Sleep management system and sleep management method

Abstract

A sleep management system according to the present invention includes: a plurality of sensors including a first sensor and a second sensor for collecting data on a user; a hub device that receives first data from the first sensor and second data from the second sensor, acquires first processing data on the basis of processing the first data, and acquires second processing data on the basis of processing the second data; and a user device that receives the first processing data and the second processing data from the hub device, acquires a sleep stage of a user and sleep state information about body movements of the user on the basis of the first processing data and the second processing data, determines the occurrence of REM sleep behavior disorder in the user on the basis of the sleep stage of the user and the body movements of the user, and performs a first operation on the basis of the occurrence of REM sleep behavior disorder.

Description

Sleep management system and sleep management method

[0001] The disclosed invention relates to a sleep management system and a sleep management method.

[0002] REM sleep behavior disorder (RBD) is a type of parasomnia in which abnormal behavior occurs during sleep.

[0003] REM sleep behavior disorder occurs during REM sleep, and the dreams associated with REM sleep behavior disorder are often unpleasant and often involve violent acts. These violent actions in dreams can be projected onto reality, potentially causing physical harm to the individual or injuring a sleeping partner.

[0004] Previously, REM sleep behavior disorder was examined through polysomnography conducted at hospitals.

[0005] Polysomnography is expensive, requires wearing various contact sensors on the body, which makes it uncomfortable for the subject, and requires sleeping in a hospital, which is a hassle.

[0006] Provided are a sleep management system and sleep management method that can more easily and effectively examine REM sleep behavior disorder, which is an unconscious behavior during the REM sleep stage.

[0007] The technical challenges achievable by the present disclosure are not limited to those described above. Additional aspects will be described in part in the following description, and in part will become apparent from the description or may be learned by practicing the presented embodiments.

[0008] According to one aspect of the present disclosure, a sleep management system may include a plurality of sensors including a first sensor and a second sensor for collecting user data; a hub device for receiving first data from the first sensor and second data from the second sensor, obtaining first processed data based on processing the first data, and obtaining second processed data based on processing the second data; and a user device for receiving the first processed data and the second processed data from the hub device, obtaining sleep state information about a sleep stage and a body movement of the user based on the first processed data and the second processed data, determining whether a REM sleep behavior disorder occurs in the user based on the sleep stage and the body movement of the user, and performing a first operation based on the occurrence of the REM sleep behavior disorder.

[0009] According to one aspect of the present disclosure, a sleep management method may include: a hub device receiving first data from a first sensor and second data from a second sensor; the hub device obtaining first processed data based on processing the first data and obtaining second processed data based on processing the second data; the hub device transmitting the first processed data and the second processed data to a user device; the user device obtaining sleep state information about a sleep stage and a body movement of the user based on the first processed data and the second processed data received from the hub device; the user device determining whether a REM sleep behavior disorder has occurred in the user based on the sleep stage and the body movement of the user; and the user device performing a first operation based on the occurrence of the REM sleep behavior disorder.

[0010] The above and other aspects, features, and advantages of specific embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings, in which:

[0011] FIG. 1 illustrates an example of a network of a sleep management system according to one embodiment.

[0012] FIG. 2 schematically illustrates an example of the structure of a sleep management system according to one embodiment.

[0013] FIG. 3 illustrates an example of a control block diagram of a sleep management system according to one embodiment.

[0014] FIGS. 4 and 5 illustrate examples of multiple sensors of a sleep management system according to one embodiment.

[0015] FIG. 6 illustrates an example of a flowchart of a sleep management method according to one embodiment.

[0016] FIG. 7 illustrates another example of a flowchart of a sleep management method according to one embodiment.

[0017] FIG. 8 is a diagram illustrating a process for processing data collected from multiple sensors of a sleep management system according to one embodiment.

[0018] Figure 9 is a diagram for explaining sleep stages.

[0019] FIG. 10 illustrates an example of obtaining information on a user's REM sleep behavior disorder in a sleep management method according to one embodiment.

[0020] FIG. 11 illustrates an example of sleep state information and REM sleep behavior disorder information derived by a sleep management system according to one embodiment.

[0021] FIG. 12 illustrates an example of a predetermined operation according to the occurrence of a user's REM sleep behavior disorder in a sleep management method according to one embodiment.

[0022] FIG. 13 illustrates an example of a predetermined operation for alleviating a user's REM sleep behavior disorder according to one embodiment.

[0023] FIG. 14 illustrates another example of a predetermined action for alleviating a user's REM sleep behavior disorder according to one embodiment.

[0024] FIG. 15 illustrates an example of a predetermined operation for inducing a user's wake-up according to one embodiment.

[0025] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings. The same reference numerals are used for the same components in the drawings, and their redundant description will be omitted. The embodiments described in this disclosure are exemplary embodiments, and thus, the present disclosure is not limited thereto and may be implemented in various other forms. Unless otherwise specified in the context, the singular form should be understood to include plural references. Terms, including technical or scientific terms, used in this disclosure may have the same meaning as commonly understood by those skilled in the art.

[0026] The various embodiments of this document and the terminology used herein are not intended to limit the technical features described in this document to specific embodiments, but should be understood to include various modifications, equivalents, or alternatives of the embodiments. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise.

[0027] In this document, each of the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" may include any one of the items listed together in that phrase, or all possible combinations thereof.

[0028] The term "and / or" may include any combination of multiple related described components or any one of multiple related described components.

[0029] For example, a phrase such as "A, B, and / or C" may include any one of the items listed in that phrase, or all possible combinations of them.

[0030] Terms such as "first," "second," or "first" or "second" may be used simply to distinguish one component from another and do not qualify the components in any other respect (e.g., importance or order).

[0031] When a component (e.g., a first component) is referred to as being "coupled" or "connected" to another component (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0032] The terms "include" or "have" are intended to specify the presence of a feature, number, step, operation, component, part or combination thereof described in this document, but do not preclude the presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

[0033] When a component is said to be “connected,” “coupled,” “supported,” or “in contact with” another component, this includes not only cases where the components are directly connected, coupled, supported, or in contact, but also cases where the components are indirectly connected, coupled, supported, or in contact through a third component.

[0034] When we say that a component is "on" another component, this includes not only cases where the component is in contact with the other component, but also cases where there is another component between the two components.

[0035] The symbols attached to each step are used to identify each step, and these symbols do not indicate the order of each step, and each step may be performed in a different order than the specified order unless the context clearly indicates a specific order. Hereinafter, a sleep management system according to various embodiments will be described in detail with reference to the attached drawings.

[0036] FIG. 1 illustrates an example of a network of a sleep management system according to one embodiment.

[0037] Referring to FIG. 1, a sleep management system according to one embodiment may include a hub device (1) and a user device (2).

[0038] Additionally, the sleep management system according to one embodiment may include a server device (3) and / or a home appliance (4).

[0039] The hub device (1) may include a communication module capable of communicating with a user device (2), a server device (3), and / or a home appliance (4), at least one processor for processing data, and at least one memory storing a program for controlling the operation of the hub device (1).

[0040] The hub device (1) can obtain processed data based on processing data collected from multiple sensors. According to one embodiment, the hub device (1) can process data collected from multiple sensors using a machine learning model.

[0041] According to one embodiment, the hub device (1) can transmit processing data to the user device (2). For example, the hub device (1) can transmit processing data to the user device (2) through direct communication without going through the server device (3).

[0042] The home appliance (4) may include various types of electronic products. For example, the home appliance (4) may include at least one of a display device (41), a furniture control device (42), a lighting device (43), an automatic curtain opening / closing device (44), an air conditioner (45), a speaker (46), and an air purifier (47). The aforementioned home appliances are merely examples, and therefore, in addition to the aforementioned home appliances, various types of electronic products, such as a clothing manager, may also be included in the home appliance (4).

[0043] The home appliance (4) can be remotely controlled by the server device (3).

[0044] The furniture control device (42) may include an actuator that can change the user's posture by changing the structure of the furniture and / or a vibration element that can transmit vibration to a user lying or sitting on the furniture. For example, the furniture control device (42) may include an actuator that can control the reclining angle of a recliner bed, a recliner chair, and / or a recliner sofa.

[0045] The lighting device (43) may include a light source whose light intensity and / or light color can be controlled.

[0046] The automatic curtain opening / closing device (44) may include an actuator for automatically opening or closing the curtain.

[0047] The server device (3) may include a communication module capable of communicating with the hub device (1), the user device (2), and / or the home appliance (4).

[0048] The server device (3) may include at least one processor capable of processing data received from the hub device (1), the user device (2), and / or the home appliance (4), and at least one memory capable of storing a program for processing the data or processed data. The server device (3) may be implemented as various computing devices such as a workstation, a cloud, a data drive, a data station, etc. The server device (3) may be implemented as one or more servers that are physically or logically separated based on functions, detailed configurations of functions, or data, and may transmit and receive data through communication between each server and process the transmitted and received data.

[0049] The server device (3) can store and / or manage user accounts, register hub devices (1), user devices (2), and / or home appliances (4) by linking them to user accounts, and perform functions of managing or controlling the registered hub devices (1) and home appliances (4). For example, a user can access the server device (3) through the user device (2) and create a user account. The user account can be identified by an ID and password set by the user. The user can access the server device (3) through the user device (2) and manage the user account. The server device (3) can register hub devices (1), user devices (2), and / or home appliances (4) to the user account according to a set procedure. For example, the server device (3) can link identification information (e.g., serial number or MAC address, etc.) of the hub device (1) to the user account, thereby registering, managing, and controlling the hub device (1). Likewise, the server device (3) can register and control the user device (2) and home appliance (4) to the user account.

[0050] The server device (3) can receive various information from the hub device (1), user device (2), and / or home appliance (4) registered to the user account.

[0051] For example, the server device (3) may include a first server and a second server. The first server may create and / or manage user account information, and register and / or manage information of the hub device (1), the user device (2), and / or the home appliance (4) in the user account information. The second server may receive registration information of the user device (2) and the home appliance (4) from the first server, and control the user device (2) and / or the home appliance (4).

[0052] As another example, the second server may perform the management function of the hub device (1) and home appliance (4) registered in the first server on behalf of the first server.

[0053] The number of server devices (3) is not limited thereto, and the server devices (3) may include multiple servers for performing the same operation and / or different operations.

[0054] The user device (2) may include a communication module capable of communicating with a hub device (1), a server device (3), and / or a home appliance (4). The user device (2) may include a user interface for receiving user input or outputting information to the user. The user device (2) may include at least one processor for controlling the operation of the user device (2) and at least one memory for storing a program for controlling the operation of the user device (2).

