Sleep management system and sleep management method

Abstract

This sleep management system comprises: a plurality of sensors including a first sensor and a second sensor for collecting data of a user; a hub device for receiving first data collected by the first sensor and second data collected by the second sensor, acquiring first processed data on the basis of processing the first data, and acquiring second processed data on the basis that the second data is processed; a user device which receives the first processed data and the second processed data from the hub device, and which inputs the first processed data and the second processed data into a machine-learning model so as to acquire sleep state information related to a sleep state of the user; and a server device for receiving 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 because it is performed while attaching various sensors to the body.

[0005] For this reason, various devices that can analyze a person's sleep state are being developed, but the accuracy of measuring sleep state of these devices is significantly lower than that of polysomnography.

[0006] The disclosed invention provides a sleep management system and a sleep management method for more accurately determining a user's sleep time using a pressure sensor.

[0007] A sleep management system according to one embodiment of the present disclosure may include a plurality of pressure sensors (51) for detecting pressure by a user; a hub device (1) for receiving pressure data collected by the plurality of pressure sensors (51) and obtaining pressure processing data based on processing the pressure data; and a user device (2) for receiving the pressure processing data from the hub device (1), determining muscle stiffness (M) of the user corresponding to an area of ​​the user's body in contact with the plurality of pressure sensors (51) based on the pressure processing data, and determining whether the user is sleeping based on the muscle stiffness (M) of the user.

[0008] A sleep management method according to one embodiment of the present disclosure may include: a hub device (1) receiving pressure data collected by a plurality of pressure sensors (51); the hub device (1) obtaining pressure processing data based on processing the pressure data; the hub device (1) transmitting the pressure processing data to a user device (2); the user device (2) determining muscle stiffness (M) of the user based on the pressure processing data received from the hub device (1); and the user device (2) determining whether the user is asleep based on the muscle stiffness (M).

[0009] According to one aspect of the disclosed invention, a user's sleeping point can be accurately measured even without attaching various sensors to the body.

[0010] 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.

[0011] 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.

[0012] According to one aspect of the disclosed invention, pressure processing data is processed by a plurality of machine learning models stored in a user device, thereby obtaining accurate data related to the user's sleeping time and sleep state.

[0013] 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.

[0014] FIG. 1 illustrates an example of a network of a sleep management system according to one embodiment.

[0015] FIG. 2 schematically illustrates an example of the structure of a sleep management system according to one embodiment.

[0016] FIG. 3 illustrates an example of a control block diagram of a sleep management system according to one embodiment.

[0017] FIGS. 4 and 5 illustrate examples of multiple sensors of a sleep management system according to one embodiment.

[0018] FIG. 6 illustrates an example of a flowchart of a sleep management method according to one embodiment.

[0019] FIG. 7 is a diagram illustrating a process for processing data collected from a pressure sensor of a sleep management system according to one embodiment.

[0020] Figure 8 is a flowchart illustrating a sleep management method according to one embodiment.

[0021] FIG. 9 illustrates an example of sleep onset time and sleep state information derived by a sleep management system according to one embodiment.

[0022] FIG. 10 illustrates an example of sleep summary information derived by a sleep management system according to one embodiment.

[0023] 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.

[0024] In connection with the description of the drawings, similar reference numerals may be used for similar or related components.

[0025] 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.

[0026] 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.

[0027] The term “and / or” includes any combination of a plurality of related described elements or any one of a plurality of related described elements.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034]

[0035] *The sleep management system according to various embodiments is specifically described below with reference to the attached drawings.

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

[0037] Referring to FIG. 1, a sleep management system according to one embodiment may include a hub device (1), a user device (2), a server device (3), and / or a home appliance (4).

[0038] 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).

[0039] The hub device (1) can obtain processed data based on processing data collected from multiple sensors. In one embodiment, the hub device (1) can process data collected from multiple sensors using a machine learning model.

[0040] 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).

[0041] 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).

[0042] The home appliance (4) can be remotely controlled by the server device (3).

[0043] 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.

[0044] The lighting device (43) may include a light source whose light intensity and / or light color can be controlled.

