WO2022034940A1 - Blood pressure measurement apparatus and method therefor - Google Patents

Abstract

The present disclosure discloses a blood pressure estimation apparatus comprising: a sensing unit for obtaining a bio-signal of a measurement target; and a processor for obtaining a representative bio-signal, on the basis of a similarity between a calibration signal and the bio-signal, and estimating blood pressure by using an artificial intelligence model trained to, when feature data of the bio-signal is input, output a blood pressure estimation value as a result value.

Description

Blood pressure measuring device and method

The present disclosure relates to an apparatus for measuring blood pressure and a method for measuring blood pressure.

Blood pressure refers to the force that the blood supplied from the heart stimulates the arterial wall, and blood pressure is used as a measure to determine an individual's cardiovascular health status.

As a method of measuring blood pressure, cuff-type blood pressure monitors commonly used in medical institutions and homes have a problem of causing inconvenience to users because they are used by applying pressure to the arteries. A method of monitoring blood pressure using a heart rate sensor is being studied.

However, the method of measuring blood pressure by acquiring a pulse wave from a pulse wave sensor and analyzing the acquired pulse wave is a method of measuring a blood pressure according to a heartbeat according to the heartbeat according to the physical structure and health status of each individual. Blood pressure may also vary, and there is a problem in that it is not possible to provide a personalized blood pressure measuring device in consideration of this point. In addition, when measuring blood pressure using a sensor, noise such as motion artifacts according to a user's movement cannot be distinguished by frequency filtering, so it is difficult to obtain an accurate pulse wave.

An object of the present disclosure is to accurately measure a current user's blood pressure using pre-generated data (called calibration data in the present disclosure) generated by the user.

An object of the present disclosure is to accurately acquire pulse wave data based on a user's heartbeat by using calibration data.

An object of the present disclosure is to provide a wearable device capable of measuring blood pressure.

An object of the present disclosure is to provide a personalized blood pressure measurement device.

The blood pressure measuring apparatus of the present disclosure obtains a representative biosignal (unified waveform) based on the similarity between a calibration signal and the biosignal of a sensing unit for obtaining a biosignal of a measurement target, and when characteristic data of the biosignal is input, the blood pressure estimate value It may include a processor for estimating blood pressure using the artificial intelligence model learned to output as a result value.

In this case, the calibration signal may refer to a representative unit calibration signal that is generated based on a plurality of unit calibration signals included in the pre-obtained biosignal of the measurement target, and is generated based on the plurality of unit calibration signals.

In addition, the processor extracts a plurality of unit bio-signals included in the bio-signals, obtains unit bio-signals in which a similarity between each of the plurality of unit bio-signals and the calibration signal is equal to or greater than a preset value, and the obtained unit bio-signals It is possible to obtain a representative biosignal based on

Also, the processor may measure a heart rate by using the biosignal, scale the calibration signal to correspond to the heart rate, and obtain similarities between the scaled calibration signal and each of the plurality of unit biosignals.

In addition, the processor may normalize unit bio-signals having a similarity with the calibration signal equal to or greater than a preset value, and obtain a representative bio-signal based on the normalized unit bio-signals.

Also, the processor may obtain a representative biosignal as a median or average value of the normalized unit biosignals.

In this case, the calibration signal may refer to a representative unit calibration signal generated by normalizing the plurality of unit calibration signals and using a median value or an average value of the normalized unit calibration signals.

Also, the processor may extract the characteristic data of the representative bio-signal and the characteristic data of the calibration signal based on the characteristics of the waveform included in the representative bio-signal and the calibration signal.

At this time, the characteristics of the waveform include at least one of the ratio of the inflection point of the waveform to the systolic peak point of the waveform, the ratio of the region formed before and after the inflection point, the time between the diastolic peak and the systolic peak, and the ratio of the detected amplitude can do.

In this case, when at least one of the characteristic data of the representative biosignal and the characteristic data of the calibration signal is input to the artificial intelligence model, the artificial intelligence model may output the blood pressure estimation value as a result value.

The artificial intelligence model may include any one of an artificial neural network algorithm, a k-nearest neighbor algorithm, a Bayesian network algorithm, an SVM algorithm, and a recurrent neural network algorithm.

In addition, the blood pressure estimating apparatus may further include a memory for storing body information including at least one of age, sex, weight, height, calibration signal, biosignal, and blood pressure of the measurement target.

Also, the blood pressure estimating apparatus may further include a communication unit configured to receive body information including at least one of age, gender, weight, height, calibration signal, biosignal, and blood pressure of the measurement target.

Also, the blood pressure estimating apparatus may further include a display unit for outputting the estimated blood pressure.

A method of operating an apparatus for estimating blood pressure according to the present disclosure includes generating a calibration signal; Obtaining a bio-signal of a measurement target, obtaining a representative bio-signal based on a similarity between a calibration signal and the bio-signal, and estimating blood pressure when the characteristic data of the bio-signal is input using an artificial intelligence model can

The generating of the calibration signal includes extracting a plurality of unit calibration signals included in the biosignal of the measurement target obtained in advance, and generating a representative unit calibration signal based on the plurality of unit calibration signals. can do.

The obtaining of the representative bio-signal based on the similarity between the calibration signal and the bio-signal may include: extracting a plurality of unit bio-signals included in the bio-signal; The method may include obtaining a unit bio-signal having a similarity between each of the plurality of unit bio-signals and the calibration signal equal to or greater than a preset value, and obtaining a representative bio-signal based on the obtained unit bio-signals.

In addition, the step of estimating the blood pressure when the characteristic data of the biosignal is input using the artificial intelligence model,

extracting the characteristic data of the representative bio-signal and the characteristic data of the calibration signal based on the characteristics of the waveform included in the representative bio-signal and the calibration signal, and the characteristic data of the representative bio-signal and the characteristic data of the calibration signal The method may include outputting the blood pressure estimation value as a result value when at least one of them is input to the artificial intelligence model.

The present disclosure removes unnecessary parts of an acquired biosignal when estimating blood pressure using pre-generated calibration data, so that noise from a distorted signal generated when measuring blood pressure can be effectively removed.

Since the calibration data of the present disclosure is obtained and generated in advance from a user who wants to measure blood pressure, it is possible to provide a personalized blood pressure measuring apparatus reflecting the user's physical structure.

According to the present disclosure, the performance of the blood pressure estimating apparatus can be gradually improved by updating the artificial intelligence model by additionally acquiring calibration data.

1 shows an artificial intelligence device 100 according to an embodiment of the present disclosure.

2 shows an artificial intelligence server 200 according to an embodiment of the present disclosure.