[0055] The user device (2) may be carried by the user or placed in the user's home or office, etc. The user device (2) may include, but is not limited to, a personal computer, a terminal, a mobile phone, a smart phone, a handheld device, a wearable device, a display device, etc.

[0056] The memory of the user device (2) may store a program, i.e., an application, that can process data received from the hub device (1). The application may be sold installed on the user device (2) or downloaded and installed from an external server.

[0057] A user can access a server device (3) by executing an application installed on a user device (2), create a user account, and perform communication with the server device (3) based on the logged-in user account to register a hub device (1) and / or a home appliance (4).

[0058] For example, if the home appliance (4) is operated so that the home appliance (4) can be connected to the server device (3) according to the procedure guided by the application installed on the user device (2), the home appliance (4) can be registered in the user account by registering the identification information (e.g., serial number or MAC address) of the home appliance (4) in the corresponding user account on the server device (3). The home appliance (4) can also be registered in the user account in the same manner. It goes without saying that the information required to register a device such as the home appliance (4) in the user account may be other information that can identify the device in addition to the serial number or MAC address of the device.

[0059] The user device (2) can receive various information from the server device (3) or from the hub device (1) or home appliance (4) registered directly to the user account.

[0060] A network can include both wired and wireless networks. Wired networks include cable networks or telephone networks, while wireless networks can include any network that transmits and receives signals via radio waves. Wired and wireless networks can be interconnected.

[0061] A network may include a wide area network (WAN) such as the Internet, a local area network (LAN) formed around an access point (AP), and a short-range wireless network that does not use an access point (AP). A short-range wireless network is a Bluetooth TM , IEEE 802.15.1), Zigbee (IEEE 802.15.4), Wi-Fi Direct, Near Field Communication (NFC), Z-Wave, etc., but are not limited thereto.

[0062] An access point (AP) can connect a hub device (1), a user device (2), and / or a home appliance (4) to a wide area network (WAN) to which a server device (3) is connected. The hub device (1), the user device (2), and / or the home appliance (4) can be connected to the server device (3) via the wide area network (WAN).

[0063] Access Point (AP) is a Wi-Fi TM , IEEE 802.11), Bluetooth TM, IEEE 802.15.1), Zigbee (IEEE 802.15.4), etc., to communicate with a hub device (1), a user device (2), and / or a home appliance (4), and can connect to a wide area network (WAN) using wired communication, but the wireless communication method of the access point (AP) is not limited to this.

[0064] In one embodiment, the hub device (1) can communicate with the user device (2) via a short-range wireless network that does not go through an access point (AP).

[0065] For example, the hub device (1) may be connected to the user device (2) via a short-range wireless network (e.g., Wi-Fi Direct, Bluetooth, NFC). As another example, the hub device (1) may be connected to the user device (2) via a wide area network (WAN) using a long-range wireless network (e.g., a cellular communication module).

[0066] Fig. 2 schematically illustrates an example of the structure of a sleep management system according to one embodiment. Fig. 3 illustrates an example of a control block diagram of a sleep management system according to one embodiment.

[0067] Referring to FIGS. 2 and 3, the hub device (1) can receive data collected from a plurality of sensors (5).

[0068] The plurality of sensors (5) may include sensors for collecting user data (e.g., a first sensor (51), a second sensor (52), a third sensor (53), a fourth sensor (54), and / or a fifth sensor (55)).

[0069] Each of the plurality of sensors (5) can collect user data and transmit the collected user data to the hub device (1).

[0070] In one embodiment, the user's data may include data related to the user's sleep.

[0071] Data related to the user's sleep may include pressure data for measuring pressure changes corresponding to changes in the user's posture, displacement data corresponding to displacement of the body that changes according to the user's breathing, heart rate data corresponding to the user's heartbeat, oxygen saturation data corresponding to the user's oxygen saturation, electrocardiogram data corresponding to the user's electrocardiogram, acceleration data corresponding to acceleration that changes according to the user's movement, and / or eye-movement data corresponding to the movement of the user's eyes.

[0072] The plurality of sensors (5) may include at least two of a pressure sensor for collecting pressure data for measuring pressure changes corresponding to changes in the user's posture, a UWB sensor for measuring displacement data corresponding to displacement of the body that changes according to the user's breathing, a PPG sensor for collecting heart rate data corresponding to the user's heartbeat and / or oxygen saturation data corresponding to the user's oxygen saturation, an electrocardiogram sensor for collecting electrocardiogram data corresponding to the user's electrocardiogram, an acceleration sensor for collecting acceleration data corresponding to acceleration that changes according to the user's body movement, and / or a radar sensor for collecting eye-movement data corresponding to the user's eye movement.

[0073] In the terms "first, second, third, fourth, and fifth" in the first sensor (51), second sensor (52), third sensor (53), fourth sensor (54), and fifth sensor (55), the terms "first," "second," "third," "fourth," and "fifth" are intended to indicate that each sensor is a different sensor.

[0074] Each of the first sensor (51), second sensor (52), third sensor (53), fourth sensor (54), and fifth sensor (55) may be any one of a pressure sensor, an ultra-wideband (UWB) sensor, a radar sensor, an electrocardiogram sensor, a photoplethysmography (PPG) sensor, or an acceleration sensor. The PPG sensor may include an oxygen saturation sensor and / or a heart rate sensor.

[0075] The first sensor (51), second sensor (52), third sensor (53), fourth sensor (54), and fifth sensor (55) can be divided into contact sensors and non-contact sensors.

[0076] For example, a pressure sensor, a PPG sensor, an electrocardiogram sensor, or an acceleration sensor can be a contact sensor. An ultra-wideband (UWB) sensor or a radar sensor can be a non-contact sensor.

[0077] The plurality of sensors (5) may include at least one contact sensor and at least one non-contact sensor.

[0078] For convenience of explanation, the first sensor (51) is defined as a pressure sensor, the second sensor (52) as a UWB sensor, the third sensor (53) as a radar sensor, the fourth sensor (54) as at least one of a PPG sensor and / or an electrocardiogram sensor, and the fifth sensor (55) as an acceleration sensor.

[0079] The plurality of sensors (5) may further include additional sensors (e.g., microphones, cameras) in addition to the first sensor (51) to the fifth sensor (55), or may not include at least one of the first sensor (51) to the fifth sensor (55).

[0080] Data collected from multiple sensors (5) can be transmitted to the hub device (1).

[0081] In one embodiment, data collected by at least one sensor among a plurality of sensors (5) can be transmitted to the hub device (1) via wired communication, and data collected by the remaining sensors can be transmitted to the hub device (1) via wireless communication.

[0082] Accordingly, the hub device (1) can be connected to at least one of the plurality of sensors (5) by wire and connected to the remaining sensors wirelessly.

[0083] In one embodiment, the fourth sensor (54) may be included in a smart sensor device (e.g., a wearable device). The smart sensor device may include a wireless communication module and the fourth sensor (54). For example, the smart sensor device may include a smart watch in the form of a watch, and / or a smart ring in the form of a ring, but the form of the smart sensor device is not limited thereto.

[0084] The smart sensor device can establish wireless communication with the hub device (1) and transmit data collected from the fourth sensor (54) to the hub device (1) via wireless communication.

[0085] In one embodiment, the smart sensor device may include a fourth sensor (54) and a fifth sensor (55). The smart sensor device may establish wireless communication with the hub device (1) and transmit data collected from the fourth sensor (54) and the fifth sensor (55) to the hub device (1) via wireless communication.

[0086] In one embodiment, data collected from multiple sensors (5) can be transmitted to a hub device (1) via wired communication.

[0087] According to various embodiments, the plurality of sensors (5) may include imaging sensors (e.g., cameras). However, in one embodiment, all of the plurality of sensors (5) may be non-imaging sensors.

[0088] According to one embodiment of the present disclosure, all of the plurality of sensors (5) correspond to non-imaging sensors, thereby preventing invasion of the user's privacy.

[0089] In one embodiment, the hub device (1) may include at least one memory (120) storing a program for processing data collected from a plurality of sensors (5), and at least one processor (110) capable of processing data collected from a plurality of sensors (5) based on the program stored in the at least one memory (120).

[0090] At least one processor (110) can store a machine learning model for processing data collected from multiple sensors (5).

[0091] In one embodiment, the machine learning model may be a machine learning model for feature extraction that extracts features of data when data collected from a plurality of sensors (5) is input, and outputs processed data including the extracted features.

[0092] Features of data may include elements that a machine learning model extracts from the data to perform classification or prediction.

[0093] In one embodiment, the machine learning model may be a machine learning model for determining sleep stages, which outputs processed data including data on the user's sleep stage when data collected from a plurality of sensors (5) is input.

[0094] For example, at least one processor (120) may include a first machine learning model (11) for processing first data collected from a first sensor (51), a second machine learning model (12) for processing second data collected from a second sensor (52), a third machine learning model (13) for processing third data collected from a third sensor (53), a fourth machine learning model (14) for processing fourth data collected from a fourth sensor (54), and / or a fifth machine learning model (15) for processing fifth data collected from a fifth sensor (55).

[0095] At least one processor (110) can obtain first processed data based on processing first data collected from the first sensor (51).

[0096] The first processed data may include feature data extracted from the first data and / or data regarding sleep stages extracted from the first data. The capacity of the first processed data may be smaller than that of the first data.

[0097] At least one processor (110) can obtain second processed data based on processing second data collected from a second sensor (52).

[0098] The second processed data may include feature data extracted from the second data and / or data on sleep stages extracted from the second data. The capacity of the second processed data may be smaller than that of the second data.

[0099] At least one processor (110) can obtain third processing data based on processing third data collected from a third sensor (53).

[0100] The third processed data may include feature data extracted from the third data and / or data on sleep stages extracted from the third data. The capacity of the third processed data may be smaller than that of the third data.

[0101] At least one processor (110) can obtain fourth processed data based on processing fourth data collected from the fourth sensor (54).

[0102] The fourth processed data may include feature data extracted from the fourth data and / or data on sleep stages extracted from the fourth data. The capacity of the fourth processed data may be smaller than that of the fourth data.

[0103] At least one processor (110) can obtain fifth processed data based on processing fifth data collected from the fifth sensor (55).

[0104] The fifth processed data may include feature data extracted from the fifth data and / or data on sleep stages extracted from the fifth data. The capacity of the fifth processed data may be smaller than that of the fifth data.

[0105] According to the present disclosure, by first processing data in a hub device (1) and then transmitting it to a user device (2), the amount of data processing that the user device (2) must bear can be reduced.