[0045] The automatic curtain opening / closing device (44) may include an actuator for automatically opening or closing the curtain.

[0046] 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).

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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).

[0051] 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.

[0052] 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.

[0053] 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).

[0054] 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.

[0055] 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.

[0056] 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).

[0057] 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 include other information that can identify the device in addition to the serial number or MAC address of the device.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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).

[0062] 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.

[0063] 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).

[0064] 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).

[0065] FIG. 2 schematically illustrates an example of the structure of a sleep management system according to one embodiment.

[0066] FIG. 3 illustrates an example of a control block diagram of a sleep management system according to one embodiment.

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

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

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

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

[0071] Data related to the user's sleep may include pressure data for measuring pressure changes corresponding to changes in the user's posture, displacement data corresponding to 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.

[0072] The plurality of sensors (5) may include at least two of a pressure sensor for collecting pressure data for measuring pressure changes corresponding to changes in the user's posture, a UWB sensor for measuring displacement data corresponding to displacement of the body that changes according to the user's breathing, 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.

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

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

[0075] 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.

[0076] 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).

[0077] Data collected from multiple sensors (5) can be transmitted to the hub device (1).

[0078] In one embodiment, data collected by at least one sensor among a plurality of sensors (5) can be transmitted to a hub device (1) via wired communication, and data collected by the remaining sensors can be transmitted to the hub device (1).

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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).

[0083] In one embodiment, data collected from multiple sensors (5) can be transmitted to a hub device (1) via wired communication.

[0084] 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.

[0085] 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.

[0086] In one embodiment, the hub device (1) may include at least one processor (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 processor (120).

[0087] At least one processor (110) can store a machine learning model for processing data collected from multiple sensors (5).

[0088] 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.

[0089] Features of data may include elements that a machine learning model extracts from the data to perform classification or prediction.

[0090] 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.

[0091] At least one processor (110) can obtain first processed data based on processing first data collected from the first sensor (51).

[0092] 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.

[0093] At least one processor (110) can obtain second processed data based on processing second data collected from a second sensor (52).

[0094] 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.

[0095] At least one processor (110) can obtain third processing data based on processing third data collected from a third sensor (53).

[0096] 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.

[0097] At least one processor (110) can obtain fourth processed data based on processing fourth data collected from the fourth sensor (54).

[0098] 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.

[0099] *At least one processor (110) can obtain fifth processed data based on processing fifth data collected from the fifth sensor (55).

[0100] 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.

[0101] According to the present disclosure, by first processing data in a hub device (1) and then transmitting it to a user device (2), the amount of data processing that the user device (2) must bear can be reduced.

[0102] 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.

[0103] The hub device (1) may include a printed circuit board including at least one processor (110), at least one processor (120), and a communication unit (130). At least some of the plurality of sensors (5) may be connected to the printed circuit board by wires.

[0104] The hub device (1) may include a housing covering a printed circuit board.

[0105] 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).

[0106] 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).

[0107] 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).

[0108] 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).

[0109] 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).

[0110] At least one processor (110) can control multiple sensors (5).

[0111] For example, at least one processor (110) can control at least one wired sensor among a plurality of sensors (5).

[0112] 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.

[0113] Waking up a sensor may include activating the sensor.

[0114] 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.

[0115] The user device (2) can receive data acquired by the hub device (1) from the hub device (1).

[0116] The data acquired by the hub device (1) may include processed data obtained by processing data collected from a plurality of sensors (5).

[0117] 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).

[0118] At least one memory (220) can store a machine learning model for processing data received from the hub device (1).

[0119] 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.

[0120] 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.

[0121] 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).

[0122] The user device (2) can receive processing data from the hub device (1) through the communication unit (230).

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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).

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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).

[0137] The user device (2) may include a user interface (240) for communication with the user.

[0138] According to various embodiments, it is also possible for the hub device (1) to primarily process data collected from multiple sensors (5), and then secondarily process the primarily processed data to obtain sleep state information.

[0139] 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 data to a server, and then acquire sleep state information after the server secondarily processes the primarily processed data.

