KR102253946B1 - Computer device for prediction sleep state based on data measured in user's sleep environment - Google Patents

Abstract

Disclosed is a computing device for predicting a sleep state based on data measured in a user's sleep environment. The computing device of the present invention comprises: a processor for receiving sleep sensing data of a user, and inputting the sleep sensing data to a sleep evaluation model to calculate sleep analysis information on the user; a memory for storing program codes executable by the processor; and a network unit for transceiving data with a user terminal. In addition, the sleep sensing data includes user respiration information obtained during a predetermined time period related to the sleep environment of the user. The sleep analysis information includes at least one of apnea severity information related to the degree of apnea occurrence by the user and disease prediction information related to the possibility of disease occurrence. Accordingly, the efficiency of health care can be improved.

Description

A computing device for predicting sleep based on data measured in the user's sleeping environment {COMPUTER DEVICE FOR PREDICTION SLEEP STATE BASED ON DATA MEASURED IN USER'S SLEEP ENVIRONMENT}

The present disclosure provides information on a sleep state based on a user's sleep data acquired in a sleep environment, and more specifically, providing analysis information and health management information corresponding to a user's sleep state using an artificial neural network It is for sake.

There are many ways to maintain and improve your health, such as exercise and diet, but it is most important to manage your sleep, which takes about 30% or more of the day. However, modern people are unable to get a good night's sleep due to irregular eating habits, lifestyle habits, and stress despite the simple replacement of labor by machines and life, and suffer from sleep disorders such as insomnia, excessive sleep, sleep apnea syndrome, nightmares, nightmares, and sleepwalking. Are receiving.

According to the National Health Insurance Corporation, the number of patients with sleep disorders in Korea increased by an average of about 8% per year from 2014 to 2018, and about 570,000 patients were treated for sleep disorders in Korea in 2018.

In particular, in the case of sleep apnea, which is the most common but dangerous among sleep disorders, 1 in 6 adults in Korea suffers from it. Sleep apnea is considered to be a major cause of heart disease, mental disease, and brain disease, as well as disturbing sleep.

As sound sleep is recognized as an important factor affecting physical or mental health, interest in sound sleep is increasing.However, in order to improve sleep disorders, you need to visit a specialized medical institution in person, and separate test costs are required, and continuous Due to the difficulty of management, users' efforts for treatment are insufficient.

Accordingly, Korean Patent Publication 2003-0032529 receives user's body information and outputs vibration and/or ultrasonic waves in a frequency band detected by repetitive learning according to the user's body state during sleep, enabling optimal sleep induction. Disclosed is a sleep induction device and a sleep induction method.

However, in the conventional technology, there is a concern that the quality of sleep may be reduced due to inconvenience caused by the wearable equipment, and periodic management of the equipment (eg, charging, etc.) is required. In addition, the conventional technology simply provides information that induces optimal sleep, and as the correlation between the information obtained from sleep and the accompanying diseases is not considered, it can provide predictive information on the possibility of future disease occurrence. Therefore, medical diagnosis and information transmission are impossible.

Therefore, in the industry, it is possible to continuously diagnose sleep disorders by collecting data related to the user's sleeping environment in a non-contact manner, and at the same time, the occurrence of accompanying diseases (e.g., heart disease, mental disease, and brain disease). There may be a demand for computing devices that can predict the likelihood.

The present disclosure was conceived in response to the above-described background technology, and provides information related to the sleep state based on data measured in the sleep environment of each user, and the possibility of occurrence of sleep diseases and accompanying diseases accompanying sleep diseases. It is to predict and provide medical diagnosis information to the user.

Problems to be solved by the present disclosure are not limited to the problems mentioned above, and other problems that are not mentioned will be clearly understood by those skilled in the art from the following description.

In one embodiment of the present disclosure for solving the above-described problem, a computing device for predicting a sleep state based on data measured in a user's sleep environment is disclosed. The computing device includes a processor configured to receive the user's sleep sensing data and input the sleep sensing data to a sleep evaluation model to calculate sleep analysis information of the user, a memory storing program codes executable in the processor, and And a network unit that transmits and receives data to and from a user terminal, and the sleep sensing data includes breathing information of the user acquired during a predetermined time period in relation to the sleep environment of the user, and the sleep analysis information includes: It may include at least one of apnea severity information on a user's apnea incidence degree and disease prediction information on a probability of disease occurrence.

In an alternative embodiment, further comprising a sensor unit for acquiring the user's sleep sensing data, wherein the sensor unit receives at least one transmission module for transmitting a radio wave of a specific frequency and a reflected wave generated in response to the radio wave of the specific frequency Including a receiving module, and by detecting a phase difference or a frequency change according to the moving distance of the reflected wave, it is possible to obtain the sleep sensing data from the user in a non-contact manner.

In an alternative embodiment, the processor receives a sleep diagnosis data set including a plurality of sleep diagnosis data corresponding to each of a plurality of users, and the user's breathing for a predetermined time period from each of the plurality of sleep diagnosis data , Extracting information on heart rate and movement to generate a learning input data set, and extracting at least one of information on apnea index, information on sleep state, and information on whether or not a disease occurs from each of the plurality of sleep diagnosis data A learning output data set is generated, the learning input data set and the learning output data set are matched to generate a labeled training data set, and the sleep by learning one or more network functions using the labeled training data set. You can create an evaluation model.

In an alternative embodiment, the sleep evaluation model, wherein the processor inputs each of the training input data sets to the one or more network functions, and output data calculated by the one or more network functions and each of the training input data sets An error is derived by comparing each of the training output data sets corresponding to a label, and the weight of the one or more network functions is adjusted in a backpropagation method based on the error, and the learning of the one or more network functions is performed in advance. When more than the determined epoch is performed, it is generated by determining whether to stop learning using verification data, and by testing the performance of the one or more network functions using a test data set to determine whether to activate the one or more network functions. I can.

In an alternative embodiment, the sleep evaluation model is a model for predicting the user's sleep state and the likelihood of disease occurrence based on the sleep sensing data, and includes one or more network functions, and the one or more network functions, A Dilated-Convolutional Neural Network for reinforcing a long-term relationship without loss of input data may be included.

In an alternative embodiment, the extended convolutional neural network enhances the long-term association by expanding a receptive field of a filter related to the input of the one or more network functions, and the filter related to the input. Zero padding may be added to maintain lengths of inputs and outputs of the one or more network functions.

In an alternative embodiment, the disease prediction information includes prediction information on at least one of a sleep disorder, a mental disorder, a brain disorder, and a cardiovascular disorder, and the processor includes the user input to the sleep evaluation model. A correlation between the sleep sensing data and the disease prediction information may be derived based on the amount of change in the disease prediction information output by changing the respiratory information for a predetermined time period included in the sleep sensing data of.

In an alternative embodiment, the sleep sensing data includes motion information and heart rate information for a predetermined period of time, and the sleep analysis information includes at least one sleep state according to time change in relation to the user's sleep environment. May contain sleep phase information for changes.

In an alternative embodiment, the sensor unit further comprising at least one environment sensing module for acquiring indoor environment information including information on at least one of the user's body temperature, room temperature, room humidity, room sound, and room illuminance. In addition, the processor may generate sleep degradation factor information based on the sleep stage information and the indoor environment information.

In an alternative embodiment, the processor identifies a first time point at which the user's sleep quality is deteriorated based on the sleep stage information, and identifies a singular point related to the amount of change in the indoor environment information corresponding to the first time point. And generate the sleep degradation factor information based on the identified singularity.

In an alternative embodiment, the processor relates to the variability of the indoor environment information based on whether a change amount of each of the one or more pieces of information included in the indoor environment information exceeds a predetermined threshold change amount corresponding to the first time point. Singularity can be identified.

In an alternative embodiment, further comprising an indoor environment creation unit for adjusting at least one of temperature, humidity, sound, and illuminance to create an indoor environment related to the user's sleeping environment, and the processor includes information on the sleep deterioration factor On the basis of, an environment control signal for controlling the indoor environment creation unit may be generated.

In an alternative embodiment, the processor determines to generate health management information for improving the user's sleep environment and health based on the sleep evaluation information and transmit it to the user terminal of the user, and the health management information is , Information on eating habits, information on exercise amount, and information on an optimal sleeping environment may include at least one of information.

In another embodiment of the present disclosure, a method of predicting a sleep state and a disease based on data measured in a user's sleep environment performed by one or more processors of a computing device is disclosed. The method includes: receiving, by the processor, the user's sleep sensing data, calculating, by the processor, the sensing data as an input of a sleep evaluation model, and calculating the sleep analysis information of the user, and the processor, the sleep analysis information It may include the step of determining to transmit to the user terminal of the user.

