Electronic device, method, and non-transitory computer-readable storage medium for detecting breathing state during sleep of user

Abstract

A method is disclosed. This method comprises the operations of: while a user is in a sleeping state, emitting a first light corresponding to a first band and a second light corresponding to a second band different from the first band toward the body of the user by using at least one of a plurality of light-emitting units included in a wearable device worn on the body of the user; on the basis that the first light and the second light are received in a light-receiving unit included in the wearable device via the body, acquiring first sensor data corresponding to the received first light and second sensor data corresponding to the received second light; identifying the breathing state during sleep of the user on the basis of at least a portion of a first component extracted from the first sensor data and a second component extracted from the second sensor data; and providing an indication showing the breathing state to the user through the wearable device or an external electronic device functionally connected to the wearable device.

Description

Electronic device, method, and non-transient computer-readable storage medium for detecting a user's breathing state during sleep

[0001] The present disclosure relates to an electronic device, a method, and a non-transient computer-readable storage medium for detecting a user's breathing state during sleep.

[0002] Sleep apnea or sleep hypopnea refers to a breathing disorder in which breathing temporarily stops or decreases during sleep. Sleep apnea can be detected during sleep through the user's biosignals measured by wearable devices. Wearable devices detect sleep apnea using biosignals such as oxygen saturation (SpO2), heart rate, or the user's movement.

[0003] The information described above may be provided as related art for the purpose of aiding understanding of the present disclosure. No claim or determination is made as to whether any of the foregoing may be applied as prior art related to the present disclosure.

[0004] A method according to one embodiment of the present disclosure may include, while a user is in a sleep state, using at least one of a plurality of light-emitting parts included in a wearable device worn on the user's body, emitting a first light corresponding to a first band and a second light corresponding to a second band different from the first band toward the user's body; acquiring first sensor data corresponding to the received first light and second sensor data corresponding to the received second light based on the fact that the first light and the second light are received by a light-receiving part included in the wearable device via the body; identifying the user's breathing state during sleep based on at least a portion of a first component extracted from the first sensor data and a second component extracted from the second sensor data; and providing an indication indicating the breathing state to the user through the wearable device or an external electronic device functionally connected to the wearable device.

[0005] An electronic device according to one embodiment of the present disclosure comprises at least one processor including a communication circuit, a memory for storing instructions, and a processing circuitry, wherein when the instructions are executed individually or collectively by the at least one processor, the electronic device may receive a ratio between a first component of first sensor data and a second component of second sensor data obtained through a biometric sensor of a wearable device worn on a user's body through the communication circuit, obtain a breathing state of the user during sleep based on the ratio, and provide an indication indicating the breathing state to the user through the electronic device or the wearable device, wherein the first sensor data may include data obtained based on receiving a first light corresponding to a first band emitted through the biometric sensor through the user's body, and the second sensor data may include data obtained based on receiving a second light corresponding to a second band emitted through the biometric sensor through the user's body.

[0006] A non-transient computer-readable storage medium storing instructions for the electronic device to perform an operation when executed by at least one processor of the electronic device according to one embodiment of the present disclosure, wherein the operation may include: emitting a first light corresponding to a first band and a second light corresponding to a second band different from the first band toward the user’s body using at least one of a plurality of light-emitting parts included in a wearable device worn on the user’s body while the user is in a sleep state; acquiring first sensor data corresponding to the received first light and second sensor data corresponding to the received second light based on the fact that the first light and the second light are received by a light-receiving part included in the wearable device via the body; identifying the breathing state of the user during sleep based on at least a portion of a first component extracted from the first sensor data and a second component extracted from the second sensor data; and providing an indication indicating the breathing state to the user through the wearable device or an external electronic device functionally connected to the wearable device.

[0007] In relation to the description of the drawings, the same or similar reference numerals may be used for identical or similar components.

[0008] FIG. 1 is a diagram schematically illustrating the operation of an electronic device detecting a user's breathing state during sleep, according to one embodiment.

[0009] FIG. 2 is a block diagram of an exemplary electronic device capable of performing the operations described in this document according to one embodiment.

[0010] FIG. 3 is a block diagram of an electronic device according to one embodiment.

[0011] FIG. 4 is a block diagram illustrating the configuration of a wearable device according to one embodiment.

[0012] FIG. 5 is a flowchart illustrating an example of an operation for determining a user's breathing state during sleep according to one embodiment.

[0013] FIG. 6 is a diagram illustrating sensor data for light according to one embodiment.

[0014] FIG. 7 is a diagram illustrating the DC component of light according to one embodiment.

[0015] FIG. 8 is a diagram illustrating the ratio between a first DC component and a second DC component according to one embodiment.

[0016] FIG. 9 is a diagram for comparing a graph of a user's oxygen saturation and a graph of the ratio between a first DC component and a second DC component, according to one embodiment.

