Sleep management system and sleep management method

Abstract

This sleep management system includes: a plurality of sensors including a first sensor and a second sensor for collecting data on a user; a hub device which receives first data collected by the first sensor and second data collected by the second sensor, acquires first processed data on the basis of having processed the first data, and acquires second processed data on the basis of having processed the second data; and a user device which receives the first processed data and the second processed data from the hub device, acquires sleep state information corresponding to the sleep stage of the user and body movements of the user on the basis of the first processed data and the second processed data, determines whether the user is exhibiting non-REM sleep impaired behavior on the basis of the sleep stage of the user and the body movements of the user, and performs a predetermined operation on the basis of the occurrence of the non-REM sleep impaired behavior.

Description

Sleep management system and sleep management method

[0001] Embodiments of the present disclosure relate to a sleep management system and a sleep management method.

[0002] Non-REM sleep behavior disorder (NREM) is a sleep-wake disorder characterized by involuntary movements during non-REM sleep. Examples include sleepwalking and night terrors.

[0003] In particular, sleepwalkers can move around unconsciously, which puts them at risk of injury or safety accidents, and they can also escape through open doors or rooms, so caution is required.

[0004] Previously, non-REM sleep behavior disorder was examined in subjects 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] One aspect of the disclosed invention provides a sleep management system and sleep management method that can more easily and effectively examine non-REM sleep behavior disorder, which is an unconscious behavior during the non-REM sleep stage.

[0007] The technical problems to be achieved in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present invention belongs from the description below.

[0008] A sleep management system according to one aspect of the disclosed invention 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 collected by the first sensor and second data collected by 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 corresponding to a sleep stage and a body movement of the user based on the first processed data and the second processed data, determining whether a non-REM sleep behavior disorder of the user occurs based on the sleep stage of the user and the body movement of the user, and performing a predetermined operation based on the occurrence of the non-REM sleep behavior disorder.

[0009] A sleep management method according to one aspect of the disclosed invention may include: a hub device receiving first data collected by a first sensor and second data collected by 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; and the user device obtaining sleep state information corresponding to 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, determining whether a non-REM sleep behavior disorder occurs in the user based on the sleep stage and the body movement of the user, and performing a predetermined operation based on the occurrence of the non-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] Figures 4 and 5 illustrate an example of a plurality of 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 is a diagram illustrating a process for processing data collected from multiple sensors of a sleep management system according to one embodiment.

[0017] Figure 8 is a diagram for explaining sleep stages.

[0018] FIG. 9 illustrates an example of obtaining information on a user's non-REM sleep behavior disorder in a sleep management method according to one embodiment.

[0019] FIG. 10 illustrates an example of sleep state information and non-REM sleep behavior disorder information derived by a sleep management system according to one embodiment.

[0020] FIG. 11 illustrates an example of a predetermined operation according to the occurrence of a user's non-REM sleep behavior disorder in a sleep management method according to one embodiment.

[0021] Figure 12 illustrates an example of a predetermined action to alleviate a user's non-REM sleep behavior disorder.

[0022] Figure 13 illustrates another example of a predetermined action to alleviate a user's non-REM sleep behavior disorder.

[0023] Figure 14 illustrates an example of a predetermined action to induce a user's wake-up.

[0024] The various embodiments and terminology used in this document are not intended to limit the technical features described herein to specific embodiments, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiments. The embodiments described in this disclosure are exemplary embodiments, and thus, the disclosure is not limited thereto.

[0025] In connection with the description of the drawings, similar reference numerals may be used for similar or related components.

[0026] The singular form of a noun corresponding to an item may include one or more of said items, unless the relevant 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 do not indicate the order of the steps, and the steps may be performed in a different order than stated unless the context clearly indicates a specific order.

[0036] Hereinafter, embodiments according to the present invention will be described in detail with reference to the attached drawings.

[0037] Below, a sleep management system according to various embodiments is specifically described with reference to the attached drawings.

[0038] FIG. 1 illustrates an example of a network of a sleep management system according to one embodiment.

[0039] Referring to FIG. 1, a sleep management system according to one embodiment may include a hub device (1) and a user device (2).

[0040] Additionally, the sleep management system according to one embodiment may include a server device (3) and / or a home appliance (4).

[0041] The hub device (1) may include a communication interface 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).

[0042] The hub device (1) can obtain processed data based on processing data collected from multiple sensors. In one embodiment, the hub device (1) can process data collected from multiple sensors using a machine learning model.

[0043] In 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).

[0044] 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).

[0045] The home appliance (4) can be remotely controlled by the server device (3).

[0046] 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.