[0106] The hub device (1) may include a communication unit (130) including a wired communication module for performing wired communication with a plurality of sensors (5), and / or a wireless communication module for performing wireless communication with a user device (2), a server device (3), and / or a home appliance.

[0107] The hub device (1) may include a printed circuit board including at least one processor (110), at least one processor (120), and a communication unit (130). At least some of the plurality of sensors (5) may be connected to the printed circuit board by wires.

[0108] The hub device (1) may include a housing covering a printed circuit board.

[0109] The hub device (1) is likely to be installed in a location where user operation is difficult. Accordingly, in one embodiment, the hub device (1) may not include a user interface device (input / output device).

[0110] In order to prepare for the case where the hub device (1) is installed in a place where the user can easily operate it, in one embodiment, the hub device (1) may include a user interface device (input / output device).

[0111] In one embodiment, a user can operate a user interface device provided on the hub device (1) to connect the hub device (1) to an access point (AP).

[0112] In one embodiment, a user can activate the communication unit (130) of the hub device (1) by operating a user interface device provided on the hub device (1).

[0113] In one embodiment, a user can turn on the power of the hub device (1) by operating a user interface device provided on the hub device (1).

[0114] At least one processor (110) can control multiple sensors (5).

[0115] For example, at least one processor (110) can control at least one wired sensor among a plurality of sensors (5).

[0116] At least one processor (110) can wake up at least one wired sensor among a plurality of sensors (5) based on the satisfaction of a sensor wake-up condition.

[0117] Waking up a sensor may include activating the sensor.

[0118] At least one processor (110) may switch at least one wired sensor among the plurality of sensors (5) to a standby state based on the satisfaction of a sensor standby condition. Switching the sensor to a standby state may include deactivating the sensor or operating it in a low-power mode.

[0119] The user device (2) can receive data acquired by the hub device (1) from the hub device (1) via wireless communication.

[0120] The data acquired by the hub device (1) may include processed data obtained by processing data collected from a plurality of sensors (5).

[0121] In one embodiment, the user device (2) may include at least one memory (220) storing a program for processing data received from the hub device (1), and at least one processor (210) capable of processing data received from the hub device (1) based on the program stored in the at least one memory (220).

[0122] At least one memory (220) can store a machine learning model for processing data received from the hub device (1).

[0123] At least one memory (220) can store a sleep management application that can be downloaded from an external server. The sleep-related application can be a downloadable app, and at least some of the sleep-related application can be temporarily stored or temporarily created on a device-readable storage medium, such as a memory of a manufacturer's server, an application store server, or a relay server.

[0124] A sleep management application may include a machine learning model. The machine learning model included in the sleep management application may be updated by an external server.

[0125] In one embodiment, the user device (2) may include a communication unit (230) including at least one communication module for establishing communication with a smart sensor device including at least some of a hub device (1), a server device (3), a home appliance (4), and / or a plurality of sensors (5).

[0126] The user device (2) can receive processing data from the hub device (1) through the communication unit (230).

[0127] In one embodiment, at least one processor (210) may establish communication between the communication unit (130) of the hub device (1) and the communication unit (230) of the user device (2) in response to the communication unit (230) (e.g., a short-range wireless communication module) being activated.

[0128] In one embodiment, at least one processor (210) can control the user interface (240) to provide feedback requesting activation of the communication unit (230) in response to the sleep management application being executed while the communication unit (230) (e.g., a short-range wireless communication module) is not activated.

[0129] At least one processor (210) can wirelessly receive data from the hub device (1) via a communication unit (230) (e.g., a short-range wireless communication module).

[0130] At least one processor (210) can process data received from the hub device (1) using a machine learning model stored in at least one memory (220).

[0131] The data received from the hub device (1) may include first processing data, second processing data, third processing data, fourth processing data and / or fifth processing data.

[0132] At least one processor (210) can obtain sleep state information related to the user's sleep state based on the first processing data, the second processing data, the third processing data, the fourth processing data, and / or the fifth processing data.

[0133] At least one processor (210) can input first processing data, second processing data, third processing data, fourth processing data, and / or fifth processing data into a machine learning model stored in at least one memory (220) to obtain sleep state information related to the user's sleep state.

[0134] Sleep state information related to the user's sleep state may include sleep state information related to the user's sleep stage and the user's body movements.

[0135] Sleep state information related to the user's sleep stage and the user's body movements may include information about the user's sleep stage and information about the user's body movements.

[0136] The machine learning model stored in at least one memory (220) may be a machine learning model for determining a user's sleep state, which outputs sleep state information related to the user's sleep state when first processing data, second processing data, third processing data, fourth processing data, and / or fifth processing data are input.

[0137] At least one processor (210) can input the first processing data, the second processing data, the third processing data, the fourth processing data, and / or the fifth processing data into the machine learning model and store the acquired sleep state information in at least one memory (220).

[0138] At least one processor (210) can determine whether the user has REM sleep behavior disorder based on the user's sleep stage and the user's body movements.

[0139] REM sleep behavior disorder (RSD) occurs during REM sleep and can be accompanied by aggressive and violent behavior. Therefore, REM sleep behavior disorder can cause physical harm to the individual while dreaming or harm a sleeping partner. If a person engages in potentially dangerous sleep behaviors, such as dream-related body or limb movements during REM sleep or acts out dreams, they may be diagnosed with REM sleep behavior disorder.

[0140] At least one processor (210) may input at least one processing data corresponding to data collected by a contact sensor among the first processing data, the second processing data, the third processing data, the fourth processing data, and / or the fifth processing data into a machine learning model to obtain information about a first body movement of the user, and may input at least one processing data corresponding to data collected by a non-contact sensor among the first processing data, the second processing data, the third processing data, the fourth processing data, and / or the fifth processing data into a machine learning model to obtain information about a second body movement of the user.

[0141] Information about the first body movement may include at least one of information about the presence or absence of the user's first body movement, information about the intensity of the first body movement, information about the size of the first body movement, or information about the movement of the first body movement.

[0142] Information about the second body movement may include at least one of information about the presence or absence of the second body movement, information about the intensity of the second body movement, information about the magnitude of the second body movement, or information about the movement of the second body movement.

[0143] At least one processor (210) may determine whether both the first body movement and the second body movement are greater than a preset movement based on information about the user's first body movement and information about the user's second body movement when the user's sleep stage is a REM sleep stage, and if both the first body movement and the second body movement are greater than the preset movement, it may be determined that a REM sleep behavior disorder has occurred.

[0144] At least one processor (210) may obtain REM sleep behavior disorder information related to the user's REM sleep behavior disorder in response to the user's REM sleep behavior disorder occurring.

[0145] At least one processor (210) can store the user's REM sleep behavior disorder information in at least one memory (220).

[0146] The user's REM sleep behavior disorder information may include at least one of information regarding whether REM sleep behavior disorder has occurred, information regarding the degree of occurrence of REM sleep behavior disorder, or information regarding the time of occurrence of REM sleep behavior disorder.

[0147] At least one processor (210) can control at least one of the communication unit (230) and the user interface (240) to perform a predetermined action in response to the occurrence of a REM sleep behavior disorder.

[0148] At least one processor (210) can notify a registered medical institution in conjunction with the user device (2) of the REM sleep behavior disorder through a communication unit (230) (e.g., a cellular communication module) in response to the occurrence of REM sleep behavior disorder.

[0149] At least one processor (210) may alert a user of the REM sleep behavior disorder via a user interface (240) in response to the occurrence of the REM sleep behavior disorder.

[0150] At least one processor (210) can control the communication unit (230) to transmit sleep state information and REM sleep behavior disorder information to the server device (3).

[0151] The communication unit (230) may include a first communication module for establishing communication with the hub device (1), a second communication module for establishing communication with the server device (3), and a third communication module for establishing communication with the medical institution. Accordingly, the communication unit (230) may perform communication with the hub device (1) through the first communication method, and may perform communication with the server device (3) through the second communication method, while also performing communication with an external medical institution.

[0152] According to one embodiment of the present disclosure, data collected from a plurality of sensors (5) is primarily processed by a hub device (1) and secondarily processed by a user device (2), and then sleep state information and REM sleep behavior disorder information are obtained, and the sleep state information and REM sleep behavior disorder information can be transmitted to a server device (3) in real time.

[0153] According to one embodiment of the present disclosure, data collected from a plurality of sensors (5) related to the user's private life may not be directly transmitted to the server device (3).

[0154] The user device (2) may include a user interface (240) for communication with the user.

[0155] According to various embodiments, it is also possible for the hub device (1) to primarily process data collected from a plurality of sensors (5), and then secondarily process the primarily processed data to obtain sleep state information and REM sleep behavior disorder information.

[0156] According to various embodiments, it is also possible for the hub device (1) to primarily process data collected from a plurality of sensors (5), transmit the primarily processed processing data to the server device (3), and then the server device (3) to secondarily process the primarily processed processing data and then obtain sleep state information and REM sleep behavior disorder information.

[0157] According to various embodiments, it is also possible for the hub device (1) to transmit data collected from a plurality of sensors (5) to the user device (2), and for the user device (2) to primarily process the data received from the hub device (1), and then to secondarily process the primarily processed data and then obtain sleep state information and REM sleep behavior disorder information.

[0158] According to various embodiments, it is also possible for the hub device (1) to transmit data collected from a plurality of sensors (5) to the server device (3), and for the server device (3) to primarily process the data received from the hub device (1), and then to secondarily process the primarily processed data and then obtain sleep state information and REM sleep behavior disorder information.

[0159] The user interface (240) can obtain user input. The user interface (240) can provide various information regarding the operation of the user device (2). The user interface (240) can include an input interface and an output interface.

[0160] The input interface can convert sensory information received from the user into electrical signals. The electrical signals can correspond to user input. The user input can include various commands. The input interface can transmit electrical signals (voltage or current) corresponding to the user input to at least one processor (210).

[0161] The input interface may include various input devices capable of converting tactile information into electrical signals. For example, the input interface may be provided as a physical button or a touchscreen. The input interface may also include a microphone capable of converting auditory information into electrical signals.

[0162] The input interface can receive user input to run a sleep management application.

[0163] The output interface can output information related to the operation of the user device (2). The output interface can display information input by the user or information provided to the user on various screens. The output interface can display information related to the operation of the user device (2) in the form of at least an image or text. For example, the output interface can output the interface of a sleep management application. In addition, the output interface can display a graphical user interface (GUI) that enables control of the user device (2). That is, the output interface can display a user interface element (UI element), such as an icon.

[0164] The output interface can output an interface corresponding to a sleep management application.

[0165] For example, the output interface may include a liquid crystal display panel (LCD Panel), a light emitting diode panel (LED Panel), an organic light emitting diode panel (OLED Panel), or a micro LED panel. The output interface may include a touch display that also functions as an input device.

