The service providing unit 130 searches and extracts a corresponding bio-signal feature value using a query language corresponding to the feature value condition requested through the transceiver unit 110. [ Then, the service providing unit 130 provides the extracted biometric signal characteristic value as a result of the biometric signal analysis through the web service of the transceiver unit 110.

The transmission and reception unit 110 includes a raw data storage unit 121, a feature value extraction unit 122, and a data warehouse 123.

First, the Hadoop distributed file system 120 classifies and stores raw data of a bio-signal according to a bio-signal (for example, electrocardiogram, respiration, acceleration, SpO2, etc.). Here, the raw data storage unit 121 analyzes the raw data of the bio-signal received by the transmitter-receiver unit 110 to generate a repository for each bio-signal and stores the raw data of the received bio- / RTI > The raw data storage unit 121 may generate a repository according to the type of the biosignal in the repository of the HDFS, and may store raw data of the biosignal in the secondary classified according to the user.

The feature value extracting unit 122 extracts the feature value of the bio-signal by filtering the raw data of the bio-signal stored in the raw data storage unit 121.

The data warehouse 123 has a data table divided into directories according to partitions. Then, the data warehouse 123 divides the feature value extracted by the feature value extracting unit 122 into a partition for each partition of the data table and stores it.

For example, the feature value extracting unit 122 detects the R peak value among the P wave, the QRS wave, and the T wave when the raw data of the biological signal is the raw data of the electrocardiogram signal, and the detected RR interval (RR interval) A heart rate variability (HRV), a low frequency and a high frequency band intensity (LF / HF), and a standard deviation of a predetermined normal RR interval (SDNN) for a predetermined recording time The feature value of one bio-signal can be extracted.

As another example, when the raw data of the biological signal is raw data of the acceleration signal, the feature value extracting unit 122 extracts feature values of at least one of the exercise time, the calorie consumption, the moving distance, the number of steps, and the behavior pattern can do.

On the other hand, the data warehouse 123 stores the feature values of the divided bio-signals corresponding to the feature value condition of at least one of the user ID, the measurement year, the measurement month, the measurement date and the measurement time as a data warehouse Can be separately stored in a partition-by-partition directory of the partition 123.

Then, the service provider 130 searches and extracts the corresponding bio-signal feature value using the query language of the SQL-On-Hadoop corresponding to the requested feature value condition. That is, the service providing unit 130 analyzes the feature value of the bio-signal stored in the data warehouse 123 and provides a service based on the bio-signal. For example, when the user terminal 101 requests a desired biometric signal feature value from the transceiver 110 through the network, the service provider 130 searches for and extracts feature values stored in the data warehouse 123.

For example, when the data warehouse 123 is a Hive, the service providing unit 130 may search for and extract the corresponding biometric signal feature value using the query language of HiveQL provided in the hive have.

2 is an explanatory diagram of various bio-signals generated from a human body applied to the embodiment of the present invention.

The types of the biological signals generated from the human body are shown in Fig. Biomedical signals are signals generated by the human body, and bio-signal measuring instruments can measure various kinds of bio-signals through devices capable of measuring bio-signals. They include electrocardiogram, breathing, body temperature, weight, blood pressure, pulse and body fat. In the case of an electrocardiogram or a pulse of the type of a biological signal, two or more electrodes are attached to the skin of a person and measured through a potential difference flowing therebetween. The sensor can be attached to the body and can be measured in various ways such as lifetime measurement and exercise measurement. The number of samples per second is called the sampling frequency. The higher the sampling frequency is, the more accurate the measurement is possible.

In addition, biometric data collected through these measurements can be systematically managed by database for disease prediction and health management

3 is an explanatory view of a smart garment applied to a bio-signal measuring device according to an embodiment of the present invention.

With the fusion of textile technology and IT technology in accordance with the paradigm of new digital society, smart apparel can measure and transmit biometric information as bio signal measurement, processing technology and information transmission technology can be made anytime and anywhere. A smart garment which is very easy to measure and transmit such a biological signal is a wearable wearable garment capable of collecting various bio-signals (for example, electrocardiogram, respiration, body temperature, blood pressure and exercise amount)

As shown in FIG. 3, the smart garment includes a signal transmission network for connecting the sensor and the communication module. A digital seal is used to connect the sensor and the module. For example, a digital yarn can be made of a metal fiber material of good rolling resistance and a fiber diameter of 100 mu m or less. As a result, the digital room can be a data transmission room suitable for smart clothes by eliminating the rigidity which is a characteristic inherent in the metal, and improving flexibility and flexing resistance.

