Sleep management system and sleep management method

Abstract

This sleep management system comprises: a plurality of sensors that includes first and second sensors for collecting data for a user; a hub device that receives first data collected by the first sensor and second data collected by the second sensor, acquires first processed data by processing the first data, and acquires second processed data by processing the second data; a user device that receives the first processed data and the second processed data from the hub device, and inputs the first processed data and the second processed data to a machine-learning model to acquire sleep state information related to sleep states of the user; and a server device that receives the sleep state information from the user device.

Description

Sleep management system and sleep management method

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

[0002] Sleep is essential to human health and well-being, playing a variety of roles, including helping the body recover, forming new memories, maintaining concentration, and clearing away accumulated brain waste.

[0003] Maintaining good quality sleep is very important for a healthy and smooth life.

[0004] To maintain good quality sleep, demand for polysomnography, a test that detects diseases or sleep disorders that occur during sleep, is increasing. However, polysomnography is expensive, takes a long time, and can cause discomfort to the subject (person) because it is performed while attaching various sensors to the body.

[0005] For this reason, various devices capable of analyzing human sleep are being developed. However, the accuracy of these devices in measuring sleep is significantly lower than that of polysomnography. Therefore, there is a need for devices and methods that can more accurately measure human sleep.

[0006] According to one aspect of the disclosed invention, a person's sleep state can be accurately measured in daily life without attaching various sensors to the body.

[0007] According to one aspect of the disclosed invention, since analysis of a person's sleep state is performed at a user device, invasion of an individual's privacy can be prevented.

[0008] According to one aspect of the disclosed invention, since data processing is performed by a plurality of devices, the level of data processing load that each device must bear can be reduced.

[0009] According to one aspect of the disclosed invention, accurate data related to the user's sleep state 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.

[0010] According to one aspect of the disclosed invention, it is possible to induce a user to have good quality sleep.

[0011] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly understood by a person having ordinary skill in the art to which the present disclosure belongs from the description below.

[0012] A sleep management system according to one embodiment of the present disclosure comprises a plurality of sensors for collecting user data, a hub device for processing data collected from the plurality of sensors, a user device for processing data processed by the hub device to obtain sleep state information related to a sleep state of the user, and a server device for controlling at least one home appliance based on the sleep state information obtained by the user device, wherein the plurality of sensors include a first sensor for collecting first data and a second sensor for collecting second data, and the hub device receives the first data and the second data, obtains first processed data based on processing the first data, obtains second processed data based on processing the second data, and transmits the first processed data and the second processed data to the user device, and the user device receives the first processed data and the second processed data from the hub device, inputs the first processed data and the second processed data into a machine learning model of the user device to obtain sleep state information related to the sleep state of the user, and transmits the sleep state information to the server device. Can be transmitted.

[0013] A sleep management method according to one embodiment of the present disclosure 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 by processing the first data and obtaining second processed data by processing the second data; the hub device transmitting the first processed data and the second processed data to a user device; the user device inputting the first processed data and the second processed data received from the hub device into a machine learning model of the user device to obtain sleep state information related to the sleep state of the user; and the user device transmitting the sleep state information to a server device.

[0014] According to one embodiment of the present disclosure, a user device includes a communication unit that communicates with a hub device that processes user data collected from a plurality of sensors; a memory; and at least one processor electrically connected to the communication unit and the memory; wherein the memory can store instructions that, when executed by the at least one processor, cause the at least one processor to receive processed data processed by the hub device through the communication unit and input the processed data into a machine learning model of the user device to obtain sleep state information related to the sleep state of the user.

[0015] According to one embodiment of the present disclosure, a non-transitory storage medium stores computer-readable commands, wherein the commands, when executed by a processor, cause the processor to: receive, through a communication unit, processed data processed by a hub device that processes user data collected from a plurality of sensors; input the processed data into a machine learning model to obtain sleep state information related to a sleep state of the user; and control at least one home appliance based on the sleep state information.

[0016] FIG. 1 illustrates an example of a network of a sleep management system according to one embodiment.

[0017] FIG. 2 schematically illustrates an example of the structure of a sleep management system according to one embodiment.

[0018] FIG. 3 illustrates an example of a control block diagram of a sleep management system according to one embodiment.

[0019] FIGS. 4 and 5 illustrate examples of multiple sensors of a sleep management system according to one embodiment.

[0020] FIG. 6 illustrates an example of a flowchart of a sleep management method according to one embodiment.

[0021] FIG. 7 is a diagram illustrating a process for processing data collected from multiple sensors of a sleep management system according to one embodiment.

[0022] Figure 8 is a diagram for explaining sleep stages.

[0023] FIG. 9 illustrates an example of sleep state information derived by a sleep management system according to one embodiment.

[0024] FIG. 10 illustrates an example of sleep summary information derived by a sleep management system according to one embodiment.

[0025] FIG. 11 and FIG. 12 illustrate an example of a flowchart of a sleep management method according to one embodiment.

[0026] Figure 13 illustrates an example of a predetermined operation to induce sleep in a user.

[0027] Figure 14 illustrates an example of a certain action to relieve user stress.

[0028] Figure 15 illustrates an example of a predetermined operation to alleviate a user's sleep disturbance.

[0029] Figure 16 illustrates an example of a predetermined action to induce a user's wake-up.

[0030] It should be understood that the various embodiments and terms used in this document are not intended to limit the technical features described in this document to specific embodiments, but rather to include various modifications, equivalents, or substitutes of the embodiments.

[0031] In connection with the description of the drawings, similar reference numerals may be used for similar or related components.

[0032] 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.

[0033] 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.

[0034] The term “and / or” includes any combination of a plurality of related described elements or any one of a plurality of related described elements.

[0035] 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.

[0036] 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).

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] Below, a sleep management system according to various embodiments is specifically described with reference to the attached drawings.

[0042] FIG. 1 illustrates an example of a network of a sleep management system according to one embodiment.

[0043] Referring to FIG. 1, a sleep management system according to one embodiment may include a hub device (1), a user device (2), a server device (3), and / or a home appliance (4).

[0044] The hub device (1) may include a communication module capable of communicating with a user device (2), a server device (3), and / or a home appliance (4), at least one processor for processing data, and at least one memory storing a program for controlling the operation of the hub device (1).

[0045] The hub device (1) can obtain processed data by 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.

[0046] 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).

[0047] 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).

[0048] The home appliance (4) can be remotely controlled by the server device (3).

[0049] 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.

[0050] The lighting device (43) may include a light source whose light intensity and / or light color can be controlled.

[0051] The automatic curtain opening / closing device (44) may include an actuator for automatically opening or closing the curtain.

[0052] The server device (3) may include a communication module capable of communicating with the hub device (1), the user device (2), and / or the home appliance (4).

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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).

[0057] 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.

[0058] 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.

[0059] The user device (2) may include a communication module capable of communicating with a hub device (1), a server device (3), and / or a home appliance (4). The user device (2) may include a user interface for receiving user input or outputting information to the user. The user device (2) may include at least one processor for controlling the operation of the user device (2) and at least one memory for storing a program for controlling the operation of the user device (2).

[0060] 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.

[0061] 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.

[0062] 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).

[0063] For example, if the home appliance (4) is operated so that it can be connected to the server device (3) according to the procedure guided by the application installed on the user device (2), the server device (3) can register the home appliance (4) to the user account by registering the identification information (e.g., serial number or MAC address) of the home appliance (4) to the corresponding user account, thereby registering the home appliance (4). The home appliance (4) can also be registered to the user account in the same manner. It goes without saying that the information required to register a device such as a home appliance (4), home appliance (4), etc. to a user account may use other information that can identify the device in addition to the serial number or MAC address of the device.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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).

[0068] 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.

[0069] 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).