In another embodiment of the present disclosure, a computer program stored in a computer-readable storage medium is disclosed. When the computer program is executed on more than one processor, it performs the following operations for predicting a sleep state based on data acquired in the user's sleep environment, the operations: receiving the user's sleep sensing data , An operation of calculating, by the processor, the user's sleep analysis information by using the sensing data as an input of a sleep evaluation model, and an operation of determining, by the processor, to transmit the sleep analysis information to the user's user terminal. .

Other specific matters of the present disclosure are included in the detailed description and drawings.

The present disclosure has been devised in response to the above-described background technology, and can provide information related to sleep disorders based on data measured in each user's sleep environment, and at the same time, the possibility of occurrence of sleep disorders and accompanying diseases. By predicting and providing medical diagnosis information to the user, it is possible to provide an effect of improving the efficiency of health management.

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

Various aspects are now described with reference to the drawings, wherein like reference numbers are used collectively to refer to like elements. In the examples that follow, for illustrative purposes, a number of specific details are presented to provide a comprehensive understanding of one or more aspects. However, it will be apparent that such aspect(s) may be practiced without these specific details.
1 is a conceptual diagram illustrating a system in which various aspects of a computing device for providing sleep analysis information for providing sleep analysis information based on sleep environment sensing data related to a user's sleep environment according to an embodiment of the present disclosure may be implemented.
2 is a block diagram of a computing device for providing sleep analysis information based on sleep environment sensing data related to a user's sleep environment according to an embodiment of the present disclosure.
3 is an exemplary diagram of a computing device that acquires sleep sensing data related to a user's sleep environment according to an embodiment of the present disclosure.
4 is an exemplary diagram illustrating a learning process of a sleep evaluation model related to an embodiment of the present disclosure.
5 is a schematic diagram illustrating one or more network functions related to an embodiment of the present disclosure.
6 is a flowchart illustrating a method of providing sleep analysis information based on sleep sensing data related to a user's sleep environment according to an embodiment of the present disclosure.

Advantages and features of the present disclosure, and a method of achieving them will be apparent with reference to the embodiments described below in detail together with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below, but may be implemented in a variety of different forms. It is provided to fully inform the technical person of the scope of the present disclosure, and the present disclosure is only defined by the scope of the claims.

The terms used in the present specification are for describing exemplary embodiments and are not intended to limit the present disclosure. In this specification, the singular form also includes the plural form unless specifically stated in the phrase. As used herein, “comprises” and/or “comprising” do not exclude the presence or addition of one or more other elements other than the mentioned elements. Throughout the specification, the same reference numerals refer to the same elements, and "and/or" includes each and all combinations of one or more of the mentioned elements. Although "first", "second", and the like are used to describe various elements, it goes without saying that these elements are not limited by these terms. These terms are only used to distinguish one component from another component. Therefore, it goes without saying that the first component mentioned below may be the second component within the technical idea of the present disclosure.

Unless otherwise defined, all terms (including technical and scientific terms) used in the present specification may be used with meanings that can be commonly understood by those of ordinary skill in the art to which this disclosure belongs. In addition, terms defined in a commonly used dictionary are not interpreted ideally or excessively unless explicitly defined specifically.

The term "unit" or "module" used in the specification refers to a hardware component such as software, FPGA or ASIC, and the "unit" or "module" performs certain roles. However, "unit" or "module" is not meant to be limited to software or hardware. The “unit” or “module” may be configured to be in an addressable storage medium, or may be configured to reproduce one or more processors. Thus, as an example, "sub" or "module" refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays and variables. Components and functions provided within "sub" or "modules" may be combined into a smaller number of components and "sub" or "modules" or into additional components and "sub" or "modules". Can be further separated.

In the present specification, a computer means all kinds of hardware devices including at least one processor, and may be understood as encompassing a software configuration operating in the corresponding hardware device according to embodiments. For example, the computer may be understood as including all of a smartphone, a tablet PC, a desktop, a laptop, and a user client and application running on each device, and is not limited thereto.

Those of skill in the art would further describe the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein, including electronic hardware, computer software, or a combination of both. It should be recognized that it can be implemented as To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or as software depends on the specific application and design restrictions imposed on the overall system. Skilled technicians can implement the described functionality in various ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

Each of the steps described herein is described as being performed by a computer, but the subject of each step is not limited thereto, and at least some of the steps may be performed by different devices according to embodiments.

1 is an exemplary diagram illustrating a system in which various aspects of a computing device for predicting a sleep state based on measured data in a user's sleep environment according to an embodiment of the present disclosure may be implemented.

A system according to embodiments of the present disclosure may include a computing device 100, a user terminal 10, an external server 20, and a network. The computing device 100, the user terminal 10, and the external server 20 according to the embodiments of the present disclosure may mutually transmit and receive data for the system according to the exemplary embodiments of the present disclosure through a network.

Networks according to embodiments of the present disclosure include Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), and Very High Speed DSL (VDSL). ), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and local area network (LAN).

In addition, the networks presented here include Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multi Access (OFDMA), Single Carrier-FDMA (SC-FDMA), and Various wireless communication systems such as other systems can be used.

The network according to the embodiments of the present disclosure may be configured regardless of its communication mode, such as wired or wireless, and is composed of various communication networks such as a short-range communication network (PAN) and a local area network (WAN). Can be. In addition, the network may be a known World Wide Web (WWW), and a wireless transmission technology used for short-range communication such as infrared (IrDA) or Bluetooth may be used. The techniques described herein may be used not only in the networks mentioned above, but also in other networks.

According to an embodiment of the present disclosure, the computing device 100 may acquire sleep sensing data related to the user's sleep environment, and generate sleep analysis information related to the user's sleep state based on the sleep sensing data. Specifically, the computing device 100 may generate a sleep evaluation model for outputting sleep analysis information based on sleep sensing data by learning one or more network functions using a labeled learning data set. That is, the computing device 100 may acquire sleep sensing data related to the user's sleep environment, and may generate sleep analysis information by processing the sleep sensing data as an input of a sleep evaluation model that is a learned neural network model. In this case, the sleep analysis information provided by the computing device 100 may include at least one of apnea severity information about the degree of apnea occurrence related to a user's sleep, sleep stage information about a change in sleep state, and disease prediction information about the possibility of disease occurrence. It can contain one. In other words, the computing device 100 may provide information related to a sleep disorder and information for identifying a sleep stage based on data measured in a user's individual sleep environment, and at the same time, a companion disease accompanying the sleep disorder. By predicting the likelihood of occurrence and providing medical diagnosis information to the user, it is possible to provide an effect of improving the efficiency of health management. A detailed configuration of the computing device 100 of the present disclosure and effects according to the configuration will be described in detail later with reference to FIG. 2 below.

In addition, the computing device 100 may be a terminal or a server, and may include any type of device. The computing device 100 is a digital device, and may be a digital device equipped with a processor, such as a laptop computer, a notebook computer, a desktop computer, a web pad, and a mobile phone, and equipped with a memory and a computing capability. The computing device 100 may be a web server that processes a service. The types of servers described above are only examples, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the user terminal 10 is a terminal capable of receiving information related to a user's sleep through information exchange with the computing device 100, and may mean a terminal possessed by the user. For example, the user terminal 10 may be a terminal related to a user who wants to improve his or her health through information related to his/her sleeping habit. Through the user terminal 10, the user may receive information related to his/her sleep, prediction information on the possibility of occurrence of a companion disease, information for reducing the likelihood of occurrence of a disease, or information for improving health.

These user terminals 10 include customer terminals (UE), mobiles, PCs capable of wireless communication, mobile phones, kiosks, cellular phones, cellular phones, cellular terminals, subscriber units, subscriber stations, mobile stations, terminals, remote stations, PDAs, and remote terminals. , Access terminals, user agents, cellular telephones, wireless telephones, session initiation protocol (SIP) telephones, wireless local loop (WLL) stations, portable devices with wireless access capabilities, and any wireless mechanisms capable of using a wireless model. It may be referred to as a device of, but is not limited thereto. In addition, the user terminal 10 may be referred to as a wired fax machine, a PC equipped with a wired model, a wired telephone, an arbitrary device capable of using a wired connection mechanism such as a terminal capable of wired communication, but is not limited thereto. Does not.