[0140] According to various embodiments, it is also possible for the hub device (1) to transmit data collected from a plurality of sensors (5) to the user device (2), the user device (2) to primarily process the data received from the hub device (1), and to secondarily process the primarily processed data and then obtain sleep state information.

[0141] According to various embodiments, it is also possible for the hub device (1) to transmit data collected from a plurality of sensors (5) to the server device (3), the server device (3) to primarily process the data received from the hub device (1), and to secondarily process the primarily processed data and then obtain sleep state information.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] The input interface can receive user input to run a sleep management application.

[0146] 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.

[0147] The output interface can output an interface corresponding to a sleep management application.

[0148] 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.

[0149] The output interface and input interface may be provided as separate devices or as a single device (e.g., a touch display).

[0150] The server device (3) can receive data acquired by the user device (2) from the user device (2).

[0151] The data acquired by the user device (2) may include the sleep state information described above.

[0152] 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).

[0153] At least one memory (320) can store sleep state information received from the user device (2).

[0154] 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).

[0155] At least one processor (310) can generate sleep summary information based on sleep state information accumulated and stored in at least one memory (320).

[0156] 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).

[0157] At least one processor (310) can control the home appliance (4) based on sleep state information received from the user device (2).

[0158] 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.

[0159] 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.

[0160] At least one memory (320) can store sleep summary information generated based on sleep state information.

[0161] 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).

[0162] The server device (3) can receive sleep status information from the user device (2) through the communication unit (330).

[0163] The server device (3) can transmit a control command to the home appliance (4) through the communication unit (330).

[0164] The server device (3) can transmit sleep summary information to the user device (2) through the communication unit (330).

[0165] 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.

[0166] FIGS. 4 and 5 illustrate examples of multiple sensors of a sleep management system according to one embodiment.

[0167] 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.).

[0168] At least some of the plurality of sensors (5) may be provided on furniture (10) on which a user can sit or lie.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The mattress may further include springs and / or sponges. The springs and / or sponges may be provided in the accommodation space of the cover.

[0175] The structure (e.g., length, arrangement, etc.) of the first sensor (51) may be changed depending on the size of the mattress.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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).

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] The third sensor (53) can measure the movement of the user's eyes based on mmWave reflected from the user's eyes.

[0197] That is, the third sensor (53) can obtain eye-movement data corresponding to the movement of the user's eyes.

[0198] 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.

[0199] The fourth sensor may have a detection area facing the body of a user lying or sitting on the furniture (10).

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] In one embodiment, the fifth sensor (55) may be installed on the furniture (10).

[0207] The fifth sensor (55) can obtain acceleration data corresponding to the user's body movement.

[0208] 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.

[0209] As described above, the first sensor (51) may include a pressure sensor. Below, a sleep management method based on data collected from the pressure sensor (51) will be described.

[0210] FIG. 6 illustrates an example of a flowchart of a sleep management method according to one embodiment.

[0211] FIG. 7 is a diagram illustrating a process for processing data collected from a pressure sensor of a sleep management system according to one embodiment.

[0212] Referring to FIGS. 6 and 7, the hub device (1) can collect pressure data from a pressure sensor (51) (S1). The pressure data can include an electrical signal corresponding to a displacement caused by pressure.

[0213] When the pressure sensor (51) remains activated, the power consumption supplied to the pressure sensor (51) may be high. Maintaining the pressure sensor (51) in an activated state may include maintaining a state in which the pressure sensor (51) is capable of acquiring pressure data by receiving power.

[0214] In one embodiment, when a plurality of pressure sensors (51) are provided, the hub device (1) can keep at least some of the plurality of pressure sensors (51) in an inactive state, but can switch the plurality of pressure sensors (51) to an active state based on the satisfaction of a predetermined condition.

[0215] For example, the hub device (1) can determine whether a user is present in the furniture (10) based on processing data collected from a plurality of pressure sensors (51). The presence of a user in the furniture (10) may include the user lying down or sitting on the furniture (10).