3 shows an artificial intelligence system 1 according to an embodiment of the present disclosure.

4 shows an AI device according to an embodiment of the present disclosure.

5 shows a flowchart according to an embodiment of the present disclosure.

6 is a flowchart illustrating a process of acquiring calibration information according to an embodiment of the present disclosure.

7 is a flowchart illustrating a process of acquiring a representative biosignal according to an embodiment of the present disclosure.

8 is a diagram for explaining a process of obtaining a representative biosignal with a waveform according to an embodiment of the present disclosure.

9 is a diagram illustrating an artificial intelligence model according to an embodiment of the present disclosure.

10 is a diagram for describing characteristic data of a waveform according to an embodiment of the present disclosure.

Hereinafter, the detail of this invention is demonstrated.

The embodiments described below are only examples of the present invention, and the present invention may be modified in various forms. Accordingly, the specific features and functions disclosed below do not limit the scope of the claims.

Hereinafter, the embodiments disclosed in the present specification will be described in detail with reference to the accompanying drawings, but the same or similar components are assigned the same reference numbers regardless of reference numerals, and redundant description thereof will be omitted. The suffixes "module" and "part" for components used in the following description are given or mixed in consideration of only the ease of writing the specification, and do not have distinct meanings or roles by themselves. In addition, in describing the embodiments disclosed in the present specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in this specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in the present specification, and the technical spirit disclosed in the present specification is not limited by the accompanying drawings, and all changes included in the spirit and scope of the present disclosure , should be understood to include equivalents or substitutes.

Terms including an ordinal number such as 1st, 2nd, etc. may be used to describe various elements, but the elements are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

When a component is referred to as being “connected” or “connected” to another component, it may be directly connected or connected to the other component, but it is understood that other components may exist in between. it should be On the other hand, when it is said that a certain element is "directly connected" or "directly connected" to another element, it should be understood that the other element does not exist in the middle.

<Artificial Intelligence (AI)>

Artificial intelligence refers to a field that studies artificial intelligence or methodologies that can create it, and machine learning refers to a field that defines various problems dealt with in the field of artificial intelligence and studies methodologies to solve them. do. Machine learning is also defined as an algorithm that improves the performance of a certain task through constant experience.

An artificial neural network (ANN) is a model used in machine learning, and may refer to an overall model having problem-solving ability, which is composed of artificial neurons (nodes) that form a network by combining synapses. An artificial neural network may be defined by a connection pattern between neurons of different layers, a learning process that updates model parameters, and an activation function that generates an output value.

The artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include neurons and synapses connecting neurons. In the artificial neural network, each neuron may output a function value of an activation function for input signals, weights, and biases input through synapses.

Model parameters refer to parameters determined through learning, and include the weight of synaptic connections and the bias of neurons. In addition, the hyperparameter refers to a parameter that must be set before learning in a machine learning algorithm, and includes a learning rate, the number of iterations, a mini-batch size, an initialization function, and the like.

The purpose of learning the artificial neural network can be seen as determining the model parameters that minimize the loss function. The loss function may be used as an index for determining optimal model parameters in the learning process of the artificial neural network.

Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method.

Supervised learning refers to a method of training an artificial neural network in a state where a label for the training data is given, and the label is the correct answer (or result value) that the artificial neural network should infer when the training data is input to the artificial neural network. can mean Unsupervised learning may refer to a method of training an artificial neural network in a state where no labels are given for training data. Reinforcement learning can refer to a learning method in which an agent defined in an environment learns to select an action or sequence of actions that maximizes the cumulative reward in each state.

Among artificial neural networks, machine learning implemented as a deep neural network (DNN) including a plurality of hidden layers is also called deep learning (deep learning), and deep learning is a part of machine learning. Hereinafter, machine learning is used in a sense including deep learning.

<Robot>

A robot can mean a machine that automatically handles or operates a task given by its own capabilities. In particular, a robot having a function of recognizing an environment and performing an operation by self-judgment may be referred to as an intelligent robot.

Robots can be classified into industrial, medical, home, military, etc. depending on the purpose or field of use.

The robot may be provided with a driving unit including an actuator or a motor to perform various physical operations such as moving the robot joints. In addition, the movable robot includes a wheel, a brake, a propeller, and the like in the driving unit, and may travel on the ground or fly in the air through the driving unit.

<Self-Driving>

Autonomous driving refers to a technology that drives itself, and an autonomous driving vehicle refers to a vehicle that travels without or with minimal manipulation of a user.

For example, autonomous driving includes technology for maintaining a driving lane, technology for automatically adjusting speed such as adaptive cruise control, technology for automatically driving along a predetermined route, technology for automatically setting a route when a destination is set, etc. All of these can be included.

The vehicle includes a vehicle having only an internal combustion engine, a hybrid vehicle having both an internal combustion engine and an electric motor, and an electric vehicle having only an electric motor, and may include not only automobiles, but also trains, motorcycles, and the like.

In this case, the autonomous vehicle can be viewed as a robot having an autonomous driving function.

<Extended Reality (XR: eXtended Reality)>

The extended reality is a generic term for virtual reality (VR), augmented reality (AR), and mixed reality (MR). VR technology provides only CG images of objects or backgrounds in the real world, AR technology provides virtual CG images on top of images of real objects, and MR technology is a computer that mixes and combines virtual objects in the real world. graphic technology.

MR technology is similar to AR technology in that it shows both real and virtual objects. However, there is a difference in that in AR technology, a virtual object is used in a form that complements a real object, whereas in MR technology, a virtual object and a real object are used with equal characteristics.

XR technology can be applied to HMD (Head-Mount Display), HUD (Head-Up Display), mobile phone, tablet PC, laptop, desktop, TV, digital signage, etc. can be called

1 shows an artificial intelligence device 100 according to an embodiment of the present disclosure.

AI device 100 is TV, projector, mobile phone, smart phone, desktop computer, notebook computer, digital broadcasting terminal, PDA (personal digital assistants), PMP (portable multimedia player), navigation, tablet PC, wearable device, set-top box (STB) ), a DMB receiver, a radio, a washing machine, a refrigerator, a desktop computer, a digital signage, a robot, a vehicle, etc., may be implemented as a stationary device or a movable device.

Referring to FIG. 1 , the terminal 100 includes a communication unit 110 , an input unit 120 , a learning processor 130 , a sensing unit 140 , an output unit 150 , a memory 170 and a processor 180 , etc. may include

The communication unit 110 may transmit/receive data to and from external devices such as other AI devices 100a to 100e or the AI server 200 using wired/wireless communication technology. For example, the communication unit 110 may transmit and receive sensor information, a user input, a learning model, a control signal, and the like with external devices.

