KR20200013940A - Health care monitor-control system using blockchain network - Google Patents

Abstract

Provided is a health care control system using blockchain networks. A health care connection server receives sensing data from a healthcare device sensing a body state of a user to check a health state of the user. When an occurrence of an emergency situation in health of the user is determined, a block containing bio-information is generated to broadcast the block to healthcare blockchain networks. The healthcare blockchain networks transmit the blocks received from the healthcare connection server to members of the blockchain networks. A health care control server performs a process for coping with the emergency situation based on the block transmitted through the health care blockchain networks.

Description

Health care monitor-control system using blockchain network}

The present invention relates to a healthcare control system using a blockchain network, and more particularly, to determine a user's health state using sensing data for checking a user's health state and to determine information related to the determined health state. It relates to a healthcare control system using a blockchain network that can be delivered using.

The health care band is a wristband that can easily check the amount of exercise of the user, similar to the health care watch. Healthcare devices such as health care bands or health care patches record how many steps a user walks a day, alert you to your heart rate during exercise, analyze sleep patterns, measure key figures directly related to your health, It may also provide services for the prevention and management of symptoms. Recently, due to the development of communication technology, the data measured in the healthcare band may be provided to a smartphone or a computer, and may be collected and analyzed.

However, there is a problem in that the security of the data measured by the healthcare device is weak by transmitting data through a general data network. Therefore, there is a need for technology improvement that can quickly check the user's health status and take first aid measures while maintaining security related to the user's health.

Patent Publication No. 10-2018-0057233 (published May 30, 2018)

The technical problem to be solved by the present invention to solve the above-mentioned problems, the block to determine the health state of the user using the sensing data for checking the health state of the user and to maintain the security of the information related to the determined health state The present invention provides a healthcare control system using a blockchain network to be delivered using a chain network.

The problems of the present invention are not limited to those mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

As a means for solving the above-described technical problem, according to an embodiment of the present invention, the healthcare control system using a block chain network, by receiving the sensing data from the healthcare device for sensing the user's physical state by the health state of the user Check, and if it is determined that an emergency has occurred in the health of the user, a healthcare connection server for generating a block containing the biometric information of the user and broadcasting it to the healthcare blockchain network; A healthcare blockchain network for delivering blocks received from the healthcare connection server to blockchain network members; And a healthcare control server that performs a procedure for coping with the emergency based on the block received through the healthcare blockchain network.

The healthcare connection server mines and stores a block hash necessary for block generation, and generates and broadcasts a block describing the biometric information using the stored block hash.

The healthcare connection server collects sensing data from at least one of a sensor provided in the healthcare device and another sensor of the same type as the sensor, and applies the collected sensing data to a machine learning algorithm to support SVM (Support). Vector machine) is input to the sensing data received from the sensor of the healthcare device, to determine the health state of the user.

The SVM includes a heart rate SVM learned for heart rate and a body temperature SVM learned for body temperature, the learned heart rate SVM includes a normal range and error range of heart rate, and the learned body temperature SVM is a normal range of body temperature. Including an emergency range, the healthcare connection server, if the sensing data received from the sensor is heart rate data, the heart rate data belongs to the learned heart rate SVM and the received sensing data is the temperature data In this case, the health state of the user is determined using at least one of the ranges in which the temperature data belongs to the learned temperature SVM.

The healthcare connection server and the healthcare control server operate as members of the healthcare blockchain network.

According to the present invention, by receiving the sensing data for checking the health state of the user from the health care device to determine the health state of the user and delivers information related to the determined health state to the healthcare control server using a block chain network Can quickly and accurately identify and respond to user health, while keeping health-related information secure.

The effects of the present invention are not limited to those mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a view showing a healthcare control system using a blockchain network according to an embodiment of the present invention,
2 is a block diagram illustrating in detail the healthcare connection server shown in FIG. 1;
3 is a diagram for explaining a general SVM
4 is a flowchart illustrating a healthcare control method of the healthcare control system using a blockchain network according to an embodiment of the present invention;
5 is a flowchart illustrating an operation of learning a heart rate SVM by a healthcare connection server according to an embodiment of the present invention using a machine learning algorithm;
6 is a flowchart illustrating an operation of the healthcare connection server learning the body temperature SVM using a machine learning algorithm, and
FIG. 7 is a flowchart for describing an operation of determining a health state of a user using each SVM derived by FIGS. 5 and 6.