[0216] The hub device (1) can switch a plurality of pressure sensors (51) to a standby state based on the determination that there is no user in the furniture (10).

[0217] For example, the hub device (1) may deactivate some of the plurality of pressure sensors (51) and operate the rest in a low power mode. Deactivating the sensors may include cutting off power supplied to the sensors.

[0218] 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.

[0219] The hub device (1) can determine whether a user is present in the furniture (10) based on processing data collected by a sensor operating in low power mode among a plurality of pressure sensors (51).

[0220] The hub device (1) can wake up multiple pressure sensors (51) based on the determination that a user is present in the furniture (10).

[0221] A plurality of pressure sensors (51) can transmit collected data to the hub device (1) based on their activation.

[0222] The pressure sensor (51) can transmit pressure data to the hub device (1). In one embodiment, at least some of the plurality of pressure sensors (51) can transmit sensor data to the hub device (1) via wired communication, and the remaining some of the plurality of pressure sensors (51) can transmit sensor data to the hub device (1).

[0223] The hub device (1) can primarily process pressure data collected from multiple pressure sensors (51) (S2). To this end, the hub device (1) can be equipped with a machine learning model.

[0224] Data collected from multiple sensors (5) can be processed by a machine learning model mounted on the hub device (1).

[0225] The hub device (1) may be equipped with a machine learning model for extracting features from pressure data collected from a pressure sensor (51). The machine learning model may be pre-trained to extract features from pressure data collected by the pressure sensor.

[0226] The machine learning model can use pressure data collected by the pressure sensor (51) as input data and output pressure processing data as output data. The pressure processing data may include data including specific numerical values ​​by processing pressure data including electrical signals corresponding to displacement caused by pressure. For example, the pressure processing data may include the absolute magnitude of the pressure detected by the pressure sensor.

[0227] According to the present disclosure, the hub device (1) can primarily process data collected from the pressure sensor (51) and transmit it to the user device (2) even in a communication method with a low data transmission capacity.

[0228] The hub device (1) can process data collected from the pressure sensor (51) and transmit pressure processing data to the user device (2) (S3).

[0229] The hub device (1) can transmit pressure processing data to the user device (2). In one embodiment, the communication unit (130) of the hub device (1) can include a first communication module for receiving pressure data collected from a pressure sensor (51) in a first wireless communication manner, and a second communication module for transmitting the pressure processing data to the user device (2) in a second wireless communication manner.

[0230] The first wireless communication method and the second wireless communication method may be the same or different from each other.

[0231] According to one embodiment of the present disclosure, the hub device (1) has both a first communication module for receiving data from a pressure sensor (51) and a second communication module for communicating with a user device (2), so that it can communicate with the pressure sensor (51) and the user device (2) simultaneously.

[0232] The user device (2) can process pressure processing data received from the hub device (1) (S4). For this purpose, the user device (2) can be equipped with a machine learning model.

[0233] Pressure processing data received from the hub device (1) can be processed by a machine learning model (20) mounted on the user device (2).

[0234] The machine learning model (20) 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 (20) 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 (20) mounted on the user device (2) may be trained for the purpose of estimating the user's sleep state based on pressure processing data processed by the hub device (1).

[0235] The machine learning model (20) mounted on the user device (2) can use the pressure processing data output from the hub device (1) as input data to output information on the user's sleeping time and sleep state as output data.

[0236] The user's sleep state information output by the machine learning model (20) installed in the user device (2) may include at least one of information about sleep stages, information about the user's stress index, or information about the user's sleep disorder.

[0237] In addition, the machine learning model (20) mounted on the user device (2) can use pressure processing data as input data and output the user's body data (B) as output data.

[0238] The user's body data (B) output by the machine learning model (20) mounted on the user device (2) may include at least one of the user's height, weight distribution, cross-sectional area of ​​the coronal plane, and the user's posture.

[0239] The machine learning model (20) mounted on the user device (2) may include multiple machine learning models, each including different algorithms. The multiple machine learning models may include a first machine learning model (21), a second machine learning model (22), a third machine learning model (23), and a fourth machine learning model (24).