4 is an explanatory diagram of a smart clothing system applied to the embodiment of the present invention.

As shown in FIG. 4, the smart clothing system measures biomedical signals using a biosensor in smart clothing. The smart apparel system then transmits the measured bio-signals to the device via the communication module. Then, the device is a system for transmitting a service to a server to provide a service.

5 is an exemplary view of a continuously incoming electrocardiogram signal according to an embodiment of the present invention.

Among all the information that can be measured through the bio-signal measurement device with respect to the signal generated from the human body, there is an electrocardiogram (ECG) signal as bio-signals that can be obtained from the heart. In addition, there is an acceleration (ACC) signal as a bio-signal that can be obtained from human motion.

Here, the raw data of the bio-signal collected through the bio-signal measuring device can be largely classified into discontinuous data and continuous data. Non-continuous data refers to data having simple characteristic values such as body temperature, blood pressure, pulse, and oxygen saturation. Continuous data refers to data that continuously flows, such as electrocardiogram, breathing, and acceleration.

As shown in FIG. 5, the raw data of the bio-signal having the linearly-introduced linear structure has a form and an amount of data that are not suitable for storing in the database. In the case of non-continuous data, the shorter the measurement period, the greater the amount of data, which is not suitable for database storage.

6 is an explanatory diagram of a web service applied to the embodiment of the present invention.

Web services are one or more functional components that can be accessed by using Internet standard protocols such as XML or HTTP.

As shown in FIG. 6, the user terminal 101 communicates with the web server 610 using SOAP, which is a basic message transmission means of the web service. This SOAP message has an envelope of the root element and consists of a SOAP header and a sub-element of the SOAP body. The body of the SOAP describes the message content to be transmitted as a mandatory message. The reason for using SOAP is based on industry standard XML, and it is a protocol that can be used by any application.

7 is an exemplary view of a SOAP message of a web service applied to an embodiment of the present invention.

As shown in FIG. 7, the wearable bio-signal measurement instrument transmits the measured electrocardiogram signal to the bio-signal analysis system 100 using the SOAP message of the web service. The parameters of the SOAP message include the measured ID, the name of the transmitted file, and the electrocardiogram signal data.

8 is a configuration diagram of a hive system in the Hadoop distributed file system according to the embodiment of the present invention.

Hadoop, a Java-based open source framework, is used for low-cost and cost-effective data processing for storing and distributing large amounts of biomedical data. Because Hadoop stores data copies, it has the advantage of being able to recover from data loss or failure. Hadoop's data processing method is a distributed computing system that stores data on multiple servers and simultaneously processes the data on each server where the data is stored. Map-Reduce is a sub-project of Hadoop that is divided into a Map stage for simple data division and a Reduce stage for collecting processed data. It is a computational programming model that parallelizes in a configured cluster.

Hive is a data warehouse infrastructure operating on Hadoop that provides data summarization, query and analysis capabilities. Hive has an important function that can easily perform data retrieval and analysis tasks that need to be done through Map-Reduce creation in general Hadoop ecosystem by using HiveQL similar to SQL . This allows the hive to query large data, allowing for easy access and analysis of Hadoop's data. HiveQL supports SQL type queries such as join, table creation, query and insert, so that users can easily query data.

The data in the hive is represented in the same table format as the relational database system. Table schema is stored and managed in Metastore as Metadata.

As shown in FIG. 8, the hive is a representative data warehouse using SQL-On-Hadoop. Hive can query and insert data using a SQL-like query language called HiveQL. The hive has components such as Java Database Connectivity (JDBC), MetaStore, and common language infrastructure (CLI). JDBC is a query compiler and execution engine that converts SQL query statements into MapReduce. MetaStore stores meta information table, partition, and column information for managing data on HDFS in DB table format. The CLI represents a console window for easy access to the hive.

For example, Apache Hive is a technology that can be analyzed in a query language called HiveQL. It is composed of map-ridus-based execution part, metadata information about data store, and query part from user or application program.