[0017] FIG. 10 is a diagram illustrating an operation to identify a user's breathing state during sleep based on a ratio graph according to one embodiment.

[0018] FIGS. 11a and FIGS. 11b illustrate an example of an operation that provides an indication of a breathing state when a user's breathing state during sleep is identified as sleep apnea, according to one embodiment.

[0019] FIGS. 12a and FIGS. 12b illustrate an example of an operation that provides an indication of a breathing state when a user's breathing state during sleep is identified as a normal breathing state, according to one embodiment.

[0020] FIGS. 13a and FIGS. 13b are drawings illustrating an example of an indication provided to a user when a sleep apnea detection function is activated, according to one embodiment.

[0021] FIG. 14 is a drawing for explaining a module for identifying a user's breathing state during sleep according to one embodiment.

[0022] FIG. 15 is a flowchart illustrating an operation to provide an indication of a breathing state to a user based on information about the user's breathing state obtained from a wearable device, according to one embodiment.

[0023] FIG. 16 is a flowchart illustrating an operation to provide an indication of breathing status to a user based on information about a ratio obtained from a wearable device, according to one embodiment.

[0024] FIG. 17 is a drawing illustrating an example of a wearable device according to one embodiment.

[0025] The present disclosure will be described in detail below with reference to the attached drawings.

[0026] The electronic device according to the various embodiments disclosed in this document may be of various forms. The electronic device may include, for example, a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a consumer electronics device. The electronic device according to the embodiments of this disclosure is not limited to the devices described above.

[0027] The various embodiments of this document and the terms used therein are not intended to limit the technical features described in this document to specific embodiments, and should be understood to include various modifications, equivalents, or substitutions of said embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. 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. In this document, phrases such as "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 each include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used simply to distinguish said components from other said components and do not limit said components in any other aspect (e.g., importance or order). Where any (e.g., 1st) component is referred to as "coupled" or "connected" to another (e.g., 2nd) component, with or without the terms "functionally" or "communicationly," it means that said any component may be connected to said other component directly (e.g., via a wire), wirelessly, or through a third component.

[0028] Throughout the specification, singular expressions include plural expressions unless the context clearly indicates otherwise. Wherever a part of the specification states that it "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0029] The term “module” as used in the various embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit, for example. A module may be a component formed integrally, or a minimum unit of said component or a part thereof that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0030] FIG. 1 is a diagram schematically illustrating the operation of an electronic device detecting a user's breathing state during sleep, according to one embodiment.

[0031] Referring to FIG. 1, the electronic device (100) can identify the breathing state during sleep of a user (10) wearing a wearable device (200) based on biometric data received from a wearable device (200). The breathing state of the user (10) may include, for example, a normal breathing state, a hypopnea state, and an apnea state.

[0032] Normal respiratory state may refer to a condition in which respiratory airflow and blood oxygen saturation are maintained within a normal range during sleep. Normal respiratory state may include a condition in which respiratory airflow does not decrease or decreases by less than about 30%, and a condition in which blood oxygen saturation decreases by about 3% or less. Generally, the normal respiratory rate for an adult may be approximately 12 breaths per minute.

[0033] Hypopnea refers to a state in which respiratory airflow and blood oxygen saturation decrease partially during sleep. Hypopnea may refer to a state in which respiratory airflow decreases by approximately 30% or more and blood oxygen saturation decreases by approximately 3% or more for 10 seconds or longer. In a state of hypopnea, oxygen saturation may temporarily drop from approximately 90% to less than approximately 94%.

[0034] Apnea refers to a state in which breathing completely stops for a certain period during sleep. Apnea may include a state in which airflow decreases by approximately 90% or more and blood oxygen saturation decreases by approximately 3% or more for 10 seconds or longer. Apnea may include obstructive apnea and central apnea. During apnea, blood oxygen saturation may drop to less than approximately 90%.

[0035] Meanwhile, the criteria for the user's (10) breathing condition are not limited to the examples described above. The criteria for the breathing condition may be determined differently for each user based on the user's (10) physical factors (e.g., height, weight, age, gender), physiological factors (e.g., structure of the airway, hormonal changes), environmental factors (e.g., sleeping position, water intake, smoking, drugs, rhinitis), and other factors (e.g., altitude, cardiovascular disease).

[0036] In addition, the breathing state of the user during sleep in this disclosure is not limited to the examples described above, including hyperventilation and bradyventilation.

[0037] According to one embodiment, the electronic device (100) may be one of various types of electronic devices, such as a laptop, a smartphone, a tablet, a cellular phone, and other similar computing devices. According to one embodiment, the wearable device (200) may include a smart ring, a smart watch, or wireless earphones. However, it is not limited thereto, and the wearable device (200) may include an electronic device that can be worn (or attached) to a user, such as a smart belt, smart glasses, a smart band, or an attachable device (e.g., a patch, a digital tattoo).