According to an embodiment of the present disclosure, the external server 20 may be a server that stores health checkup information or sleep checkup information for a plurality of users. For example, the external server 20 may be at least one of a hospital server and an information server, and may be a server that stores information on a plurality of polysomnographic records, electronic health records, and electronic medical records. For example, the sleep polysomnography record includes information on brain waves, eye movements, muscle movements, respiration, electrocardiogram, etc. during sleep of the sleep test subject, and the results of sleep diagnosis corresponding to the information (e.g., sleep apnea, sleep disorder, etc.). ) Can be included. In addition, the polysomnography record may include information on whether or not the person to be examined has a disease. For example, the subject of the polysomnography test has heart disease (ischemic myocardial infarction, heart attack, arrhythmia, etc.), brain disease (bleeding Stroke, ischemic stroke, transient ischemia, cerebral infarction, and the like) and mental disorders (depression, anxiety disorder, manic depression, panic disorder, dementia, delusional disorder, etc.). Information stored in the external server 20 may be used as training data, verification data, and test data for training the neural network in the present disclosure.

The computing device 100 of the present disclosure may receive health checkup information or sleep checkup information from the external server 20, and may build a learning data set based on the corresponding information. The computing device 100 may generate a sleep evaluation model for calculating sleep analysis information corresponding to a user by learning a sleep evaluation model including one or more network functions through the training data set. A detailed description of a configuration for constructing a training data set for training a neural network and a learning method using the training data set according to the present disclosure will be described later with reference to FIG. 2.

The external server 20 is a digital device, and may be a digital device equipped with a processor, such as a laptop computer, a notebook computer, a desktop computer, a web pad, and a mobile phone, and equipped with a memory and a computing capability. The external server 20 may be a web server that processes a service. The types of servers described above are only examples, and the present disclosure is not limited thereto.

Hereinafter, a method of providing sleep analysis information by the computing device 100 based on sleep environment sensing data will be described in more detail below with reference to FIG. 2.

2 is a block diagram of a computing device for providing sleep analysis information based on sleep environment sensing data related to a user's sleep environment according to an embodiment of the present disclosure.

As shown in FIG. 2, the computing device 100 may include a network unit 110, a memory 120, a sensor unit 130, an environment creation unit 140, and a processor 150. Components included in the above-described computing device 100 are exemplary, and the scope of the present disclosure is not limited to the above-described components. That is, additional components may be included or some of the above-described components may be omitted according to implementation aspects of the embodiments of the present disclosure.

According to an embodiment of the present disclosure, the computing device 100 may include a user terminal 10 and a network unit 110 that transmits and receives data to and from the external server 20. The network unit 110 may transmit and receive data for performing a method of providing sleep analysis information based on the sleep environment sensing data according to an embodiment of the present disclosure. That is, the network unit 110 may provide a communication function between the computing device 100 and the user terminal 10 and the external server 20. For example, the network unit 110 may receive sleep examination records and electronic health records for a plurality of users from a hospital server. Additionally, the network unit 110 may allow information transfer between the computing device 100 and the user terminal 10 and the external server 20 by calling a procedure to the computing device 100.

The network unit 110 according to an embodiment of the present disclosure includes a public switched telephone network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), and VDSL ( Various wired communication systems such as Very High Speed DSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and local area network (LAN) can be used.

In addition, the network unit 110 presented in the present specification includes Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multi Access (OFDMA), and SC-FDMA ( Single Carrier-FDMA) and various wireless communication systems can be used.

In the present disclosure, the network unit 110 may be configured regardless of its communication mode, such as wired and wireless, and may be configured with various communication networks such as a short-range communication network (PAN) and a local area network (WAN). I can. In addition, the network may be a known World Wide Web (WWW), and a wireless transmission technology used for short-range communication such as infrared (IrDA) or Bluetooth may be used. The techniques described herein may be used not only in the networks mentioned above, but also in other networks.

According to an embodiment of the present disclosure, the memory 120 may store a computer program for performing a method of providing sleep analysis information according to an embodiment of the present disclosure, and the stored computer program is It can be read and driven. In addition, the memory 120 may store information in any form generated or determined by the processor 150 and information in any form received by the network unit 110. In addition, the memory 120 may store data related to a user's sleep. For example, the memory 120 may temporarily or permanently store input/output data (eg, sleep sensing data related to a user's sleep environment, sleep analysis information, health promotion information, etc.).

According to an embodiment of the present disclosure, the memory 120 is a flash memory type, a hard disk type, a multimedia card micro type, and a card type memory (for example, SD or XD memory), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read) -Only Memory), magnetic memory, magnetic disk, and optical disk. The computing device 100 may operate in connection with a web storage that performs a storage function of the memory 120 on the Internet. The description of the above-described memory is only an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the computing device 100 may acquire sleep sensing data using the sensor unit 130. The sensor unit 130 may acquire sleep sensing data related to the user's sleep environment. The sleep sensing data may include user's breathing information acquired during a predetermined time period in relation to the user's sleep environment. In addition, the sleep sensing data may further include motion information and heart rate information of the user acquired during a predetermined time period in relation to the user's sleep environment. That is, the sensor unit 130 may include a sensor module for acquiring at least one of breathing information, motion information, and heart rate information from the user in relation to the user's sleeping environment.

According to an embodiment of the present disclosure, the sensor unit 130 includes one or more transmission modules that transmit radio waves of a specific frequency and a reception module that receives reflected waves generated in response to radio waves of a specific frequency (eg, microwave). It may be provided, including. In this case, the sensor unit 130 may obtain sleep sensing data from a user in a non-contact manner by detecting a phase difference or a frequency change according to a moving distance of a reflected wave corresponding to a radio wave transmitted from the transmission module. For example, when a radio wave transmitted through the transmission module hits an object and is reflected, a phase difference or frequency may be changed. For example, when the object is closer to the transmission module, the frequency of the reflected wave may be shortened, and when the object gradually increases, the frequency of the reflected wave may be increased. That is, the sensor unit 130 may acquire sleep sensing information related to the user's breathing by detecting the movement of the user's body (eg, abdomen or chest) based on a phase difference of the reflected wave or a change in frequency. For example, as illustrated in FIG. 3, when the user sleeps, the sensor unit 130 may be located in an area of a space in which the user sleeps. The sensor unit 130 may transmit a radio wave having a specific frequency through a transmission module, and receive a reflected wave reflected from a user's body in response to the radio wave through the receiving module. In addition, the sensor unit 130 may acquire at least one of user's breathing information, motion information, and heart rate information based on a phase difference or frequency change of the reflected wave received through the receiving module. However, the position where the sensor unit of the present disclosure is provided is not limited to the position shown in FIG. 3, and may be provided in plural in various areas within the space where the user sleeps.

In addition, as described above, the sensor unit 130 may include a transmission module and a reception module to obtain sleep sensing data through a radio frequency (RF) sensing method, but the present disclosure is not limited thereto. In an additional embodiment, the sensor unit 130 of the present disclosure is configured to further include a sensor module for transmitting and detecting WiFi radio waves, and a sensor module for detecting airflow related to the user's breathing to sense sleep. Data can be acquired.

According to an embodiment of the present disclosure, the sensor unit 130 includes information on at least one of a user's body temperature, indoor temperature, indoor airflow, indoor humidity, indoor sound, and indoor illuminance in relation to the user's sleeping environment. It may include one or more environment sensing modules for acquiring indoor environment information. The indoor environment information is information related to a user's sleeping environment, and may be information that serves as a criterion for considering the influence of an external factor on the user's sleep through a sleep state related to a change in the user's sleep stage. The one or more environment sensing modules may include, for example, at least one sensor module among a temperature sensor, an airflow sensor, a humidity sensor, an acoustic sensor, and an illuminance sensor. However, the present invention is not limited thereto, and various sensors that may affect the user's sleep may be further included.

According to an embodiment of the present disclosure, the computing device 100 may adjust a user's sleeping environment through the environment creation unit 140. Specifically, the environment creation unit 140 may include one or more driving modules, and operates a driving module related to at least one of temperature, wind direction, humidity, sound, and illuminance based on the environmental control signal received from the processor 150 By doing so, it is possible to adjust the sleeping environment of the user. The environment control signal may be a signal generated by the processor 150 based on a sleep state determination according to a change in the user's sleep stage, for example, lowering the temperature or raising the humidity in relation to the user's sleeping environment. Or, it may be a signal to lower the illuminance or lower the sound. The detailed description of the above-described environmental control signal is only an example, and the present disclosure is not limited thereto.

The one or more driving modules may include, for example, at least one of a temperature control module, a wind direction control module, a humidity control module, a sound control module, and an illuminance control module. However, the present invention is not limited thereto, and the one or more driving modules may further include various driving modules capable of causing a change in a user's sleeping environment. That is, the environment creation unit 140 may adjust the sleeping environment of the user by driving one or more driving modules based on the environment control signal of the processor 150.