9 is a diagram illustrating an example of a file name and a storage format of raw data of a bio-signal according to an embodiment of the present invention.

As shown in Fig. 9, a wearable wearable device, a living body signal measuring device (e.g., smart clothes, etc.) measures an electrocardiogram signal. The electrocardiogram signal measured through the sensor attached to the bio-signal measuring instrument is stored in the Hadoop Distributed File System (HDFS) of Hadoop in the bio-signal analysis system 100 through the web service. For example, the file name of the raw data is the date of measurement, the start time of measurement, the measured time, the measurement frequency,

Figure pat00001
'.

10 is an exemplary view of an R peak value detected from an electrocardiogram signal according to an embodiment of the present invention.

As shown in FIG. 10, the transceiving unit 110 can receive 200 pieces of ECG signal raw data from the smart clothing used for ECG bio-signal measurement in one second. Accordingly, the file stored in the transmission / reception unit 110 includes the raw data value of the electrocardiogram signal measured for a predetermined time.

Here, the raw data of the bio signal having a linear structure is not meaningful only by the raw data itself. The electrocardiogram signal is a raw data having a linear structure. An electrocardiogram signal having a linear structure has an R-R interval as shown in FIG. The R-R interval is one of the important factors for analyzing the electrocardiogram signal, which is necessary for analyzing the electrocardiogram signal.

Thus, the transceiver unit 110 processes the bio-signal raw data and extracts a feature value. For example, the transceiving unit 110 detects R peak values in the P wave, QRS wave, and T wave that are the main signals from the raw data of the measured electrocardiogram signal, and calculates the HR value using the RR interval I ask. In addition, the transceiver unit 110 can obtain a LF (Low frequency) / HF (High frequency) feature value using an R-R interval and can calculate an SDNN using a standard deviation of an R-R interval.

The transmission / reception unit 110 may store the feature values obtained through filtering in a subdirectory of the data warehouse directory of the hive.

11 is an explanatory diagram of characteristic values extractable from electrocardiogram data according to an embodiment of the present invention.

As shown in FIG. 11, the transceiver 110 can extract feature values such as SDNN, LF / HF, and HRV using electrocardiogram data. Therefore, the characteristic values that can be extracted are determined, and therefore, definite definition is possible. The raw data of the bio-signal can be processed in a batch form since the service analysis is performed through the collected raw data.

Hives are therefore more efficient than dealing with non-relational databases (NoSQL) -based map-reduction. A non-relational database (NoSQL) -based map-reshaping parses and processes a myriad of unspecified data, and consists of a pair of text-type keys and values, do. On the other hand, it is more efficient for a hive to use raw data where a schema exists and can be defined clearly. In addition, Hive has the advantage of easily analyzing and processing big data using HiveQL similar to SQL.

12 is an explanatory diagram of a process of creating a partitioned management table according to an embodiment of the present invention.

The data warehouse 123 has a partitioned data table. A table structure designed to store feature values stored in the data warehouse 123 within a measurement date which is a lower directory of a measurement year through partitioning is shown in FIG.

A typical file-based table needs to retrieve each record to see the results. On the other hand, if the feature values extracted in the partitioned data table in the transceiver 110 according to the embodiment of the present invention are stored in separate partitions, only the files in the partitioned area can be retrieved, thereby improving the processing speed.

13 is an explanatory diagram of a process of creating a partitioned management table according to an embodiment of the present invention.

As shown in Fig. 13, the data file extracted from the raw data of the electrocardiogram signal by filtering is stored in accordance with the table format. Then, the transceiver unit 110 can upload the data to the hive using the HiveQL.

FIG. 14 is an explanatory diagram of a query processing process for extracting a feature value necessary for providing an analysis service according to the embodiment of the present invention.

The service providing unit 130 may extract feature values necessary for providing a service of analyzing a bio-signal among the data uploaded to the hive. A query processing procedure for this is shown in Fig.

Hereinafter, an embodiment in which a bio-signal analysis system 100 according to an embodiment of the present invention extracts a feature value from raw data of a received electrocardiogram signal, stores the extracted feature value, and provides an analysis service of the electrocardiogram signal I will explain it as an example.