Objects, other objects, features and advantages of the present invention will be readily understood through the following preferred embodiments associated with the accompanying drawings. However, the present invention is not limited to the embodiments described herein and may be embodied in other forms. Rather, the embodiments introduced herein are provided to ensure that the disclosed contents are thorough and complete, and that the spirit of the present invention can be sufficiently delivered to those skilled in the art.

In the present specification, when a component is mentioned as being on another component, it means that it may be formed directly on the other component or a third component may be interposed therebetween.

In addition, when it is mentioned that a first element (or component) is operated or executed on a second element (or component), the first element (or component) means that the second element (or component) It is to be understood that the operation or execution in the environment in which the operation or execution is performed or the operation or execution is performed directly or indirectly through interaction with the second element (or component).

When an element, component, device, or system is referred to as including a component made up of a program or software, the element, component, device, or system may be executed or operated by the element, component, device, or system even if no explicit mention is made. It should be understood to include hardware necessary to run (eg, memory, CPU, etc.) or other programs or software (eg, an operating system or drivers needed to run the hardware, etc.).

In addition, it is to be understood that an element (or component) may be implemented in software, hardware, or any form of software and hardware, unless otherwise specified in the implementation of any element (or component).

Also, the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, the words 'comprises' and / or 'comprising' do not exclude the presence or addition of one or more other components.

Hereinafter, with reference to the accompanying drawings for the specific technical content to be carried out in the present invention will be described in detail.

In describing the following specific embodiments, various specific details are set forth in order to explain and understand the invention in more detail. However, one of ordinary skill in the art can understand that the present invention can be used without these various specific details.

In some cases, it is mentioned in advance that parts of the invention which are commonly known in the description of the invention and which are not highly related to the invention are not described in order to prevent confusion in explaining the invention without cause.

Each configuration of the healthcare control system using the blockchain network shown in FIG. 1 represents a functional and logical separation, which means that each configuration is divided into separate physical devices or written in separate codes. It will be readily understood by the average expert in the technical field of the present invention.

One or more programs for implementing a healthcare control system using the blockchain network may be installed in a predetermined data processing device to implement the technical idea of the present invention.

In addition, the healthcare connection server 100 and the healthcare control server 300 of the healthcare control system using a blockchain network according to an embodiment of the present invention is a desktop PC (personal computer), server, laptop PC (Laptop PC), It may be one of all electronic devices capable of installing and executing a program, such as a netbook computer.

1 is a diagram illustrating a healthcare control system using a blockchain network according to an embodiment of the present invention.

As shown in FIG. 1, the healthcare control system using the blockchain network determines the health state of a user using sensing data and artificial intelligence learning from sensors (not shown) included in the healthcare device 10. , The emergency information of the state of health is transmitted to the healthcare control server 300 by using the blockchain network, the healthcare control server 300 to contact the hospital or 119 located in close proximity to the user to quickly respond to the emergency Can be.

Referring to FIG. 1, a healthcare control system using a blockchain network according to an embodiment of the present invention includes a healthcare device 10, a healthcare connection server 100, a healthcare blockchain network 200, and a healthcare control server ( 300).

The healthcare device 10 may be a mobile terminal that can be attached to a user's body or clothing, or can sense a biosignal of a user, such as a wearable device or a smartphone in the form of a wrist band.

The healthcare device 10 includes one or more sensors for sensing a user's biosignal, such as a user's heart rate, body temperature, sleep pattern, and stride length, and displays the measured position data of the healthcare device 10 and The identification information of the healthcare device 10 is transmitted to the healthcare connection server 100.

The healthcare connection server 100 checks a user's health state using sensing data received in real time from a sensor provided in the healthcare device 10, and if it is determined that an emergency has occurred in the user's health, the user's living body A block containing information may be generated and broadcast to the healthcare blockchain network 200.