[0070] For example, the hub device (1) may be connected to the user device (2) via a short-range wireless network (e.g., Wi-Fi Direct, Bluetooth, NFC). As another example, the hub device (1) may be connected to the user device (2) via a wide area network (WAN) using a long-range wireless network (e.g., a cellular communication module).

[0071] 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.

[0072] Referring to FIGS. 2 and 3, the hub device (1) can receive data collected from a plurality of sensors (5).

[0073] The plurality of sensors (5) may include sensors for collecting user data (e.g., a first sensor (51), a second sensor (52), a third sensor (53), a fourth sensor (54), and / or a fifth sensor (55)).

[0074] Each of the plurality of sensors (5) can collect user data and transmit the collected user data to the hub device (1).

[0075] In one embodiment, the user's data may include data related to the user's sleep.

[0076] 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 body displacement that changes according to the user's breathing, 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.

[0077] 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, an oxygen saturation sensor for collecting 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 movement, and / or a radar sensor for collecting eye-movement data corresponding to the movement of the user's eyes.

[0078] 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.

[0079] 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 oxygen saturation sensor, an electrocardiogram sensor, or an acceleration sensor.

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

[0081] The plurality of sensors (5) may further include additional sensors (e.g., microphones, cameras) in addition to the first sensor (51) to the fifth sensor (55), or may not include at least one of the first sensor (51) to the fifth sensor (55).

[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) by 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 module and the fourth sensor (54). For example, the smart sensor device may include a smart watch in the form of a watch, and / or a smart ring in the form of a ring, but the form of the smart sensor device is not limited thereto.

[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.

[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 memory (120) 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 memory (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 by 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 by 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 by 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 processing data by 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 by 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 (primarily) 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 module for performing wired communication with a plurality of sensors (5), and / or a wireless communication module for performing wireless communication with a user device (2), a server device (3), and / or a home appliance.

[0109] The hub device (1) may include a printed circuit board including at least one processor (110), at least one memory (120), and a communication unit (130). At least some of the plurality of sensors (5) may be connected to the printed circuit board by wires.

[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). In one embodiment, the user device (2) can receive data acquired by the hub device (1) from the hub device (1) via wireless communication. According to various embodiments, the hub device (1) and the user device (2) may be connected by wire via a connection cable. The user device (2) can also receive data acquired by the hub device (1) from the hub device (1) via wired 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 module for establishing communication with a smart sensor device including at least some of a hub device (1), a server device (3), a home appliance (4), and / or a plurality of sensors (5).

[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 module) being activated.

[0130] In one embodiment, at least one processor (210) can control the user interface (240) to provide feedback requesting activation of the communication unit (230) in response to the sleep management application being executed while the communication unit (230) (e.g., a short-range wireless communication module) is not activated.

[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 module).

[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 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.

[0135] Sleep state information related to the user's sleep state may include at least one of information about the user's sleep stage, information about the user's stress index, or information about the user's sleep disorder.

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

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

[0138] In one embodiment, at least one processor (210) may generate sleep summary information based on sleep state information accumulated and stored in at least one memory (220). At least one processor (210) may control a user interface (240) to output sensory information corresponding to the sleep summary information based on a predetermined condition being satisfied (e.g., receiving a user input for confirming the sleep summary information).

[0139] At least one processor (210) can control the communication unit (230) to transmit the acquired sleep state information by inputting 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 to the server device (3).

[0140] The communication unit (230) may include a first communication module for establishing communication with the hub device (1) and a second communication module for establishing communication with the server device (3). Accordingly, the communication unit (230) may perform communication with the hub device (1) via the first communication method while simultaneously performing communication with the server device (3) via the second communication method.

[0141] 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 is obtained, and the sleep state information can be transmitted to a server device (3) in real time.

[0142] 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).

[0143] The user device (2) may include a user interface (240) for communication with the user.

[0144] 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 (secondarily) process the (primarily) processed data to obtain sleep state information.

[0145] 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 obtain sleep state information after the server (secondarily) processes the (primarily) processed processing data.

[0146] According to various embodiments, it is also possible for the hub device (1) to transmit data collected from multiple sensors (5) to the user device (2), the user device (2) to (primarily) process the data received from the hub device (1), and (secondarily) process the (primarily) processed data to obtain sleep state information.

[0147] 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 (secondarily) process the (primarily) processed data to obtain sleep state information.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] The input interface can receive user input to run a sleep management application.

[0152] 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.

[0153] The output interface can output an interface corresponding to a sleep management application.

[0154] 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.

[0155] The output interface and input interface may be provided as separate devices or as a single device (e.g., a touch display).

[0156] The server device (3) can receive data acquired by the user device (2) from the user device (2) via wireless communication.

[0157] The data acquired by the user device (2) may include the sleep state information described above.

[0158] In one embodiment, the server device (3) may include at least one memory (320) storing a program for processing sleep state information received from the user device (2), and at least one processor (310) capable of processing sleep state information received from the user device (2) based on the program stored in the at least one memory (320).

[0159] At least one memory (320) can store sleep state information received from the user device (2).

[0160] At least one memory (320) can store a program for generating sleep summary information based on sleep state information received from the user device (2).

[0161] At least one processor (310) can generate sleep summary information based on sleep state information accumulated and stored in at least one memory (320).

[0162] At least one memory (320) can store a program for controlling a home appliance (4) based on sleep state information received from a user device (2).

[0163] At least one processor (310) can control the home appliance (4) based on sleep state information received from the user device (2).

[0164] According to various embodiments, a program for generating sleep summary information based on sleep state information and a program for controlling a home appliance (4) based on sleep state information may be stored in different servers.

[0165] For example, a first server included in the server device (3) can store a program for generating sleep summary information based on sleep state information, and a second server included in the server device (3) can store a program for controlling a home appliance (4) based on sleep state information.

[0166] At least one memory (320) can store sleep summary information generated based on sleep state information.

[0167] In one embodiment, the server device (3) may include a communication unit (330) including at least one communication module for establishing communication with a smart sensor device including at least some of a hub device (1), a user device (2), a home appliance (4), and / or a plurality of sensors (5).

[0168] The server device (3) can receive sleep status information from the user device (2) through the communication unit (330).

[0169] The server device (3) can transmit a control command to the home appliance (4) through the communication unit (330).

[0170] The server device (3) can transmit sleep summary information to the user device (2) through the communication unit (330).

[0171] According to one embodiment of the present disclosure, the user's sleep can be managed in various ways as the server device (3) performs various operations based on sleep state information.

[0172] FIGS. 4 and 5 illustrate examples of multiple sensors of a sleep management system according to one embodiment.

[0173] Referring to FIGS. 4 and 5, in one embodiment, some of the plurality of sensors (5) (e.g., the first sensor (51), the second sensor (52), and / or the third sensor (53)) may be installed in furniture (10), and other some of the plurality of sensors (5) (e.g., the fourth sensor (54), and / or the fifth sensor (55)) may be provided in a smart sensor device (e.g., a smart watch, a smart ring, etc.).

[0174] At least some of the plurality of sensors (5) may be provided on furniture (10) on which a user can sit or lie.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The mattress may further include springs and / or sponges. The springs and / or sponges may be provided in the accommodation space of the cover.

[0181] The structure (e.g., length, arrangement, etc.) of the first sensor (51) may be changed depending on the size of the mattress.

[0182] 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.

[0183] The seating portion may include a portion that the user's buttocks close to, the backrest portion may include a portion that the user's back closes to, the headrest may include a portion that the user's head closes to, and the leg portion may include a portion that the user's legs close to.

[0184] 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).

[0185] 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).

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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).

[0196] 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).

[0197] The third sensor 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.

[0198] 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).

[0199] 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.

[0200] 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.

[0201] 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.

[0202] The third sensor (53) can measure the movement of the user's eyes based on mmWave reflected from the user's eyes.

[0203] That is, the third sensor (53) can obtain eye-movement data corresponding to the movement of the user's eyes.