[0047] The lighting device (43) may include a light source whose light intensity and / or light color can be controlled.

[0048] The automatic curtain opening / closing device (44) may include an actuator for automatically opening or closing the curtain.

[0049] The server device (3) may include a communication interface capable of communicating with the hub device (1), the user device (2), and / or the home appliance (4).

[0050] 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 the 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.

[0051] 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.

[0052] 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.

[0053] 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).

[0054] 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.

[0055] 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.

[0056] The user device (2) may include a communication interface 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 a 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 in which a program for controlling the operation of the user device (2) is stored.

[0057] 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.

[0058] 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.

[0059] 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).

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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). Short-range wireless networks may include, but are not limited to, Bluetooth (IEEE 802.15.1), Zigbee (IEEE 802.15.4), Wi-Fi Direct, Near Field Communication (NFC), Z-Wave, etc.

[0064] 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).

[0065] The access point (AP) communicates with a hub device (1), a user device (2), and / or a home appliance (4) using wireless communication such as Wi-Fi (IEEE 802.11), Bluetooth (IEEE 802.15.1), or Zigbee (IEEE 802.15.4), 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.

[0066] 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).

[0067] 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 interface).

[0068] 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.

[0069] Referring to FIGS. 2 and 3, the hub device (1) can receive data collected from a plurality of sensors (5).

[0070] The plurality of sensors (5) may include sensors for collecting user data, for example, a first sensor (51), a second sensor (52), a third sensor (53), a fourth sensor (54), and / or a fifth sensor (55).

[0071] Each of the plurality of sensors (5) can collect user data and transmit the collected user data to the hub device (1).

[0072] In one embodiment, the user's data may include data related to the user's sleep.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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. However, the embodiment is not limited thereto.

[0077] 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.

[0078] 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.

[0079] The plurality of sensors (5) may include at least one contact sensor and at least one non-contact sensor.

[0080] For convenience of explanation, the first sensor (51) may be a pressure sensor, the second sensor (52) may be a UWB sensor, the third sensor (53) may be a radar sensor, the fourth sensor (54) may be a PPG sensor and / or an electrocardiogram sensor, and the fifth sensor (55) may be an acceleration sensor.

[0081] The plurality of sensors (5) may further include additional sensors, for example, microphones and 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).

[0082] Data collected from multiple sensors (5) can be transmitted to the hub device (1).

[0083] 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.

[0084] Accordingly, the hub device (1) can be connected to at least one of the plurality of sensors (5) via a wire and connected to the remaining sensors wirelessly.

[0085] 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 interface 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.

[0086] 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.

[0087] 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.

[0088] In one embodiment, data collected from multiple sensors (5) can be transmitted to a hub device (1) via wired communication.

[0089] 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. However, the embodiment is not limited thereto.

[0090] 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.

[0091] 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).

[0092] At least one processor (110) can store a machine learning model for processing data collected from multiple sensors (5).

[0093] 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.

[0094] Features of data may include elements that a machine learning model extracts from the data to perform classification or prediction.

[0095] 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.

[0096] 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).

[0097] At least one processor (110) can obtain first processed data based on processing first data collected from the first sensor (51).

[0098] 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.

[0099] At least one processor (110) can obtain second processed data based on processing second data collected from a second sensor (52).

[0100] 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.

[0101] At least one processor (110) can obtain third processing data based on processing third data collected from a third sensor (53).

[0102] 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.

[0103] At least one processor (110) can obtain fourth processed data based on processing fourth data collected from the fourth sensor (54).

[0104] 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.

[0105] At least one processor (110) can obtain fifth processed data based on processing fifth data collected from the fifth sensor (55).

[0106] 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.

[0107] 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.

[0108] The hub device (1) may include a communication unit (130) including a wired communication interface for performing wired communication with a plurality of sensors (5), and / or a wireless communication interface for performing wireless communication with a user device (2), a server device (3), and / or a home appliance.

[0109] 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 via a wire.

[0110] The hub device (1) may include a housing covering a printed circuit board.

[0111] 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).

[0112] 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).

[0113] 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).

[0114] 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).

[0115] 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).

[0116] At least one processor (110) can control multiple sensors (5).

[0117] For example, at least one processor (110) can control at least one wired sensor among a plurality of sensors (5).

[0118] 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.

[0119] Waking up a sensor may include activating the sensor.

[0120] 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.

[0121] The user device (2) can receive data acquired by the hub device (1) from the hub device (1) via wireless communication.

[0122] The data acquired by the hub device (1) may include processed data obtained by processing data collected from a plurality of sensors (5).

[0123] 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).