15 is an exemplary view of information stored in a SOAP message according to an embodiment of the present invention;

As shown in FIG. 15, a living body signal measuring device, which is a wearable wearable device, measures an electrocardiogram signal. The bio-signal measuring device is connected to a user terminal (for example, a smart phone, etc.) 101 via Bluetooth, and the measured electrocardiogram signal is stored in the user terminal 101. For example, the user terminal 101 generates a SOAP message using the 'ksoap2_android' library, and transmits the generated SOAP message to the bio-signal analysis system 100 through the web service.

The transceiver 110 according to an embodiment of the present invention receives raw data of a bio-signal using the POST method among the GET method or the POST method, which is a method of transmitting / receiving data by HTTP. The POST method is a method of transmitting a message in a body of HTTP. Since the POST method can transmit a larger amount of data than the GET method, the bio-signal measurement instrument transmits a SOAP message in a body of HTTP and transmits the SOAP message to the body of the HTTP, and the transceiver unit 110 receives the SOAP message.

16 is an exemplary view of a source code for storing an electrocardiogram signal according to an embodiment of the present invention.

As shown in FIG. 16, 'AttachementECG_Hadoop' has attachment information. The bio-signal analysis system 100 stores the raw data of the electrocardiogram signal in the HDFS, which is a data store of Hadoop, through 'attachedECG_Hadoop'.

17 is an explanatory diagram of a feedback process of feature values of an input bio-signal according to an embodiment of the present invention.

As shown in FIG. 17, the feature value extracting unit 122 uses the user ID, file name, and binary data received from the SOAP message as parameters for analyzing the feature extraction algorithm. The feature value extracting unit 122 extracts HRV, SDNN, LF / HF, and the like using the measurement information and the electrocardiogram signal value. In the case of HRV extractable from ECG signals, bradycardia with a mean heart rate of 20 or less during recording time means bradycardia, 60 to more than 80 means normal, and 90 or more means tachycardia. In the case of SDNN, the state of the feature value can be confirmed according to the steps of 50 or more, 30-50, 20-30, 20 or less. In the case of LF / HF, it is divided into 3 or more and less than 3, and the state prediction about the feature value is possible.

18 is an explanatory diagram of a main signal of an electrocardiogram signal according to an embodiment of the present invention.

As shown in FIG. 18, the feature value extracting unit 122 extracts the R peak value of the P wave, QRS wave, and T wave which are the main signals in the electrocardiogram signal measured in units of 5 minutes and stored as a file in the HDFS . The feature value extractor 122 extracts feature values such as HRV (Heart Rate Variability), SDNN (Standard Deviation of NN Interval), LF (Low Frequency), and HF (High Frequency) using an RR interval .

19 and 20 are diagrams illustrating an example of a path where the raw data of the electrocardiogram signal is stored in the HDFS and raw data of the electrocardiogram signal in the bio-signal analysis system according to the embodiment of the present invention.

As shown in Fig. 19, the raw data of the electrocardiogram signal measured from the wearable wearable device and stored in the HDFS of the bio-signal analysis system 100 is an unprocessed numerical value continuously inputted. The raw data for these ECG signals is stored in the HDFS wellnessDB / user ID / ECGRawData / measurement date path.

The transmission / reception unit 110 confirms the storage path (location) as shown in FIG. 19 through the SOAP message analyzed by the transmission / reception unit 110. The transceiver 110 stores the electrocardiogram signal in the HDFS by matching the user ID to the data storage where the raw data of the electrocardiogram signal is stored.

As shown in Fig. 20, the raw data of the electrocardiogram signal is irregular data which is meaningless in itself. Therefore, the feature value extracting unit 122 extracts feature values that are numerical values that can be analyzed from the raw data

21 is an exemplary diagram of a feature value extraction source code of an electrocardiogram signal according to an embodiment of the present invention.

As shown in Fig. 21, an ECG parser (ECG parser) inheriting a bio-signal parser (VitalSignParser) is created in the feature value extraction source code. The ECG parser (ECGParser) passes the HRV class in the startParsing method as a parameter to the file path and the sampling rate.

22 is an exemplary diagram of method source code for extracting feature values in an HRV class according to an embodiment of the present invention.

As shown in FIG. 22, the feature value extracting unit 122 stores raw data in a form of an array in a raw data variable by a readFile in the HRV class. Then, the feature value extracting unit 122 extracts a formalized data value, that is, a feature value, which can be analyzed through doQRSDetection.