In this case, the communication technology used by the communication unit 110 includes GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity) ), Bluetooth, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), and the like.

The input unit 120 may acquire various types of data.

In this case, the input unit 120 may include a camera for inputting an image signal, a microphone for receiving an audio signal, a user input unit 120 for receiving information from a user, and the like. Here, by treating the camera or the microphone as a sensor, a signal obtained from the camera or the microphone may be referred to as sensing data or sensor information.

The input unit 120 may acquire training data for model training and input data to be used when acquiring an output using the training model. The input unit 120 may acquire raw input data, and in this case, the processor 180 or the learning processor 130 may extract an input feature by preprocessing the input data.

The learning processor 130 may train a model composed of an artificial neural network by using the training data. Here, the learned artificial neural network may be referred to as a learning model. The learning model may be used to infer a result value with respect to new input data other than the training data, and the inferred value may be used as a basis for a decision to perform a certain operation.

In this case, the learning processor 130 may perform AI processing together with the learning processor 240 of the AI server 200 .

In this case, the learning processor 130 may include a memory 170 integrated or implemented in the AI device 100 . Alternatively, the learning processor 130 may be implemented using a memory 170 , an external memory 170 directly coupled to the AI device 100 , or a memory 170 maintained in an external device.

The sensing unit 140 may acquire at least one of internal information of the AI device 100 , information on the surrounding environment of the AI device 100 , and user information by using various sensors.

At this time, sensors included in the sensing unit 140 include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, and a lidar. , radar, etc.

The output unit 150 may generate an output related to sight, hearing, or touch.

In this case, the output unit 150 may include a display unit that outputs visual information, a speaker that outputs auditory information, and a haptic module that outputs tactile information.

The memory 170 may store data supporting various functions of the AI device 100 . For example, the memory 170 may store input data obtained from the input unit 120 , learning data, a learning model, a learning history, and the like.

The processor 180 may determine at least one executable operation of the AI device 100 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the processor 180 may control the components of the AI device 100 to perform the determined operation.

To this end, the processor 180 may request, search, receive, or utilize the data of the learning processor 130 or the memory 170, and perform a predicted operation or an operation determined to be preferable among the at least one executable operation. It is possible to control the components of the AI device 100 to execute.

In this case, when the connection of the external device is required to perform the determined operation, the processor 180 may generate a control signal for controlling the corresponding external device and transmit the generated control signal to the corresponding external device.

The processor 180 may obtain intention information with respect to a user input, and determine a user's requirement based on the obtained intention information.

In this case, the processor 180 uses at least one of a speech to text (STT) engine for converting a voice input into a character string or a natural language processing (NLP) engine for obtaining intention information of a natural language, Intention information corresponding to the input may be obtained.

At this time, at least one of the STT engine and the NLP engine may be configured as an artificial neural network, at least a part of which is learned according to a machine learning algorithm. And, at least one or more of the STT engine or the NLP engine is learned by the learning processor 130, or learned by the learning processor 240 of the AI server 200, or learned by distributed processing thereof. it could be

The processor 180 collects history information including user feedback on the operation contents or operation of the AI device 100 and stores it in the memory 170 or the learning processor 130, or the AI server 200 It can be transmitted to an external device. The collected historical information may be used to update the learning model.

The processor 180 may control at least some of the components of the AI device 100 in order to drive an application program stored in the memory 170 . Furthermore, in order to drive the application program, the processor 180 may operate two or more of the components included in the AI device 100 in combination with each other.

2 shows an artificial intelligence server 200 according to an embodiment of the present disclosure.

Referring to FIG. 2 , the AI server 200 may refer to a device that trains an artificial neural network using a machine learning algorithm or uses a learned artificial neural network. Here, the AI server 200 may be configured with a plurality of servers to perform distributed processing, and may be defined as a 5G network. In this case, the AI server 200 may be included as a part of the AI device 100 to perform at least a part of AI processing together.

The AI server 200 may include a communication unit 210 , a memory 230 , a learning processor 240 , a processor 260 , and the like.

The communication unit 210 may transmit/receive data to and from an external device such as the AI device 100 .

The memory 230 may include a model storage unit 231 . The model storage unit 231 may store a model (or artificial neural network, 231a) being trained or learned through the learning processor 240 .

The learning processor 240 may train the artificial neural network 231a using the training data. The learning model may be used while being mounted on the AI server 200 of the artificial neural network, or may be used while being mounted on an external device such as the AI device 100 .

The learning model may be implemented in hardware, software, or a combination of hardware and software. When a part or all of the learning model is implemented in software, one or more instructions constituting the learning model may be stored in the memory 230 .

The processor 260 may infer a result value with respect to new input data using the learning model, and may generate a response or a control command based on the inferred result value.

3 shows an artificial intelligence system 1 according to an embodiment of the present disclosure.

Referring to FIG. 3 , the AI system 1 includes at least one of an AI server 200 , a robot 100a , an autonomous vehicle 100b , an XR device 100c , a smart phone 100d , or a home appliance 100e . It is connected to the cloud network 10 . Here, the robot 100a to which the AI technology is applied, the autonomous driving vehicle 100b, the XR device 100c, the smart phone 100d, or the home appliance 100e may be referred to as AI devices 100a to 100e.

The cloud network 10 may constitute a part of the cloud computing infrastructure or may refer to a network existing in the cloud computing infrastructure. Here, the cloud network 10 may be configured using a 3G network, a 4G or Long Term Evolution (LTE) network, or a 5G network.

That is, each of the devices 100a to 100e and 200 constituting the AI system 1 may be connected to each other through the cloud network 10 . In particular, each of the devices 100a to 100e and 200 may communicate with each other through the base station, but may directly communicate with each other without passing through the base station.

The AI server 200 may include a server performing AI processing and a server performing an operation on big data.

The AI server 200 includes at least one of the AI devices constituting the AI system 1, such as a robot 100a, an autonomous vehicle 100b, an XR device 100c, a smartphone 100d, or a home appliance 100e, and It is connected through the cloud network 10 and may help at least a part of AI processing of the connected AI devices 100a to 100e.

In this case, the AI server 200 may train the artificial neural network according to a machine learning algorithm on behalf of the AI devices 100a to 100e, and directly store the learning model or transmit it to the AI devices 100a to 100e.

At this time, the AI server 200 receives input data from the AI devices 100a to 100e, infers a result value with respect to the input data received using the learning model, and provides a response or control command based on the inferred result value. It can be generated and transmitted to the AI devices 100a to 100e.