[0166] The output interface and input interface may be provided as separate devices or as a single device (e.g., a touch display).

[0167] The server device (3) can receive data acquired by the user device (2) from the user device (2) via wireless communication.

[0168] The data acquired by the user device (2) may include sleep state information and REM sleep behavior disorder information.

[0169] In one embodiment, the server device (3) may include at least one memory (320) storing a program for processing sleep state information and REM sleep behavior disorder information received from the user device (2), and at least one processor (310) capable of processing the sleep state information and REM sleep behavior disorder information received from the user device (2) based on the program stored in the at least one memory (320).

[0170] At least one memory (320) can store sleep state information and REM sleep behavior disorder information received from the user device (2).

[0171] At least one memory (320) can store a program for generating sleep summary information based on sleep state information and REM sleep behavior disorder information received from the user device (2).

[0172] At least one processor (310) can generate sleep summary information based on sleep state information and REM sleep behavior disorder information accumulated and stored in at least one memory (320).

[0173] At least one memory (320) can store a program for controlling a home appliance (4) based on at least one of sleep state information or REM sleep behavior disorder information received from a user device (2).

[0174] At least one processor (310) can control the home appliance (4) based on at least one of sleep state information or REM sleep behavior disorder information received from the user device (2).

[0175] According to various embodiments, a program for generating sleep summary information based on at least one of sleep state information or REM sleep behavior disorder information and a program for controlling a home appliance (4) based on at least one of sleep state information or REM sleep behavior disorder information may be stored in different servers.

[0176] For example, a first server included in the server device (3) can store a program for generating sleep summary information based on at least one of sleep state information or REM sleep behavior disorder information, and a second server included in the server device (3) can store a program for controlling a home appliance (4) based on at least one of sleep state information or REM sleep behavior disorder information.

[0177] At least one memory (320) can store sleep summary information generated based on at least one of sleep state information or REM sleep behavior disorder information.

[0178] In one embodiment, the server device (3) may include a communication unit (330) including at least one communication module for establishing communication with a smart sensor device including at least some of a hub device (1), a user device (2), a home appliance (4), and / or a plurality of sensors (5).

[0179] The server device (3) can receive sleep state information and REM sleep behavior disorder information from the user device (2) through the communication unit (330).

[0180] The server device (3) can transmit a control command to the home appliance (4) through the communication unit (330).

[0181] The server device (3) can transmit sleep summary information and REM sleep behavior disorder information to the user device (2) through the communication unit (330).

[0182] FIGS. 4 and 5 illustrate examples of multiple sensors of a sleep management system according to one embodiment.

[0183] Referring to FIGS. 4 and 5, in one embodiment, some of the plurality of sensors (5) (e.g., the first sensor (51), the second sensor (52), and / or the third sensor (53)) may be installed in furniture (10), and other some of the plurality of sensors (5) (e.g., the fourth sensor (54), and / or the fifth sensor (55)) may be provided in a smart sensor device (e.g., a smart watch, a smart ring, etc.).

[0184] At least some of the plurality of sensors (5) may be provided on furniture (10) on which a user can sit or lie.

[0185] Furniture (10) on which a user can sit or lie down may include, for example, a bed, a chair, and / or a sofa, but of course, any type of furniture on which a user can sit or lie down may be adopted as furniture (10) according to one embodiment without limitation.

[0186] In one embodiment, furniture (10), such as a bed, chair, and / or sofa, may include an actuator that can change the user's posture by changing its structure and / or a vibration element that can transmit vibration to the user.

[0187] The first sensor (51) may include a pressure sensor. The pressure sensor may include a piezoelectric element that generates an electrical signal corresponding to a displacement caused by pressure.

[0188] The first sensor (51) can be installed at a location where it can measure the pressure generated by the user's body (e.g., whole body) depending on whether the user is lying down or sitting.

[0189] For example, if the furniture (10) corresponds to a bed, the first sensor (51) may be installed on the mattress, which is the part where pressure is generated by the user's body. The mattress may have a flat polygonal or circular shape, and may include a cover that forms the exterior and has a receiving space, and a pad that is installed in the receiving space of the cover and on which the first sensor (51) is placed. The mattress may be placed on the floor, a chair, a sofa, or a bed.

[0190] The mattress may further include springs and / or sponges. The springs and / or sponges may be provided in the accommodation space of the cover.

[0191] The structure (e.g., length, arrangement, etc.) of the first sensor (51) may be changed depending on the size of the mattress.

[0192] As another example, if the furniture (10) corresponds to a chair, the first sensor (51) may be provided in the seating area, backrest area, headrest, and / or leg area where pressure is generated by the user's body.

[0193] The seating portion may include a portion that the user's buttocks close, the backrest portion may include a portion that the user's back closes, the headrest may include a portion that the user's head closes, and the leg portion may include a portion that the user's legs close.

[0194] The location of the first sensor (51) is not limited to the examples shown in FIGS. 4 and 5, and the first sensor (51) may be installed in various locations capable of measuring pressure generated by the body of a user lying or sitting on the furniture (10).

[0195] The first sensor (51) can measure the pressure generated by a user lying or sitting on the furniture (10). For example, the first sensor (51) can measure the pressure distribution generated by a user lying or sitting on the furniture (10). The first sensor (51) can obtain pressure data corresponding to the pressure generated by a user lying or sitting on the furniture (10).

[0196] The second sensor (52) may include a UWB sensor. The UWB sensor may include a UWB signal emitting unit that transmits an ultra-wideband signal (UWB signal) and a UWB signal receiving unit that receives a UWB signal reflected by the user's body.

[0197] The second sensor (52) may have a detection area facing the body (e.g., torso) of a user lying or sitting on the furniture (10). The second sensor (52) may have a detection area capable of detecting body displacement according to the user's breathing. The second sensor (52) may be provided on a frame of the furniture (10) to have a detection area facing the body (e.g., torso) of the user, but the location of the second sensor (52) is not limited thereto.

[0198] For example, the second sensor (52) may have a detection area directed toward a part of the body of a user lying or sitting on the furniture (10).

[0199] For example, if the furniture (10) corresponds to a bed, the second sensor (52) may have a detection area facing the central area of ​​the bed.

[0200] As another example, if the furniture (10) corresponds to a chair, the second sensor (52) may have a detection area facing the backrest of the chair.

[0201] The second sensor (52) can transmit an ultra-wideband signal (UWB signal) toward the user's body and receive a UWB signal reflected from the user's body.

[0202] The second sensor (52) can measure the displacement of the user's body based on the UWB signal reflected from the user's body. For example, the second sensor (52) can measure the displacement of the user's body based on the time of flight (ToF; Time of Fight) of the UWB signal. As another example, the second sensor (52) can measure the displacement of the user's body based on the change in the wavelength (and frequency) of the UWB signal by utilizing the Doppler effect.

[0203] That is, the second sensor (52) can obtain displacement data corresponding to the displacement of the body that changes according to the user's breathing.

[0204] The third sensor (53) may include a radar sensor. The third sensor (53) may include a radar signal irradiation unit that transmits a radar signal (e.g., mmWave signal, millimeter wave wave) and a radar signal reception unit that receives a radar signal (e.g., mmWave signal) reflected from the user's body.

[0205] The frequency band (e.g., 28 GHz) of the radar signal output from the third sensor (53) may be higher than the frequency band (e.g., 6.0 to 8.8 GHz) of the UWB signal output from the second sensor (52).

[0206] The frequency bandwidth of the radar signal output from the third sensor (53) may be narrower than the frequency bandwidth of the UWB signal output from the second sensor (52).

[0207] The third sensor (53) may have a detection area facing the body (e.g., face) of a user lying or sitting on the furniture (10). The third sensor (53) may have a detection area capable of detecting the movement of the user's eyes. The third sensor (53) may be provided on a frame of the furniture (10) to have a detection area facing the body (e.g., torso) of the user, but the location of the third sensor (53) is not limited thereto.

[0208] The third sensor (53) may have a detection area directed toward a part of the body of a user lying or sitting on the furniture (10).

[0209] For example, if the furniture (10) corresponds to a bed, the third sensor (53) may have a detection area facing the head area of ​​the bed.

[0210] As another example, if the furniture (10) corresponds to a chair, the third sensor (53) may have a detection area directed toward the headrest of the chair.

[0211] The third sensor (53) can transmit a radar signal (mmWave signal, millimeter wave wave) toward the user's body and receive a mmWave signal reflected from the user's body.

[0212] The third sensor (53) can measure the movement of the user's eyes based on mmWave reflected from the user's eyes.

[0213] That is, the third sensor (53) can obtain eye-movement data corresponding to the movement of the user's eyes.

[0214] The fourth sensor (54) may include a PPG sensor and / or an electrocardiogram sensor. The PPG sensor and / or the electrocardiogram sensor may include a light source that irradiates light and a light receiving unit that receives light reflected from the user's body.

[0215] The fourth sensor (54) may have a detection area facing the body of a user lying or sitting on the furniture (10).

[0216] The fourth sensor (54) may be provided in a smart sensor device (e.g., a smart watch, a smart ring, etc.) wearable by a user.

[0217] The fourth sensor (54) operates in a non-invasive manner, and can irradiate light toward a part of the user's body (e.g., wrist) and receive light reflected from the user's body.

[0218] The fourth sensor (54) can measure the user's heart rate, the user's blood oxygen saturation, or the user's electrocardiogram based on the intensity of light reflected from the user's body.

[0219] Some of the light directed at a part of the body is absorbed by blood vessels, and depending on the absorption rate of the light or the pattern of the absorbed light, the user's heart rate, the user's blood oxygen saturation, or the user's electrocardiogram can be measured.

[0220] The fifth sensor (55) may include an acceleration sensor. The acceleration sensor may include a MEMS sensor, a 3-axis acceleration sensor, and / or a 6-axis acceleration sensor.

[0221] According to various embodiments, the fifth sensor (55) may be provided in a wearable smart sensor device (e.g., a smart watch, a smart ring, etc.) by a user, similar to the fourth sensor (54).

[0222] In one embodiment, the fifth sensor (55) may be installed on the furniture (10).

[0223] The fifth sensor (55) can obtain acceleration data corresponding to the user's body movement.

[0224] The hub device (1) may be installed on furniture (10) or installed in a location adjacent to the furniture (10) and may be connected to some of the plurality of sensors (5) via a wire. In addition, the hub device (1) may perform wireless communication with a smart sensor device (e.g., a wearable device) worn by a user.