23 is an exemplary view of a feature value storage source code extracted from an electrocardiogram signal according to an embodiment of the present invention.

As shown in FIG. 23, the raw data of the electrocardiogram signal is stored in a file in a form suitable for the schema information stored in the meta store to store the feature values obtained through filtering in the hive.

24 is an exemplary diagram of source code for loading a file storing a feature value according to an embodiment of the present invention into a hive.

As shown in FIG. 24, the transceiver 110 loads the hive into the hive using the HiveQL_Start method to analyze the stored file.

25 is an exemplary view of a file loaded into a hive according to an embodiment of the present disclosure;

As shown in FIG. 25, a partitioned table in a hive enhances performance and stores data in a logical form such as a hierarchical structure.

At this time, if the file (row unit) is read and processed, if all the data are loaded and processed, the map-reducing operation becomes more frequent. Therefore, the hive according to the embodiment of the present invention can improve the processing speed by reducing the data to be read from the file through the partitioning. The user ID, the measurement year, and the measurement month are generated, and the generated feature value data is partitioned and stored in the hive to be loaded.

FIG. 26 is an exemplary diagram of a feature value stored in a hive according to an embodiment of the present invention. FIG.

As shown in FIG. 26, HRV (array), LF / HF (array) by the measurement date, hour, minute, second and delimiter (:) by a comma (SDNN), a sampling-rate (int), and the like are stored in the memory. For example, the type of feature value data may be all of the data types used in JAVA.

Hereinafter, an embodiment of a bio-signal analysis service (for example, an autonomic nervous system service, a momentum management, and a user behavior pattern analysis service) served by the bio-signal analysis system 100 according to an embodiment of the present invention will be described.

The bio-signal analysis system 100 according to the embodiment of the present invention can be applied to an autonomic nervous system service using an electrocardiogram signal measured by a wearable wearable device (for example, smart clothing, etc.) To provide momentum management and analysis of user behavior patterns.

Figure 27 is an illustration of an SOAP message requested for an autonomic nervous system service according to an embodiment of the present disclosure;

The raw data of the bio-signal collected through the measurement is transmitted to the bio-signal analysis system 100 through the SOA-based web service. The transmitted raw data is stored as raw data of the electrocardiogram signal in HDFS, which is a big data store. The feature value extracting unit 122 stores the filtered feature values in the hive that is the data warehouse 123.

As shown in FIG. 27, three parameter information about the user ID, the start date and the end date are expressed in the body element of the Soap message.

28 is an exemplary diagram of HiveQL used for extracting feature values of an autonomic nervous system service according to an embodiment of the present invention.

As shown in FIG. 28, when the user requests the Soap message from the user terminal 101 as a client to the bio-signal analysis system 100, the feature values stored in the hive are analyzed through the HiveQL .

The service provider 130 of the bio-signal analysis system 100 extracts data on feature values between the start date and the end date using 'unix_timestamp'. The autonomic nervous system service extracts feature values for year, month, day, measurement time, LF / HF, SDNN, and average HRV (AVG HRV) from the 'wellness.hrv' table.

29 is an illustration of HiveQL for a momentum management service according to an embodiment of the present disclosure.

As shown in FIG. 29, HiveQL for the momentum management service is shown. The service providing unit 130 searches the data warehouse 123 for characteristic values of year, month, day, measurement time, distance, calories, and number of steps.

30 is an exemplary diagram of HiveQuels for a user behavior pattern analysis service according to an embodiment of the present invention.

As shown in FIG. 30, HiveQL for the user behavior pattern analysis service is shown. For example, the service providing unit 130 extracts feature values for year, month, day, measurement time, non-smart device use, staying still, walking, running, and jumping using HiveQL for a user behavior pattern do.

31 is an explanatory diagram of the number of numbers corresponding to each action element of the user according to the embodiment of the present invention.

As shown in FIG. 31, in the case of the behavior pattern of the user, the number is expressed as 1 to 5 for five behavior factors. The number of numbers corresponding to each behavior pattern element is shown.

32 is an exemplary view of a soap message in which an autonomic nervous system management result according to an embodiment of the present invention is received.

The service providing unit 130 returns the feature values extracted through the HiveQL to the user terminal 101 in the form of a Soap message. The user terminal 101 is shown to the user through parsing of its required values.