[0204] The fourth sensor (54) may include an oxygen saturation sensor and / or an electrocardiogram sensor. The oxygen saturation 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.

[0205] The fourth sensor may have a detection area facing the body of a user lying or sitting on the furniture (10).

[0206] 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.

[0207] 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.

[0208] The fourth sensor (54) can measure the oxygen saturation in the user's blood or the user's electrocardiogram based on the intensity of light reflected from the user's body.

[0209] 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 oxygen saturation in the user's blood or the user's electrocardiogram can be measured.

[0210] 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.

[0211] 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).

[0212] In one embodiment, the fifth sensor (55) may be installed on the furniture (10).

[0213] The fifth sensor (55) can obtain acceleration data corresponding to the user's body movement.

[0214] 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.

[0215] 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.

[0216] Referring to FIGS. 6 and 7, the hub device (1) can collect data from a plurality of sensors (5) (S1).

[0217] 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.

[0218] 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.

[0219] For example, the hub device (1) can determine whether a user is present in the furniture (10) by 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).

[0220] 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).

[0221] 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.

[0222] 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.

[0223] The hub device (1) can determine whether a user is present in the furniture (10) by processing data collected by a sensor (e.g., the first sensor (51)) operating in a low power mode among a plurality of sensors (5).

[0224] The hub device (1) can wake up multiple sensors (5) based on the determination that a user is present in the furniture (10).

[0225] A plurality of sensors (5) can transmit collected data to the hub device (1) based on activation.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] The data collected from the plurality of sensors (5) corresponds to raw data and may have a large volume or may include data related to the user's private life. Accordingly, the hub device (1) according to one embodiment of the present disclosure can (primarily) process the raw data collected from the plurality of sensors (5). In addition, in order for the user device (2) to process all of the raw data collected from the plurality of sensors (5), it must be able to communicate with all of the plurality of sensors (5). However, since the number of communication modules of the user device (2) is limited, a separate hub device (1) capable of receiving and processing such data is required.

[0230] Data collected from multiple sensors (5) can be processed by a machine learning model (first machine learning model (11), second machine learning model (12), third machine learning model (13), fourth machine learning model (14), or fifth machine learning model (15)) mounted on the hub device (1).

[0231] 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).

[0232] The first machine learning model (11) may be pre-trained to extract features from pressure data collected by a pressure sensor. The second machine learning model (12) may be pre-trained to extract features from displacement data collected by a UWB sensor. The third machine learning model (13) may be pre-trained to extract features from eye movement data collected by a radar sensor. The fourth machine learning model (14) may be pre-trained to extract features from oxygen saturation data and / or electrocardiogram data collected by an oxygen saturation sensor and / or an electrocardiogram sensor. The fifth machine learning model (15) may be pre-trained to extract features from acceleration data collected by an acceleration sensor.

[0233] 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 movement, posture, respiration rate, and heart rate inferred from the first data. As another example, the first processed data may include information regarding the user's sleep stage inferred from the first data, information regarding the user's stress index, or information regarding the user's sleep disorder.

[0234] 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 information regarding the user's sleep stage, information regarding the user's stress index, or information regarding the user's sleep disorder inferred from the second data.

[0235] 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 about eye movements inferred from the third data. As another example, the third processed data may include information about the user's sleep stage, information about the user's stress index, or information about the user's sleep disorder inferred from the third data.

[0236] 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 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, information regarding the user's stress index, or information regarding the user's sleep disorder inferred from the fourth data.

[0237] 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 about movement inferred from the fifth data. As another example, the fifth processed data may include information about the user's sleep stage inferred from the fifth data, information about the user's stress index, or information about the user's sleep disorder.

[0238] According to the present disclosure, the hub device (1) can transmit the data collected from the plurality of sensors (5) to the user device (2) even in a communication method with a low data transmission capacity by (primarily) processing the data collected from the plurality of sensors (5).

[0239] According to the present disclosure, the amount of data processing to be borne by the processor (210) of the user device (2) can be alleviated by (primarily) processing UWB data, particularly the large volume of raw data.

[0240] The hub device (1) can process data collected from multiple sensors (5) and transmit the processed data to the user device (2) (S3).

[0241] According to the present disclosure, even if the user device (2) is equipped with only a communication module capable of communicating with the hub device (1), it can consequently process data collected from a plurality of sensors (5).

[0242] The hub device (1) can transmit processing data to the user device (2) via wireless communication. In one embodiment, the communication unit (130) of the hub device (1) can include a first communication module for receiving data collected from some sensors (e.g., the fourth sensor (54)) among the plurality of sensors (5) via a first wireless communication method, and a second communication module for transmitting the processing data to the user device (2) via a second wireless communication method.

[0243] The first wireless communication method and the second wireless communication method may be the same or different from each other.

[0244] According to one embodiment of the present disclosure, a hub device (1) has both a first communication module for receiving data from some of a plurality of sensors (5) and a second communication module for communicating with a user device (2), so that it can communicate with a plurality of sensors (5) and a user device (2) at the same time.

[0245] 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.

[0246] Processing data received from the hub device (1) can be processed by a machine learning model (21) installed in the user device (2).

[0247] 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).

[0248] 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.

[0249] 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.

[0250] The first processing data, the second processing data, the third processing data, the fourth processing data and / or the fifth processing data may include probability information about the user's sleep stage.

[0251] For example, the first processing data, the second processing data, the third processing data, the fourth processing data, and / or the fifth processing data may include probability data that the user's sleep stage corresponds to a wake stage, a REM sleep stage, an N1 stage, an N2 stage, an N3 stage, or an N4 stage of a non-REM sleep stage.

[0252] The first processing data may include a probability value a1 that the user's sleep stage is an awake stage, a probability value b1 that the user's sleep stage is a REM sleep stage, a probability value c1 that the user's sleep stage is an N1 stage of a non-REM sleep stage, a probability value d1 that the user's sleep stage is an N2 stage of a non-REM sleep stage, a probability value e1 that the user's sleep stage is an N3 stage of a non-REM sleep stage, and a probability value f1 that the user's sleep stage is an N4 stage of a non-REM sleep stage.

[0253] The second processing data may include a probability value a2 that the user's sleep stage is an awake stage, a probability value b2 that the user's sleep stage is a REM sleep stage, a probability value c2 that the user's sleep stage is an N1 stage of a non-REM sleep stage, a probability value d2 that the user's sleep stage is an N2 stage of a non-REM sleep stage, a probability value e2 that the user's sleep stage is an N3 stage of a non-REM sleep stage, and a probability value f2 that the user's sleep stage is an N4 stage of a non-REM sleep stage.

[0254] The third processing data may include a probability value a3 that the user's sleep stage is an awake stage, a probability value b3 that the user's sleep stage is a REM sleep stage, a probability value c3 that the user's sleep stage is an N1 stage of a non-REM sleep stage, a probability value d3 that the user's sleep stage is an N2 stage of a non-REM sleep stage, a probability value e3 that the user's sleep stage is an N3 stage of a non-REM sleep stage, and a probability value f3 that the user's sleep stage is an N4 stage of a non-REM sleep stage.

[0255] The fourth processing data may include a probability value a4 that the user's sleep stage is an awake stage, a probability value b4 that the user's sleep stage is a REM sleep stage, a probability value c4 that the user's sleep stage is an N1 stage of a non-REM sleep stage, a probability value d4 that the user's sleep stage is an N2 stage of a non-REM sleep stage, a probability value e4 that the user's sleep stage is an N3 stage of a non-REM sleep stage, and a probability value f4 that the user's sleep stage is an N4 stage of a non-REM sleep stage.