[0240] According to one embodiment of the present disclosure, the user device (2) can obtain the user's body data (B) based on the pressure processing data received from the hub device (1). The first machine learning model (21) mounted on the user device (2) can use the pressure processing data output from the hub device (1) as input data and output the user's body data (B) as output data.

[0241] For example, as illustrated in FIGS. 4 and 5, pressure sensors (51) may be installed in a grid form on a bed or mattress so as to measure the pressure generated by the user's body when the user lies down or sits. In this case, pressure processing data processed by the hub device (1) from the pressure data collected from the pressure sensor (51) may include the magnitude of the pressure and the position of the pressure sensor (51) where the corresponding pressure is generated. Accordingly, the first machine learning model (21) mounted on the user device (2) may use the pressure processing data as input data and output at least one of the user's height, weight distribution, coronal plane cross-sectional area, and the user's posture as output data.

[0242] The user's body data (B) output from the first machine learning model (21) can be transmitted to the second machine learning model (22).

[0243]

[0244] According to one embodiment of the present disclosure, the user device (2) can obtain the user's muscle stiffness (M) based on pressure processing data received from the hub device (1). The machine learning model (20) mounted on the user device (2) can use the pressure processing data as input data and output the user's muscle stiffness (M) as output data.

[0245] The second machine learning model (22) mounted on the user device (2) can output the user's muscle stiffness (M) as output data by using the pressure processing data output from the hub device (1) and the user's body data (B) transmitted from the first machine learning model (21) as input data. That is, the user device (2) can obtain the user's muscle stiffness (M) by inputting the pressure processing data and the user's body data (B) into the second machine learning model (22).

[0246] The user's muscle stiffness (M) is a numerical representation of the muscle tone that changes depending on the user's sleep state due to the actions of sleep hormones and the parasympathetic nervous system. Muscle tone indicates the degree to which muscles resist passive stretching in a resting state. The user's muscle stiffness (M) is related to the pressure changes caused by the user's body depending on the user's sleep state. Muscle tone typically decreases as the user approaches sleep. Accordingly, the area of ​​the user's body in contact with the pressure sensor may increase, causing changes in pressure distribution.

[0247] Therefore, the user's muscle stiffness (M) may be a numerical value corresponding to the area of ​​the user's body in contact with multiple pressure sensors. The user's muscle stiffness (M) can be utilized as a learning parameter to obtain information on the user's sleep state and timing of falling asleep through machine learning.

[0248] The second machine learning model (22) may have completed learning about the relationship between pressure processing data including pressure distribution and muscle stiffness (M).

[0249] The user's muscle stiffness (M) output from the second machine learning model (22) can be transferred to at least one of the third machine learning model (23) and the fourth machine learning model (24).

[0250] According to one embodiment of the present disclosure, the user device (2) can determine whether the user is sleeping based on the user's muscle stiffness (M) output from the second machine learning model (22). The user device (2) can determine that the user is sleeping based on the muscle stiffness (M) being below a predetermined reference value.

[0251] The third machine learning model (23) mounted on the user device (2) can use the user's muscle stiffness (M) as input data and output the user's sleeping time as output data. That is, the user device (2) can obtain the user's sleeping time by inputting the user's muscle stiffness (M) into the third machine learning model (23).

[0252] According to one embodiment of the present disclosure, the user device (2) can determine the user's sleep state information based on the user's muscle stiffness (M) output from the second machine learning model (22). The fourth machine learning model (24) mounted on the user device (2) can use the user's muscle stiffness (M) as input data and output the user's sleep state information as output data.

[0253] When the user device (2) determines that the user has fallen asleep, it can obtain sleep state information by inputting muscle stiffness (M) determined based on pressure processing data acquired after the user's falling asleep into the fourth machine learning model (24).

[0254] The user device (2) can transmit information on the time of entering sleep and sleep state to the server device (3) (S5).

[0255] According to the present disclosure, instead of direct data related to the user's private life being transmitted to the server device (3), only the time of falling asleep and sleep state information related to the user's sleep state are transmitted to the server device (3), thereby making it easier to obtain the user's consent for data collection.