According to another embodiment of the present disclosure, the environment creation unit 140 may be implemented through linkage through the Internet of Things (IOT). Specifically, the environment creation unit 140 may be implemented by linking with various devices that can change the indoor environment in relation to the space in which the user is located for sleep. For example, the environment creation unit 140 may be implemented as a smart air conditioner, a smart heater, a smart boiler, a smart window, a smart humidifier, a smart dehumidifier, and a smart lighting based on connection through the Internet of Things. Specific description of the above-described environmental composition unit is only an example, the present disclosure is not limited anymore.

According to an embodiment of the present disclosure, the processor 150 may be configured with one or more cores, and a central processing unit (CPU) and a general purpose graphics processing unit (GPGPU) of a computing device , A tensor processing unit (TPU) may include a processor for data analysis and deep learning.

The processor 150 may read a computer program stored in the memory 120 to process data for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 150 may perform an operation for learning a neural network. The processor 150 processes input data for learning in deep learning (DL), extracts features from the input data, calculates errors, and uses backpropagation to update the weights of the neural network. You can perform calculations.

In addition, at least one of the CPU, GPGPU, and TPU of the processor 150 may process learning of the network function. For example, the CPU and GPGPU can work together to learn network functions and classify data using network functions. In addition, in an embodiment of the present disclosure, learning of a network function and data classification using a network function may be processed by using processors of a plurality of computing devices together. In addition, the computer program executed in the computing device according to an embodiment of the present disclosure may be a CPU, a GPGPU, or a TPU executable program.

In the present specification, the network function may be used interchangeably with an artificial neural network or a neural network. In the present specification, the network function may include one or more neural networks, and in this case, the output of the network function may be an ensemble of outputs of one or more neural networks.

In the present specification, the model may include a network function. The model may include one or more network functions, and in this case, the output of the model may be an ensemble of the outputs of one or more network functions.

The processor 150 may read a computer program stored in the memory 120 to provide a sleep evaluation model according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 150 may perform calculation for calculating sleep analysis information based on sleep sensing data. According to an embodiment of the present disclosure, the processor 150 may perform calculations for training a sleep evaluation model.

According to an embodiment of the present disclosure, the processor 150 may typically process the overall operation of the computing device 100. The processor 150 can provide or process appropriate information or functions to the user terminal by processing signals, data, information, etc. that are input or output through the above-described components, or by running an application program stored in the memory 120. have.

According to an embodiment of the present disclosure, the processor 150 may receive sleep sensing data related to a user's sleep environment from the sensor unit 130. The sleep sensing data is information acquired through the sensor unit 130 and may include breathing information of the user for a predetermined time period in relation to the sleep environment of the user. For example, the sleep sensing data may be user's breathing information acquired for 60 minutes. The sleep sensing data may be information about the user's breathing for 60 minutes measured in a 10 Hz period, and may be time series information having a matrix size of 3600 x 10. Description of specific values for the sleep sensing data is only an example, and the present disclosure is not limited thereto. The sleep sensing data may be input data processed as input to the sleep evaluation model in order to calculate a result value (ie, sleep analysis information) through a sleep evaluation model that is a neural network model.

In an additional embodiment, the sleep sensing data may further include user motion information and heart rate information acquired through the sensor unit 130 for a predetermined time period in relation to the user's sleep environment. That is, according to the implementation aspect of the sensor unit 130, the sleep sensing data may include at least one of user's breathing information, motion information, and heart rate information. In this case, the sleep sensing data may include, for example, breathing information of the user for 60 minutes measured in a 10 Hz period, motion information of the user for 60 minutes measured in a 1 Hz period, and heart rate information. The detailed numerical description of the above-described sleep sensing data is only an example, and the present disclosure is not limited thereto.

According to an embodiment, the sleep analysis information calculated by the sleep evaluation model based on sleep sensing data in the present disclosure includes three elements acquired through the sensor unit 130 (ie, respiration information, motion information, and heart rate information). ), it can be calculated with higher accuracy. However, three elements (ie, breathing information, motion information, and heart rate information) acquired through the sensor unit 130 as input of a sleep evaluation model for calculating sleep analysis information in the present disclosure are not necessarily required. .

According to an embodiment of the present disclosure, the processor 150 may calculate sleep analysis information. Specifically, the processor 150 may calculate the user's sleep analysis information by inputting the user's sleep sensing data into the sleep evaluation model. In this case, the sleep evaluation model is a model for predicting the user's sleep state and the likelihood of disease occurrence, and may provide sleep analysis information based on data obtained from the user's sleep.

The sleep evaluation model is a model for calculating information on at least one of a user's sleep state and a possibility of disease occurrence based on sleep sensing data, and may include one or more network functions.

The sleep evaluation model consists of one or more network functions, and one or more network functions may consist of a set of interconnected computational units, which may generally be referred to as'nodes'. These'nodes' may also be referred to as'neurons'. One or more network functions include at least one or more nodes. Nodes (or neurons) constituting one or more network functions may be interconnected by one or more'links'.

In a neural network, one or more nodes connected through a link may relatively form a relationship between an input node and an output node. The concepts of an input node and an output node are relative, and any node in an output node relationship with respect to one node may have an input node relationship in a relationship with another node, and vice versa. As described above, the input node to output node relationship can be created around the link. One or more output nodes may be connected to one input node through a link, and vice versa.

In the relationship between the input node and the output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting the input node and the output node may have a weight. The weight may be variable, and in order for the neural network to perform a desired function, it may be changed by a user or an algorithm. For example, when one or more input nodes are interconnected to one output node by respective links, the output node includes values input to input nodes connected to the output node and a link corresponding to each input node. The output node value may be determined based on the weight.

As described above, in a neural network, one or more nodes are interconnected through one or more links to form an input node and an output node relationship in the neural network. The characteristics of the neural network may be determined according to the number of nodes and links in the neural network, the association relationship between the nodes and the links, and a weight value assigned to each of the links. For example, when there are the same number of nodes and links, and two neural networks having different weight values between the links exist, the two neural networks may be recognized as being different from each other.

Some of the nodes constituting the neural network may configure one layer based on distances from the initial input node. For example, a set of nodes having a distance n from an initial input node may constitute n layers. The distance from the first input node may be defined by the minimum number of links that must be traversed to reach the node from the first input node. However, the definition of such a layer is arbitrary for explanation, and the order of the layer in the neural network may be defined in a different manner from that described above. For example, the layer of nodes may be defined by the distance from the last output node.

The first input node may mean one or more nodes to which data is directly input without going through a link in a relationship with other nodes among nodes in the neural network. Alternatively, in a relationship between nodes based on a link in a neural network network, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may mean one or more nodes that do not have an output node in a relationship with other nodes among nodes in the neural network. In addition, the hidden node may refer to nodes constituting a neural network other than the first input node and the last output node. The neural network according to an embodiment of the present disclosure may be a neural network in which the number of nodes of the input layer may be greater than the nodes of the hidden layer close to the output layer, and the number of nodes decreases as the input layer proceeds to the hidden layer.

A neural network may include one or more hidden layers. The hidden node of the hidden layer can take the output of the previous layer and the output of the neighboring hidden node as inputs. The number of hidden nodes for each hidden layer may be the same or different. The number of nodes of the input layer may be determined based on the number of data fields of the input data, and may be the same as or different from the number of hidden nodes. Input data input to the input layer may be calculated by a hidden node of the hidden layer and may be output by a fully connected layer (FCL) that is an output layer.

In more detail, the processor 150 may build a training data set for training a sleep evaluation model. The processor 150 may receive a sleep diagnosis data set including each of a plurality of sleep diagnosis data corresponding to each of a plurality of users. These sleep diagnosis data may be information received from the external server 20. The sleep diagnosis data may include information related to a sleep examination of a specific user, and may be, for example, information related to a polysomnia test. Information related to the user's polysomnography may include information on EEG, safety, jaw EMG, respiration, electrocardiogram, arterial blood oxygen saturation, anterior peroneal muscle EMG, and other physiological and physical variables for several hours.

Also, the information related to the polysomnography may include information on a sleep analysis result analyzed through the above information. For example, the diagnosis result of the information acquired during the user's sleep period, the information that the Apnea-Hypopnea Index (AHI) related to the sleep apnea index is 21, and the sleep stage of the user at a specific time is REM sleep. It may contain the information. The apnea-hypnea index is information used as a criterion for determining sleep apnea. In general, if the AHI is less than 5, it is normal, if the AHI is 5 or more and less than 15, mild sleep apnea, and the AHI is 15 or more and less than 30. Moderate sleep apnea, AHI of 30 or higher can be classified as severe sleep apnea.