[0256] The fifth processing data may include a probability value a5 that the user's sleep stage is an awake stage, a probability value b5 that the user's sleep stage is a REM sleep stage, a probability value c5 that the user's sleep stage is an N1 stage of a non-REM sleep stage, a probability value d5 that the user's sleep stage is an N2 stage of a non-REM sleep stage, a probability value e5 that the user's sleep stage is an N3 stage of a non-REM sleep stage, and a probability value f5 that the user's sleep stage is an N4 stage of a non-REM sleep stage.

[0257] The machine learning model (21) mounted on the user device (2) can ultimately determine the user's sleep stage by assigning different weights to each probability value (a1, a2, a3, a4, a5, b1, b2, b3, b4, b5, c1, c2, c3, c4, c5, d1, d2, d3, d4, d5, e1, e2, e3, e4, e5, f1, f2, f3, f4, f5) included in the first to fifth processing data.

[0258] For example, since the user's eye movements are important in determining whether the user's sleep stage is a REM sleep stage, the machine learning model (21) can give the highest weight to the probability value b3 included in the third processing data among the probability values ​​b1, b2, b3, b4, and b5 in determining whether the user's sleep stage is a REM sleep stage.

[0259] As another example, since the user's body movement is important in determining whether the user's sleep stage is N4, the highest weight can be given to the probability value f1 included in the first processing data and the probability value f5 included in the fifth processing data among f1, f2, f3, f4, and f5 in determining whether the user's sleep stage is N4.

[0260] The first processing data, the second processing data, the third processing data, the fourth processing data and / or the fifth processing data may include a user's stress index value.

[0261] For example, the first processing data may include the user's stress index value g1, the second processing data may include the user's stress index value g2, the third processing data may include the user's stress index value g3, the fourth processing data may include the user's stress index value g4, and the fifth processing data may include the user's stress index value g5.

[0262] The machine learning model (21) mounted on the user device (2) can determine the user's stress index by assigning different weights to each of the stress index values ​​included in the first processing data to the fifth processing data.

[0263] For example, since the user's heart rate or electrocardiogram is important in determining the user's stress index, the machine learning model (21) can give the highest weight to the stress index value g2 included in the second processing data and the stress index value g4 included in the fourth processing data among the stress index values ​​g1, g2, g3, g4, and g5 in determining the user's stress index.

[0264] The first processing data, the second processing data, the third processing data, the fourth processing data and / or the fifth processing data may include a probability value for the user's sleep disorder.

[0265] For example, the first processed data may include the user's snoring probability value h1, the sleepwalking probability value i1, and / or the apnea-hypopnea index j1, the second processed data may include the user's snoring probability value h2, the sleepwalking probability value i2, and / or the apnea-hypopnea index j2, the third processed data may include the user's snoring probability value h3, the sleepwalking probability value i3, and / or the apnea-hypopnea index j3, the fourth processed data may include the user's snoring probability value h4, the sleepwalking probability value i4, and / or the apnea-hypopnea index j4, and the fifth processed data may include the user's snoring probability value h5, the sleepwalking probability value i5, and / or the apnea-hypopnea index j5.

[0266] The machine learning model (21) mounted on the user device (2) can determine whether the user snores by assigning different weights to each snoring probability value (h1, h2, h3, h4, and h5) included in the first to fifth processing data.

[0267] The machine learning model (21) mounted on the user device (2) can determine whether the user exhibits sleepwalking by assigning different weights to each of the sleepwalking probability values ​​(i1, i2, i3, i4, and i5) included in the first to fifth processing data.

[0268] The machine learning model (21) mounted on the user device (2) can determine the user's apnea-hypopnea index by assigning different weights to each of the apnea-hypopnea indices (j1, j2, j3, j4, and j5) included in the first to fifth processing data.

[0269] The sleep state information output by the machine learning model (21) installed in the user device (2) may include at least one of information about the user's sleep stage, information about the user's stress index, or information about the user's sleep disorder.

[0270] According to the present disclosure, since data output by a machine learning model (first machine learning model (11), second machine learning model (12), third machine learning model (13), fourth machine learning model (14), or fifth machine learning model (15)) mounted on a hub device (1) is (secondarily) input to a machine learning model (21) mounted on a user device (2) to output sleep state information, the user's sleep state can be accurately estimated. That is, according to the present disclosure, a large amount of data can be processed in stages to accurately estimate the user's sleep state.

[0271] The user device (2) can transmit sleep state information to the server device (3) (S5).

[0272] According to various embodiments, the user device (2) may generate a control command for controlling the home appliance (4) based on the sleep state information and transmit the control command for controlling the home appliance (4) to the server. That is, step S6, which will be described later, may be performed by the user device (2).

[0273] In one embodiment, the user device (2) generates a control command for controlling the home appliance (4) based on sleep state information, transmits the control command to the server device (3), and the server device (3) can transmit the control command received from the user device (2) to the home appliance (4) (S7).

[0274] According to the present disclosure, instead of direct data related to the user's private life being transmitted to the server device (3), only sleep state information related to the user's sleep state is transmitted to the server device (3), thereby making it easier to obtain the user's consent for data collection.

[0275] The server device (3) can generate a control command for controlling the home appliance (4) based on the sleep state information (S6). 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.

[0276] 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.

[0277] The process (S6) in which the server device (3) generates a control command for controlling the home appliance (4) based on the sleep state information is described in detail later with reference to FIGS. 11 to 16. The server device (3) can transmit a control command for controlling the home appliance (4) to the home appliance (4) (S7).

[0278] According to various embodiments, the process (S6) of generating a control command for controlling the home appliance (4) based on sleep state information and the process 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).

[0279] The home appliance (4) can perform a predetermined operation corresponding to a control command received from the server device (3) (S8).

[0280] 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 a machine learning model (first machine learning model (11), second machine learning model (12), third machine learning model (13), fourth machine learning model (14), or fifth machine learning model (15)) mounted on the hub device (1) can be transmitted to the server device (3).

[0281] Figure 8 is a diagram for explaining sleep stages.

[0282] Human sleep stages can be divided into wakefulness stages, rapid eye movement (REM) stages, and non-rapid eye movement (NREM) stages.

[0283] The awakening stage corresponds to the stage in which a person is awake.

[0284] REM sleep is a rapid eye movement sleep stage, a shallow sleep similar to being awake, and is a stage of sleep characterized by rapid eye movement.

[0285] In adults, REM sleep typically accounts for approximately 20–25% of total sleep, occurring approximately 90–120 minutes per night.

[0286] During REM sleep, the brain's neural activity is quite similar to that of awake states. However, because the body is in a relaxed state, it is involuntary. For this reason, REM sleep is also called paradoxical sleep, meaning that brain waves are not suppressed during REM sleep.

[0287] Non-REM sleep is a non-rapid eye movement sleep stage, and unlike REM sleep, there is almost no eye movement during non-REM sleep.

[0288] 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 the lack of muscle inhibition can lead to sleep disorders (e.g., sleepwalking).

[0289] 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.

[0290] As another example, non-REM sleep stages may be divided into stage 1 (stage N1), stage 2 (stage N2), and stage 3 (stage N3).

[0291] During non-REM sleep, brain wave activity gradually slows and physiological functions decline. During non-REM sleep, brain tissue and epithelial cells regenerate, body energy is restored, and hormones for skeletal growth, protein synthesis, and tissue regeneration are secreted.

[0292] Stage N1 is the most liminal stage of sleep, marking the body's gradual transition into sleep. Stage N1 occurs before deep sleep and represents a state of non-deep sleep.

[0293] Stage N2 is a transition from borderline sleep to a deeper sleep. Brainwave patterns shift from slightly slower beta waves to slower theta waves, and eye movements cease, but intermittent rapid eye movements are absent.

[0294] Stage N3 is the initial stage of deep sleep, where brainwave patterns shift to delta waves, muscle tone is relaxed, and physical movement is minimal. Snoring may occur during Stage N3.

[0295] Stage N4 is a deep sleep from which awakening is extremely difficult. During stage N4, growth hormone secretion increases, which supports skeletal growth, protein synthesis, and tissue regeneration. Symptoms such as sleepwalking and enuresis may also occur.