The healthcare connection server 100 may be, for example, an edge computing system, a user mobile terminal, a cloud server, or the like connected to communicate with the healthcare device 100.

The healthcare connection server 100 and the healthcare control server 300 may operate as members of the blockchain network 200. Therefore, the healthcare connection server 100 receives and distributes the newly generated block propagated from the blockchain network 200 or propagates the new block generated by the healthcare connection server 100 to the blockchain network 200. You can also request it to be distributed.

If the health connection server 100 is determined that the health of the user is not an emergency or worsened in comparison with the previous health from the received sensing data, the health care device 10 and the current state of the user to be noted, etc. The healthcare device 10 may output information received from the healthcare connection server 100 so that the user can recognize the information.

FIG. 2 is a detailed block diagram illustrating the healthcare connection server 100 illustrated in FIG. 1.

Referring to FIG. 2, the healthcare connection server 100 according to an embodiment of the present invention includes a bus 110, a block hash mining unit 120, a block hash storage unit 130, a communication unit 140, and sensing data collection. The unit 145, the memory 150, and the processor 160 may be included.

The bus 110 connects the block hash mining unit 120, the block hash storage unit 130, the communication unit 140, the sensing data collection unit 145, the memory 150, and the processor 160 to each other, and a control message. And circuitry for transmitting various signals such as status information, and / or data.

The block hash mining unit 120 mines the block hash necessary for generating the block, and the block hash storage unit 130 may store the mined block hash.

The communicator 140 includes an interface circuit for communicating with the healthcare device 10 and the healthcare blockchain network 200. For example, the communication unit 140 may receive sensing data from one or more sensors (not shown) included in the healthcare device 10, and broadcast the block to be described later to the healthcare blockchain network 200.

The sensing data collector 145 may collect and store sensing data received from the healthcare device 10. The sensing data collected and stored may include heart rate data, temperature data, stride length, sleep pattern data, location data, identification information of the healthcare device 10, and sensed time sensed by each sensor.

The memory 150 may include volatile memory and / or nonvolatile memory. The memory 150 may store instructions or data related to components, one or more programs and / or software, an operating system, etc. to implement and / or provide the operations, functions, and the like provided by the healthcare connection server 100. Can be.

The program stored in the memory 150 may include a block mining program that mines a block hash and generates a new block using the mined block hash.

In addition, the program stored in the memory 150 is a machine that derives a support vector machine (SVM) by performing machine learning for each healthcare device 10 using the sensing data collected from the healthcare device 10 as learning data. It may also include a learning algorithm.

In addition, the program stored in the memory 150 may include a health determination program that determines the health state of the user using the SVM learned about the healthcare device 10, and generates and propagates a block. In addition, the SVM machine learning algorithm is described as an example, but the present invention is not limited thereto, and the health state of the user may be determined through a situation recognition algorithm and a state prediction algorithm using artificial intelligence (deep learning, rush learning, etc.). .

The processor 160 may control the overall operation of the healthcare connection server 100 by executing one or more programs stored in the memory 150.

To this end, the processor 160 may include a learner 161, an emergency situation determiner 163, a biometric information generator 165, and a block generator 167.

The learning unit 161 executes a program that implements a machine learning algorithm to collect sensing data from at least one of a sensor included in the healthcare device 10 and other sensors of the same type as the sensor, and collect the sensed data. By applying to a machine learning algorithm, the SVM can be learned for each type of sensor provided in the healthcare device 10. Accordingly, the learning unit 161 may collect and store sensing data from a plurality of users who use the healthcare device 10 to learn the SVM, and then check the health state using the SVM for each user.

In addition, the learning unit 161 may independently learn the SVM for each type of sensing data. In the embodiment of the present invention, the heart rate SVM learned about the heart rate and the body temperature SVM learned about the body temperature are taken as examples. Of course not. The learning unit 161 learns the SVM using the normal range and the error range of the heart rate data among the collected sensing data, and the normal range is the actual sensed value of the heart rate data sensed by the user in a healthy state. As a range for determining that the heart rate is included in the normal range, for example, ± 40% of the actual sensed heart rate data value may be determined as a reference of the error range. The error range may be set differently for each type of sensor.