The processor 150 may generate a learning input data set by extracting information about a user's breathing, heart rate, and movement for a predetermined time period from each of the plurality of sleep diagnosis data. For example, the processor 150 may extract information related to the user's breathing for 1 hour from the sleep diagnosis data. In this case, the learning input data may be information about the user's breathing for 60 minutes measured in a 10 Hz period, and may be time series information having a matrix size of 3600 x 10.

That is, the learning input data set may include information on at least one of information on respiration, heart rate, and movement for a predetermined period of time extracted from each of the plurality of sleep diagnosis data.

In addition, the processor 150 may generate learning output data by extracting at least one of information on an apnea index, information on a sleep state, and information on whether or not a disease has occurred from each of the plurality of sleep diagnosis data.

For a specific example, the processor 150 may extract information on the apnea index of user A (eg, AHI of 19) from sleep diagnosis data of user A. For another example, the processor 150 may extract information that user B has a heart disease related to arrhythmia from sleep diagnosis data of user B. For another example, the processor 150 may extract information on the sleep stage for each point of time of user C from the sleep diagnosis data of user C. Detailed description of the above-described learning output data is merely an example, and the present disclosure is not limited thereto.

That is, the processor 150 may generate a learning output data set based on each of the plurality of sleep diagnosis data. In other words, the learning output data set may be information related to a sleep measurement result (or a sleep diagnosis result) extracted from each of the plurality of diagnosis data.

Further, the processor 150 may generate a labeled training data set by matching the training input data set and the training output data set. For example, the first learning input data is generated by extracting information on breathing measured during a predetermined period of time (eg, time series data related to breathing measured for 1 hour) from the sleep diagnosis data of the first user, and 1 When the sleep diagnosis result corresponding to the information measured during the predetermined period of time from the user's sleep diagnosis data extracts information that the AHI is 19 to generate the first learning output data, the processor 150 A labeled training data set may be generated by matching first training input data acquired from a first user, which is a user, and first training output data. That is, the processor 150 may generate a labeling training data set by matching each training input data and each corresponding training output data.

According to an embodiment of the present disclosure, the processor 150 may train a sleep evaluation model. Specifically, the processor 150 may perform learning on one or more network functions constituting the lifetime evaluation model by using the labeled training data set. Specifically, the processor 150 inputs each of the training input data sets to one or more network functions, and compares each of the output data calculated by the one or more network functions with each of the training output data sets corresponding to the labels of each of the training input data sets. Thus, the error can be derived. That is, in training of a neural network, training input data may be input to an input layer of one or more network functions, and training output data may be compared with outputs of one or more network functions. The processor 150 may train a neural network based on an error between an operation result of one or more network functions with respect to the training input data and the training output data (label).

In addition, the processor 150 may adjust the weights of one or more network functions based on the error in a backpropagation method. That is, the processor 150 may adjust the weights such that the outputs of the one or more network functions are close to the training output data based on an error between the training output data and the operation result of the one or more network functions with respect to the training input data.

When learning of one or more network functions is performed by more than a predetermined epoch, the processor 150 may determine whether to stop learning by using the verification data. The predetermined epoch may be part of the overall learning target epoch. The verification data may consist of at least a portion of the labeled training data set. That is, the processor 150 performs training of the neural network through the training data set, and after the training of the neural network is repeated by a predetermined epoch or more, it may determine whether the learning effect of the neural network is equal to or higher than a predetermined level using the verification data I can. For example, if the processor 150 performs learning with a target repetition learning number of 10 times using 100 learning data, it performs repetitive learning 10 times, which is a predetermined epoch, and then uses 10 verification data. If a change in the output of the neural network is less than a predetermined level during the three iterations by performing repetitive learning, it is determined that further learning is meaningless, and the learning can be terminated. That is, the verification data may be used to determine completion of learning based on whether the effect of learning for each epoch in the iterative learning of the neural network is more than a certain level or less. The number of training data and verification data described above and the number of repetitions are only examples, and the present disclosure is not limited thereto.

The processor 150 may test the performance of one or more network functions using a test data set to determine whether to activate one or more network functions, thereby generating a sleep evaluation model. The test data may be used to verify the performance of the neural network, and may consist of at least a part of the training data set. For example, 70% of the training data set can be used for training the neural network (i.e., training to adjust the weights to output similar results with labels), and 30% are tests to verify the performance of the neural network. It can be used as data. The processor 150 may input a test data set to the neural network on which the training has been completed, measure an error, and determine whether to activate the neural network according to whether or not a predetermined performance is higher. The processor 150 may verify the performance of the trained neural network by using test data on the trained neural network, and activate the neural network to be used in another application when the performance of the trained neural network is greater than or equal to a predetermined criterion. Further, the processor 150 may deactivate and discard the neural network when the performance of the neural network that has been trained is less than or equal to a predetermined criterion. For example, the processor 150 may determine the performance of the generated neural network model based on factors such as accuracy, precision, and recall. The above-described performance evaluation criteria are only examples, and the present disclosure is not limited thereto. According to an embodiment of the present disclosure, the processor 150 may independently train each neural network to generate a plurality of neural network models, and may evaluate performance and use only neural networks with a certain performance or higher for calculating sleep analysis information.

Hereinafter, a data operation process of one or more network functions (or neural networks) will be described in more detail with reference to FIG. 4.

In the present disclosure, the sleep environment sensing data 300 used as an input of a neural network may include the user's breathing information 320 acquired during a predetermined time period in relation to the user's sleep environment. For example, the sleep sensing data 300 may be user's breathing information 320 acquired for 60 minutes. In this case, the sleep sensing data 300 may be user's breathing information 320 for 60 minutes measured in a 10 Hz cycle, and may be time series information having a matrix size of 3600 (60 minutes converted to seconds) x 10. That is, in the present disclosure, data used as an input of a neural network may be data having a relatively long length. Since the data used as the input of the neural network is long time series data, it may be difficult to calculate the neural network.

For example, in the case of a general convolutional neural network, a receptive field may be expanded by increasing the kernel size or stacking it as deeply as possible (depth = number of filters). The receptive field may be related to an area of a filter for processing data related to an input of a neural network. For example, as the receptive field increases, the amount of information used in the process of calculating the output may increase, and as the receptive field decreases, the amount of information used in the process of calculating the output may decrease.

However, as described above, when the kernel size is increased or the depth is increased, there is a concern that overfitting may occur as the number of parameters increases. In addition, in the process of calculating a general convolutional neural network, the length of the output terminal corresponding to the input may be reduced according to the stride related to the position interval to which the position of the filter is applied. For example, when the stride is 2, the output length may be 1/2 of the input length, as the interval of the filter is 2. In addition, data loss may occur during a general CNN pooling process. For example, in the case of max pooling, since only a record having a maximum value among records included in a specific kernel size is extracted as a feature, feature values corresponding to the remaining records may be lost. In time series data, the positions of the input points must be maintained, and the length of the data at the input and output terminals may be different during the calculation process.

That is, when a sleep environment sensing data (i.e., long time series data) related to the input of the neural network of the present disclosure is to be calculated through a general convolutional neural network, there is a concern that overfitting may occur as a parameter increases, In addition, it may be difficult to learn about the temporal relationship of time series data due to changes in the length of the input and output.

In an additional embodiment, the sleep sensing data 300 may further include motion information 310 and heart rate information 330 of a user for a predetermined time. In this case, as the sleep sensing data includes the user's breathing information 320 for 60 minutes measured in a 10 Hz cycle, motion information 310 and heart rate information 330 for 60 minutes measured in a 1 Hz cycle, ( It may be time series information having a matrix size of 3600 x 10) + (3600 x 1) + (3600 x 1). In this case, as data longer than sleep sensing data including only the respiration information 320 is used as an input of the neural network, the efficiency of calculation and learning through the convolutional neural network may further decrease.

Accordingly, one or more network functions constituting the sleep evaluation model of the present disclosure include an extended convolutional neural network 430 for reinforcing a long-term association without loss of input data. I can. The extended convolutional neural network 430 enhances a long-term association by expanding a receptive field of a filter related to the input of one or more network functions, and adds zero padding to the filter related to the input to reduce one or more network functions. It can be characterized by maintaining the length of the input and output. Specifically, the extended convolutional neural network may extend the receptive field by considering a dilation rate, which is an interval between kernels, as a parameter. For example, a 3x3 kernel having a dilation rate of 2 may have the same field of view as a 5x5 kernel, and thus the receptive field may be extended. That is, by utilizing the dilation operation, increasing the receptive field while maintaining the number of parameters to reinforce a long-term correlation, it is possible to process long time series data. In addition, the extended convolutional neural network 430 may prevent loss by adding zero padding to a loss record (ie, a skipped record) that may occur during a dilation operation. Accordingly. The length of the input and output can be maintained (temporal correlation) while reducing the loss of features.