[0256] The server device (3) can generate a control command for controlling the home appliance (4) based on the time of entering sleep and 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.

[0257] 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.

[0258] The server device (3) can transmit a control command to control the home appliance (4) to the home appliance (4) (S7).

[0259] 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).

[0260] The home appliance (4) can perform a predetermined operation corresponding to a control command received from the server device (3) (S8).

[0261] Multiple machine learning models (20) stored in the user device (2) can be updated by an external server.

[0262]

[0263] Figure 8 is a flowchart illustrating a sleep management method according to one embodiment.

[0264] A plurality of pressure sensors (51) can collect pressure data and transmit it to the hub device (1). (S81)

[0265] The hub device (1) can process pressure data received from a plurality of pressure sensors (51) to obtain pressure processing data. (S82) In addition, the hub device (1) can transmit the obtained pressure processing data to the user device (2). (S83)

[0266] The user device (2) can input pressure processing data into the first machine learning model (21) to obtain the user's body data (B). (S84) In addition, the user device (2) can input the pressure processing data and the user's body data (B) into the second machine learning model (22) to obtain the user's muscle stiffness (M). (S85) In addition, the user device (2) can input the user's muscle stiffness (M) into the third machine learning model (23) to obtain the user's sleeping time. (S86) In addition, the user device (2) can input the muscle stiffness (M) obtained after the user's sleeping time into the fourth machine learning model (24) to obtain the user's sleep state information. (87)

[0267] According to the present disclosure, since the pressure processing data output by the hub device (1) is secondarily input into a plurality of machine learning models (20) mounted on the user device (2) to output information on the user's sleeping time and sleeping state, the user's sleeping time and sleeping 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 sleeping time and sleeping state.

[0268] Human sleep stages can be divided into wakefulness stages, rapid eye movement (REM) stages, and non-rapid eye movement (NREM) stages.

[0269] The awakening stage corresponds to the stage in which a person is awake.

[0270] 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.

[0271] In adults, REM sleep typically accounts for approximately 20–25% of total sleep, occurring approximately 90–120 minutes per night.

[0272] 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.

[0273] 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.

[0274] 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).

[0275] 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.

[0276] As another example, non-REM sleep stages may be divided into stage 1 (stage N1), stage 2 (stage N2), and stage 3 (stage N3).

[0277] 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.

[0278] 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.

[0279] 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.

[0280] 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.

[0281] 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.

[0282] 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.

[0283] Afterwards, in the second sleep cycle, we go through stages N1, N2, N3, N4, and then return to stages N3 and N2.

[0284] Afterwards, in the third sleep cycle, we go through stage N3 and then return to stage N2, stage N1, and REM sleep.

[0285] Afterwards, in the fourth sleep cycle, we go through stage N1 and N2 sleep, and then enter stage N1 and REM sleep again.

[0286] After that, the person goes through the N1 stage, N2 stage, and REM sleep stage and wakes up naturally.

[0287] If a person wakes up during stage N4 sleep, they may feel groggy and not feel like they've slept. Therefore, it's recommended that people wake up during stage N1 or REM sleep.

[0288] FIG. 9 illustrates an example of sleep onset time and sleep state information derived by a sleep management system according to one embodiment.

[0289] Information on the time of falling asleep and sleep status can be derived in real time by the sleep management system.

[0290] Referring to FIG. 9, the sleep onset time and sleep state information derived by the sleep management system according to one embodiment may include information on sleep stages, information on oxygen saturation, information on sleep disorders, information on stress index, information on respiration rate, information on movement, information on heart rate, and / or information on body pressure.

[0291] According to various embodiments, the user device (2) (or display device (41)) may output the time of falling asleep and sleep state information through an output interface (e.g., a display).

[0292] 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.

[0293] 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.

[0294] 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.

[0295] 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.

[0296] The user device (2) can output the time of falling asleep along with information about the sleep stage. This allows the user to more specifically check the time of falling asleep.

[0297] 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.