In addition, the learning unit 161 learns the body temperature SVM using the normal range and the emergency range of the body temperature data among the collected sensing data. In the case of body temperature, the normal range is an actual sensed value of the body temperature data sensed from the user in the current healthy state (for example, within a preset error range of 36.5 ° C.). The emergency range may be a range except an error range for determining that the emergency range is included in the normal range of the sensed body temperature. For example, if the error range for body temperature is (sensed input temperature-0.75) <input body temperature <(sensed input body temperature + 3), the emergency range is less than (sensed input temperature-0.75) and ( Sensing input temperature + 3) or more body temperature is included. 3 is a diagram for explaining a general SVM. Referring to FIG. 3, Optimal Margin corresponds to a normal range reference error range.

Heart rate SVM and body temperature SVM learning will be described later with reference to FIGS. 5 and 6.

Meanwhile, the emergency determination unit 163 inputs sensing data received from the sensors of the healthcare device 10 by the healthcare device 10 actually attached to the user to each SVM learned for each type, and the health of the user. A situation-aware algorithm for determining the state may be performed. That is, the emergency determination unit 163 determines a normal range and an error range of the sensing data through the learned SVM, or sets an emergency situation range, and when the received sensing data is out of an error range or belongs to an emergency range. An abnormality has occurred in the health of the patient, but it may not be an emergency or an emergency situation may be determined.

If it is determined that an emergency situation has occurred, the biometric information generation unit 165 receives the biometric information (heart rate, body temperature, sleep pattern, etc.) and the healthcare device 10 from the sensing data for a predetermined time received from the healthcare device 10. Generate biometric information including identification information and location information.

The block generator 167 may receive a block hash usable from the block hash storage 130 and generate a new block by describing the biometric information generated by the biometric information generator 165 in the block hash. In addition, the block generation unit 167 may broadcast the block containing the biometric information to the block chain network 200 to be propagated to the healthcare control server 300.

Referring back to FIG. 1, the healthcare blockchain network 200 may deliver a block received from the healthcare connection server 100 to members of the blockchain network 200 to be distributed and stored. Since the healthcare blockchain network 200 includes at least one healthcare connection server 100 and at least one healthcare control server 300, the healthcare connection server 100 and the healthcare control server 300 are connected to the blockchain network. Can act as a node.

The healthcare control server 300 transmits the user's location information after searching for the hospitals and / or fire fighting organizations around the user who can cope with the emergency of the user based on the blocks received through the healthcare blockchain network 200. Notify you of an emergency. When the contact information of the guardian mapped to the user of the healthcare device 10 is stored in the healthcare control server 300 or the healthcare connection server 100, the healthcare control server 300 or the healthcare connection server 100 ) Can also notify the guardian of the user's emergency.

4 is a flowchart illustrating a healthcare control method of a healthcare control system using a blockchain network according to an exemplary embodiment of the present invention.

Since the healthcare device 10, the healthcare connection server 100, the block chain network 200, and the healthcare control server 300 illustrated in FIG. 4 have been described with reference to FIGS. 1 to 3, a detailed description thereof will be omitted.

Referring to FIG. 4, the healthcare connection server 100 operates as a member of the blockchain network 200, that is, a node, and distributes and stores the newly generated block propagated to the blockchain network 200 (S405 and S410).

In addition, the healthcare connection server 100 is used by itself, that is, mining and storing the block hash required for block generation (S415, S420).

While the steps S405 to S420 are in progress, the healthcare connection server 100 may perform steps S425 to S465 in parallel.

That is, the healthcare connection server 100 receives the sensing data from the healthcare device 10 to collect and store the data according to the passage of time (S425 to S430).

The healthcare connection server 100 may determine whether a user's health state, in particular, an emergency situation occurs through a situation recognition algorithm based on the collected and stored sensing data (S435, S440).

In detail, the healthcare connection server 100 applies a machine learning algorithm to collected and stored sensing data to learn SVM for each sensor, and inputs sensing data of a type corresponding to the learned SVM. Determine your health. In operation S435, an operation of learning the SVM for each sensor, that is, for each type of sensing data may be performed.