[0124] At least one memory (220) can store a machine learning model for processing data received from the hub device (1).

[0125] 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.

[0126] 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.

[0127] In one embodiment, the user device (2) may include a communication unit (230) including at least one communication interface 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).

[0128] The user device (2) can receive processing data from the hub device (1) through the communication unit (230).

[0129] 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 interface) being activated.

[0130] In one embodiment, at least one processor (210) can control a 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 interface) is not activated.

[0131] 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 interface).

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] At least one processor (210) can determine whether the user has a non-REM sleep behavior disorder based on the user's sleep stage and the user's body movements.

[0141] Non-REM sleep behavior disorder may include sleepwalking disorder. For example, sleepwalking disorder may be sleepwalking. Non-REM sleep behavior disorder may include sleep-startling disorder. For example, sleep-startling disorder may be night terrors. Non-REM sleep behavior disorder may include both sleepwalking disorder and sleep-startling disorder.

[0142] 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.

[0143] 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 magnitude of the first body movement, or information about the movement of the first body movement.

[0144] 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.

[0145] 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, and if both the first body movement and the second body movement are greater than the preset movement and the user's sleep stage is a non-REM sleep stage, it may be determined that a non-REM sleep behavior disorder has occurred.

[0146] At least one processor (210) may obtain non-REM sleep behavior disorder information related to the user's non-REM sleep behavior disorder in response to the occurrence of the user's non-REM sleep behavior disorder.

[0147] At least one processor (210) can store the user's non-REM sleep behavior disorder information in at least one memory (220).

[0148] The user's non-REM sleep behavior disorder information may include at least one of information regarding whether non-REM sleep behavior disorder occurs, information regarding the type of non-REM sleep behavior disorder, or information regarding the time of occurrence of non-REM sleep behavior disorder.

[0149] 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 non-REM sleep behavior disorder.

[0150] At least one processor (210) can, in response to the occurrence of a non-REM sleep behavior disorder, notify an external emergency center registered in conjunction with the user device (2) of non-REM sleep behavior disorder information through a communication unit (230) (e.g., a cellular communication interface).

[0151] At least one processor (210) may alert the user of non-REM sleep behavior disorder information via the user interface (240) in response to the occurrence of a non-REM sleep behavior disorder.

[0152] At least one processor (210) can control the communication unit (230) to transmit sleep state information and non-REM sleep behavior disorder information to the server device (3).

[0153] The communication unit (230) may include a first communication interface for establishing communication with the hub device (1), a second communication interface for establishing communication with the server device (3), and a third communication interface for establishing communication with an external emergency center. 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 the external emergency center.

[0154] 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 non-REM sleep behavior disorder information are obtained, and the sleep state information and non-REM sleep behavior disorder information can be transmitted to a server device (3) in real time.

[0155] 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).

[0156] The user device (2) may include a user interface (240) for communication with the user.

[0157] 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 non-REM sleep behavior disorder information.

[0158] 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 non-REM sleep behavior disorder information.

[0159] 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 non-REM sleep behavior disorder information.

[0160] 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), the server device (3) to primarily process the data received from the hub device (1), and to secondarily process the primarily processed data to obtain sleep state information and non-REM sleep behavior disorder information.

[0161] 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.

[0162] 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).

[0163] 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 include a microphone capable of converting auditory information into electrical signals.

[0164] The input interface can receive user input to run a sleep management application.

[0165] 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.

[0166] The output interface can output an interface corresponding to the sleep management application.

[0167] 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.

[0168] The output interface and input interface may be provided as separate devices or as a single device (e.g., a touch display).

[0169] The server device (3) can receive data acquired by the user device (2) from the user device (2) via wireless communication.

[0170] Data acquired by the user device (2) may include sleep state information and non-REM sleep behavior disorder information.

[0171] In one embodiment, the server device (3) may include at least one memory (320) storing a program for processing sleep state information and non-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 non-REM sleep behavior disorder information received from the user device (2) based on the program stored in the at least one memory (320).

[0172] At least one memory (320) can store sleep state information and non-REM sleep behavior disorder information received from the user device (2).

[0173] At least one memory (320) can store a program for generating sleep summary information based on sleep state information and non-REM sleep behavior disorder information received from the user device (2).

[0174] At least one processor (310) can generate sleep summary information based on sleep state information and non-REM sleep behavior disorder information accumulated and stored in at least one memory (320).

[0175] 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 non-REM sleep behavior disorder information received from a user device (2).

[0176] At least one processor (310) can control the home appliance (4) based on at least one of sleep state information or non-REM sleep behavior disorder information received from the user device (2).