[0296] 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.

[0297] Afterwards, in the second sleep cycle, we go through stages N1, N2, N3, N4, and then return to stages N3 and N2.

[0298] Afterwards, in the third sleep cycle, we go through stage N3 and then return to stage N2, stage N1, and REM sleep.

[0299] Afterwards, in the fourth sleep cycle, we go through stage N1 and N2 sleep, and then enter stage N1 and REM sleep again.

[0300] After that, the person goes through the N1 stage, N2 stage, and REM sleep stage and wakes up naturally.

[0301] If a person awakens during stage N4 sleep, they may still feel groggy, as if they hadn't slept at all. Therefore, it's important to wake up during stage N1 or REM sleep.

[0302] FIG. 9 illustrates an example of sleep state information derived by a sleep management system according to one embodiment.

[0303] Sleep status information can be derived in real time by a sleep management system.

[0304] Referring to FIG. 9, sleep state information derived by a sleep management system according to one embodiment may include information about sleep stages, information about oxygen saturation, information about sleep disorders, information about stress index, information about respiration rate, information about movement, information about heart rate, and / or information about body pressure.

[0305] According to various embodiments, the user device (2) (or display device (41)) may output sleep state information through an output interface (e.g., a display). For example, the user device (2) may obtain sleep state information based on

[0306] 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.

[0307] 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.

[0308] 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.

[0309] 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.

[0310] 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.

[0311] When information about oxygen saturation is output by the user device (2) (or the display device (41)), the numerical value of the oxygen saturation may be output in the form of a percentage. In addition, when information about oxygen saturation is output by the user device (2) (or the display device (41)), whether the user's oxygen saturation is normal according to medical standards may be displayed.

[0312] Information about sleep disorders may include information about the current user's sleep disorders and / or information about sleep disorders that have developed over time.

[0313] Sleep disorders may include information on breathing-related sleep disorders, such as apnea and hypopnea. Information on breathing-related sleep disorders, such as apnea and hypopnea, may include the Apnea Hypopnea Index (AHI).

[0314] When information on sleep disorders related to breathing is output by the user device (2) (or display device (41)), the Apnea Hypopnea Index (AHI) may be output, and whether the user's Apnea Hypopnea Index is normal according to medical standards may be displayed.

[0315] 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.

[0316] 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.

[0317] 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.

[0318] 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.

[0319] 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.

[0320] 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).

[0321] 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.

[0322] 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.

[0323] 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.

[0324] 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.

[0325] 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.

[0326] 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.

[0327] FIG. 10 illustrates an example of sleep summary information derived by a sleep management system according to one embodiment.

[0328] Sleep summary information may include information compiled from sleep status information acquired in real time by a sleep management system.

[0329] For example, sleep summary information may include sleep score information corresponding to sleep quality, sleep time information, sleep efficiency information, sleep latency information, sleep continuity information, and / or sleep stage information.

[0330] A sleep score corresponding to sleep quality can be determined based on sleep status information acquired in real time during the user's sleep. For example, the user device (2) and / or the server device (3) can calculate a sleep score based on sleep status information acquired in real time during the user's sleep. Information regarding the sleep score can be calculated based on the user's sleep stage, oxygen saturation, sleep disturbance, stress index, respiratory rate, movement, heart rate, and / or body pressure during sleep.

[0331] Sleep time information may include information about the time from when the user transitions from an awake state to a sleep state to when the user finally awakens.

[0332] As another example, sleep time information may include information about the time from the time of lying down on the furniture (10) to the time of finally waking up.

[0333] Information about sleep efficiency can be determined based on the proportion of time occupied by a given stage (e.g., non-REM sleep stage) among sleep stages. The user device (2) and / or the server device (3) can calculate sleep efficiency based on sleep state information acquired in real time during the user's sleep. In one embodiment, the user device (2) and / or the server device (3) can determine sleep efficiency based on the proportion of time occupied by a given stage (e.g., non-REM sleep stage) among the user's sleep stages.

[0334] Information about sleep onset latency (SOL) may include information about the time it takes for a sleep stage to change from a wakeful stage to a non-REM sleep stage.

[0335] Information about sleep continuity can be determined based on the time that the sleep stage remains in the non-REM sleep stage without changing to the wake stage (and / or REM sleep stage). The user device (2) and / or the server device (3) can determine sleep continuity based on sleep state information acquired in real time during the user's sleep. In one embodiment, the user device (2) and / or the server device (3) can determine sleep continuity based on the time that a predetermined stage (e.g., non-REM sleep stage) among the user's sleep stages is maintained.

[0336] Information about sleep stages may include information about changes in sleep stages over time. Information about sleep stages may include information about the percentage of time each sleep stage occupies during the user's sleep, information about whether the user's sleep cycle falls within a normal range, and information about the degree of physical recovery, mental recovery, and / or periodic awakening determined based on the percentage of time each sleep stage occupies during the user's sleep.

[0337] Sleep summary information is not limited to the examples described above and may include various information related to the user's sleep. For example, sleep summary information may include information on the user's snoring duration and the number of times the user changes posture.

[0338] The sleep state information and sleep summary information described with reference to FIGS. 9 and 10 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.).

[0339] For example, a user can run a sleep management application using a user device (2) and check sleep status information and sleep summary information through an interface provided by the sleep management application.

[0340] 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 when the user wakes up.

[0341] According to various embodiments, a user may share sleep status information and / or sleep summary information with other users via the user device (2). For example, a user may run a sleep management application and transmit sleep status information and / or sleep summary information to another user's device via an interface provided by the sleep management application.

[0342] FIG. 11 and FIG. 12 illustrate an example of a flowchart of a sleep management method according to one embodiment.

[0343] As described with reference to FIG. 6, in one embodiment, the server device (3) can control the home appliance (4) based on sleep state information.

[0344] In one embodiment, the server device (3) may control the home appliance (4) based on a control command received from the user device (2).

[0345] As described above, the user device (2) may directly generate a control command based on the sleep state information, the server device (3) may generate a control command based on the sleep state information, or both the user device (2) and the server device (3) may generate a control command based on the sleep state information.

[0346] Accordingly, it goes without saying that the operations performed by the server device (3) to be described below can be performed by the user device (2). At least one memory (220) of the user device (2) can store commands that enable at least one processor (210) to perform the operations described above and the operations to be described below.

[0347] Referring to FIGS. 11 and 12, the hub device (1) can wake up a plurality of sensors (5) based on the satisfaction of a predetermined condition (1000).

[0348] For example, the hub device (1) can wake up multiple sensors (5) based on determining that a user is present in the furniture (10).

[0349] As another example, the user device (2) may transmit a sleep management start request to the hub device (1) based on receiving a user input corresponding to the start of sleep, and the hub device (1) may wake up a plurality of sensors (5) based on receiving the sleep management start request through the user device (2).

[0350] As another example, the user device (2) can receive information about the expected sleep time from the user and transmit it to the hub device (1). The hub device (1) can wake up multiple sensors (5) based on reaching the expected sleep time.

[0351] The server device (3) can determine whether the current time corresponds to sleep time (1100).

[0352] For example, a user can preset a sleep time via the user device (2). The sleep time can be set as a time range or by time. For example, the user can set the sleep time to "10:00 PM - 11:00 PM" or "10:00 PM."

[0353] The server device (3) can determine that the current time corresponds to the sleep time if the current time corresponds to the sleep time set by the user.

[0354] As another example, when a user inputs a command corresponding to the start of sleep through the user device (2), the server device (3) can determine that the current time corresponds to the sleep time.

[0355] A command corresponding to the start of sleep can be received via the user interface (240) of the user device (2). For example, the command corresponding to the start of sleep can be received by the user device (2) via touch input and / or voice input.