Alternatively, the AI devices 100a to 100e may infer a result value with respect to input data using a direct learning model, and generate a response or a control command based on the inferred result value.

Hereinafter, various embodiments of the AI devices 100a to 100e to which the above-described technology is applied will be described. Here, the AI devices 100a to 100e shown in FIG. 3 can be viewed as specific examples of the AI device 100 shown in FIG. 1 .

<AI+Robot>

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc. to which AI technology is applied.

The robot 100a may include a robot control module for controlling an operation, and the robot control module may mean a software module or a chip implemented as hardware.

The robot 100a acquires state information of the robot 100a by using sensor information obtained from various types of sensors, detects (recognizes) surrounding environments and objects, generates map data, moves path and travels A plan may be determined, a response to a user interaction may be determined, or an action may be determined.

Here, the robot 100a may use sensor information obtained from at least one sensor among LiDAR, radar, and camera in order to determine a movement route and a travel plan.

The robot 100a may perform the above-described operations using a learning model composed of at least one artificial neural network. For example, the robot 100a may recognize a surrounding environment and an object using a learning model, and may determine an operation using the recognized surrounding environment information or object information. Here, the learning model may be directly learned from the robot 100a or learned from an external device such as the AI server 200 .

In this case, the robot 100a may perform an operation by generating a result using a direct learning model, but transmits sensor information to an external device such as the AI server 200 and receives the result generated accordingly to perform the operation You may.

The robot 100a determines a movement path and travel plan using at least one of map data, object information detected from sensor information, or object information obtained from an external device, and controls the driving unit to apply the determined movement path and travel plan. Accordingly, the robot 100a may be driven.

The map data may include object identification information for various objects disposed in a space in which the robot 100a moves. For example, the map data may include object identification information for fixed objects such as walls and doors and movable objects such as flowerpots and desks. In addition, the object identification information may include a name, a type, a distance, a location, and the like.

In addition, the robot 100a may perform an operation or drive by controlling the driving unit based on the user's control/interaction. In this case, the robot 100a may acquire intention information of an interaction according to a user's motion or voice utterance, determine a response based on the acquired intention information, and perform the operation.

<AI + Autonomous Driving>

The autonomous driving vehicle 100b may be implemented as a mobile robot, a vehicle, an unmanned aerial vehicle, etc. by applying AI technology.

The autonomous driving vehicle 100b may include an autonomous driving control module for controlling an autonomous driving function, and the autonomous driving control module may mean a software module or a chip implemented as hardware. The autonomous driving control module may be included as a component of the autonomous driving vehicle 100b, but may also be configured and connected as separate hardware to the outside of the autonomous driving vehicle 100b.

The autonomous vehicle 100b obtains state information of the autonomous vehicle 100b using sensor information obtained from various types of sensors, detects (recognizes) surrounding environments and objects, generates map data, A movement route and a driving plan may be determined, or an operation may be determined.

Here, the autonomous driving vehicle 100b may use sensor information obtained from at least one sensor among a lidar, a radar, and a camera, similarly to the robot 100a, in order to determine a moving route and a driving plan.

In particular, the autonomous vehicle 100b may receive sensor information from external devices to recognize an environment or object for an area where the field of view is obscured or an area over a certain distance, or receive information recognized directly from external devices. .

The autonomous vehicle 100b may perform the above-described operations by using a learning model composed of at least one artificial neural network. For example, the autonomous vehicle 100b may recognize a surrounding environment and an object using a learning model, and may determine a driving route using the recognized surrounding environment information or object information. Here, the learning model may be directly learned from the autonomous vehicle 100b or learned from an external device such as the AI server 200 .

In this case, the autonomous vehicle 100b may generate a result by using the direct learning model and perform the operation, but it operates by transmitting sensor information to an external device such as the AI server 200 and receiving the result generated accordingly. can also be performed.

The autonomous vehicle 100b uses at least one of map data, object information detected from sensor information, or object information obtained from an external device to determine a movement path and a driving plan, and controls the driving unit to determine the movement path and driving The autonomous vehicle 100b may be driven according to a plan.

The map data may include object identification information for various objects disposed in a space (eg, a road) in which the autonomous vehicle 100b travels. For example, the map data may include object identification information for fixed objects such as street lights, rocks, and buildings, and movable objects such as vehicles and pedestrians. In addition, the object identification information may include a name, a type, a distance, a location, and the like.

Also, the autonomous vehicle 100b may perform an operation or drive by controlling the driving unit based on the user's control/interaction. In this case, the autonomous vehicle 100b may acquire intention information of an interaction according to a user's motion or voice utterance, determine a response based on the obtained intention information, and perform the operation.

<AI+XR>

The XR device 100c is AI technology applied, so a head-mount display (HMD), a head-up display (HUD) provided in a vehicle, a television, a mobile phone, a smart phone, a computer, a wearable device, a home appliance, and a digital signage , a vehicle, a stationary robot, or a mobile robot.

The XR device 100c analyzes 3D point cloud data or image data acquired through various sensors or from an external device to generate location data and attribute data for 3D points, thereby providing information on surrounding space or real objects. It can be obtained and output by rendering the XR object to be output. For example, the XR apparatus 100c may output an XR object including additional information on the recognized object to correspond to the recognized object.

The XR apparatus 100c may perform the above-described operations using a learning model composed of at least one artificial neural network. For example, the XR apparatus 100c may recognize a real object from 3D point cloud data or image data using a learning model, and may provide information corresponding to the recognized real object. Here, the learning model may be directly learned from the XR device 100c or learned from an external device such as the AI server 200 .

In this case, the XR device 100c may perform an operation by generating a result using the direct learning model, but it transmits sensor information to an external device such as the AI server 200 and receives the result generated accordingly to perform the operation. can also be done

<AI+Robot+Autonomous Driving>

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc. to which AI technology and autonomous driving technology are applied.

The robot 100a to which AI technology and autonomous driving technology are applied may mean a robot having an autonomous driving function or a robot 100a that interacts with the autonomous driving vehicle 100b.

The robot 100a having an autonomous driving function may collectively refer to devices that move by themselves according to a given movement line without user's control or by determining a movement line by themselves.

The robot 100a with the autonomous driving function and the autonomous driving vehicle 100b may use a common sensing method to determine one or more of a moving route or a driving plan. For example, the robot 100a having an autonomous driving function and the autonomous driving vehicle 100b may determine one or more of a movement route or a driving plan by using information sensed through lidar, radar, and camera.