[0298] When information regarding oxygen saturation is output by the user device (2) (or display device (41)), the numerical value of oxygen saturation may be output in the form of a percentage. In addition, when information regarding oxygen saturation is output by the user device (2) (or display device (41)), whether the user's oxygen saturation is normal according to medical standards may be displayed.

[0299] 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.

[0300] 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).

[0301] 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.

[0302] 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.

[0303] 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.

[0304] 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.

[0305] 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.

[0306] 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.

[0307] 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).

[0308] 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.

[0309] 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.

[0310] 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.

[0311] 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.

[0312] 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.

[0313] 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.

[0314] FIG. 10 illustrates an example of sleep summary information derived by a sleep management system according to one embodiment.

[0315] Sleep summary information may include information compiled from sleep status information acquired in real time by a sleep management system.

[0316] 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.

[0317] 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.

[0318] Sleep time information may include information about the time from the time of falling asleep, which indicates the time when the user transitioned from an awake state to a sleep state, to the time when the user finally woke up.

[0319] 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.

[0320] 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.

[0321] 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.

[0322] 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.

[0323] 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.

[0324] 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.

[0325] 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.).

[0326] 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.

[0327] 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.

[0328] 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.

[0329] According to one embodiment of the present disclosure, a user's point of view can be accurately measured even without attaching various sensors to the body.

[0330] According to one embodiment of the present disclosure, since analysis of a person's sleep state is performed at a user device, invasion of an individual's privacy can be prevented.

[0331] According to one embodiment of the present disclosure, since data processing is performed by a plurality of devices, the level of data processing load that each device must bear can be reduced.

[0332] According to one embodiment of the present disclosure, pressure processing data is processed by a plurality of machine learning models stored in a user device, thereby obtaining accurate data related to the user's sleeping time and sleep state.

[0333] A sleep management system according to one embodiment of the present disclosure may include a plurality of pressure sensors (51) for detecting pressure by a user; a hub device (1) for receiving pressure data collected by the plurality of pressure sensors (51) and obtaining pressure processing data based on processing the pressure data; and a user device (2) for receiving the pressure processing data from the hub device (1), determining muscle stiffness (M) of the user corresponding to an area of ​​the user's body in contact with the plurality of pressure sensors (51) based on the pressure processing data, and determining whether the user is sleeping based on the muscle stiffness (M) of the user.

[0334] The user device (2) can determine that the user is asleep based on the muscle stiffness (M) being below a predetermined reference value.

[0335] The user device (2) can input the pressure processing data into a first machine learning model (21) to obtain the user's body data (B), and input the pressure processing data and the user's body data (B) into a second machine learning model (22) to obtain the user's muscle stiffness (M).

[0336] The above user device (2) can obtain the user's sleeping point of view by inputting the muscle stiffness (M) into the third machine learning model (23).

[0337] The user device (2) can store a sleep management application including the third machine learning model (23).

[0338] The above sleep management application may be downloadable from an external server.

[0339] The user device (2) can obtain sleep state information by inputting muscle stiffness (M) determined based on pressure processing data acquired after the user's sleeping time into the fourth machine learning model (24), when it is determined that the user has fallen asleep.

[0340] The above sleep state information 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.

[0341] A sleep management method according to one embodiment of the present disclosure may include: a hub device (1) receiving pressure data collected by a plurality of pressure sensors (51); the hub device (1) obtaining pressure processing data based on processing the pressure data; the hub device (1) transmitting the pressure processing data to a user device (2); the user device (2) determining muscle stiffness (M) of the user based on the pressure processing data received from the hub device (1); and the user device (2) determining whether the user is asleep based on the muscle stiffness (M).

[0342] Determining whether the user is sleeping based on the muscle stiffness (M) may include the user device (2) determining that the user is sleeping based on the muscle stiffness (M) being below a predetermined reference value.

[0343] Determining the user's muscle stiffness (M) based on the above pressure processing data may include: the user device (2) inputting the pressure processing data into a first machine learning model (21) to obtain the user's body data (B); and the user device (2) inputting the pressure processing data and the user's body data (B) into a second machine learning model (22) to obtain the user's muscle stiffness (M).