[0177] According to various embodiments, a program for generating sleep summary information based on at least one of sleep state information or non-REM sleep behavior disorder information and a program for controlling a home appliance (4) based on the sleep state information or non-REM sleep behavior disorder information may be stored on different servers.

[0178] 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 non-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 non-REM sleep behavior disorder information.

[0179] At least one memory (320) can store sleep summary information generated based on at least one of sleep state information or non-REM sleep behavior disorder information.

[0180] In one embodiment, the server device (3) may include a communication unit (330) including at least one communication interface 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).

[0181] The server device (3) can receive sleep state information and non-REM sleep behavior disorder information from the user device (2) through the communication unit (330).

[0182] The server device (3) can transmit a control command to the home appliance (4) through the communication unit (330).

[0183] The server device (3) can transmit sleep summary information and non-REM sleep behavior disorder information to the user device (2) through the communication unit (330).

[0184] Figures 4 and 5 illustrate an example of a plurality of sensors of a sleep management system according to one embodiment.

[0185] Referring to FIGS. 4 and 5, in one embodiment, some of the plurality of sensors (5), for example, the first sensor (51), the second sensor (52), and / or the third sensor (53), may be installed in the furniture (10), and other some of the plurality of sensors (5), for example, the fourth sensor (54), and / or the fifth sensor (55), may be provided in a smart sensor device, for example, a smart watch, a smart ring, or the like.

[0186] At least some of the plurality of sensors (5) may be provided on furniture (10) on which a user can sit or lie.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] The mattress may further include springs and / or sponges. The springs and / or sponges may be provided in the accommodation space of the cover.

[0193] The structure (e.g., length, arrangement, etc.) of the first sensor (51) may be changed depending on the size of the mattress.

[0194] 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.

[0195] 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.

[0196] 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).

[0197] 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).

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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).

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] The third sensor (53) can measure the movement of the user's eyes based on mmWave reflected from the user's eyes.

[0215] That is, the third sensor (53) can obtain eye-movement data corresponding to the movement of the user's eyes.

[0216] 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.

[0217] The fourth sensor (54) may have a detection area facing the body of a user lying or sitting on the furniture (10).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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).

[0224] In one embodiment, the fifth sensor (55) may be installed on the furniture (10).

[0225] The fifth sensor (55) can obtain acceleration data corresponding to the user's body movement.

[0226] 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.

[0227] Fig. 6 illustrates an example of a flowchart of a sleep management method according to one embodiment. Fig. 7 is a diagram illustrating a process for processing data collected from multiple sensors of a sleep management system according to one embodiment.

[0228] Referring to FIGS. 6 and 7, the hub device (1) can collect data from a plurality of sensors (5) (S1).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] The hub device (1) can wake up multiple sensors (5) based on the determination that a user is present in the furniture (10).

[0237] A plurality of sensors (5) can transmit collected data to the hub device (1) based on activation.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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).

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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 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.

[0246] 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 or information regarding the user's body movements inferred from the third data.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] The hub device (1) can process data collected from multiple sensors (5) and transmit the processed data to the user device (2) (S3).

[0251] 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 interface 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 interface for transmitting the processing data to the user device (2) via a second wireless communication method.

[0252] The first wireless communication method and the second wireless communication method may be the same or different from each other.

[0253] According to one embodiment of the present disclosure, a hub device (1) has both a first communication interface for receiving data from some of a plurality of sensors (5) and a second communication interface for communicating with a user device (2), so that it can communicate with a plurality of sensors (5) and the user device (2) simultaneously.

[0254] 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.

[0255] Processing data received from the hub device (1) can be processed by a machine learning model (21) installed in the user device (2).

[0256] 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).

[0257] 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.

[0258] 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.

[0259] 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.

[0260] The user device (2) can obtain non-REM sleep behavior disorder information related to the user's non-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 non-REM sleep stage and the user's body movements are greater than a preset movement, the user device (2) can determine that a non-REM sleep behavior disorder such as sleep gait disorder or sleep startle disorder has occurred.

[0261] The user device (2) can store the user's non-REM sleep behavior disorder information in at least one memory (220).

[0262] 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, the occurrence of non-REM sleep behavior disorder can be determined based on the user's accurate sleep state, so the occurrence of non-REM sleep behavior disorder can be estimated more easily and effectively, as well as more accurately.

[0263] The user device (2) can alert the user of the non-REM sleep behavior disorder through the user interface (240) in response to the occurrence of the non-REM sleep behavior disorder (S6).

[0264] The user device (2) can alert the user of non-REM sleep behavior disorder at the time when non-REM sleep behavior disorder occurs.