[0225] FIG. 6 illustrates an example of a flowchart of a sleep management method according to one embodiment. FIG. 7 illustrates another example of a flowchart of a sleep management method according to one embodiment. FIG. 8 is a diagram illustrating a process for processing data collected from multiple sensors of a sleep management system according to one embodiment.

[0226] Referring to FIGS. 6 to 8, the hub device (1) can collect data from multiple sensors (5) (S1).

[0227] When multiple sensors (5) are maintained in an activated state, the power consumption supplied to the multiple sensors (5) may be high. Maintaining the multiple sensors (5) in an activated state may include maintaining a state in which the multiple sensors (5) are capable of acquiring sensor data by being supplied with power.

[0228] In one embodiment, the hub device (1) can keep at least some of the plurality of sensors (5) in a deactivated state, but can switch the plurality of sensors (5) to an activated state based on the satisfaction of a predetermined condition.

[0229] For example, the hub device (1) can determine whether a user is present in the furniture (10) based on processing data collected from multiple sensors (5). The presence of a user in the furniture (10) may include the user lying down or sitting on the furniture (10).

[0230] The hub device (1) can switch multiple sensors (5) to a standby state based on the determination that there is no user in the furniture (10).

[0231] For example, the hub device (1) can deactivate the remaining sensors except for the first sensor (51) and operate the first sensor (51) in a low power mode. Deactivating the sensor may include cutting off power supplied to the sensor.

[0232] Operating the sensor in a low power mode may involve setting the sensor's operating cycle (e.g., data collection cycle) to a longer period.

[0233] The hub device (1) can determine whether a user is present in the furniture (10) based on processing data collected by a sensor (e.g., the first sensor (51)) operating in a low power mode among the plurality of sensors (5).

[0234] The hub device (1) can wake up multiple sensors (5) based on the determination that a user is present in the furniture (10).

[0235] A plurality of sensors (5) can transmit collected data to the hub device (1) based on activation.

[0236] The first sensor (51) can transmit first data to the hub device (1), the second sensor (52) can transmit second data to the hub device (1), the third sensor (53) can transmit third data to the hub device (1), the fourth sensor (54) can transmit fourth data to the hub device (1), and the fifth sensor (55) can transmit fifth data to the hub device (1).

[0237] In one embodiment, at least some of the plurality of sensors (5) can transmit sensor data to the hub device (1) via wired communication, and some of the remaining sensors (5) can transmit sensor data to the hub device (1) via wireless communication.

[0238] The hub device (1) can primarily process data collected from multiple sensors (5) (S2). To this end, the hub device (1) can be equipped with a machine learning model.

[0239] Data collected from multiple sensors (5) can be processed by a machine learning model (11, 12, 13, 14, 15) mounted on the hub device (1).

[0240] The hub device (1) may be equipped with a first machine learning model (11) for extracting features from first data collected from a first sensor (51), a second machine learning model (12) for extracting features from second data collected from a first sensor, a third machine learning model (13) for extracting features from third data collected from a third sensor (53), a fourth machine learning model (14) for extracting features from fourth data collected from a fourth sensor (54), and / or a fifth machine learning model (15) for extracting features from first data collected from a fifth sensor (55).

[0241] The first machine learning model (11) can be pre-trained to extract features from pressure data collected by a pressure sensor. The second machine learning model (12) can be pre-trained to extract features from displacement data collected by a UWB sensor. The third machine learning model (13) can be pre-trained to extract features from eye movement data collected by a radar sensor. The fourth machine learning model (14) can be pre-trained to extract features from heart rate data, oxygen saturation data, and / or electrocardiogram data collected by a PPG sensor and / or an electrocardiogram sensor. The fifth machine learning model (15) can be pre-trained to extract features from acceleration data collected by an acceleration sensor.

[0242] The first machine learning model (11) can use the first data collected by the first sensor (51) as input data and output first processed data as output data. The first processed data may include, for example, information regarding the user's posture, respiration rate, and heart rate inferred from the first data. As another example, the first processed data may include at least one of information regarding the user's sleep stage inferred from the first data or information regarding the user's body movements.

[0243] The second machine learning model (12) can use the second data collected by the second sensor (52) as input data and output second processed data as output data. The second processed data may include, for example, information regarding respiration rate and / or heart rate inferred from the second data. As another example, the second processed data may include at least one of information regarding the user's sleep stage inferred from the second data or information regarding the user's body movements.

[0244] The third machine learning model (13) can use third data collected by the third sensor (53) as input data and output third processed data as output data. The third processed data may include, for example, information regarding eye movements inferred from the third data. As another example, the third processed data may include at least one of information regarding the user's sleep stage inferred from the third data, information regarding the user's eye movements, and / or information regarding the user's body movements.

[0245] The fourth machine learning model (14) can use the fourth data collected by the fourth sensor (54) as input data and output fourth processed data as output data. The fourth processed data may include, for example, information regarding heart rate, oxygen saturation, and / or electrocardiogram inferred from the fourth data. As another example, the fourth processed data may include information regarding the user's sleep stage or information regarding the user's body movements inferred from the fourth data.

[0246] The fifth machine learning model (15) can use the fifth data collected by the fifth sensor (55) as input data and output fifth processed data as output data. The fifth processed data may include, for example, information regarding movement inferred from the fifth data. As another example, the fifth processed data may include at least one of information regarding the user's sleep stage inferred from the fifth data or information regarding the user's body movement.

[0247] According to the present disclosure, the hub device (1) primarily processes data collected from a plurality of sensors (5), thereby enabling data collected from a plurality of sensors (5) to be transmitted to a user device (2) even in a communication method with a low data transmission capacity.

[0248] The hub device (1) can process data collected from multiple sensors (5) and transmit the processed data to the user device (2) (S3).

[0249] The hub device (1) can transmit processing data to the user device (2) via wireless communication. In one embodiment, the communication unit (130) of the hub device (1) can include a first communication module for receiving data collected from some sensors (e.g., the fourth sensor (54)) among the plurality of sensors (5) via a first wireless communication method, and a second communication module for transmitting the processing data to the user device (2) via a second wireless communication method.

[0250] The first wireless communication method and the second wireless communication method may be the same or different from each other.

[0251] According to one embodiment of the present disclosure, a hub device (1) has both a first communication module for receiving data from some of a plurality of sensors (5) and a second communication module for communicating with a user device (2), so that it can communicate with a plurality of sensors (5) and a user device (2) at the same time.

[0252] The user device (2) can process the processing data received from the hub device (1) (S4). To this end, the user device (2) can be equipped with a machine learning model.

[0253] Processing data received from the hub device (1) can be processed by a machine learning model (21) installed in the user device (2).

[0254] The machine learning model (21) mounted on the user device (2) may include an artificial neural network (deep neural network) model having multiple layers (e.g., an input layer, a hidden layer, and an output layer). The machine learning model (21) mounted on the user device (2) may be configured as a perceptron structure having a structure that receives multiple signals as input and outputs one signal. The machine learning model (21) mounted on the user device (2) may be trained for the purpose of estimating the user's sleep state based on processing data processed by the hub device (1).

[0255] The machine learning model (21) mounted on the user device (2) can use the processed data output by the machine learning model of the hub device (1) as input data and output sleep state information related to the user's sleep state as output data.

[0256] The machine learning model (21) mounted on the user device (2) can output sleep state information by using the first processing data, the second processing data, the third processing data, the fourth processing data, and / or the fifth processing data as input data.

[0257] The sleep state information output by the machine learning model (21) mounted on the user device (2) may include at least one of information about the user's sleep stage or information about the user's body movements.

[0258] The user device (2) can obtain REM sleep behavior disorder information related to the user's REM sleep behavior disorder based on the user's sleep stage and the user's body movements (S5). If the user's sleep stage is a REM sleep stage and the user's body movements are greater than a preset movement, the user device (2) can determine that REM sleep behavior disorder has occurred.

[0259] The user device (2) can store the user's REM sleep behavior disorder information in at least one memory (220).

[0260] According to the present disclosure, since data output by a machine learning model (11, 12, 13, 14, 15) primarily loaded on a hub device (1) is secondarily input into a machine learning model (21) loaded on a user device (2) to output sleep state information, the user's sleep state can be accurately estimated. According to the present disclosure, a large amount of data can be processed in stages to accurately estimate the user's sleep state. Therefore, since the occurrence of REM sleep behavior disorder can be determined based on the user's accurate sleep state, the occurrence of REM sleep behavior disorder can be estimated more easily and effectively, as well as more accurately.

[0261] The user device (2) can alert the user of the REM sleep behavior disorder through the user interface (240) in response to the occurrence of the REM sleep behavior disorder (S6).

[0262] The user device (2) can alert the user of REM sleep behavior disorder at the time when REM sleep behavior disorder occurs.

[0263] When a REM sleep behavior disorder occurs, the user device (2) can store REM sleep behavior disorder information in at least one memory (220) and then warn the user of REM sleep behavior disorder when the user's sleep stage is an awake stage.

[0264] The user device (2) may suggest a hospital diagnosis to the user when REM sleep behavior disorder occurs.

[0265] In response to the occurrence of REM sleep behavior disorder, the user device (2) can transmit REM sleep behavior disorder information to a medical institution (6) registered in conjunction with the user device (2) through a communication unit (230) (e.g., a cellular communication module) to notify the external medical institution (6) of the REM sleep behavior disorder (S7).

[0266] The user device (2) can notify the hospital of the occurrence of the user's REM sleep behavior disorder when the user's REM sleep behavior disorder occurs.

[0267] Medical institutions (6) can take measures to prevent safety accidents of users upon receiving information on REM sleep behavior disorder, thereby safely protecting users.

[0268] Meanwhile, the user device (2) can transmit REM sleep behavior disorder information to the server device (3) (S8). At this time, the user device (2) can transmit sleep state information and REM sleep behavior disorder information to the server device (3).

[0269] According to the present disclosure, instead of direct data related to the user's private life being transmitted to the server device (3), only the user's sleep status information is transmitted to the server device (3), thereby making it easier to obtain the user's consent for data collection.

[0270] The server device (3) can generate a control command for controlling a home appliance (4) based on REM sleep behavior disorder information (S9). At this time, the home appliance (4) can include at least one home appliance (4) registered in conjunction with the user device (2). The server device (3) can store and / or manage user accounts and register the user device (2) and the home appliance (4) by linking them to the user account.

[0271] At least one home appliance (4) registered in conjunction with the user device (2) may include a home appliance (4) registered to the user account to which the user device (2) is registered.

[0272] The process (S9) of generating a control command for controlling a home appliance (4) based on REM sleep behavior disorder information by a server device (3) is described in detail later with reference to FIGS. 12 to 15. The server device (3) can transmit a control command for controlling the home appliance (4) to the home appliance (4) (S10).