In addition, one or more network functions may include 1D-CNN (440). The 1D-CNN 440 may be a network function for reducing the dimension of long time series data. The 1D-CNN 440 may be a network function for reducing (or compressing) the dimension of data transmitted from the extended convolutional neural network 430 by being located at the rear of the neural network.

That is, the input data (e.g., sleep environment sensing data and learning input data) input to the input layer undergoes a calculation process through the extended convolutional neural network 430, and the dimension is reduced through the 1D-CNN 440, It may be output by a fully connected layer (FCL) 450 that is an output layer.

The sleep evaluation model of the present disclosure can enhance long-term correlation by increasing the receptive field while maintaining the number of parameters required for calculation through an extended convolutional neural network, and reduce the loss of features occurring in the calculation process. At the same time, the length of the input and output can be maintained (temporal correlation). Accordingly, the processor 150 is a sleep evaluation model for calculating sleep analysis information 500 based on sleep sensing data by performing learning on a neural network model configured including an extended convolutional neural network through the training data set. Can be created.

The sleep evaluation model may output sleep analysis information 500 by inputting sleep-related sensing data. The sleep analysis information 500 may include at least one of apnea severity information about a user's sleep-related apnea incidence, sleep stage information on a change in sleep state, and disease prediction information on a probability of disease occurrence.

The apnea severity information may include information on an apnea-hypopnea index (AHI) measured for a predetermined time in relation to the user's sleeping environment. For example, the apnea severity information may include information that the apnea-hypnea index for 90 minutes is 8, and based on the index, it may be determined that the user corresponds to mild sleep apnea. The sleep stage information may include information on changes in one or more sleep states according to time changes in relation to the user's sleep environment. For example, the sleep stage information may include information on a sleep stage for each viewpoint of a user for a predetermined time or information on a ratio of a sleep stage. The disease prediction information may include prediction information on at least one of sleep disorders, mental disorders, brain disorders, and cardiovascular disorders. For example, the disease prediction information may include information that a user's probability of developing a brain disease within 3 years is 40%. The detailed description of the above-described apnea severity information, sleep stage information, and disease prediction information is only an example, and the present disclosure is not limited thereto.

That is, the sleep evaluation model of the present disclosure is Raw data to output (or End to End deep learning) that outputs output data (i.e., sleep analysis information) without a separate preprocessing process for input data (sleep sensing data), which is time series information. ) Can be a model. In other words, the pre-processing of data may be omitted in the data operation process using a neural network.

In addition, sleep sensing data processed as an input of the sleep evaluation model may include user's breathing information measured during a predetermined time period. That is, the sleep evaluation model of the present disclosure may be a neural network model that calculates sleep analysis information as described above based on only a minimum number of elements (ie, user's breathing information). In general, a variety of information (e.g., EEG, safety, jaw EMG, respiration, electrocardiogram, arterial blood oxygen saturation, anterior peroneal muscle EMG, other physiological and physical variables, etc.) may be required for sleep evaluation (or analysis). . The present disclosure is to provide convenience for a user to obtain sleep evaluation information, and may provide a neural network model (ie, a sleep evaluation model) that uses only information about breathing when a user sleeps as input data. Accordingly, a sensing process for acquiring a plurality of input data can be reduced. This facilitates the user's sleep evaluation (or analysis) in real life, thereby providing convenience for continuous health management.

In an additional embodiment, the sleep sensing data may further include user motion information and heart rate information measured during a predetermined time period. In this case, the sleep evaluation model may be a neural network model trained to provide sleep analysis information based on three factors (ie, user's breathing information, motion information, and heart rate information). In this case, since the sleep sensing data includes the user's breathing information, motion information, and heart rate information, the input data of the neural network may include three elements. That is, as three factors are considered as input data, the accuracy of the sleep analysis information calculated through the sleep evaluation model may be further improved.

According to an embodiment of the present disclosure, the processor 150 may generate health management information for improving a user's sleeping environment and health based on the sleep analysis information. The health management information may include at least one of information on eating habits, information on exercise amount, and information on an optimal sleeping environment.

For a specific example, when the sleep analysis information includes information that it corresponds to mild sleep apnea as the AHI is calculated as 13, the processor 150 is'information recommending weight loss through exercise or a drug that relaxes muscles. It is possible to generate health care information including'information that avoidance of the disease'.

For another example, when the sleep analysis information includes information that it corresponds to severe sleep apnea as the AHI is calculated as 32, the processor 150 provides health management information including'information that positive pressure therapy is recommended'. Can be generated.

For another example, if the sleep analysis information includes information that the probability of occurrence of arrhythmia within the next 3 years is 70%, the processor 150 is'use vegetable oil such as soybean oil, perilla oil, sesame oil rather than animal oil, and egg, Health, including information on eating habits to consume foods with high cholesterol content such as fish 2 to 3 times a week or less, or information on exercise amount that avoids excessive strength training and recommends 30 minutes aerobic exercise a day. You can create management information. The detailed description of the information included in the above-described sleep analysis information and health care information is only an example, and the present disclosure is not limited thereto.

In addition, the processor 150 may determine to transmit the health management information generated based on the sleep analysis information to the user terminal 10. Accordingly, the user can easily recognize information for improving apnea according to his or her sleep or lowering the probability of developing a comorbid disease.

According to an embodiment of the present disclosure, the processor 150 changes the breathing information for a predetermined period of time included in the user's sleep sensing data input to the sleep evaluation model, and sleeps based on the amount of change in disease prediction information output. Correlation between sensing data and disease prediction information can be derived.

For example, the processor 150 may adjust breathing information for a predetermined period of time included in sleep sensing data related to the user's sleeping environment. In this case, as the input of the neural network model (ie, the sleep evaluation model) is changed, disease prediction information may be changed and output. That is, the processor 150 may derive a correlation between sleep analysis information that is changed and output according to a change in sleep sensing data related to an input of a neural network. For example, the correlation between sleep sensing data and disease prediction information is information that the probability of developing cardiovascular disease increases as the cycle in which the user's apnea occurs during sleep is shortened (that is, even if the apnea index is the same, Depending on the disease, the probability of developing a disease may vary, etc.). A detailed description of the correlation between the above-described sleep sensing data and disease prediction information is only an example, and the present disclosure is not limited thereto.

In other words, by deriving a correlation between respiratory information and disease prediction information using a neural network in addition to commonly known breathing during sleep and a disease predicted in response to the corresponding breath, it is possible to provide meaningful insight into the relationship between breathing and disease.

According to an embodiment of the present disclosure, the processor 150 may generate sleep degradation factor information based on sleep stage information and indoor environment information. The indoor environment information is information about a user's sleeping environment acquired by the sensor unit 130, and includes information on at least one of the user's body temperature, the indoor temperature of the room where the user is located, indoor humidity, indoor sound, and indoor illuminance. Can include.

The sleep degradation factor information may be information on an external factor that impairs the user's sleep quality during the user's sleep process. For example, the sleep deterioration factor information may include information that the quality of sleep of a user is impaired as the indoor temperature decreases at a specific point in time. For another example, it may include information that the quality of sleep of the user is impaired as the illuminance increases at a specific point in time. The detailed description of the above-described sleep deterioration factor information is only an example, and the present disclosure is not limited thereto.

More specifically, the processor 150 may identify a first time point at which the user's sleep quality is deteriorated based on the sleep stage information. In general, the sleep stage may be divided into one or more stages according to the user's level of sound sleep. For example, the sleep stage may be divided into 1 to 4 stages, and as the stage increases, the user may take a deep sleep. These sleep steps may be repeated several times based on a certain period of time (eg, 1 hour and 30 minutes) during the user's daily sleep. In this case, the processor 150 may identify a first time point at which the user's sleep quality is deteriorated through sleep stage information included in the sleep analysis information calculated through the sleep evaluation model. For a specific example, when the user's sleep stage is changed from the third stage to the first stage at 3:10 AM (that is, when the sleep is changed from deep sleep to shallow sleep regardless of the repetition period of sleep), the processor 150 May identify the corresponding time point (that is, 3:10) as the first time point in which the quality of the user's sleep is deteriorated. The detailed description of the above-described first viewpoint is only an example, and the present disclosure is not limited thereto.

In addition, the processor 150 may identify a singular point related to the amount of change in the indoor environment information corresponding to the first time point. The indoor environment information is information related to the environment of the user's sleeping space, and may include information on at least one of the user's body temperature, indoor temperature, indoor airflow, indoor humidity, indoor sound, and indoor illuminance, and the sensor It may be obtained through the unit 130.