[0265] When a non-REM sleep behavior disorder occurs, the user device (2) can store non-REM sleep behavior disorder information in at least one memory (220) and then warn the user of the non-REM sleep behavior disorder when the user's sleep stage is an awake stage.

[0266] The user device (2) may suggest a hospital diagnosis to the user when non-REM sleep behavior disorder occurs.

[0267] In response to the occurrence of non-REM sleep behavior disorder, the user device (2) can transmit non-REM sleep behavior disorder information to an external emergency center (6) registered in conjunction with the user device (2) through a communication unit (230) (e.g., a cellular communication interface) to notify the external emergency center (6) of the non-REM sleep behavior disorder (S7).

[0268] The user device (2) can notify the hospital of the occurrence of the user's non-REM sleep behavior disorder when the non-REM sleep behavior disorder occurs.

[0269] An external emergency center (6) can take measures to prevent safety accidents for users upon receiving information about non-REM sleep behavior disorder, thereby safely protecting users.

[0270] The user device (2) can transmit non-REM sleep behavior disorder information to the server device (3) (S8). At this time, the user device (2) can transmit sleep state information and non-REM sleep behavior disorder information to the server device (3).

[0271] 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.

[0272] The server device (3) can generate a control command for controlling a home appliance (4) based on information about non-REM sleep behavior disorder (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.

[0273] 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.

[0274] The process (S9) in which the server device (3) generates a control command for controlling a home appliance (4) based on non-REM sleep behavior disorder information is described in detail later with reference to FIGS. 11 to 14. The server device (3) can transmit a control command for controlling the home appliance (4) to the home appliance (4) (S10).

[0275] According to various embodiments, the process (S9) of generating a control command for controlling a home appliance (4) based on non-REM sleep behavior disorder information and the process (S10) of transmitting the control command for controlling the home appliance (4) to the home appliance (4) can of course also be performed by the user device (2).

[0276] The home appliance (4) can perform a predetermined operation corresponding to a control command received from the server device (3) (S11).

[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 8 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 deep sleep, a stage from which it is very difficult to wake up.

[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] During non-REM sleep, dreams are rare, and muscle movements are not inhibited as in REM sleep. Those who do not properly progress through the sleep stages become trapped in non-REM sleep, and because muscle movements are not inhibited, they can develop non-REM sleep behavioral disorders such as sleepwalking and night terrors.

[0297] Non-REM sleep behavior disorder (NREM) typically occurs during stages N3 and N4 of NREM sleep, which are slow-wave sleep stages, but can also occur during stage N2. NREM sleep behavior disorder can include sleepwalking disorder and sleep-startle disorder. Sleepwalking disorder, also known as sleepwalking, is a disorder in which people engage in complex physical movements, including walking, while sleeping. The eyes are usually open, but the gaze is fixed, and upon awakening, the person has no recollection of the previous behavior. Sleep-startle disorder, also known as night terrors, is a disorder in which the person awakens from sleep with intense fear and panic, accompanied by vocalizations, movements, and a heightened autonomic nervous system response. The person wakes up screaming in terror, sitting up, and usually has no recollection of the episode.

[0298] Non-REM sleep behavior disorder (NREM sleep behavior disorder) involves physical movement during non-REM sleep. Therefore, monitoring a user's physical movement during non-REM sleep can help diagnose the presence of NREM sleep behavior disorder.

[0299] FIG. 9 illustrates an example of obtaining information on a user's non-REM sleep behavior disorder in a sleep management method according to one embodiment.

[0300] Referring to FIG. 9, the user device (2) can determine whether the user's body movement is detected (400).

[0301] The user device (2) can determine whether the user's body movement occurs after the user enters a deep sleep state. If the user's sleep stage enters a non-REM sleep stage, the user can be determined to have entered deep sleep.

[0302] 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.

[0303] The user device (2) can determine the user's current sleep stage and current body movement based on the sleep state information.

[0304] When a user's body movement is detected (400, for example), the user device (2) can obtain information about the user's first body movement based on a contact sensor (402).

[0305] 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.

[0306] The user device (2) can obtain information about the user's second body movement based on a non-contact sensor (404).

[0307] 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.

[0308] The user device (2) can determine whether both the first body movement and the second body movement are greater than a preset movement (406). 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.

[0309] The user device (2) can determine whether the sleep stage is a non-REM sleep stage (408) if both the first body movement and the second body movement are greater than the preset movement (406, example).