[0273] According to various embodiments, the process of generating a control command for controlling a home appliance (4) based on REM sleep behavior disorder information (S9) and the process of transmitting the control command for controlling the home appliance (4) to the home appliance (4) (S10) can of course also be performed by the user device (2).

[0274] The home appliance (4) can perform a predetermined operation corresponding to a control command received from the server device (3) (S11).

[0275] According to various embodiments, the server device (3) may control the home appliance (4) to perform an action to alert the user of REM sleep behavior disorder in response to the occurrence of REM sleep behavior disorder. The server device (3) may alert the user of REM sleep behavior disorder through the home appliance (4) when the REM sleep behavior disorder occurs or when the user's sleep stage is an awake stage. The server device (3) may suggest a hospital diagnosis to the user when the REM sleep behavior disorder occurs.

[0276] According to various embodiments, the server device (3) may perform an operation of transmitting REM sleep behavior disorder information to a registered medical institution linked to the user device (2) via wireless communication in response to the occurrence of REM sleep behavior disorder. When REM sleep behavior disorder occurs, the server device (3) may notify the hospital of the situation in which the user's REM sleep behavior disorder has occurred.

[0277] The machine learning model stored in the user device (2) can be updated by an external server. To this end, according to various embodiments, data output by the machine learning model (11, 12, 13, 14, 15) mounted on the hub device (1) can be transmitted to the server device (3).

[0278] Figure 9 is a diagram for explaining sleep stages.

[0279] Human sleep stages can be divided into wakefulness stages, rapid eye movement (REM) stages, and non-rapid eye movement (NREM) stages.

[0280] The awakening stage corresponds to the stage in which a person is awake.

[0281] REM sleep, also known as rapid eye movement sleep, is a light sleep similar to waking sleep and is characterized by rapid eye movement. REM sleep follows non-REM sleep and can begin approximately 90 minutes after falling asleep. Heart rate and brain wave activity are similar to those of a waking state, muscle tone is reduced, and breathing becomes rapid and irregular.

[0282] Non-REM sleep is a non-rapid eye movement sleep stage, in which heart rate, breathing, and eye movement slow down and muscles relax.

[0283] Non-REM sleep stages can be divided into stage 1 (stage N1), stage 2 (stage N2), stage 3 (stage N3), and stage 4 (stage N4). Within non-REM sleep stages, stages 1 (stage N1) and 2 (stage N2) can be divided into light sleep stages, while stages 3 (stage N3) and 4 (stage N4) can be divided into deep sleep stages.

[0284] As another example, non-REM sleep stages may be divided into stage 1 (stage N1), stage 2 (stage N2), and stage 3 (stage N3).

[0285] During non-REM sleep, brain wave activity gradually slows down and physiological functions decline.

[0286] Stage N1 is the most liminal stage of sleep, marking the entrance to sleep. Stage N1 occurs before deep sleep and corresponds to the stage in which slow eye movements are observed.

[0287] Stage N2 is the stage where we move from the borderline sleep stage to a deeper sleep.

[0288] Stage N3 is the initial stage of deep sleep, a stage in which muscle tone is relaxed and physical movement is minimal.

[0289] Stage N4 is a deep sleep that can be difficult to wake up from.

[0290] Slow-wave sleep (stages N3-N4) occurs most frequently in the first third of the sleep cycle, while REM sleep is mainly observed in the last third.

[0291] Typically, when a person falls asleep, the first sleep cycle begins. In this first sleep cycle, the person enters non-REM sleep from the wakeful stage, then moves through stages N1, N2, N3, and N4, and then returns to N3, N2, N1, and REM sleep.

[0292] Afterwards, in the second sleep cycle, we go through stages N1, N2, N3, N4, and then return to stages N3 and N2.

[0293] Afterwards, in the third sleep cycle, we go through stage N3 and then return to stage N2, stage N1, and REM sleep.

[0294] Afterwards, in the fourth sleep cycle, we go through stage N1 and N2 sleep, and then enter stage N1 and REM sleep again.

[0295] After that, the person goes through the N1 stage, N2 stage, and REM sleep stage and wakes up naturally.

[0296] REM sleep behavior disorder can occur during REM sleep.

[0297] From the time you fall asleep until you wake up, REM sleep occurs 4 to 5 times at intervals of about 90 minutes, and usually lasts from 10 minutes to 30 minutes.

[0298] REM sleep behavior disorder occurs more than two hours after falling asleep and is associated with dreams, with dream behaviors actually occurring and awakening from the dream immediately leading to a state of wakefulness.

[0299] REM sleep behavior disorder is characterized by increased muscle tone during REM sleep and excessive movement and abnormal behavior related to dreams.

[0300] People with REM sleep behavior disorder may have unpleasant and violent dreams, such as confrontations with someone, being attacked, or being chased, and may act out these dreams, falling out of bed and injuring themselves, or attacking and injuring their bedmates.

[0301] REM sleep behavior disorder is characterized by extreme physical movements during REM sleep. Therefore, monitoring a user's physical movements during REM sleep can help diagnose whether they have REM sleep behavior disorder.

[0302] FIG. 10 illustrates an example of obtaining information on a user's REM sleep behavior disorder in a sleep management method according to one embodiment.

[0303] Referring to FIG. 10, the user device (2) can determine whether the user's sleep stage is a REM sleep stage (400).

[0304] The user device (2) can continuously determine the user's sleep stage after the user enters sleep mode and determine whether the user's sleep stage is a REM sleep stage.

[0305] The user device (2) can obtain sleep status information related to the user's sleep stage and body movements by inputting the processed data obtained based on processing data collected from multiple sensors (5) into a machine learning model.

[0306] The user device (2) can determine the user's current sleep stage based on the sleep state information.

[0307] The user device (2) can determine that the user has entered REM sleep when the user's sleep stage transitions from the non-REM sleep stage to the REM sleep stage.

[0308] The user device (2) can determine whether the user's body movement is detected (402) if the sleep stage is a REM sleep stage (400, example).

[0309] The user device (2) can determine the user's current body movement based on sleep status information.

[0310] When a user's body movement is detected (402, for example), the user device (2) can obtain information about the user's first body movement based on a contact sensor (404).

[0311] The user device (2) can acquire information about the first body movement of the user by inputting at least one processed data corresponding to data collected by a contact sensor among the first processed data, the second processed data, the third processed data, the fourth processed data and / or the fifth processed data obtained based on processing the first data, the second data, the third processed data, the fourth processed data and / or the fifth processed data collected from a plurality of sensors (5) into a machine learning model. For example, the information about the first body movement of the user can be acquired by inputting the processed data acquired based on processing data collected from at least one of a pressure sensor or an acceleration sensor, which is a contact sensor, into the machine learning model.

[0312] The user device (2) can obtain information about the user's second body movement based on a non-contact sensor (406).

[0313] The user device (2) can acquire information about the second body movement of the user by inputting at least one processed data corresponding to data collected by a non-contact sensor among the first processed data, the second processed data, the third processed data, the fourth processed data and / or the fifth processed data obtained based on processing the first data, the second data, the third data, the fourth data and / or the fifth data collected from a plurality of sensors (5) into a machine learning model. For example, the processed data acquired based on processing data collected from a UWB sensor, which is a non-contact sensor, can be input into the machine learning model to acquire information about the second body movement of the user.

[0314] The user device (2) can determine whether both the first body movement and the second body movement are greater than a preset movement (408). At this time, the user device (2) can compare the first body movement and the second body movement with the same preset movement. In addition, the user device (2) can also compare the first body movement and the second body movement with different preset movements. For example, the user device (2) can compare the first body movement with the preset first movement to determine whether the first body movement is greater than the preset first movement, and can compare the second body movement with the preset second movement to determine whether the second body movement is greater than the preset second movement.

[0315] The user device (2) can determine that a REM sleep behavior disorder has occurred if both the first body movement and the second body movement are greater than the preset movement (408, example) (410).

[0316] The user device (2) can count the number of times that both the first body movement and the second body movement are greater than a preset movement during the REM sleep stage, and if the counted number is greater than the preset number, it can be determined that a REM sleep behavior disorder has occurred.

[0317] The user device (2) can obtain REM sleep behavior disorder information related to the user's REM sleep behavior disorder (412).

[0318] The REM sleep behavior disorder information may include at least one of information regarding whether REM sleep behavior disorder has occurred, information regarding the degree of occurrence of REM sleep behavior disorder, or information regarding the time of occurrence of REM sleep behavior disorder.

[0319] The user device (2) can determine whether REM sleep behavior disorder has occurred, the degree of occurrence, the time of occurrence, etc. by analyzing the pattern of body movements such as the intensity, size, and movement of the first body movement and the second body movement. For example, if the user moves the body or limbs related to a dream while sleeping during the REM sleep stage and moves the body corresponding to a potentially dangerous action or moves the body corresponding to an action of performing an action in the dream, it can be determined that REM sleep behavior disorder has occurred.

[0320] FIG. 11 illustrates an example of sleep state information and REM sleep behavior disorder information derived by a sleep management system according to one embodiment.

[0321] Referring to Fig. 11, sleep state information and REM sleep behavior disorder information can be derived in real time by the sleep management system.

[0322] According to various embodiments, the user device (2) (or display device (41)) may output sleep state information and / or REM sleep behavior disorder information through an output interface (e.g., a display).

[0323] Sleep state information may include information about sleep stages, information about movement, information about oxygen saturation, information about stress index, information about respiration rate, information about heart rate, and / or information about body pressure.

[0324] Information about sleep stages may include information about the user's current sleep stage and / or information about the user's sleep stages over time.

[0325] Information about the user's current sleep stage may include information about whether the user's current sleep stage corresponds to a wake stage, a REM sleep stage, or stage 1, stage 2, stage 3, or stage 3 of non-REM sleep.

[0326] Information about the user's sleep stages over time may include information about changes in sleep stages from a time point preset by the user using the user device (2), a time point when the user lies down or sits on furniture (10) to fall asleep, a time point when the user falls asleep, and / or a time point when the user runs the sleep management application to the present time point.

[0327] When information about the user's sleep stage over time is output by the user device (2) (or display device (41)), the information about the user's sleep stage over time can be displayed in the form of a graph, and the x-axis of the graph can represent time and the y-axis can represent the user's sleep stage.

[0328] Information about oxygen saturation may include information about the user's current oxygen saturation and / or information about the user's oxygen saturation over time.

[0329] When information regarding oxygen saturation is output by the user device (2) (or display device (41)), the numerical value of oxygen saturation may be output in the form of a percentage. In addition, when information regarding oxygen saturation is output by the user device (2) (or display device (41)), whether the user's oxygen saturation is normal according to medical standards may be displayed.