[0356] According to various embodiments, when a user inputs an automatic sleep management request through the user device (2), the server device (3) may determine that the current time corresponds to sleep time based on the satisfaction of a predetermined condition. In one embodiment, the server device (3) may determine whether the current time corresponds to sleep time based on the result information. For example, the server device (3) may determine that the current time corresponds to sleep time if the user maintains a predetermined posture (e.g., lying down or prone) for a predetermined period of time.

[0357] If the current time corresponds to sleep time (example of 1100), the server device (3) can control the home appliance (4) based on sleep status information received from the user device (2).

[0358] In one embodiment, the server device (3) can control the home appliance (4) to perform a predetermined operation for inducing sleep (1300) when the current time corresponds to a sleep time (example of 1100) and the user's sleep stage corresponds to a predetermined stage (e.g., an awake stage) (example of 1200).

[0359] For example, the user device (2) can transmit a sleep start request to the server device (3) based on receiving a command corresponding to sleep start from the user, and the server device (3) can receive the sleep start request and control the home appliance (4) to perform a predetermined operation for sleep induction when the user's sleep stage corresponds to a predetermined stage (e.g., an awake stage).

[0360] The predetermined action for sleep induction may include a predetermined action that each of at least one home appliance (4) can perform.

[0361] Certain actions for inducing sleep may be preset and may be changed by the user through the user device (2).

[0362] Figure 13 illustrates an example of a predetermined operation to induce sleep in a user.

[0363] Referring to FIG. 13, the server device (3) can control the display device (41) to perform a predetermined operation for inducing sleep.

[0364] For example, the server device (3) can control the display device (41) so that the brightness of the image output from the display device gradually darkens. 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 inducing sleep).

[0365] According to various embodiments, the server device (3) may also control the speaker (46) to play predetermined music (e.g., music for inducing sleep).

[0366] The server device (3) can control the lighting device (43) to perform a predetermined operation for inducing sleep. For example, the server device (3) can control the lighting device so that the brightness of the light output from the lighting device (43) gradually darkens. As another example, the server device (3) can control the lighting device so that the color of the light output from the lighting device (43) changes to a predetermined color (e.g., a color having a color temperature of 2000 K or less).

[0367] The server device (3) can control the automatic curtain opening / closing device (44) to perform a predetermined operation for inducing sleep. For example, the server device (3) can control the automatic curtain opening / closing device (44) to close the curtain.

[0368] The furniture control device (42) may 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) (see FIGS. 14 and 15).

[0369] The server device (3) can control the furniture control device (42) to perform a predetermined operation for inducing sleep.

[0370] For example, the server device (3) can control the vibration element (42a) to output vibration at a predetermined cycle that can relieve the user's tension.

[0371] 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 predetermined posture (lying upright) that is comfortable for sleeping.

[0372] The server device (3) can control the air conditioner (45) and / or the air purifier (47) to perform a predetermined operation for inducing sleep. For example, the server device (3) can control the air conditioner and / or the air purifier (47) to operate in sleep mode.

[0373] When the air conditioner (45) and / or air purifier (47) is operated in sleep mode, noise can be minimized, for example, by controlling the fan speed to slow.

[0374] The specific operations for sleep induction that can be performed by the home appliance (4) are not limited to the examples described above, and can of course be changed depending on the user's settings and the type of home appliance (4).

[0375] Referring again to FIG. 11, the server device (3) can determine whether the user's sleep stage corresponds to an awake stage (1400).

[0376] If the user's sleep stage does not correspond to the awakening stage (example of 1400), the server device (3) can control the home appliance (4) to perform a predetermined action to alleviate the user's stress index or alleviate sleep disorders.

[0377] In one embodiment, the server device (3) can control the home appliance (4) to perform a predetermined operation to alleviate the user's stress index or alleviate sleep disorders when the user's sleep stage corresponds to the REM sleep stage or the non-REM sleep stage.

[0378] More preferably, the server device (3) can control the home appliance (4) to perform a predetermined operation to alleviate the user's stress index or alleviate sleep disorders only when the user's sleep stage corresponds to the non-REM sleep stage.

[0379] According to one embodiment of the present disclosure, by controlling the home appliance (4) to perform a predetermined operation to alleviate the user's stress index or alleviate sleep disorders only when the user's sleep stage corresponds to the non-REM sleep stage, the user can be prevented from being woken up by the operation of the home appliance (4) during the REM sleep stage.

[0380] Certain actions for stress relief that can be performed by the 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).

[0381] The server device (3) can control the home appliance (4) to perform a predetermined action to relieve the user's stress in response to the user's stress index exceeding a predetermined value (example of 1500) (1550).

[0382] Figure 14 illustrates an example of a certain action to relieve user stress.

[0383] Referring to FIG. 14, the server device (3) can control the lighting device (43) to perform a predetermined operation to relieve the user's stress.

[0384] 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).

[0385] The server device (3) can control the air conditioner (45) to perform a predetermined operation to relieve user stress.

[0386] 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.

[0387] The server device (3) can control the furniture control device (42) to perform a predetermined operation to relieve the user's stress. 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).

[0388] For example, the server device (3) can control the vibration element (42a) to output vibration corresponding to the user's heart rate.

[0389] According to one embodiment of the present disclosure, a user's stress index can be alleviated during sleep, thereby inducing a user to have good quality sleep.

[0390] Referring again to FIG. 11, the server device (3) can control the home appliance (4) to perform a predetermined action to alleviate the developed sleep disorder in response to the user experiencing a predetermined sleep disorder (example of 1600) (1650).

[0391] Certain operations for alleviating sleep disorders that can be performed by home appliances (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).

[0392] Figure 15 illustrates an example of a predetermined operation to alleviate a user's sleep disturbance.

[0393] Referring to FIG. 15, the server device (3) can control the air conditioner (45) to perform a predetermined operation to alleviate sleep disturbance.

[0394] For example, the server device (3) can turn off the air conditioner (45).

[0395] The server device (3) can control the furniture control device (42) to perform a predetermined operation to alleviate sleep disorders. 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).

[0396] 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).

[0397] According to one embodiment of the present disclosure, a user can be induced to have good quality sleep by alleviating sleep disorders that occur during sleep.

[0398] Referring again to FIG. 12, the server device (3) can control the home appliance (4) to perform a predetermined operation for waking up based on the sleep stage not corresponding to the wake-up stage (No at 1700) and the current time corresponding to the wake-up time (Yes at 1800) (1950).

[0399] For example, a user can preset a wake-up time via the user device (2). The wake-up time can be set as a time range or as a time of day. For example, the user can set the wake-up time to "7:00 AM - 8:00 AM" or "7:00 AM."

[0400] The server device (3) can determine that the current time corresponds to the wake-up time if the current time corresponds to the wake-up time set by the user.

[0401] As another example, when a user inputs a command corresponding to the start of the wake-up through the user device (2), the server device (3) can determine that the current time corresponds to the wake-up time.

[0402] A command corresponding to the start of the wake-up can be received via the user interface (240) of the user device (2). For example, a command corresponding to the start of the wake-up can be received by the user device (2) via touch input and / or voice input.

[0403] According to various embodiments, when a user inputs an automatic sleep management request through the user device (2), the server device (3) may determine that the current time corresponds to the wake-up time based on the satisfaction of a predetermined condition. In one embodiment, the server device (3) may determine whether the current time corresponds to the wake-up time based on the result information. For example, the server device (3) may determine that the current time corresponds to the wake-up time if the user's sleep stage maintains the awake stage for a predetermined period of time (e.g., 10 minutes).

[0404] As explained earlier, a person needs to wake up during stage N1 or REM sleep.

[0405] The server device (3) can determine the possibility of entering the N1 stage of a predetermined sleep stage (e.g., REM sleep stage and / or non-REM sleep stage) within the wake-up time based on the sleep state information (1900).