[0310] The user device (2) can determine that a non-REM sleep behavior disorder has occurred if the sleep stage is a non-REM sleep stage (406, example) (410). At this time, the user device (2) can determine that a non-REM sleep behavior disorder has occurred if the sleep stage is N2, as well as N3 and N4 stages, where slow-wave sleep occurs, among non-REM sleep stages.

[0311] The user device (2) counts the number of times that both the first body movement and the second body movement are greater than a preset movement in the non-REM sleep stage, and if the counted number is greater than the preset number, it can be determined that a non-REM sleep behavior disorder has occurred.

[0312] The user device (2) can obtain non-REM sleep behavior disorder information related to the user's non-REM sleep behavior disorder (412).

[0313] The non-REM sleep behavior disorder information may include at least one of information regarding whether non-REM sleep behavior disorder occurs, information regarding the type of non-REM sleep behavior disorder, or information regarding the time of occurrence of non-REM sleep behavior disorder.

[0314] The user device (2) can determine whether the non-REM sleep behavior disorder is sleepwalking or night terrors by analyzing the pattern of body movements, such as the intensity, size, and movement of the first and second body movements. For example, if body movements to the extent of walking while sleeping are detected during the non-REM sleep stage, sleepwalking can be determined to have occurred. If body movements to the extent of sitting up together with vocalization and movement are detected during sleep, night terrors can be determined to have occurred.

[0315] FIG. 10 illustrates an example of sleep state information and non-REM sleep behavior disorder information derived by a sleep management system according to one embodiment.

[0316] Referring to Fig. 10, sleep state information and non-REM sleep behavior disorder information can be derived in real time by the sleep management system.

[0317] According to various embodiments, the user device (2) (or display device (41)) may output sleep state information and / or non-REM sleep behavior disorder information through an output interface (e.g., a display).

[0318] 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.

[0319] 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.

[0320] 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.

[0321] 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.

[0322] 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.

[0323] 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.

[0324] 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.

[0325] 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.

[0326] 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.

[0327] 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.

[0328] 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.

[0329] 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.

[0330] 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).

[0331] 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.

[0332] Information about movement may include information about body movement and information about eye movement.

[0333] 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.

[0334] 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.

[0335] 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.

[0336] 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.

[0337] 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.

[0338] The user's non-REM sleep behavior disorder information may include at least one of information regarding whether non-REM sleep behavior disorder occurs, information regarding the type of non-REM sleep behavior disorder, or information regarding the time of occurrence of non-REM sleep behavior disorder.

[0339] When information on current non-REM sleep behavior disorder is output by the user device (2) (or display device (41)), the occurrence, type, and time of occurrence of the user's non-REM sleep behavior disorder may be output in numerical or text form. Typical non-REM sleep behavior disorders, such as sleepwalking and night terrors, may be output.

[0340] Sleep state information and / or non-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.).

[0341] For example, a user can run a sleep management application using a user device (2) and check sleep state information and / or non-REM sleep behavior disorder information through an interface provided by the sleep management application.

[0342] 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 non-REM sleep behavior disorder information when the user wakes up.

[0343] According to various embodiments, a user may share sleep state information and / or non-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 non-REM sleep behavior disorder information to another user's device via an interface provided by the sleep management application.

[0344] FIG. 11 illustrates an example of a predetermined operation according to the occurrence of a user's non-REM sleep behavior disorder in a sleep management method according to one embodiment.

[0345] In one embodiment, the server device (3) can control the home appliance (4) based on non-REM sleep behavior disorder information.

[0346] Referring to FIG. 11, the server device (3) can determine whether the user's non-REM sleep behavior disorder has occurred based on non-REM sleep behavior disorder information (500).

[0347] If the server device (3) determines that the user's non-REM sleep behavior disorder has occurred (500, example), it can control the home appliance (4) to perform a predetermined action to alleviate the user's non-REM sleep behavior disorder (502).

[0348] According to one embodiment of the present disclosure, by controlling the home appliance (4) to perform a predetermined operation to alleviate the user's non-REM sleep behavior disorder, the user can alleviate the non-REM sleep behavior disorder through the operation of the home appliance (4).

[0349] Certain actions to alleviate non-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).

[0350] The server device (3) can determine whether the user's non-REM sleep behavior disorder has been alleviated (504). The server device (3) can determine whether the non-REM sleep behavior disorder has been alleviated based on whether the user's body movement changes have slowed down after a predetermined action to alleviate the non-REM sleep behavior disorder.

[0351] If the user's non-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 non-REM sleep behavior disorder (506).

[0352] If the user's non-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).

[0353] Figure 12 illustrates an example of a predetermined action to alleviate a user's non-REM sleep behavior disorder.