[0330] Information about the user's stress level may include information about the user's stress level. When information about the user's stress level is output by the user device (2) (or display device (41)), the user's stress level may be output numerically or in the form of comparative terms (e.g., "high," "medium," "low"), and whether the user's stress level is normal according to medical standards may be indicated.

[0331] Information about respiration rate may include information about the user's current respiration rate and / or information about the user's respiration rate over time.

[0332] When information about the current respiratory rate is output by the user device (2) (or display device (41)), the user's respiratory rate can be output numerically, and whether the user's respiratory rate is normal according to medical standards can be displayed.

[0333] When information about the user's breathing rate over time is output by the user device (2) (or display device (41)), the information about the user's breathing rate over time can be displayed in the form of a graph, and the x-axis of the graph can represent time and the y-axis can represent the user's breathing rate.

[0334] Information about movement may include information about the degree of movement of the user, and / or information about the degree of movement of the user over time.

[0335] When information about the current degree of movement is output by the user device (2) (or display device (41)), the degree of the user's movement may be output numerically or in the form of comparative words (e.g., high, medium, low).

[0336] When information about the degree of movement over time is output by the user device (2) (or display device (41)), the information about the degree of movement of the user over time can be displayed in the form of a graph, and the x-axis of the graph can represent time and the y-axis can represent the degree of movement of the user.

[0337] Information about movement may include information about body movement and information about eye movement.

[0338] Information about heart rate may include information about the user's current heart rate and / or information about the user's heart rate over time.

[0339] When information about the current heart rate is output by the user device (2) (or display device (41)), the user's heart rate can be output numerically, and whether the user's heart rate is normal according to medical standards can be displayed.

[0340] When information about the user's heart rate over time is output by the user device (2) (or display device (41)), the information about the user's heart rate over time can be displayed in the form of a graph, and the x-axis of the graph can represent time and the y-axis can represent a pulse corresponding to the user's heartbeat.

[0341] Information about body pressure may include information about the user's body posture. This information may include whether the user is lying flat, lying on their left side, lying on their right side, lying prone, or sitting.

[0342] When information about body pressure is output by the user device (2) (or display device (41)), a term indicating the user's posture may be used, and a pressure distribution table based on the furniture (10) on which the user is lying or sitting may be output.

[0343] The user's REM sleep behavior disorder information may include at least one of information regarding whether REM sleep behavior disorder has occurred, information regarding the degree of occurrence of REM sleep behavior disorder, or information regarding the time of occurrence of REM sleep behavior disorder.

[0344] When information on current REM sleep behavior disorder is output by the user device (2) (or display device (41)), whether the user's REM sleep behavior disorder has occurred, the degree of occurrence, the time of occurrence, etc. may be output in numerical or text form.

[0345] Sleep state information and / or REM sleep behavior disorder information may be provided to the user by the user device (2) or may be provided to the user by a home appliance (4) (e.g., a display device (41), a speaker (46), etc.).

[0346] For example, a user can run a sleep management application using a user device (2) and check sleep status information and / or REM sleep behavior disorder information through an interface provided by the sleep management application.

[0347] As another example, the server device (3) can control a home appliance (4) (e.g., a display device (41), a speaker (46), etc.) to provide sleep summary information and / or REM sleep behavior disorder information when the user wakes up.

[0348] According to various embodiments, a user may share sleep state information and / or REM sleep behavior disorder information with other users via the user device (2). For example, a user may run a sleep management application and transmit sleep state information and / or REM sleep behavior disorder information to another user's device via an interface provided by the sleep management application.

[0349] FIG. 12 illustrates an example of a predetermined operation according to the occurrence of a user's REM sleep behavior disorder in a sleep management method according to one embodiment.

[0350] In one embodiment, the server device (3) can control the home appliance (4) based on REM sleep behavior disorder information.

[0351] Referring to FIG. 12, the server device (3) can determine whether the user has REM sleep behavior disorder based on REM sleep behavior disorder information (500).

[0352] If the server device (3) determines that the user has a REM sleep behavior disorder (500, example), it can control the home appliance (4) to perform a predetermined action to alleviate the user's REM sleep behavior disorder (502).

[0353] According to one embodiment of the present disclosure, by controlling the home appliance (4) to perform a predetermined operation to alleviate the user's REM sleep behavior disorder, the user can alleviate the REM sleep behavior disorder through the operation of the home appliance (4).

[0354] Certain actions to alleviate REM sleep behavior disorder that can be performed by a home appliance (4) are not limited to the examples described above, and may of course be changed depending on the user's settings and the type of home appliance (4).

[0355] The server device (3) can determine whether the user's REM sleep behavior disorder has been alleviated (504). The server device (3) can determine whether the REM sleep behavior disorder has been alleviated based on the slowing of the user's body movement changes after a predetermined action to alleviate the REM sleep behavior disorder.

[0356] If the user's REM sleep behavior disorder is alleviated (504, example), the server device (3) can control the home appliance (4) to stop a predetermined operation for alleviating the REM sleep behavior disorder (506).

[0357] If the user's REM sleep behavior disorder is not alleviated (504, No), the server device (3) can control the home appliance (4) to perform a predetermined action to wake the user up (508).

[0358] Figure 13 illustrates an example of a predetermined action to alleviate a user's REM sleep behavior disorder.

[0359] Referring to FIG. 13, the server device (3) can control the lighting device (43) to perform a predetermined operation to alleviate the user's REM sleep behavior disorder.

[0360] For example, the server device (3) can control the lighting device (43) to output light of a predetermined color (e.g., a color having a color temperature of 1000 K or less).

[0361] The server device (3) can control the air conditioner (45) to perform a predetermined operation to alleviate the user's REM sleep behavior disorder.

[0362] For example, the server device (3) can control the air conditioner (45) to slightly increase the speed of the fan so that the wind output from the air conditioner (45) can be delivered to the user.

[0363] The server device (3) can control the furniture control device (42) to perform a predetermined operation to alleviate the user's REM sleep behavior disorder. The furniture control device (42) can include a vibration element (42a) that can transmit vibration to a user lying or sitting on the furniture (10).

[0364] For example, the server device (3) can control the vibration element (42a) to output vibration corresponding to the user's heart rate.

[0365] According to one embodiment of the present disclosure, by alleviating a user's REM sleep behavior disorder, the risk of injury to the user or to the sleeping partner can be reduced and quality sleep can be induced.

[0366] Figure 14 illustrates another example of a predetermined action to alleviate a user's REM sleep behavior disorder.

[0367] Referring to FIG. 14, the server device (3) can control the air conditioner (45) to perform a predetermined operation to alleviate the user's REM sleep behavior disorder.

[0368] For example, the server device (3) can turn off the air conditioner (45).

[0369] The server device (3) can control the furniture control device (42) to perform a predetermined operation to alleviate the user's REM sleep behavior disorder. The furniture control device (42) can include an actuator (42b) that can change the user's posture by changing the structure of the furniture (10).

[0370] For example, the server device (3) can control the actuator (42b) to change the structure of the furniture so that the user's posture changes to a predetermined posture that is comfortable for breathing (a posture in which the user's head is higher than the torso and legs).

[0371] According to one embodiment of the present disclosure, by alleviating a user's REM sleep behavior disorder, the risk of injury to the user or to the sleeping partner can be reduced and quality sleep can be induced.

[0372] Figure 15 illustrates an example of a predetermined action to induce a user's wake-up.

[0373] Referring to FIG. 15, the server device (3) can control the display device (41) to perform a predetermined operation for waking up in order to reduce the risk of injury when the user's REM sleep behavior disorder is not alleviated and persists for a long time or occurs frequently.

[0374] For example, the server device (3) can control the display device (41) so that the brightness of the image output from the display device (41) gradually increases. As another example, the server device (3) can control the display device (41) so that the display device (41) plays predetermined music (e.g., music for waking up).

[0375] According to various embodiments, the server device (3) may also control the speaker (46) to play a predetermined music (e.g., music for waking up).

[0376] The server device (3) can control the lighting device (43) to perform a predetermined operation for weather induction. For example, the server device (3) can control the lighting device (43) so that the brightness of the light output from the lighting device (43) gradually increases. As another example, the server device (3) can control the lighting device (43) so that it outputs light similar to natural light.

[0377] The server device (3) can control the automatic curtain opening / closing device (44) to perform a predetermined operation for weather induction. For example, the server device (3) can control the automatic curtain opening / closing device (44) to open the curtains.

[0378] The server device (3) can control the furniture control device (42) to perform a predetermined operation for waking up. The furniture control device (42) can include a vibration element (42a) that can transmit vibration to a user lying or sitting on the furniture (10) and / or an actuator (42b) that can change the user's posture by changing the structure of the furniture (10).

[0379] For example, the server device (3) can control the vibration element (42a) to output vibration at a predetermined cycle corresponding to the user's heart rate.

[0380] As another example, the server device (3) can control the actuator (42b) to change the structure of the furniture so that the user's posture changes to a comfortable predetermined posture (a posture in which the upper body is higher than the lower body).

[0381] The server device (3) can control the air conditioner (45) and / or the air purifier (47) to perform a predetermined operation for weather induction. For example, the server device (3) can control the air conditioner and / or the air purifier (47) to operate in a weather mode.

[0382] When the air conditioner (45) and / or the air purifier (47) operates in the weather mode, it can provide a refreshing feeling to the user, for example, by quickly controlling the speed of the fan.

[0383] The predetermined operation for waking up that can be performed by the home appliance (4) is not limited to the above-described example, and can of course be changed depending on the user's settings and the type of home appliance (4).

[0384] According to one embodiment of the present disclosure, a user's REM sleep behavior disorder can be examined more easily and effectively in daily life without having to wear various sensors on the body.

[0385] According to one embodiment of the present disclosure, a user's REM sleep behavior disorder can be alleviated by controlling a home appliance to perform a predetermined action to alleviate the user's REM sleep behavior disorder.

[0386] According to one embodiment of the present disclosure, by alleviating a user's REM sleep behavior disorder, the risk of injury to the user or to the sleeping partner can be reduced and quality sleep can be induced.

[0387] According to one embodiment of the present disclosure, accurate data related to a user's REM sleep behavior disorder can be obtained by utilizing values ​​output by a machine learning model stored in a hub device as input values ​​of a machine learning model stored in a user device.