The robot 100a interacting with the autonomous driving vehicle 100b exists separately from the autonomous driving vehicle 100b and is linked to an autonomous driving function inside the autonomous driving vehicle 100b or connected to the autonomous driving vehicle 100b. An operation associated with the user on board may be performed.

At this time, the robot 100a interacting with the autonomous driving vehicle 100b acquires sensor information on behalf of the autonomous driving vehicle 100b and provides it to the autonomous driving vehicle 100b, or obtains sensor information and obtains information about the surrounding environment or By generating object information and providing it to the autonomous driving vehicle 100b, the autonomous driving function of the autonomous driving vehicle 100b may be controlled or supported.

Alternatively, the robot 100a interacting with the autonomous driving vehicle 100b may monitor a user riding in the autonomous driving vehicle 100b or control a function of the autonomous driving vehicle 100b through interaction with the user. . For example, when it is determined that the driver is in a drowsy state, the robot 100a may activate an autonomous driving function of the autonomous driving vehicle 100b or assist in controlling a driving unit of the autonomous driving vehicle 100b. Here, the function of the autonomous driving vehicle 100b controlled by the robot 100a may include not only an autonomous driving function, but also a function provided by a navigation system or an audio system provided in the autonomous driving vehicle 100b.

Alternatively, the robot 100a interacting with the autonomous driving vehicle 100b may provide information or assist a function to the autonomous driving vehicle 100b from the outside of the autonomous driving vehicle 100b. For example, the robot 100a may provide traffic information including signal information to the autonomous driving vehicle 100b, such as a smart traffic light, or interact with the autonomous driving vehicle 100b, such as an automatic electric charger for an electric vehicle. You can also automatically connect an electric charger to the charging port.

<AI+Robot+XR>

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, a drone, etc. to which AI technology and XR technology are applied.

The robot 100a to which the XR technology is applied may mean a robot that is a target of control/interaction within an XR image. In this case, the robot 100a is distinguished from the XR device 100c and may be interlocked with each other.

When the robot 100a, which is the target of control/interaction within the XR image, obtains sensor information from sensors including a camera, the robot 100a or the XR device 100c generates an XR image based on the sensor information. and the XR apparatus 100c may output the generated XR image. In addition, the robot 100a may operate based on a control signal input through the XR device 100c or a user's interaction.

For example, the user can check the XR image corresponding to the viewpoint of the remotely linked robot 100a through an external device such as the XR device 100c, and adjust the autonomous driving path of the robot 100a through interaction or , control motion or driving, or check information of surrounding objects.

<AI+Autonomous Driving+XR>

The autonomous vehicle 100b may be implemented as a mobile robot, a vehicle, an unmanned aerial vehicle, etc. by applying AI technology and XR technology.

The autonomous driving vehicle 100b to which the XR technology is applied may mean an autonomous driving vehicle equipped with a means for providing an XR image or an autonomous driving vehicle subject to control/interaction within the XR image. In particular, the autonomous driving vehicle 100b, which is the target of control/interaction within the XR image, is distinguished from the XR device 100c and may be interlocked with each other.

The autonomous driving vehicle 100b having means for providing an XR image may obtain sensor information from sensors including a camera, and output an XR image generated based on the acquired sensor information. For example, the autonomous vehicle 100b may provide an XR object corresponding to a real object or an object in a screen to the passenger by outputting an XR image with a HUD.

In this case, when the XR object is output to the HUD, at least a portion of the XR object may be output to overlap the real object to which the passenger's gaze is directed. On the other hand, when the XR object is output to the display provided inside the autonomous vehicle 100b, at least a portion of the XR object may be output to overlap the object in the screen. For example, the autonomous vehicle 100b may output XR objects corresponding to objects such as a lane, other vehicles, traffic lights, traffic signs, two-wheeled vehicles, pedestrians, and buildings.

When the autonomous driving vehicle 100b, which is the subject of control/interaction in the XR image, acquires sensor information from sensors including a camera, the autonomous driving vehicle 100b or the XR device 100c performs An XR image is generated, and the XR apparatus 100c may output the generated XR image. In addition, the autonomous vehicle 100b may operate based on a control signal input through an external device such as the XR device 100c or a user's interaction.

4 shows the AI device 100 according to an embodiment of the present disclosure.

A description overlapping with FIG. 1 will be omitted.

In the present disclosure, the artificial intelligence apparatus 100 includes an edge device.

Referring to FIG. 4 , the input unit 120 includes a camera 121 for inputting an image signal, a microphone 122 for receiving an audio signal, and a user input unit 120 for receiving information from a user (User). Input Unit, 123) may be included.

The voice data or image data collected by the input unit 120 may be analyzed and processed as a user's control command.

The input unit 120 is for inputting image information (or signal), audio information (or signal), data, or information input from a user. For input of image information, the AI device 100 may include one or more Cameras 121 may be provided.

The camera 121 processes an image frame such as a still image or a moving image obtained by an image sensor in a video call mode or a shooting mode. The processed image frame may be displayed on the display unit 151 or stored in the memory 170 .

The microphone 122 processes an external sound signal as electrical voice data. The processed voice data may be utilized in various ways according to a function (or a running application program) being performed by the AI device 100 . Meanwhile, various noise removal algorithms for removing noise generated in the process of receiving an external sound signal may be applied to the microphone 122 .

The user input units 120 and 123 are for receiving information from a user, and when information is input through the user input units 120 and 123 , the processor 180 controls the AI device 100 to correspond to the input information. You can control the action.

The user input units 120 and 123 are mechanical input means (or mechanical keys, for example, buttons located on the front/rear or side of the AI device 100 , dome switches, jog wheels, jogs). switch, etc.) and a touch input means. As an example, the touch input means consists of a virtual key, a soft key, or a visual key displayed on the touch screen through software processing, or a part other than the touch screen. It may be made of a touch key (touch key) disposed on the.

The sensing unit 140 may be referred to as a sensing unit 140 .

The output unit 150 includes a display unit (Display Unit, 151), a sound output unit (150) (Sound Output Unit, 152), a haptic module (Haptic Module, 153), and an optical output unit (150) (Optical Output Unit, 154). ) may include at least one of.

The display unit 151 displays (outputs) information processed by the AI device 100 . For example, the display unit 151 may display execution screen information of an application program driven in the AI device 100 or UI (User Interface) and GUI (Graphic User Interface) information according to the execution screen information.

The display unit 151 may implement a touch screen by forming a layer structure with the touch sensor or being integrally formed. Such a touch screen may function as the user input units 120 and 123 that provide an input interface between the AI device 100 and the user, and may provide an output interface between the terminal 100 and the user.