[0354] Referring to FIG. 12, the server device (3) can control the lighting device (43) to perform a predetermined operation to alleviate the user's non-REM sleep behavior disorder.

[0355] 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).

[0356] The server device (3) can control the air conditioner (45) to perform a predetermined operation to alleviate the user's non-REM sleep behavior disorder.

[0357] 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.

[0358] The server device (3) can control the furniture control device (42) to perform a predetermined operation to alleviate the user's non-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).

[0359] For example, the server device (3) can control the vibration element (42a) to output vibration corresponding to the user's heart rate.

[0360] According to one embodiment of the present disclosure, the risk of injury or safety accidents to the user can be reduced by alleviating or reducing the user's non-REM sleep behavior disorder, and the user can be encouraged to have good quality sleep.

[0361] Figure 13 illustrates another example of a predetermined action to alleviate a user's non-REM sleep behavior disorder.

[0362] Referring to FIG. 13, the server device (3) can control the air conditioner (45) to perform a predetermined operation to alleviate or reduce the user's non-REM sleep behavior disorder.

[0363] For example, the server device (3) can turn off the air conditioner (45).

[0364] The server device (3) can control the furniture control device (42) to perform a predetermined operation to alleviate the user's non-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).

[0365] 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).

[0366] According to one embodiment of the present disclosure, the risk of injury or safety accidents to the user can be reduced by alleviating or reducing the user's non-REM sleep behavior disorder, and the user can be encouraged to have good quality sleep.

[0367] Figure 14 illustrates an example of a predetermined action to induce a user's wake-up.

[0368] Referring to FIG. 14, the server device (3) can control the display device (41) to perform a predetermined operation for waking up to alleviate or reduce the user's non-REM sleep behavior disorder.

[0369] 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).

[0370] 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).

[0371] 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.

[0372] 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.

[0373] 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).

[0374] 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.

[0375] 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).

[0376] 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.

[0377] 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.

[0378] 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).

[0379] According to one embodiment of the present disclosure, non-REM sleep behavior disorder can be examined more easily and effectively in a user's daily life without having to wear various sensors on the body.

[0380] According to one embodiment of the present disclosure, a user's non-REM sleep behavior disorder can be alleviated or reduced by controlling a home appliance to perform a predetermined action to alleviate or reduce the user's non-REM sleep behavior disorder.

[0381] According to one embodiment of the present disclosure, a user's non-REM sleep behavior disorder can be alleviated or prevented, thereby reducing the risk of injury or safety accidents to the user and inducing the user to have good quality sleep.

[0382] According to one embodiment of the present disclosure, accurate data related to a user's non-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.

[0383] 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 collected by the first sensor (51, 52, 53, 54, or 55) and second data collected by 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 corresponding to 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 non-REM sleep behavior disorder based on the user's sleep stage and the user's body movement, and performs a predetermined action based on the occurrence of the non-REM sleep behavior disorder.

[0384] 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.

[0385] 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.

[0386] The user device may determine that the non-REM sleep behavior disorder has occurred based on the user's sleep stage being a non-REM sleep stage and the user's body movement being greater than a preset movement.

[0387] 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.

[0388] The user device (2) inputs first processed data corresponding to first data collected by the contact sensor into a machine learning model (21) to obtain information about the user's first body movement, inputs second processed data corresponding to second data collected by the non-contact sensor into the machine learning model (21) to obtain information about the user's second body movement, and can determine that the non-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 and the user's sleep stage is a non-REM sleep stage.

[0389] The above user device (2) can store a sleep management application including a machine learning model (21).

[0390] The above non-REM sleep behavior disorder may include at least one of sleep walking disorder (sleepwalking) and sleep startle disorder (nocturnal terror).

[0391] The above-described action may include an action of warning the user of the occurrence of the non-REM sleep behavior disorder through the user interface (240).

[0392] The above-described action may include an action of notifying the user device (2) and a registered external emergency center (6) of the occurrence of the non-REM sleep behavior disorder.

[0393] It may include a server device (3) that receives non-REM sleep behavior disorder information corresponding to the non-REM sleep behavior disorder from the user device (2).

[0394] The server device (3) can control at least one home appliance (4) registered in the user device to perform a predetermined operation to reduce the non-REM sleep behavior disorder or wake up the user based on the non-REM sleep behavior disorder information.

[0395] A sleep management method according to one embodiment of the present disclosure may include: a hub device (1) receiving first data collected by a first sensor (51, 52, 53, 54, or 55) and second data collected by 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 corresponding to 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 non-REM sleep behavior disorder of the user has occurred based on the sleep stage of the user and the body movement of the user, and performing a predetermined operation based on the occurrence of the non-REM sleep behavior disorder.