[0406] When the wake-up time is set to a range of time, determining the likelihood of entering a predetermined sleep stage (e.g., REM sleep stage and / or N1 stage of non-REM sleep) within the wake-up time may include determining whether the user's sleep stage enters a predetermined sleep stage (e.g., REM sleep stage and / or N1 stage of non-REM sleep) within the set range of time.

[0407] If the wake-up time is set to a time, determining the likelihood of entering a predetermined sleep stage (e.g., REM sleep stage and / or N1 stage of non-REM sleep) may include determining whether the user's sleep stage is likely to enter a predetermined sleep stage (e.g., REM sleep stage and / or N1 stage of non-REM sleep) within a predetermined time (e.g., 15 minutes) before or after the time corresponding to the wake-up time.

[0408] If there is a possibility that the user's sleep stage will enter a predetermined stage (e.g., REM sleep stage and / or N1 stage of non-REM sleep stage) within the wake-up time (example of 1900), the server device (3) can control the home appliance (4) to perform a predetermined action to wake up the user based on the user's sleep stage entering a predetermined stage (e.g., REM sleep stage and / or N1 stage of non-REM sleep stage) (1960).

[0409] That is, the server device (3) can control the home appliance (4) to perform a predetermined operation to wake up the user when the current time corresponds to the wake-up time and the user's sleep stage corresponds to a predetermined stage (e.g., REM sleep stage and / or N1 stage of non-REM sleep stage).

[0410] According to the present disclosure, by waking up a user in the REM sleep stage and / or the N1 stage of the non-REM sleep stage, the user can be induced to feel refreshed upon waking up.

[0411] On the other hand, if the server device (3) is unlikely to enter a predetermined stage (e.g., REM sleep stage and / or N1 stage of non-REM sleep stage) of the user's sleep stage within the wake-up time (No of 1900), the server device (3) can control the home appliance (4) to perform a predetermined action to wake up the user if the current time corresponds to the wake-up time (1950).

[0412] In one embodiment, the server device (3) can control the home appliance (4) to perform a predetermined operation to wake up the user (1950) when the user's sleep stage corresponds to the wake-up stage and the current time corresponds to the wake-up time (example of 1750).

[0413] In one embodiment, if the user's sleep stage corresponds to the wake-up stage and the current time does not correspond to the wake-up time (No of 1750), the server device (3) can perform an action for waking up or an action for inducing sleep again according to the user's input.

[0414] For example, the server device (3) may perform an action for waking up based on receiving a user input requesting the user device (2) to start waking up, if the user's sleep stage does not correspond to the current waking up time (1950).

[0415] As another example, the server device (3) may perform an operation for inducing sleep based on the user device (2) not receiving a user input requesting to start waking up for a predetermined period of time when the user's sleep stage does not correspond to the current time for waking up (1300).

[0416] As another example, the server device (3) may perform an operation for sleep induction based on receiving a user input requesting the user device (2) to start re-sleep when the user's sleep stage does not correspond to the current wake-up time (1300).

[0417] Figure 16 illustrates an example of a predetermined action to induce a user's wake-up.

[0418] Referring to FIG. 16, the server device (3) can control the display device (41) to perform a predetermined operation for inducing sleep.

[0419] 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).

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] According to various embodiments, the operations described with reference to FIGS. 11 to 16 may also be performed by the user device (2). For example, the user device (2) may control the home appliance (4) based on sleep state information.

[0430] According to one embodiment of the present disclosure, a user can be helped to have good quality sleep.

[0431] 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 (51, 52, 53, 54, or 55), processing the first data to obtain first processed data, and processing the second data to obtain second processed data; a user device (2) for receiving the first processed data and the second processed data from the hub device (1), and inputting the first processed data and the second processed data into a machine learning model to obtain sleep state information related to the sleep state of the user; And it may include a server device (3) that receives the sleep state information from the user device (2) via wireless communication.

[0432] 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.

[0433] The above hub device (1) can receive the user's data from the plurality of sensors (5) through wired communication.

[0434] The above hub device (1) can receive the first data from the sensor (e.g., the first sensor (51), the second sensor (52), or the third sensor (53)) through wired communication, and can receive the second data from the sensor (e.g., the fourth sensor (54) or the fifth sensor (55)) through wireless communication.

[0435] The above plurality of sensors (5) may include at least two of a pressure sensor, a UWB sensor, a radar sensor, an oxygen saturation sensor, an electrocardiogram sensor, or an acceleration sensor.

[0436] The sleep state information related to the sleep state of the user may include at least one of information about the sleep stage of the user, information about the user's stress index, or information about the user's sleep disorder.

[0437] The user device (2) can store a sleep management application including the machine learning model (21).

[0438] The above sleep management application may be downloadable from an external server.

[0439] The above server device (3) can control at least one home appliance (4) registered in conjunction with the user device (2) based on the sleep state information.

[0440] The server device (3) can control at least one home appliance (4) to perform a predetermined operation to wake up the user in response to (or based on) the current time corresponding to the wake-up time and the user's sleep stage corresponding to a predetermined stage.

[0441] The server device (3) can control at least one home appliance (4) to perform a predetermined operation for inducing sleep of the user in response to (or based on) the current time corresponding to sleep time and the sleep stage of the user corresponding to a predetermined stage.

[0442] The server device (3) can control at least one home appliance (4) to perform a predetermined operation to relieve the user's stress in response to the user's stress index exceeding a predetermined value.

[0443] The server device (3) can control at least one home appliance (4) to perform a predetermined operation to alleviate the developed sleep disorder in response to the development of a predetermined sleep disorder in the user.

[0444] The above hub device (1) may include a printed circuit board connected by wires to the plurality of sensors (5).

[0445] 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 by processing the first data and obtaining second processed data by processing the second data; the hub device (1) transmitting the first processed data and the second processed data to a user device (2); the user device (2) inputting the first processed data and the second processed data received from the hub device (1) into a machine learning model to obtain sleep state information related to the sleep state of the user; and the user device (2) transmitting the sleep state information to a server device (3) via wireless communication.

[0446] The above hub device (1) may obtain the first processing data and the second processing data by inputting the first data into a first machine learning model (11, 12, 13, 14, or 15) to obtain the first processing data, and inputting the second data into a second machine learning model (11, 12, 13, 14, or 15) to obtain the second processing data.

[0447] The hub device (1) receiving the first data and the second data may include receiving the first data from the first sensor (51, 52 or 53) through wired communication and receiving the second data from the second sensor (51, 52 or 53) through wired communication.

[0448] The hub device (1) receiving the first data and the second data may include receiving the first data from the first sensor (51, 52 or 53) through wired communication and receiving the second data from the second sensor (54 or 55) through wireless communication.

[0449] The above sleep management method may further include the server device (3) controlling at least one home appliance (4) registered in conjunction with the user device (2) based on the sleep state information.

[0450] The controlling of the at least one home appliance (4) by the server device (3) may include at least one of: controlling the at least one home appliance (4) to perform a first predetermined operation for waking up the user in response to (or based on) the current time corresponding to the wake-up time and the sleep stage of the user corresponding to the first predetermined stage; controlling the at least one home appliance (4) to perform a second predetermined operation for inducing sleep of the user in response to (or based on) the current time corresponding to the sleep time and the sleep stage of the user corresponding to the second predetermined stage; controlling the at least one home appliance (4) to perform a third predetermined operation for alleviating stress of the user in response to (or based on) the user's stress index exceeding a predetermined value; or controlling the at least one home appliance (4) to perform a fourth predetermined operation for alleviating a sleep disorder that has been expressed in the user in response to (or based on) the expression of a sleep disorder.