In detail, the processor 150 may identify a singular point related to the variability of the indoor environment information based on whether the change amount of each of the one or more pieces of information included in the indoor environment information exceeds a predetermined threshold change amount corresponding to the first time point. I can.

For a specific example, when the first time point at which the user's sleep quality is deteriorated is 2:40 am, the processor 150 corresponds to the time point (that is, 2:40). You can identify the amount of change in the information. In this case, the amount of change in temperature can be identified as +3°C, and as the predetermined critical change amount corresponding to the temperature is ±2°C, the processor 150 determines that the indoor temperature is a singular point related to the variability of indoor environment information. Can be identified as. That is, the processor 150 may identify that a factor that deteriorates the user's sleep quality during sleep is temperature. In addition, the processor 150 may generate sleep degradation factor information based on the identified singularity. For example, the processor 150 may generate sleep degradation factor information indicating that the quality of a user's sleep has deteriorated due to a change in room temperature at 2:40. That is, the sleep degradation factor information generated by the processor 150 may include information on a time point at which the user's sleep quality deteriorates and information on external factors related to sleep quality degradation. Also, for example, the temperature change as described above does not affect the change in sleep stage of user A (i.e., does not degrade the sleep quality of user A), but does not affect the change in sleep stage of user B. Can (i.e., lowers the sleep quality of user B). That is, the sleep degradation factor information may reflect characteristics related to individual sleep. The detailed description of the above-described first time point, the amount of change in indoor environment information, and the predetermined amount of change in the threshold are only examples, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 150 may generate an environment control signal for controlling the environment creation unit 140 based on information on the sleep deterioration factor. For example, if the sleep deterioration factor information includes information that the sleep quality of the user is deteriorated due to a change in the room temperature at 2:40, the processor 150 responds to the corresponding time point based on the sleep deterioration factor information. Thus, the environment creation unit 140 may generate an environment control signal to adjust the temperature. That is, the processor 150 generates an environment control signal for controlling the environment creation unit 140 in order to change the indoor environment of the space where the user sleeps based on a specific time point included in the sleep reduction factor information and the sleep reduction factor. can do. Accordingly, the environment creation unit 140 may adjust the indoor environment in which the user is located by operating one or more driving modules based on the environment control signal. In this case, the sleep deterioration factor information is generated based on a change in the sleep stage of an individual, and the environmental control signal is information generated based on the sleep deterioration factor information as described above, so the sleep environment adjustment operation in the present disclosure is The individual characteristics of the user related to the sleeping environment may be reflected.

That is, the processor 150 generates an environment control signal for driving one or more driving modules included in the environment creation unit 140 based on the sleep deterioration factor information, thereby providing an optimal sleep environment corresponding to each of a plurality of users. can do. Accordingly, the user's sleep efficiency can be increased.

5 is a schematic diagram showing a network function related to an embodiment of the present disclosure.

A deep neural network (DNN) may mean a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can be used to identify potential structures in data. In other words, it is possible to grasp the potential structure of photos, texts, videos, voices, and music (e.g., what objects are in photos, what are the content and emotions of the text, what are the contents and emotions of the voice, etc.) . Deep neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), auto encoders, Generative Adversarial Networks (GANs), and restricted Boltzmann machines (RBMs). boltzmann machine), deep belief network (DBN), Q network, U network, and Siam network. The description of the above-described deep neural network is only an example, and the present disclosure is not limited thereto.

In an embodiment of the present disclosure, the network function may include an auto encoder. The auto encoder may be a kind of artificial neural network for outputting output data similar to input data. The auto encoder may include at least one hidden layer, and an odd number of hidden layers may be disposed between the input/output layers. The number of nodes of each layer may be reduced from the number of nodes of the input layer to an intermediate layer called a bottleneck layer (encoding), and then reduced and expanded symmetrically from the bottleneck layer to an output layer (symmetric with the input layer). The nodes of the dimensionality reduction layer and the dimensionality restoration layer may or may not be symmetrical. Auto encoders can perform nonlinear dimensionality reduction. The number of input layers and output layers may correspond to the number of sensors remaining after pre-processing of input data. In the auto-encoder structure, the number of nodes of the hidden layer included in the encoder may have a structure that decreases as it moves away from the input layer. If the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and the decoder) is too small, a sufficient amount of information may not be delivered, so more than a certain number (e.g., more than half of the input layer, etc.) ) May be maintained.

The neural network may be learned in at least one of supervised learning, unsupervised learning, and semi supervised learning. Learning of neural networks is to minimize output errors. In learning of a neural network, iteratively inputs training data to the neural network, calculates the output of the neural network for the training data and the error of the target, and reduces the error from the output layer of the neural network to the input layer. This is a process of updating the weight of each node of the neural network by backpropagation in the direction. In the case of teacher learning, learning data in which the correct answer is labeled in each learning data is used (ie, labeled learning data), and in the case of non-satellite learning, the correct answer may not be labeled in each learning data. That is, for example, in the case of teacher learning related to data classification, the learning data may be data in which a category is labeled with each learning data. Labeled training data is input to a neural network, and an error may be calculated by comparing an output (category) of the neural network with a label of the training data. As another example, in the case of comparative history learning regarding data classification, an error may be calculated by comparing the input training data with the neural network output. The calculated error is backpropagated in the neural network in the reverse direction (ie, from the output layer to the input layer), and the connection weight of each node of each layer of the neural network may be updated according to the backpropagation. A change amount may be determined according to a learning rate in the connection weight of each node to be updated. The computation of the neural network for the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate may be applied differently according to the number of repetitions of the learning cycle of the neural network. For example, a high learning rate is used in the early stages of training of a neural network, so that the neural network quickly secures a certain level of performance to increase efficiency, and a low learning rate can be used in the later stages of training to increase accuracy.

In the learning of a neural network, in general, the training data may be a subset of actual data (that is, data to be processed using the learned neural network), and thus, errors in the training data decrease, but errors in the actual data occur. There may be increasing learning cycles. Overfitting is a phenomenon in which errors in actual data increase due to over-learning on learning data. For example, a neural network that learns a cat by showing a yellow cat may not recognize that it is a cat when it sees a cat other than a yellow cat. Overfitting can be a cause of increased errors in machine learning algorithms. Various optimization methods can be used to prevent such overfitting. In order to prevent overfitting, methods such as increasing training data, regularization, and dropout in which some nodes of the network are omitted during the training process may be applied.

Throughout this specification, a computational model, a neural network, a network function, and a neural network may be used with the same meaning. (Hereinafter, it will be unified and described as a neural network.) The data structure may include a neural network. In addition, the data structure including the neural network may be stored in a computer-readable medium. The data structure including the neural network may also include data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, an active function associated with each node or layer of the neural network, and a loss function for learning the neural network. have. The data structure including the neural network may include any of the components disclosed above. That is, the data structure including the neural network is the data input to the neural network, the weight of the neural network, the hyper parameter of the neural network, the data obtained from the neural network, the active function associated with each node or layer of the neural network, the loss function for training the neural network, etc. It may be configured to include any combination of. In addition to the above-described configurations, the data structure including the neural network may include any other information that determines the characteristics of the neural network. In addition, the data structure may include all types of data used or generated in a neural network operation process, and is not limited to the above-described matters. Computer-readable media may include computer-readable recording media and/or computer-readable transmission media. A neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. The neural network includes at least one or more nodes.

6 is a flowchart illustrating a method of providing sleep analysis information based on sleep sensing data related to a user's sleep environment according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the computing device 100 may acquire user's sleep sensing data (610).

According to an embodiment of the present disclosure, the computing device 100 may calculate sleep analysis information of a user by using sleep sensing data as an input of a sleep evaluation model (620 ).

According to an embodiment of the present disclosure, the computing device 100 may transmit sleep analysis information to the user terminal (630).

The order of the steps illustrated in FIG. 6 may be changed as necessary, and at least one or more steps may be omitted or added. That is, the above-described steps are only one embodiment of the present disclosure, and the scope of the present disclosure is not limited thereto.

Steps of a method or algorithm described in connection with an embodiment of the present disclosure may be implemented directly in hardware, implemented as a software module executed by hardware, or a combination thereof. Software modules include Random Access Memory (RAM), Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), Flash Memory, hard disk, removable disk, CD-ROM, or It may reside on any type of computer-readable recording medium well known in the art to which the present disclosure pertains.