The sound output units 150 and 152 may output audio data received from the communication unit 110 or stored in the memory 170 in a call signal reception, a call mode or a recording mode, a voice recognition mode, a broadcast reception mode, and the like.

The sound output units 150 and 152 may include at least one of a receiver, a speaker, and a buzzer.

The haptic module 153 generates various tactile effects that the user can feel. A representative example of the tactile effect generated by the haptic module 153 may be vibration.

The light output units 150 and 154 output a signal for notifying the occurrence of an event by using the light of the light source of the AI device 100 . Examples of the event generated by the AI device 100 may be message reception, call signal reception, missed call, alarm, schedule notification, email reception, information reception through an application, and the like.

5 shows a flowchart according to an embodiment of the present disclosure.

Referring to FIG. 5 , the blood pressure measuring apparatus 100 of the present disclosure may acquire a biosignal of a measurement target and generate a calibration signal. In addition, the blood pressure of the measurement target may be measured using an external device.

The processor 180 may generate calibration information by using the obtained calibration signal and the blood pressure of the measurement target (S100). In this case, the calibration information may include body information of at least one of age, gender, weight, height, calibration signal, biosignal, and blood pressure of the measurement target.

When the user uses the blood pressure measuring apparatus, the processor 180 may obtain a biosignal of the measurement target through the sensing unit 140 (S200). The processor 180 may acquire a representative bio-signal based on the similarity between the bio-signal of the measurement target and the calibration signal ( S300 ).

In addition, the processor 180 may extract characteristic data of the calibration signal and the representative biosignal (S400), and estimate the user's blood pressure using the artificial intelligence model (S500).

Specifically, step S100 is an operation performed before the user uses the blood pressure measuring apparatus 100 of the present disclosure for measuring blood pressure, and a pulse wave (pulse wave) in which user information and an individual user's physical, health or physiological state are reflected. wave), heart rate, and blood pressure data may refer to a process of obtaining calibration information.

More specifically, the user information may include at least one of age, gender, weight, and height of a measurement target (eg, a user). In this case, the user information may be obtained by the user through the user input unit 120 .

In addition, the pulse wave data may be acquired through the sensing unit 140 that irradiates light to the measurement object and detects a signal change of the light by the measurement object. In this case, the sensing unit 140 may sense a biosignal including pulse wave data, and may include a PPG sensor as an example.

Blood pressure data may be obtained from an external device other than the blood pressure estimating apparatus 100 of the present disclosure. For example, a device capable of measuring blood pressure, such as an analog cuff type blood pressure monitor using mercury or a pressure gauge, or a digital blood pressure monitor using an electronic pressure sensor, may be included.

The processor 180 of the blood pressure estimating apparatus 100 may obtain a blood pressure value obtained from an external device through the communication unit 110 or through the user input unit 120 .

The processor 180 of the blood pressure estimating apparatus may obtain a calibration signal using the biosignal obtained from the sensing unit 140 . A detailed method of acquiring the calibration signal will be described with reference to FIG. 6 .

Meanwhile, the processor 180 may store the obtained calibration information in the memory 170 of the blood pressure estimating apparatus 100 .

Also, the calibration information may be obtained from an external device or server 200 through the communication unit 110 of the blood pressure estimating device.

6 is a flowchart illustrating a process of acquiring calibration information according to an embodiment of the present disclosure.

Referring to FIG. 6 , in order to obtain a calibration signal, the blood pressure estimating apparatus 100 may obtain a biosignal through the sensing unit 140 ( S110 ). The processor 180 may extract a plurality of unit bio-signals included in the obtained bio-signals ( S120 ). In this case, the unit bio-signal may mean pulse wave data obtained from the sensor unit according to one heartbeat.

In general, a plurality of pulse waves ( pulse wave) signal.

Meanwhile, when using the blood pressure estimating apparatus, it is important to select a biosignal caused by a heartbeat that does not include noise generated by an external factor from among a plurality of pulse wave signals included in the user's biosignal. However, there is a problem in that it is difficult to distinguish signals such as motion artifacts caused by the user's movement with the existing noise removal algorithm.

The blood pressure estimating device of the present disclosure does not include noise generated by external factors, generates a calibration signal that is a biosignal based on a heartbeat in advance, and masks the biosignal caused by the heartbeat by comparing it with the biosignal obtained when used later. will be able to pay

Therefore, when generating the calibration signal, it will be desirable for the user to maintain a fixed posture and take a stabilization so that noise caused by external factors is not detected by the sensing unit 140 .

In more detail, the processor 180 may acquire a low frequency of the biosignal acquired by the sensing unit 140 using low-pass filtering. The processor 180 may extract a plurality of unit calibration signals included in the filtered biosignal. In this case, the unit calibration signal may refer to a signal obtained by dividing pulse waves having different periods included in the biosignal for each period.

The processor 180 may normalize each of the plurality of unit calibration signals to generate a representative unit calibration signal based on the plurality of unit calibration signals ( S130 ).

The processor 180 may obtain a representative unit calibration signal by using a median value or an average value of a plurality of normalized unit calibration signals ( S140 ).

Specifically, the processor 180 obtains a phase value corresponding to the same time point of each of the plurality of normalized unit calibration signals, and sets the average value of the plurality of phase values or the average value of the plurality of phase values at the same time point of the representative unit calibration signal. It can be set to the corresponding phase value. Through the above process, one representative unit calibration signal may be generated from a plurality of unit calibration signals.

On the other hand, in the process of generating the representative unit calibration signal of the present disclosure, noise removal or unnecessary motion artifacts are selected as a pre-processing process according to signal processing, and the operation of excluding the corresponding unit calibration signal is performed It would be preferable to be

The processor 180 may extract feature data from the obtained calibration signal (S150). For example, the processor 180 may analyze various characteristic points of the biosignal by analyzing waveform characteristics of the pulse wave signal.

In this case, the feature data may be later input to the artificial intelligence model and used to estimate blood pressure, and the feature data may include physiological or geometrically significant feature data included in the representative unit calibration signal.

An example of the feature data will be described later with reference to FIG. 10 .

The processor 180 may generate calibration information including a calibration signal, characteristic data, user information, and blood pressure information obtained from an external device ( S160 ). After the generation of the calibration information is completed, the processor 180 may estimate the blood pressure using the biosignal received through the sensing unit 140 .

Fig. 5 will be described again.

The processor 180 may acquire a biosignal of a measurement target through the sensing unit 140 to measure the user's blood pressure (S200). In addition, the processor 180 may use the calibration signal obtained in S100 to obtain a representative biosignal based on the similarity between the calibration signal and the biosignal ( S300 ). In this regard, it will be described in detail with reference to FIG. 7 .