[0451] A user device (2) according to one embodiment of the present disclosure includes a communication unit (230) that communicates with a hub device (1) that processes user data collected from a plurality of sensors (5); at least one memory (220); and at least one processor (210) electrically connected to the communication unit (230) and the at least one memory (220). The at least one memory (220) may store commands that, when executed by the at least one processor (210), cause the at least one processor (210) to receive processed data processed by the hub device (1) through the communication unit (230) and input the processed data into a machine learning model to obtain sleep state information related to the sleep state of the user.

[0452] The above commands, when executed by the at least one processor (210), can cause the at least one processor (210) to control at least one home appliance (4) based on the sleep state information.

[0453] The above instructions, when executed by the at least one processor (210), may cause the at least one processor (210) to control the at least one home appliance (4) to perform a predetermined operation to wake up the user in response to (or based on) the current time corresponding to a wake-up time and the user's sleep stage corresponding to a predetermined stage.

[0454] The above instructions, when executed by the at least one processor (210), may cause the at least one processor (210) to control the at least one home appliance (4) to perform a predetermined operation to wake up the user in response to (or based on) the current time corresponding to a wake-up time and the user's sleep stage corresponding to a predetermined stage.

[0455] The above commands, when executed by the at least one processor (210), may cause the at least one processor (210) to control the at least one home appliance (4) to perform a predetermined operation for inducing sleep of the user in response to (or based on) the current time corresponding to a sleep time and the sleep stage of the user corresponding to an awake stage.

[0456] The above instructions, when executed by the at least one processor (210), may cause the at least one processor (210) to control the at least one home appliance (4) to perform a predetermined operation to alleviate the user's stress in response to (or based on) the user's stress index exceeding a predetermined value.

[0457] The above instructions, when executed by the at least one processor (210), may cause the at least one processor (210) to control the at least one home appliance (4) to perform a predetermined action to alleviate the developed sleep disorder in response to (or based on) the development of the predetermined sleep disorder in the user.

[0458] The above commands, when executed by the at least one processor (210), may cause the at least one processor (210) to transmit the sleep state information to the server (3) through the communication unit (230).

[0459] The above commands, when executed by the at least one processor (210), may cause the at least one processor (210) to transmit a control command to the server (3) to control the at least one home appliance (4) through the communication unit (230).

[0460] The above commands, when executed by the at least one processor (210), may cause the at least one processor (210) to output the sleep state information through the user interface (240).

[0461] According to one embodiment of the present disclosure, a non-transitory storage medium stores computer-readable commands, wherein the commands, when executed by a processor (210), cause the processor (210) to receive, through a communication unit (230), processed data processed by a hub device (1) that processes user data collected from a plurality of sensors (5); and input the processed data into a machine learning model to obtain sleep state information related to the user's sleep state.

[0462] The above commands, when executed by the processor (210), can cause the processor (210) to control at least one home appliance (4) based on the sleep state information.

[0463] The above instructions, when executed by the processor (210), may cause the processor (210) to control the at least one home appliance (4) to perform a predetermined operation to wake up the user in response to (or based on) the current time corresponding to a wake-up time and the user's sleep stage corresponding to a predetermined stage.

[0464] The above instructions, when executed by the processor (210), may cause the processor (210) to control the at least one home appliance (4) to perform a predetermined operation to wake up the user in response to (or based on) the current time corresponding to a wake-up time and the user's sleep stage corresponding to a predetermined stage.

[0465] The above commands, when executed by the processor (210), may cause the processor (210) to control the at least one home appliance (4) to perform a predetermined operation for inducing sleep of the user in response to (or based on) the current time corresponding to a sleep time and the sleep stage of the user corresponding to an awake stage.

[0466] The above commands, when executed by the processor (210), may cause the processor (210) to control the at least one home appliance (4) to perform a predetermined operation to alleviate the user's stress in response to (or based on) the user's stress index exceeding a predetermined value.

[0467] The above instructions, when executed by the processor (210), may cause the processor (210) to control the at least one home appliance (4) to perform a predetermined action to alleviate the developed sleep disorder in response to (or based on) the development of a predetermined sleep disorder in the user.

[0468] The above commands, when executed by the processor (210), can cause the processor (210) to transmit the sleep state information to the server (3) through the communication unit (230).

[0469] The above commands, when executed by the processor (210), can cause the processor (210) to transmit a control command to the server (3) to control at least one home appliance (4) through the communication unit (230).

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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.

[0474] The above illustrates and describes specific embodiments. However, the invention is not limited to the above-described embodiments, and those skilled in the art will readily 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 sleep management system comprising a plurality of sensors for collecting user data, a hub device for processing data collected from the plurality of sensors, a user device for processing data processed by the hub device to obtain sleep state information related to the sleep state of the user, and a server device for controlling at least one home appliance based on the sleep state information obtained by the user device. The above plurality of sensors include a first sensor that collects first data and a second sensor that collects second data, The hub device receives the first data and the second data, processes the first data to obtain first processing data, processes the second data to obtain second processing data, and transmits the first processing data and the second processing data to the user device. A sleep management system in which the user device receives the first processing data and the second processing data from the hub device, inputs the first processing data and the second processing data into a machine learning model of the user device to obtain the sleep state information, and transmits the sleep state information to the server device.

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

3. In paragraph 1, The above hub device, A sleep management system that receives data of the user from the plurality of sensors via wired communication.

4. In paragraph 1, The above hub device, A sleep management system that receives the first data from the first sensor via wired communication and receives the second data from the second sensor via wireless communication.

5. 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, an oxygen saturation sensor, an electrocardiogram sensor, or an acceleration sensor.

6. In paragraph 1, The above sleep status information is, A sleep management system comprising at least one of information about the user's sleep stage, information about the user's stress index, or information about the user's sleep disorder.

7. In paragraph 1, Each of the first processing data and the second processing data includes probability values ​​for the user's sleep stage, The machine learning model of the above user device is, A sleep management system that determines the sleep stage of a user by assigning different weights to probability values ​​of the sleep stages of the user.

8. In paragraph 1, Each of the first processing data and the second processing data includes stress index values ​​of the user, The machine learning model of the above user device is, A sleep management system that determines a user's stress index by assigning different weights to the user's stress index values.

9. In paragraph 1, Each of the first processing data and the second processing data includes apnea-hypopnea indices of the user, The machine learning model of the above user device is, A sleep management system that determines the apnea-hypopnea index of a user by assigning different weights to the apnea-hypopnea indices of the user.

10. In paragraph 1, The above sleep state information includes information about the user's sleep stage, The above server device, A sleep management system that controls at least one home appliance to perform a predetermined action to wake up the user in response to the current time corresponding to a wake-up time and the sleep stage of the user corresponding to a predetermined stage.

11. In paragraph 1, The above sleep state information includes information about the user's sleep stage, The above server device, A sleep management system that controls at least one home appliance to perform a predetermined operation for inducing sleep of the user in response to the current time corresponding to a sleep time and the sleep stage of the user corresponding to an awake stage.

12. In paragraph 1, The above sleep status information includes information about the user's stress index, The above server device, A sleep management system that controls at least one home appliance to perform a predetermined action to relieve stress of the user in response to the user's stress index exceeding a predetermined value.

13. In paragraph 1, The above sleep status information includes information about the user's sleep disorder, The above server device, A sleep management system that controls at least one home appliance to perform a predetermined action to alleviate a predetermined sleep disorder in response to the occurrence of a predetermined sleep disorder in the user.

14. 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 by processing the first data, and obtains second processing data by processing the second data; The hub device transmits the first processing data and the second processing data to the user device; and the user device obtains sleep state information related to the user's sleep state by inputting the first processing data and the second processing data into a machine learning model of the user device; A sleep management method, comprising: the user device transmitting the sleep state information to a server device.

15. In paragraph 14, The above hub device obtains the first processing data by processing the first data and obtains the second processing data by processing the second data, A sleep management method comprising: inputting the first data into a first machine learning model of the hub device to obtain the first processing data, and inputting the second data into a second machine learning model of the hub device to obtain the second processing data.

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