5 is a flowchart illustrating an operation in which the healthcare connection server 100 learns a heart rate SVM using a machine learning algorithm, which corresponds to a part of step S435 of FIG. 4 or is previously learned. It may be an operation.

Referring to FIG. 5, the healthcare connection server 100 receives sensing data related to a heart rate collected from one or more sensors provided in the healthcare device 10 (hereinafter, referred to as “heart rate data”) (S510). In operation S520, an error range of the input heart rate data (for example, 40% of the input heart rate data) is calculated. The heart rate error range is set differently according to age group or health condition and may be used for SVM learning.

The healthcare connection server 100 sets the heart rate data calculated with the input heart rate data and the error range (ie, 40% calculated) as the training data (S530), and sets a specific vector of the training data for each training data. Extract (S540).

The healthcare connection server 100 classifies the training data into the calculated heart rate data and the data calculated as a 40% percentage of the input heart rate data based on the extracted plurality of specific vectors, and machine-learns the classified training data. Deriving the heart rate SVM in which the normal range and the error range of the heart rate are determined (S550 and S560).

If the collected heart rate data is further present (S570-Yes), the healthcare connection server 100 enters step S520, and if not present, the learning ends.

FIG. 6 is a flowchart illustrating an operation of learning a body temperature SVM using a machine learning algorithm by the healthcare connection server 100 according to an exemplary embodiment of the present invention, which corresponds to a part of step S435 of FIG. 4 or is previously learned. It may be an operation.

Referring to FIG. 6, the healthcare connection server 100 receives sensing data (hereinafter, referred to as 'temperature data') related to body temperature collected from at least one sensor provided in the healthcare apparatus 10 (S610). To calculate the error range (for example, -0.75 ~ +3 of the input body temperature) data of the input body temperature data, and to determine the emergency range by excluding the calculated error range data by filtering (S620). The normal range and the error range of the body temperature may be set differently for each age group or health condition and commercialized. That is, the body temperature SVM also can be learned for each emergency range by varying the normal range and the emergency range of the body temperature, the healthcare connection server 100 corresponds to the user's usual health state (ie, normal body temperature) The body temperature SVM can be used to check the user's health status. The same is true for heart rate SVM.

The healthcare connection server 100 determines the data (that is, the remaining body temperature data by filtering) calculated as the emergency range among the input body temperature data as the training data (S630), and extracts a specific vector of the training data for each training data. (S640).

The healthcare connection server 100 classifies training data into -0.75 body temperature data and +3 body temperature data based on the extracted plurality of specific vectors, and the classified training data. Machine learning is performed to derive a body temperature SVM in which the normal range and the emergency range of body temperature are determined (650, S660). FIG. 7 is a flowchart illustrating an operation of determining a health state of a user using each SVM learned by FIGS. 5 and 6.

Referring to FIG. 7, the healthcare connection server 100 inputs heart rate data received in real time to the learned heart rate SVM (S710 and S720).

The healthcare connection server 100 classifies the heart rate data through the classification of the normal range and the 40% calculated error range by performing a situation awareness algorithm by inputting the heart rate data into the learned heart rate SVM (S730).

 The healthcare connection server 100 determines whether the heart rate is in a normal range according to the classification result in step S730 (S740). In operation S740, the healthcare connection server 100 determines that an abnormality in the health related to the heart rate occurs when the heart rate data is classified as being out of the error range.

In addition, the healthcare connection server 100 inputs the temperature data received in real time to the learned temperature SVM (S750, S760). At this time, the healthcare connection server 100 uses the body temperature SVM set for the user among the plurality of body temperature SVMs learned for each normal range of body temperature.

The healthcare connection server 100 inputs the temperature data to the learned body temperature SVM to perform a situation awareness algorithm to classify whether the input temperature data belongs to a normal range and an emergency range (S770).

 The healthcare connection server 100 determines whether the body temperature is in a normal range according to the classification result in step S770 (S780). In step S780, the healthcare connection server 100 determines that the abnormality in the health associated with body temperature if the body temperature data is classified as out of the emergency situation range.