As shown in Fig. 32, the service provider 130 returns autonomic nervous system data in the order of [LFHF-SDNN-HRV-measurement date-measurement time (sec)].

As described above, in the embodiment of the present invention, the user terminal 101 receives a feature value through the web service that can confirm the state of the autonomic nervous system in the electrocardiogram signal. The user terminal 101 and the bio-signal analysis system 100 can transmit and receive the SOAP message through the web server 610 that provides the web service. The user terminal 101 generates a SOAP message as shown in FIG. 33 in which the state of the autonomic nervous system can be confirmed, and transmits the generated SOAP message to the web server 610. Then, the bio-signal analysis system 100 receives the SOAP message through the web server 610. Then, the bio-signal analysis system 100 searches the table indicating the state of the autonomic nervous system in the hive, and uses the measured user ID and the select command of HiveQL as the search start date and the end date And retrieves and extracts feature value data.

The bio-signal analysis system 100 generates a SOAP message using the extracted feature value data, and transmits the SOAP message to the user terminal 101, which is a client that has requested the response message.

33 is an exemplary view of a soap message in which a momentum management result value according to an embodiment of the present invention is received.

As shown in Fig. 33, a Soap message for the momentum management is shown. The service providing unit 130 returns service data to the user terminal 101, which is a result of exercise amount management result including calorie, distance, measurement date, measurement time, and number of steps.

Then, the user terminal 101 of the user receives the Soap message, extracts necessary values through parsing of the Soap message, and provides the extracted values to the users.

34 is an explanatory diagram of an autonomic nervous system management service and a detailed view screen according to the embodiment of the present invention.

As shown in FIG. 34, the service provider 130 measures the LF / HF, SDNN, and HRV, which are electrocardiographic characteristic values stored in the hive in the case of the service data of the electrocardiogram signal, The number of days, the number of stable days, the number of unstable days, and the average pulse to the user terminal 101.

In Fig. 34, the blue dot indicates stability and the red dot indicates unstable. In addition, graphs of the values of the autonomic nervous system balance and the average pulse of the user are graphically displayed through the detailed view screen. The service providing unit 130 provides the contents of the autonomic nervous system management service through a detailed view screen displayed on the user terminal 101. [ In the case of the autonomic nervous system balance (LF / HF), the reference line is indicated by a dotted line of not more than 3, and in the case of the pulse (BPM), not less than 80 and not more than 130.

35 is an explanatory diagram of a momentum management service and a detailed view screen according to an embodiment of the present invention.

35, the service providing unit 130 transmits a momentum management service to the user terminal 101 through the analysis result of the bio-signal on the calorie, the moving distance, the measurement date, the measurement time, ).

In FIG. 35, the service providing unit 130 scales the exercise amount by day, week, and month, and provides the result through a detailed view screen displayed on the user terminal 101. Also, the service provider 130 visualizes through the numerical values and the graphs for each item through the detailed view screen.

The service provider 130 is divided into five action patterns such as standing, sitting, walking, running, and jumping, which are kept still through the analysis algorithm for the raw data of the acceleration signal collected in the case of the behavior pattern service. The service providing unit 130 extracts a necessary portion of the Soap message received from the request of the user terminal 101 through parsing and provides the exercise amount management service to the user using the extracted portion.

36 is an exemplary diagram of a SOAP message received as a result of a user behavior pattern according to an embodiment of the present disclosure;

As shown in FIG. 36, the user terminal 101 parses necessary data through the received Soap message and provides analysis service to the user.

37 is an explanatory diagram of a user behavior pattern analysis service and a detailed view screen according to an embodiment of the present invention.

As shown in FIG. 37, when a measurement date is selected through the user terminal 101, the user can confirm the behavior analysis on the corresponding date as a percentage (%). In addition, the user can check five behaviors through the detailed view screen displayed on the user terminal 101. [ In the case of the monthly statistics, the user terminal 101 displays the result of the user behavior pattern analysis service through statistics on all the data measured in the corresponding month.

38 is a flowchart illustrating a method of analyzing a big data based bio signal according to an embodiment of the present invention.

The transceiving unit 110 receives the raw data of the bio-signal measured on the basis of the big data in the user-wearing type bio-signal measuring instrument through the web service (S101).