[0344] Determining whether the user is sleeping based on the muscle stiffness (M) may include the user device (2) inputting the muscle stiffness (M) into a third machine learning model (23) to obtain the user's sleeping time point.

[0345] The user device (2) further includes storing a sleep management application including the third machine learning model (23), and the sleep management application may be downloadable from an external server.

[0346] The above sleep management method may further include: the user device (2) inputting muscle stiffness (M) determined based on pressure processing data acquired after the user's sleeping time into a fourth machine learning model (24) to obtain sleep state information in response to the user device (2) determining that the user has fallen asleep.

[0347] The above sleep state information 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.

[0348] 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.

[0349] 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.

[0350] 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.

[0351] 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.

[0352] 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. Multiple pressure sensors (51) for detecting pressure applied by the user; A hub device (1) that receives pressure data collected by the plurality of pressure sensors (51) and obtains pressure processing data based on the processing of the pressure data; and A sleep management system including a user device (2) that receives the pressure processing data from the hub device (1), determines the muscle stiffness (M) of the user corresponding to the body area of ​​the user in contact with the plurality of pressure sensors (51) based on the pressure processing data, and determines whether the user is asleep based on the muscle stiffness (M) of the user.

2. In paragraph 1, The above user device (2) is, A sleep management system that determines that the user has fallen asleep based on the muscle stiffness (M) being below a predetermined reference value.

3. In paragraph 1, The above user device (2) is, The above pressure treatment data is input into the first machine learning model (21) to obtain the user's body data (B), A sleep management system that inputs the above pressure treatment data and the user's body data (B) into a second machine learning model (22) to obtain the user's muscle stiffness (M).

4. In paragraph 1, The above user device (2) is, A sleep management system that obtains the user's sleeping time by inputting the above muscle stiffness (M) into a third machine learning model (23).

5. In paragraph 4 The above user device (2) is, A sleep management system storing a sleep management application including the third machine learning model (23).

6. In paragraph 5, The above sleep management application, A sleep management system that can be downloaded from an external server.

7. In paragraph 1, The above user device (2) is, A sleep management system that obtains sleep state information by inputting muscle stiffness (M) determined based on pressure processing data acquired after the user falls asleep into a fourth machine learning model (24) when the user is determined to have fallen asleep.

8. In paragraph 7, 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.

9. The hub device (1) receives pressure data collected by a plurality of pressure sensors (51); The above hub device (1) obtains pressure processing data based on the processing of the pressure data; The above hub device (1) transmits the pressure processing data to the user device (2); The user device (2) determines the user's muscle stiffness (M) based on the pressure processing data received from the hub device (1); and A sleep management method, comprising: the user device (2) determining whether the user is asleep based on the muscle stiffness (M).

10. In paragraph 9, Determining whether the user is sleeping or not based on the above muscle stiffness (M) is as follows: A sleep management method, comprising: determining that the user device (2) has fallen asleep based on the muscle stiffness (M) being below a predetermined reference value.

11. In paragraph 9, Based on the above pressure treatment data, the user's muscle stiffness (M) is determined. The above user device (2) inputs the pressure processing data into the first machine learning model (21) to obtain the user's body data (B); and A sleep management method comprising: the user device (2) inputting the pressure processing data and the user's body data (B) into a second machine learning model (22) to obtain the user's muscle stiffness (M).

12. In paragraph 9, Determining whether the user is sleeping or not based on the above muscle stiffness (M) is as follows: A sleep management method, comprising: the user device (2) inputting the muscle stiffness (M) into a third machine learning model (23) to obtain the user's sleeping time.

13. In paragraph 12, The user device (2) further includes storing a sleep management application including the third machine learning model (23); The above sleep management application, A sleep management method that can be downloaded from an external server.

14. In paragraph 9, A sleep management method further comprising: inputting muscle stiffness (M) determined based on pressure processing data acquired after the user falls asleep into a fourth machine learning model (24) in response to the user device (2) determining that the user has fallen asleep, thereby obtaining sleep state information.

15. In paragraph 14, The above sleep status information is, A sleep management method 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.

Images