7 is a flowchart illustrating a process of acquiring a representative biosignal according to an embodiment of the present disclosure.

Referring to FIG. 7 , the processor 180 may acquire a biosignal including pulse wave information of a measurement target (eg, a user) through the sensing unit 140 . In this case, the biosignal may include a plurality of signals in which waveforms generated by other factors such as the user's heartbeat as well as the user's movement and respiration are superimposed.

In addition, the biosignal may be divided into a plurality of unit signals having different cycles according to the heartbeat of the user.

The processor 180 may acquire a low frequency of the biosignal acquired by the sensing unit 140 using low-pass filtering.

The processor 180 may extract a unit signal included in the biosignal obtained by the sensing unit 140 (S310). In this case, the unit signal may mean a waveform generated per one heartbeat of the user.

After extracting the unit bio-signal, the processor 180 may refine the calibration signal and the unit bio-signal in order to measure the similarity between the unit bio-signal and the calibration signal.

Specifically, the processor 180 may perform smoothing of the calibration signal obtained in S100 and scaling based on the heart rate (S320). In this case, smoothing may be a type of noise removal.

Scaling is to set the cycle of the calibration signal and the unit bio-signal to be the same, and the processor 180 transforms the calibration signal based on the heart rate of the unit bio-signal, so that the calibration signal and the unit bio-signal are separated. The cycle can be set to be the same.

Also, the processor 180 may be able to normalize or scale each of the plurality of unit biosignals based on the heart rate of the biosignals.

The processor 180 may measure the similarity between each of the plurality of unit biosignals extracted in step S310 and the calibration signal generated in step S100 ( S330 ).

Also, the processor 180 may extract a unit bio-signal having the similarity equal to or greater than a preset value (S340).

Specifically, as a method of measuring the similarity between each of the plurality of unit biosignals and the calibration signal, convolution and correlation may be used.

When the similarity between the unit bio-signal and the calibration signal is equal to or greater than a preset value, the processor 180 may determine that only the signal according to the user's ideal heartbeat is included in the unit bio-signal obtained by the sensing unit 140 .

In addition, when the degree of similarity between the unit bio-signal and the calibration signal is less than a preset value, the processor 180 includes not only a signal according to the user's heartbeat but also motion artifacts in the unit bio-signal obtained from the sensing unit 140 . It is determined that signals generated from other factors overlap, and a corresponding unit biosignal may be excluded when estimating blood pressure.

The processor 180 may obtain a unit bio-signal having a similarity between the unit bio-signal and the calibration signal equal to or greater than a preset value, and may obtain a representative bio-signal by calculating a median or average value of the obtained unit bio-signals (S350).

Specifically, first, the processor 180 may normalize unit biosignals having a similarity with the calibration signal equal to or greater than a preset value. The obtained plurality of unit biosignals may be transformed to have a specific value between a predetermined value (eg, 0 to 1) through normalization. Alternatively, it may be transformed into signals having the same period as each other.

The processor 180 may obtain a representative biosignal as a median or average value of the normalized unit biosignals. For example, the processor 180 obtains a phase value corresponding to the same time point of each of the plurality of normalized unit biosignals, and calculates a median value of the plurality of phase values or an average value of the plurality of phase values at the same time point of the representative biosignal. It can be set as a phase value corresponding to .

Through the above process, one representative bio-signal may be generated from a plurality of unit bio-signals.

That is, the process may be performed in the same way as the process S140.

Hereinafter, an exemplary process in which a representative biosignal is acquired in FIG. 8 will be described with pictures.

8 is a diagram for explaining a process of obtaining a representative biosignal with a waveform according to an embodiment of the present disclosure.

Referring to FIG. 8 , the processor 180 may acquire the user's bio-signal through the sensing unit 140 ( S810 , S820 ), and the processor 180 is purified using a low-pass filter or a noise removal algorithm. A biosignal may be acquired (S830, S840). In order to obtain a representative biosignal, the processor 180 may divide the purified biosignal into a plurality of unit biosignals, and measure a similarity between each of the divided unit biosignals and a calibration signal. The processor 180 may extract a unit bio-signal having a similarity between each of the unit bio-signals and a calibration signal equal to or greater than a preset value (S850).

As shown in FIG. 8 , the waveform indicated by the arrow of S850 represents a unit bio-signal having a similarity with a calibration signal of a plurality of unit bio-signals equal to or greater than a preset value.

The processor 180 normalizes the plurality of unit bio-signals extracted in S850 and calculates a median or average value of the normalized unit bio-signals to obtain a representative bio-signal ( S860 ).

As described above, since the representative biosignal is a signal of one cycle based on the heartbeat of the user and noise caused by other factors is removed, the processor 180 may estimate the user's blood pressure based on this.

Fig. 5 will be described again.

After obtaining the representative bio-signal (S300), the processor 180 may extract calibration information and feature data from the representative bio-signal (S400). In addition, the processor 180 may estimate the user's systolic and diastolic blood pressure by inputting the extracted feature data into the artificial intelligence model ( S500 ). Hereinafter, it will be described in detail with reference to FIGS. 9 to 10 .

9 is a diagram illustrating an artificial intelligence model according to an embodiment of the present disclosure. Also, FIG. 10 is a diagram for explaining characteristic data of a waveform according to an embodiment of the present disclosure.

The processor 180 may estimate the blood pressure using the artificial intelligence model learned to output the blood pressure estimation value as a result value when the characteristic data of the biosignal is input.

Referring to FIG. 9 , the artificial intelligence model may include an input value 910 , at least one layer 920 , and a result value 930 , and includes at least one of the characteristic data of the representative biosignal and the characteristic data of the calibration signal. When one is input to the artificial intelligence model, the blood pressure estimate can be output as a result value.

In this case, the artificial intelligence model may be trained to label diastolic and systolic blood pressure as correct values when feature data included in biosignals is input during learning, and the artificial intelligence model is an artificial neural network algorithm, k-neighborhood. It may include any one of an algorithm, a Bayesian network algorithm, an SVM algorithm, and a recurrent neural network algorithm.

Specifically, the processor 180 may use at least one of characteristic data of a calibration signal, calibration information, and characteristic data of a representative biosignal as the input value 910 of the artificial intelligence model.

In this case, the processor 180 may extract the characteristic data of the representative bio-signal and the characteristic data of the calibration signal based on the characteristics of the waveform included in the representative bio-signal and the calibration signal.