The transmission / reception unit 110 analyzes the raw data of the bio-signal received by the transmission / reception unit 110 and generates a storage for each bio-signal (S102).

Then, the transceiving unit 110 stores the raw data of the bio-signal received in the storage for each generated bio-signal (S103).

Then, the transceiver 110 filters the raw data of the stored bio-signal to extract a characteristic value of the bio-signal (S104).

The transmitting and receiving unit 110 divides the extracted feature values into separate directories for each partition of the data table and stores them in operation S105.

Meanwhile, the service providing unit 130 searches for and extracts the corresponding bio-signal feature value using a query language corresponding to the requested feature value condition (S106).

Then, the service providing unit 130 provides the extracted bio-signal feature value as a bio-signal analysis result through the web service (S107).

As described above, the bio-signal analysis system 100 according to the embodiment of the present invention is for analyzing and processing a huge amount of bio-signal data. The bio-signal analysis system 100 processes the data in the Hadoop cluster using HiveQL provided in the hive for easy analysis.

The bio-signal analysis system 100 stores the raw data of the electrocardiogram signal in the HDFS, which is a repository of Hadoop, and stores the extracted feature values in the hive through filtering. Therefore, the bio-signal analysis system 100 analyzes bio-signals for the corresponding request using the HiveQuest stored in the hive, and transmits the analyzed result to the user terminal 101 ), Extracts necessary values, and provides analysis services to users.

The bio-signal analysis system 100 according to the embodiment of the present invention can be equally applied to analysis services using raw data of bio-signals other than electrocardiogram signals through addition and modification of an algorithm for extracting feature values . In addition, the bio-signal analysis system 100 can provide a variety of services based on analysis results of bio-signals.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or essential characteristics thereof. Therefore, the embodiments disclosed in the present invention are intended to illustrate rather than limit the scope of the present invention, and the scope of the technical idea of the present invention is not limited by these embodiments. The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents should be construed as falling within the scope of the present invention.

100: Biological signal analysis system
101: User terminal
110: Transmitting /
120: Hadoop Distributed File System (HDFS)
121: raw data storage unit
122: Feature value extraction unit
123: Data Warehouse
130: Service Offering
610; Web server

Claims (16)