[0388] A sleep management system according to one embodiment of the present disclosure comprises: a plurality of sensors (5) including a first sensor (51, 52, 53, 54, or 55) and a second sensor (51, 52, 53, 54, or 55) for collecting user data; a hub device (1) for receiving first data from the first sensor (51, 52, 53, 54, or 55) and second data from the second sensor, obtaining first processed data based on processing the first data, and obtaining second processed data based on processing the second data; And it may include a user device (2) that receives the first processing data and the second processing data from the hub device (1), obtains sleep state information about the user's sleep stage and the user's body movement based on the first processing data and the second processing data, determines whether the user has a REM sleep behavior disorder based on the user's sleep stage and the user's body movement, and performs a first operation based on the occurrence of the REM sleep behavior disorder.

[0389] The above plurality of sensors (5) may include at least two of a pressure sensor, a UWB sensor, a radar sensor, a PPG sensor, an electrocardiogram sensor, and an acceleration sensor.

[0390] The above hub device (1) can input the first data into a first machine learning model (11, 12, 13, 14, or 15) to obtain the first processing data, and input the second data into a second machine learning model (11, 12, 13, 14, or 15) to obtain the second processing data.

[0391] The user device may determine that the REM sleep behavior disorder has occurred based on the user's sleep stage being a REM sleep stage and the user's body movement being greater than a preset movement.

[0392] The first sensor (51, 52, 53, 54, or 55) may be a contact sensor, and the second sensor (51, 52, 53, 54, or 55) may be a non-contact sensor.

[0393] The user device (2) inputs first processed data corresponding to first data collected by the contact sensor into a machine learning model (21) based on the user's sleep stage being a REM sleep stage, thereby obtaining information on the user's first body movement, and inputs second processed data corresponding to second data collected by the non-contact sensor into the machine learning model (21) to obtain information on the user's second body movement, and based on the fact that both the first body movement and the second body movement are greater than a preset movement, it is possible to determine whether the REM sleep behavior disorder has occurred.

[0394] The first action may include an action of warning the user of the occurrence of the REM sleep behavior disorder through the user interface (240).

[0395] The above first action may include an action of notifying a medical institution (6) of the occurrence of the REM sleep behavior disorder.

[0396] It may include a server device (3) that receives REM sleep behavior disorder information from the user device (2).

[0397] The server device (3) can control at least one home appliance (4) to perform a second operation to alleviate the REM sleep behavior disorder based on the occurrence of the REM sleep behavior disorder.

[0398] The server device (3) can control at least one home appliance (4) to perform a third action to wake up the user based on the occurrence of the REM sleep behavior disorder.

[0399] The user device (2) can perform at least one of an action of warning the user of the occurrence of the REM sleep behavior disorder based on the occurrence of the REM sleep behavior disorder or an action of transmitting the REM sleep behavior disorder information to a medical institution (6).

[0400] A sleep management method according to one embodiment of the present disclosure may include: a hub device (1) receiving first data from a first sensor (51, 52, 53, 54, or 55) and second data from a second sensor (51, 52, 53, 54, or 55); the hub device (1) obtaining first processed data based on processing the first data, and obtaining second processed data based on processing the second data; the hub device (1) transmitting the first processed data and the second processed data to a user device (2); and the user device (2) obtaining sleep state information about a sleep stage and a body movement of the user based on the first processed data and the second processed data received from the hub device (1), determining whether a REM sleep behavior disorder has occurred in the user based on the sleep stage and the body movement of the user, and performing a first operation based on the occurrence of the REM sleep behavior disorder.

[0401] Determining whether the user has REM sleep behavior disorder may include determining whether the user's sleep stage is a REM sleep stage and whether the user's body movement is greater than a preset movement.

[0402] The first sensor (51, 52, 53, 54, or 55) is a contact sensor, and the second sensor (51, 52, 53, 54, or 55) is a non-contact sensor, and determining whether the user has a REM sleep behavior disorder may include: inputting first processed data corresponding to first data collected by the contact sensor into a machine learning model (21) based on the user's sleep stage being a REM sleep stage to obtain information on the user's first body movement; inputting second processed data corresponding to second data collected by the non-contact sensor into the machine learning model (21) to obtain information on the user's second body movement; and determining that the REM sleep behavior disorder has occurred based on the first body movement and the second body movement being greater than a preset movement.

[0403] The user device (2) performing the predetermined operation may include performing an operation to warn the user of the occurrence of the REM sleep behavior disorder through the user interface (240).

[0404] The user device (2) performing the predetermined operation may include performing an operation of notifying a medical institution (6) registered in conjunction with the user device (2) of the occurrence of the REM sleep behavior disorder.

[0405] The server device (3) may further include receiving REM sleep behavior disorder information related to the REM sleep behavior disorder from the user device (2).

[0406] The server device (3) may further include controlling at least one home appliance registered in conjunction with the user device to perform a predetermined action to alleviate the REM sleep behavior disorder or wake up the user in response to the occurrence of the REM sleep behavior disorder.

[0407] The server device (3) may further include controlling at least one home appliance to perform an operation of alerting the user of the REM sleep behavior disorder information through the user device in response to the occurrence of the REM sleep behavior disorder, or performing an operation of transmitting the REM sleep behavior disorder information to a medical institution registered in conjunction with the user device.

[0408] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium storing instructions executable by one or more computers. The instructions may be stored in the form of program code, and when executed by one or more processors, may generate program modules to perform the operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0409] A computer-readable recording medium includes any type of recording medium that stores instructions that can be deciphered by one or more computers. For example, it may include one or more of read-only memory (ROM), random access memory (RAM), magnetic tape, magnetic disk, flash memory, and optical data storage devices. In other words, the instructions stored in a computer-readable recording medium may be stored in a single computer-readable recording medium or distributed across one or more computer-readable recording media to implement the method.

[0410] Additionally, a computer-readable recording medium may be provided in the form of a non-transitory storage medium. Here, the term "non-transitory storage medium" simply means a tangible device that does not contain signals (e.g., electromagnetic waves). This term does not distinguish between cases where data is permanently stored in the storage medium and cases where data is temporarily stored. For example, a "non-transitory storage medium" may include a buffer in which data is temporarily stored.

[0411] According to one embodiment, the method according to various embodiments disclosed in the present document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable recording medium (e.g., compact disc read only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) via an application store (e.g., Play Store™) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product (e.g., a downloadable app) may be temporarily stored or temporarily generated on a machine-readable recording medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0412] The embodiments described above are merely specific examples intended to illustrate the technical content of embodiments of the present disclosure and aid in understanding of the embodiments of the present disclosure, and are not intended to limit the scope of the embodiments of the present disclosure. Therefore, the scope of the various embodiments of the present disclosure should be interpreted to include all modifications or variations derived based on the technical concepts of the various embodiments of the present disclosure, in addition to the embodiments disclosed in the present disclosure.

Claims

1. A plurality of sensors including a first sensor and a second sensor for collecting user data; A hub device that receives first data from the first sensor and second data from the second sensor, obtains first processed data based on processing the first data, and obtains second processed data based on processing the second data; and A sleep management system comprising: a user device which receives the first processing data and the second processing data from the hub device, obtains sleep state information about the user's sleep stage and the user's body movements based on the first processing data and the second processing data, determines whether the user has REM sleep behavior disorder based on the user's sleep stage and the user's body movements, and performs a first operation based on the occurrence of the REM sleep behavior disorder.

2. In paragraph 1, The above multiple sensors are, A sleep management system comprising at least two of a pressure sensor, a UWB sensor, a radar sensor, a PPG sensor, an electrocardiogram sensor and an acceleration sensor.

3. In paragraph 1, The above hub device, A sleep management system that inputs the first data into a first machine learning model to obtain the first processing data, and inputs the second data into a second machine learning model to obtain the second processing data.

4. In paragraph 1, The above user device, A sleep management system that determines that the user's sleep stage is a REM sleep stage and the user's body movement is greater than a preset movement, and thus determines that the user has a REM sleep behavior disorder.

5. In paragraph 1, The above first sensor is a contact sensor, The second sensor is a sleep management system that is a non-contact sensor.

6. In paragraph 5, The above user device, A sleep management system for obtaining information on a first body movement of the user by inputting first processed data corresponding to the first data collected by the contact-type sensor into a machine learning model based on the user's sleep stage being a REM sleep stage, obtaining information on a second body movement of the user by inputting second processed data corresponding to the second data collected by the non-contact sensor into the machine learning model, and determining whether the REM sleep behavior disorder occurs based on the fact that both the first body movement and the second body movement are greater than a preset movement.

7. In paragraph 1, A sleep management system wherein the first operation includes an operation of alerting a user of the occurrence of the REM sleep behavior disorder through a user interface.

8. In paragraph 1, A sleep management system wherein the first action includes an action of notifying a medical institution of the occurrence of the REM sleep behavior disorder.

9. In paragraph 1, A sleep management system comprising a server device receiving REM sleep behavior disorder information from the user device.

10. In paragraph 9, The above server device, A sleep management system that controls at least one home appliance to perform a second action for alleviating the REM sleep behavior disorder based on the occurrence of the REM sleep behavior disorder.

11. In paragraph 9, The above server device, A sleep management system that controls at least one home appliance to perform a third action to wake up the user based on the occurrence of the REM sleep behavior disorder.

12. In paragraph 9, The above user device, A sleep management system that performs at least one of an action of alerting a user to the occurrence of the REM sleep behavior disorder or an action of transmitting information about the REM sleep behavior disorder to a medical institution based on the occurrence of the REM sleep behavior disorder.

13. The hub device receives first data from the first sensor and second data from the second sensor; The hub device obtains first processing data based on processing the first data, and obtains second processing data based on processing the second data; The hub device transmits the first processing data and the second processing data to the user device; The user device obtains sleep state information about the user's sleep stage and body movement based on the first processing data and the second processing data received from the hub device; The user device determines whether the user has REM sleep behavior disorder based on the user's sleep stage and the user's body movements; A sleep management method, comprising: the user device performing a first action based on the occurrence of the REM sleep behavior disorder.

14. In paragraph 13, To determine whether the above user has REM sleep behavior disorder, A sleep management method comprising determining whether the user's sleep stage is a REM sleep stage and whether the user's body movement is greater than a preset movement.

15. In paragraph 13, The above first sensor is a contact sensor, The above second sensor is a non-contact sensor, To determine whether the above user has REM sleep behavior disorder, A sleep management method comprising: inputting first processed data corresponding to first data collected by the contact sensor into a machine learning model based on the user's sleep stage being a REM sleep stage to obtain information on the user's first body movement; inputting second processed data corresponding to second data collected by the non-contact sensor into the machine learning model to obtain information on the user's second body movement; and determining that the REM sleep behavior disorder has occurred based on the fact that both the first body movement and the second body movement are greater than a preset movement.

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