[0396] Determining whether the non-REM sleep behavior disorder has occurred may include determining that the non-REM sleep behavior disorder has occurred based on the user's sleep stage being a non-REM sleep stage and the user's body movement being greater than a preset movement.

[0397] 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 non-REM sleep behavior disorder has occurred may include inputting first processed data corresponding to first data collected by the contact sensor into a machine learning model (21) to obtain information about the first body movement of the user, inputting second processed data corresponding to second data collected by the non-contact sensor into the machine learning model (21) to obtain information about the second body movement of the user, and determining that the non-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 and the sleep stage of the user is a non-REM sleep stage.

[0398] The above non-REM sleep behavior disorder may include at least one of sleep walking disorder (sleepwalking) and sleep startle disorder (nocturnal terror).

[0399] Performing the above-described action may include performing an action to alert the user of the occurrence of the non-REM sleep behavior disorder through the user interface (240).

[0400] Performing the above-described action may include performing an action to notify an external emergency center (6) registered in conjunction with the user device of the occurrence of the non-REM sleep behavior disorder.

[0401] The server device (3) may further include receiving non-REM sleep behavior disorder information related to the non-REM sleep behavior disorder from the user device (2).

[0402] It may further include controlling at least one home appliance (4) registered in conjunction with the user device (2) to perform a predetermined action to alleviate the non-REM sleep behavior disorder or wake up the user based on the non-REM sleep behavior disorder information.

[0403] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium storing computer-executable instructions. The instructions may be stored in the form of program code, and when executed by a processor, may generate program modules to perform the operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0404] Computer-readable storage media include all types of storage media that store instructions that can be deciphered by a computer. Examples include read-only memory (ROM), random access memory (RAM), magnetic tape, magnetic disks, flash memory, and optical data storage devices.

[0405] 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.

[0406] 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.

[0407] Although the embodiments have been described with reference to the drawings, those skilled in the art will appreciate that various modifications and implementations can be made without departing from the spirit and scope of the invention as set forth in the claims below.

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 collected by the first sensor and second data collected by 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 corresponding to 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's non-REM sleep behavior disorder occurs based on the user's sleep stage and the user's body movements, and performs a predetermined operation based on the occurrence of the non-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 non-REM sleep behavior disorder has occurred based on the user's sleep stage being a non-REM sleep stage and the user's body movement being greater than a preset movement.

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 about 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, obtaining information about 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 that the non-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 and the sleep stage of the user is a non-REM sleep stage.

7. In paragraph 1, The above user device, A sleep management system storing a sleep management application including a machine learning model.

8. In paragraph 1, The above non-REM sleep behavior disorder is a sleep management system including at least one of sleep walking disorder (sleepwalking) and sleep startle disorder (nocturnal terror).

9. In paragraph 1, A sleep management system wherein the above-described actions include actions that alert a user of the occurrence of the non-REM sleep behavior disorder through a user interface.

10. In paragraph 1, A sleep management system wherein the above-described actions include actions of notifying the user device and a registered external emergency center of the occurrence of the non-REM sleep behavior disorder.

11. In paragraph 1, A sleep management system including a server device that receives non-REM sleep behavior disorder information corresponding to the non-REM sleep behavior disorder from the user device.

12. In paragraph 11, The above server device, A sleep management system that controls at least one home appliance registered in the user device to perform a predetermined action to reduce the non-REM sleep behavior disorder or to wake up the user based on the non-REM sleep behavior disorder information.

13. The hub device receives first data collected by the first sensor and second data collected by 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; A sleep management method comprising: obtaining, by the user device, sleep state information corresponding to the user's sleep stage and the user's body movements based on the first processing data and the second processing data received from the hub device; determining whether the user has non-REM sleep behavior disorder based on the user's sleep stage and the user's body movements; and performing a predetermined action based on the occurrence of the non-REM sleep behavior disorder.

14. In paragraph 13, To determine whether the above non-REM sleep behavior disorder occurs, A sleep management method comprising determining that the non-REM sleep behavior disorder has occurred based on the user's sleep stage being a non-REM sleep stage and the user's body movement being 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 non-REM sleep behavior disorder occurs, A sleep management method comprising: inputting first processed data corresponding to first data collected by the contact-type sensor into a machine learning model to obtain information about the first body movement of the user, inputting second processed data corresponding to second data collected by the non-contact sensor into the machine learning model to obtain information about the second body movement of the user, and determining that the non-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 and the sleep stage of the user is a non-REM sleep stage.

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