A transmitting and receiving unit for receiving raw data of bio-signals measured on a big data basis through a web service in a user-wearing type bio-signal measuring device;
And partitioning the characteristic values of the bio-signal extracted from the raw data of the received bio-signal into separate directories for each partition of the data warehouse Hadoop distributed file system (HDFS); And
The biometric signal feature value is retrieved and extracted using a query language corresponding to the feature value condition requested by the transceiver unit and the extracted biometric signal feature value is provided as a biometric signal analysis result through the web service of the transceiver unit Service Offering
Based biological signal analysis system. The method according to claim 1,
The transmitting /
A big-data-based bio-signal analysis system that receives raw data of a bio-signal of at least one of an ECG signal, an acceleration signal, a breathing signal, and a blood coral saturation signal, which are atypical data based on a big data. The method according to claim 1,
The Hadoop distributed file system
A raw data storage unit for analyzing raw data of the received bio-signal to generate a repository for each bio-signal, and storing raw data of the received bio-signal in a repository for each generated bio-signal;
A feature value extracting unit for extracting a feature value of a bio-signal by filtering raw data of the stored bio-signal; And
Wherein the data warehouse comprises a data table divided into directories according to partitions, and the extracted feature values are partitioned and stored in a directory for each partition of the data table,
Based biological signal analysis system. The method of claim 3,
The feature value extracting unit
A R peak value of a P wave, a QRS wave, and a T wave when the raw data of the bio-signal is the raw data of the electrocardiogram signal; At least one of heart rate variability (HRV), low frequency and high frequency band intensity (LF / HF), and standard deviation (SDNN: SDNN) Big-data-based bio-signal analysis system that extracts feature values of bio-signals. The method of claim 3,
The feature value extracting unit
Wherein the feature value of at least one of the exercise time, the calorie consumption, the movement distance, the number of steps and the behavior pattern is extracted when the raw data of the bio-signal is the raw data of the acceleration signal. The method of claim 3,
The data warehouse
The feature values of the divided bio-signals corresponding to the feature value condition of at least one of the user ID, the measurement year, the measurement month, the measurement date, and the measurement time are divided into a directory for each partition of the data warehouse corresponding to the feature value condition Big data based bio signal analysis system to store. The method according to claim 1,
The service providing unit
Wherein the bio-signal characteristic value is retrieved and extracted using a query language of the SQL-On-Hadoop corresponding to the requested feature value condition. The method according to claim 1,
The service providing unit
Wherein the bio-signal characteristic value is retrieved and extracted using a query language of HiveQL provided in the hive when the data warehouse is a hive. A raw data receiving step of receiving raw data of a bio-signal measured based on a big data on a wearable type bio-signal measuring instrument through a web service;
A bio-signal storage step of partitioning the feature value of the bio-signal extracted from the raw data of the received bio-signal into a partition for each partition of the data warehouse and storing the partition; And
A service providing step of searching and extracting the corresponding bio-signal feature value using a query language corresponding to the requested feature value condition, and providing the extracted bio-signal feature value as a result of a bio-signal analysis through a web service
Wherein the bio-signal analysis method comprises: 10. The method of claim 9,
The raw data receiving step
A method for analyzing a bio-signal based on a big data, the method comprising: receiving raw data of at least one of an ECG signal, an acceleration signal, a respiration signal, and a blood coral saturation signal, which are atypical data based on Big Data. 10. The method of claim 9,
The bio-signal storage step
Analyzing raw data of the received bio-signal to generate a repository for each bio-signal, and storing raw data of the received bio-signal in a repository for each generated bio-signal;
Extracting a characteristic value of a biological signal by filtering raw data of the stored biological signal; And
Partitioning the extracted feature values into separate directories for each partition of the data table and storing them
Wherein the bio-signal analysis method comprises: 12. The method of claim 11,
The step of extracting the feature value of the bio-
A R peak value of a P wave, a QRS wave, and a T wave when the raw data of the bio-signal is the raw data of the electrocardiogram signal; Wherein the characteristic value of at least one of the biological signals is extracted from HRV, low frequency and high frequency band intensities (LF / HF) and standard deviation (SDNN) of predetermined normal RR intervals. 12. The method of claim 11,
The step of extracting the feature value of the bio-
Wherein the feature value of at least one of the exercise time, the calorie consumption, the movement distance, the number of steps and the behavior pattern is extracted when the raw data of the bio-signal is the raw data of the acceleration signal. 12. The method of claim 11,
The step of separately storing in the partitioned directory
The feature values of the divided bio-signals corresponding to the feature value condition of at least one of the user ID, the measurement year, the measurement month, the measurement date, and the measurement time are divided into a directory for each partition of the data warehouse corresponding to the feature value condition A method for analyzing a bio-signal based on a big data to be stored. 10. The method of claim 9,
The service providing step
Wherein the biometric signal feature value is retrieved and extracted using a query language of the SQL-On-Hadoop corresponding to the requested feature value condition. 10. The method of claim 9,
The service providing step
Wherein the bio-signal feature value is retrieved and extracted using a query language of HiveQL provided in the hive when the data warehouse is a hive.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108334845A (en) * 2018-02-06 2018-07-27 北京华睿集成科技有限公司 Data positioning method and data positioning system
KR20190054741A (en) * 2017-11-14 2019-05-22 주식회사 케이티 Method and Apparatus for Quality Management of Data
KR20220032662A (en) * 2020-09-08 2022-03-15 닥터애니케어 주식회사 System for providing emergency alarming service using voice message
WO2023080288A1 (en) * 2021-11-03 2023-05-11 홍석우 Biosignal visualization service system using pan cancer cell diagnosis kit based on glucose metabolism gene microarray chip

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20190054741A (en) * 2017-11-14 2019-05-22 주식회사 케이티 Method and Apparatus for Quality Management of Data
CN108334845A (en) * 2018-02-06 2018-07-27 北京华睿集成科技有限公司 Data positioning method and data positioning system
KR20220032662A (en) * 2020-09-08 2022-03-15 닥터애니케어 주식회사 System for providing emergency alarming service using voice message
WO2023080288A1 (en) * 2021-11-03 2023-05-11 홍석우 Biosignal visualization service system using pan cancer cell diagnosis kit based on glucose metabolism gene microarray chip

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