In addition, the healthcare connection server 100 determines whether an emergency situation occurs using at least one of whether the normal range of the heart rate determined in step S740 and the normal range of body temperature determined in step S780 (S790).

Referring back to FIG. 4, the healthcare connection server 100 generates and stores biometric information of a user in which an emergency situation occurs (S445).

Then, the healthcare connection server 100 generates a block containing the biometric information by receiving and requesting the mined block hash, and then broadcasts the generated block to the block chain network (S450 to S465).

If the blockchain network 200, that is, the biometric information is transmitted from the node connected to the healthcare control server 300 among the members of the blockchain network 200 to the healthcare control server 300 (S470), the healthcare control server 300 ) Stores the biometric information (S475). In operation S470, the healthcare control server 300 may check the biometric information after receiving and parsing the block containing the biometric information.

The healthcare control server 300 may notify that the patient has occurred to a hospital or a fire fighting organization such as 119 located near the user according to the stored biometric information (S480). Alternatively, the healthcare control server 300 may perform a procedure for directly checking safety to the user before performing step S480.

According to the above-described embodiment of the present invention, the healthcare connection server 100 has been described in the case where the processor 160 learns the SVM by further using the sensing data of the healthcare device 10 provided to the user, The present invention is not limited thereto and may be installed to be applicable in the healthcare connection server 100 by learning in advance for each sensor from the manufacturer.

In the former case, the processor 160 derives the SVM more accurately in consideration of the state of the healthcare device 10 by periodically learning and updating the SVM using the sensing data received from the healthcare device 10 in use. Therefore, it is possible to more accurately determine the state of health of the user.

In addition, when two different types of sensors are provided in one healthcare device 10, the processor 160 may input sensing data corresponding to the SVM corresponding to each sensor to determine whether there is a health abnormality for each sensor. As a result, the processor 160 may determine the user health state even when using one sensor.

On the other hand, while described and illustrated in connection with a preferred embodiment for illustrating the technical spirit of the present invention, the present invention is not limited to the configuration and operation as shown and described as described above, it deviates from the scope of the technical idea It will be apparent to those skilled in the art that many modifications and variations can be made to the present invention without departing from the scope of the invention. Accordingly, all such suitable changes and modifications and equivalents should be considered to be within the scope of the present invention. Therefore, the true technical protection scope of the present invention will be defined by the technical spirit of the appended claims.

10: healthcare device 100: healthcare connection server
200: healthcare blockchain network 300: healthcare control servers

Claims (5)

Receiving the sensing data from the health care device for sensing the user's physical condition to check the health state of the user, if it is determined that an emergency situation has occurred in the health of the user, and generates a block containing the biometric information of the user A healthcare connection server broadcasting to a healthcare blockchain network;
A healthcare blockchain network for delivering blocks received from the healthcare connection server to blockchain network members; And
And a healthcare control server performing a procedure for coping with the emergency on the basis of the block received through the healthcare blockchain network. The method of claim 1,
The healthcare connection server,
And a block hash necessary for block generation, storing the block hash, and generating and broadcasting a block describing the biometric information using the stored block hash. The method of claim 1,
The healthcare connection server,
Collecting sensing data from at least one of a sensor provided in the healthcare device and another sensor of the same type as the sensor, and applying the collected sensing data to a machine learning algorithm, the healthcare to the learning SVM (Support Vector Machine) A health care control system using a blockchain network, characterized in that for determining the health state of the user by inputting the sensing data received from the sensor of the device. The method of claim 3,
The SVM includes a heart rate SVM learned for heart rate and a body temperature SVM learned for body temperature,
The learned heart rate SVM includes a normal range of heart rate and an error range, and the learned body temperature SVM includes a normal range of body temperature and an emergency range,
The healthcare connection server,
When the sensing data received from the sensor is heart rate data, the range of the heart rate data belongs to the learned heart rate SVM, and if the received sensing data is body temperature data, the range of the temperature data belongs to the learned body temperature SVM Health care control system using a block chain network, characterized in that for determining the health state of the user using at least one of. The method of claim 1,
The healthcare connection server and the healthcare control server, a healthcare control system using a blockchain network, characterized in that operating as a member of the healthcare blockchain network.

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