Components of the present disclosure may be implemented as a program (or application) and stored in a medium to be executed by being combined with a computer that is hardware. Components of the present disclosure may be executed as software programming or software elements, and similarly, embodiments include various algorithms implemented with a combination of data structures, processes, routines or other programming components, including C, C++ , Java, assembler, or the like may be implemented in a programming or scripting language. Functional aspects can be implemented with an algorithm running on one or more processors.

A person of ordinary skill in the art of the present disclosure includes various exemplary logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein, electronic hardware, (convenience). For the sake of clarity, it will be appreciated that it may be implemented by various forms of program or design code or a combination of both (referred to herein as "software"). To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. A person of ordinary skill in the art of the present disclosure may implement the described functions in various ways for each specific application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

The various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term “article of manufacture” includes a computer program, carrier, or media accessible from any computer-readable device. For example, computer-readable media include magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CD, DVD, etc.), smart cards, and flash memory. Devices (eg, EEPROM, card, stick, key drive, etc.). In addition, the various storage media presented herein include one or more devices and/or other machine-readable media for storing information. The term “machine-readable medium” includes, but is not limited to, wireless channels and various other media capable of storing, holding, and/or transmitting instruction(s) and/or data.

It is to be understood that the specific order or hierarchy of steps in the presented processes is an example of exemplary approaches. Based on the design priorities, it is to be understood that within the scope of this disclosure a specific order or hierarchy of steps in processes may be rearranged. The appended method claims provide elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

A description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those of ordinary skill in the art, and general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not to be limited to the embodiments presented herein, but is to be construed in the widest scope consistent with the principles and novel features presented herein.

Claims (15)

In a computing device for predicting a sleep state based on data measured in a user's sleep environment,
A processor configured to receive the user's sleep sensing data and input the sleep sensing data into a sleep evaluation model to calculate sleep analysis information of the user;
A memory storing program codes executable in the processor;
A network unit for transmitting and receiving data to and from a user terminal; And
A sensor unit including at least one environment sensing module for acquiring indoor environment information including information on at least one of the user's body temperature, room temperature, room humidity, room sound, and room illuminance;
Including,
The sleep sensing data,
Includes breathing information of the user acquired during a predetermined period of time in relation to the sleeping environment of the user,
The sleep analysis information,
Apnea severity information on the degree of apnea occurrence of the user, disease prediction information on the likelihood of disease occurrence, and sleep stage information on changes in one or more sleep states according to time changes in relation to the user's sleeping environment,
The processor,
Identify a first point in time at which the quality of sleep of the user is deteriorated based on the sleep stage information, identify a singular point related to the amount of change in the indoor environment information corresponding to the first point, and based on the identified singular point To generate sleep-depressing factor information,
A computing device for predicting a sleep state based on data measured in a user's sleep environment.
The method of claim 1,
A sensor unit for acquiring the user's sleep sensing data;
Including more,
The sensor unit,
One or more transmission modules for transmitting radio waves of a specific frequency; And
A receiving module for receiving a reflected wave generated in response to the radio wave of the specific frequency;
Contains, and
Obtaining the sleep sensing data from the user in a non-contact manner by detecting a phase difference or a frequency change according to the moving distance of the reflected wave,
A computing device for predicting a sleep state based on data measured in a user's sleep environment.
The method of claim 1,
The processor,
Receiving a sleep diagnosis data set including a plurality of sleep diagnosis data corresponding to each of the plurality of users,
Extracting information on the user's breathing, heart rate, and movement for a predetermined time period from each of the plurality of sleep diagnosis data to generate a learning input data set,
Extracting at least one of information about an apnea index, information about a sleep state, and information about whether or not a disease occurs from each of the plurality of sleep diagnosis data to generate a learning output data set,
Matching the training input data set and the training output data set to generate a labeled training data set, and
Generating the sleep evaluation model by learning one or more network functions using the labeled training data set,
A computing device for predicting a sleep state based on data measured in a user's sleep environment.
The method of claim 3,
The sleep evaluation model,
The processor inputs each of the training input data sets to the one or more network functions, and compares each of the output data calculated by the one or more network functions with each of the training output data sets corresponding to labels of each of the training input data sets. To derive an error, adjust the weight of the one or more network functions based on the error in a backpropagation method, and when learning of the one or more network functions is performed more than a predetermined epoch, using the verification data It is generated by determining whether to stop learning, and by testing the performance of the one or more network functions using a test data set to determine whether to activate the one or more network functions,
A computing device for predicting a sleep state based on data measured in a user's sleep environment.
The method of claim 1,
The sleep evaluation model,
A model for predicting the user's sleep state and the likelihood of disease occurrence based on the sleep sensing data, and includes one or more network functions, wherein the one or more network functions include a long-term relationship (long-term correlation) without loss of input data. term relationship) to strengthen the extended convolutional neural network (Dilated-Convolutional Neural Network),
The extended convolutional neural network,
The one or more network functions by expanding the receptive field of the filter related to the input of the one or more network functions to enhance the long-term correlation, and adding zero padding to the filter related to the input Characterized in that to maintain the length of the input and output of,
A computing device for predicting a sleep state based on data measured in a user's sleep environment.
delete The method of claim 1,
The disease prediction information,
Include predictive information for at least one of sleep disorders, mental disorders, brain disorders, and cardiovascular disorders, and
The processor,
Correlation between the sleep sensing data and the disease prediction information based on the amount of change in the disease prediction information output by changing breathing information for a predetermined time period included in the user's sleep sensing data input to the sleep evaluation model To derive,
A computing device for predicting a sleep state based on data measured in a user's sleep environment.
The method of claim 1,
The sleep sensing data,
Including motion information and heart rate information for a predetermined period of time,
A computing device for predicting a sleep state based on data measured in a user's sleep environment.
The method of claim 8,
The processor,
Generating sleep deterioration factor information based on the sleep stage information and the indoor environment information,
A computing device for predicting a sleep state based on data measured in a user's sleep environment.
delete The method of claim 1,
The processor,
Identifying a singular point related to the variability of the indoor environment information based on whether a change amount of each of the one or more pieces of information included in the indoor environment information exceeds a predetermined threshold change amount corresponding to the first time point,
A computing device that predicts a sleep state based on data measured in a user's sleep environment.
The method of claim 9,
An indoor environment creation unit that adjusts at least one of temperature, humidity, sound, and illuminance to create an indoor environment related to the user's sleeping environment;
Including more, and
The processor,
Generating an environment control signal for controlling the indoor environment creation unit based on the sleep deterioration factor information,
A computing device for predicting a sleep state based on data measured in a user's sleep environment.
The method of claim 1,
The processor,
It is determined to generate health management information for improving the sleep environment and health of the user based on the sleep evaluation information and transmit it to the user terminal of the user,
The above health care information,
Containing at least one of information about eating habits, information about exercise, and information about an optimal sleeping environment,
A computing device for predicting a sleep state based on data measured in a user's sleep environment.
A method of predicting a sleep state and a disease based on data measured in a user's sleep environment performed by one or more processors of a computing device,
Receiving, by the processor, the user's sleep sensing data;
The processor uses the sleep sensing data as an input of a sleep evaluation model, and the user's sleep analysis information--the sleep analysis information includes apnea severity information regarding the degree of occurrence of apnea of the user, disease prediction information regarding the possibility of disease occurrence, and the Calculating—including sleep stage information on changes in one or more sleep states over time in relation to the user's sleep environment;
Determining, by the processor, to transmit the sleep analysis information to the user terminal of the user;
Identifying, by the processor, a first time point at which the sleep quality of the user is deteriorated based on the sleep stage information;
Identifying, by the processor, a singular point related to a change amount of indoor environment information acquired through an environment sensing module in response to the first time point; And
Generating, by the processor, sleep deterioration factor information based on the identified singularity;
Containing,
A method of predicting a sleep state based on data measured in a user's sleep environment performed by one or more processors of a computing device.
A computer program stored in a computer-readable storage medium, wherein the computer program, when executed on one or more processors, performs the following operations for predicting a sleep state based on data acquired in a user's sleep environment, the operations :
Receiving the user's sleep sensing data;
Sleep analysis information of the user by using the sleep sensing data as an input to a sleep evaluation model--the sleep analysis information includes information on apnea severity information on the degree of occurrence of apnea of the user, disease prediction information on the possibility of disease occurrence, and sleep of the user. Calculating—including sleep stage information about changes in one or more sleep states over time with respect to the environment;
Determining to transmit the sleep analysis information to the user terminal of the user;
Identifying a first time point at which the sleep quality of the user is deteriorated based on the sleep stage information;
Identifying a singular point related to a change amount of indoor environment information acquired through an environment sensing module in response to the first viewpoint; And
Generating sleep degradation factor information based on the identified singularity;
Containing,
A computer program stored on a computer readable storage medium.

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