10 illustrates a unit biosignal obtained from a sensor unit according to two heartbeats, according to an embodiment of the present disclosure.

Specifically, referring to FIG. 10, the characteristic of the waveform is a reflection index indicating the ratio of the inflection point of the waveform to the systolic peak of the waveform, the ratio of the area formed before and after the inflection point ( area of the after inflection point and area of the before inflection point), time between diastolic peak and systolic peak, and ratios of amplitudes of s detected key positions may include at least one of

The processor 180 may extract feature data from the calibration signal and the representative biosignal using the feature elements of the waveform described with reference to FIG. 10 .

The processor 180 may obtain an estimate of the user's systolic and diastolic blood pressure by inputting the extracted feature data into the artificial intelligence model.

Meanwhile, the artificial intelligence model of the present disclosure may be learned by the blood pressure estimating apparatus 100 , and it is also possible to receive the artificial intelligence model learned from the external device or the server 200 through the communication unit 110 .

In addition, in order to secure the versatility of the artificial intelligence model of the present disclosure, data belonging to the normal blood pressure group may be basically learned, which does not correspond to data focused on the individual user, and thus blood pressure above/below the normal range of a specific user. Data may be scarce.

Accordingly, the processor 180 may improve the performance of some layers of the artificial intelligence model by using the calibration signal and blood pressure included in the calibration information.

The present disclosure described above can be implemented as computer-readable code on a medium in which a program is recorded. The computer-readable medium includes all types of recording devices in which data readable by a computer system is stored. Examples of computer-readable media include Hard Disk Drive (HDD), Solid State Disk (SSD), Silicon Disk Drive (SDD), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. There is this. In addition, the computer may include a processor 180 of the terminal.

Claims (17)

1. a sensing unit configured to acquire a biosignal of a measurement target;

   A representative bio-signal is obtained based on the similarity between the calibration signal and the bio-signal,

   and a processor for estimating blood pressure using an artificial intelligence model trained to output a blood pressure estimate as a result value when characteristic data of a biosignal is input,

   The calibration signal is

   Extracting a plurality of unit calibration signals included in the biosignal of the measurement target obtained in advance, and meaning a representative unit calibration signal generated based on the plurality of unit calibration signals,

   blood pressure estimator.
2. 2. The method of claim 1

   The processor is

   A plurality of unit bio-signals included in the bio-signal are extracted, and a unit bio-signal having a similarity between each of the plurality of unit bio-signals and the calibration signal is greater than or equal to a preset value, and a representative bio-signal is obtained based on the obtained unit bio-signals. to acquire a signal,

   blood pressure estimator.
3. 3. The method of claim 2,

   the processor is

   measuring the heart rate using the biosignal,

   scaling the calibration signal to correspond to the heart rate, and obtaining a similarity between the scaled calibration signal and the plurality of unit biosignals,

   blood pressure estimator.
4. 3. The method of claim 2

   The processor is

   Normalizing unit biosignals having a similarity with the calibration signal equal to or greater than a preset value,

   obtaining a representative bio-signal based on the normalized unit bio-signals,

   blood pressure estimator.
5. 5. The method of claim 4

   The processor is

   obtaining a representative bio-signal as a median or average value of the normalized unit bio-signals;

   blood pressure estimator.
6. 2. The method of claim 1

   The calibration signal is

   Normalizing the plurality of unit calibration signals,

   Meaning a representative unit calibration signal generated as a median or average value of the normalized unit calibration signals,

   blood pressure estimator.
7. 2. The method of claim 1

   The processor is

   Based on the characteristics of the waveform included in the representative biosignal and the calibration signal

   extracting the characteristic data of the representative bio-signal and the characteristic data of the calibration signal,

   blood pressure estimator.
8. 8. The method of claim 7

   The characteristics of the waveform are:

   at least one of a ratio of an inflection point of a waveform to a systolic peak of the waveform, a ratio of regions formed before and after the inflection point, a ratio of a time between a diastolic and a systolic peak, and a detected amplitude,

   blood pressure estimator.
9. 8. The method of claim 7

   The artificial intelligence model is

   When at least one of the characteristic data of the representative biosignal and the characteristic data of the calibration signal is input to the artificial intelligence model, the blood pressure estimation value is output as a result value,

   blood pressure estimator.
10. 2. The method of claim 1

    The artificial intelligence model includes any one of an artificial neural network algorithm, a k-nearest neighbor algorithm, a Bayesian network algorithm, an SVM algorithm, and a recurrent neural network algorithm,

    blood pressure estimator.
11. 2. The method of claim 1

    Further comprising a memory for storing body information including at least one of age, gender, weight, height, calibration signal, biosignal, and blood pressure of the measurement target,

    blood pressure estimator.
12. 2. The method of claim 1

    Further comprising a communication unit for receiving body information including at least one of age, gender, weight, height, calibration signal, biosignal, and blood pressure of the measurement target,

    blood pressure estimator.
13. 2. The method of claim 1

    Further comprising a display unit for outputting the estimated blood pressure,

    blood pressure estimator.
14. generating a calibration signal;

    acquiring a bio-signal of a measurement target, and obtaining a representative bio-signal based on a degree of similarity between a calibration signal and the bio-signal;

    Including the step of estimating blood pressure when the characteristic data of the biosignal is input using an artificial intelligence model,

    A method of operating a blood pressure estimating device.
15. 15. The method of claim 14

    The step of generating the calibration signal comprises:

    extracting a plurality of unit calibration signals included in the pre-obtained bio-signals of the measurement target; and

    generating a representative unit calibration signal based on the plurality of unit calibration signals;

    A method of operating a blood pressure estimating device.
16. 15. The method of claim 14

    The step of obtaining a representative bio-signal based on the degree of similarity between the calibration signal and the bio-signal includes:

    extracting a plurality of unit biosignals included in the biosignals; Comprising the steps of obtaining a unit bio-signal having a similarity between each of the plurality of unit bio-signals and the calibration signal equal to or greater than a preset value, and obtaining a representative bio-signal based on the obtained unit bio-signals,

    A method of operating a blood pressure estimator.
17. 15. The method of claim 14,

    The step of estimating the blood pressure when the characteristic data of the biosignal is input using the artificial intelligence model,

    extracting the characteristic data of the representative bio-signal and the characteristic data of the calibration signal based on the characteristics of the waveform included in the representative bio-signal and the calibration signal, and the characteristic data of the representative bio-signal and the characteristic data of the calibration signal Comprising the step of outputting the blood pressure estimate value as a result value when at least one of them is input to the artificial intelligence model,

    A method of operating a blood pressure estimator.

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