[0038] Conventionally, the breathing state during sleep was determined based on changes in blood oxygen saturation (SpO2) or heart rate. For example, the electronic device (100) determined the breathing state to be normal when the blood oxygen saturation was within the normal range, and determined the breathing state to be apnea or hypopnea when the blood oxygen saturation was within the abnormal range. However, the method of determining the breathing state during sleep using oxygen saturation or heart rate has a limitation in that the precision of measurement is relatively low when the user's (10) pulsation is weak. The operation of the electronic device (100) identifying the user's breathing state during sleep will be explained in detail below through the drawings.

[0039] FIG. 2 is a block diagram of an exemplary electronic device capable of performing the operations described in this document according to one embodiment.

[0040] The components, their relationships, and their functions illustrated in FIG. 2 are illustrative only and are not intended to limit the implementations described or claimed herein. The electronic device (100) may be referred to as a mobile device, a user device, a multifunction device, a portable device, or a server.

[0041] The electronic device (100) may include components comprising at least one processor (110) (hereinafter referred to as processor (110)), at least one memory (120) (hereinafter referred to as memory (120)), at least one display (140) (hereinafter referred to as display (140)), at least one image sensor (150) (hereinafter referred to as image sensor (150)), at least one communication circuit (160) (hereinafter referred to as communication circuit (160)), and / or at least one sensor (170) (hereinafter referred to as sensor (170)). The components are merely exemplary. For example, the electronic device (100) may include other components (e.g., power management integrated circuitry (PMIC), audio processing circuit, antenna, rechargeable battery, or input / output interface). For example, some components may be omitted from the electronic device (100). For example, some components may be integrated into a single component.

[0042] The processor (110) may be implemented as one or more integrated circuit (or circuitry) chips and may perform various data processing operations. The processor (110) may include at least one electrical circuit and may process instructions (or programs, data) stored in memory (120) individually or collectively in a distributed manner. The processor (110) may include a processor assembly comprising one or more processing circuits. The processor (110) may include any processing circuit that is operative to control the performance and operations of one or more components of the electronic device (100) (e.g., memory (120), display (140), image sensor (150), communication circuit (160), and / or sensor (170)). For example, the processor (110) (e.g., application processor (AP)) may be implemented as a system on chip (SoC) (e.g., a single chip or chipset). For example, the processor (110) may be implemented with a plurality of cores (or at least one core circuit), a plurality of chips, or a plurality of chipsets. For example, the processor (110) may include one or more processing circuits. For example, the processor (110) may include one or more processing circuits configured to perform the various functions of the present disclosure individually and / or collectively. As an example without limitation, at least a portion of the processor (110) may be included in a first chip of the electronic device (100), and at least another portion of the processor (110) may be included in a second chip of the electronic device (100) other than the first chip of the electronic device (100).

[0043] For example, the processor (110) may include a central processing unit (111), a graphics processing unit (112), a neural processing unit (113), an image signal processor (114), a display controller (115), a memory controller (116), a storage controller (117), a communication processor (118), and / or a sensor interface (119). These components of the processor (110) are merely exemplary. For example, the processor (110) may include other components. For example, some components of the processor (110) may be omitted from the processor (110). For example, some components of the processor (110) may be included as separate components of the electronic device (100) outside of the processor (110). For example, some components of the processor (110) (e.g., memory controller (116)) may be included in other components (e.g., at least part of memory (120), an interface (e.g. available for connection to at least one component of the electronic device (100)), a display (140) and / or an image sensor (150)).

[0044] The processor (110) may cause other components of the electronic device (100) to perform various operations by executing instructions stored in memory (120). The CPU (111) (or central processing circuit) may be configured to control the components of the processor (110) based on the execution of instructions stored in memory (120) (e.g., volatile memory (121) and / or non-volatile memory (122)). The GPU (112) (or graphics processing circuit) may be configured to execute parallel operations (e.g., rendering). The NPU (113) (or neural processing circuit, or AI (artificial intelligence) chip) may be configured to execute operations for an artificial intelligence model (e.g., convolution computation). An ISP (114) (or image signal processing circuit) may be configured to process a raw image acquired through an image sensor (150) into a format suitable for a component within the electronic device (100) or a component of the processor (110). A display controller (115) (or display control circuit, or DPU (display processing unit)) may be configured to process an image acquired from a CPU (111), GPU (112), ISP (114), or memory (120) (e.g., volatile memory (121)) into a format suitable for a display (140). A memory controller (116) (or memory control circuit) may be configured to control reading data from the volatile memory (121) and writing data to the volatile memory (121). A storage controller (117) (or storage control circuit) may be configured to control reading data from the non-volatile memory (122) and writing data to the non-volatile memory (122).The CP (118) (communication processing circuit) may be configured to process data obtained from a component of the processor (110) into a format suitable for transmitting to another electronic device via the communication circuit (160), or to process data obtained from another electronic device via the communication circuit (160) into a format suitable for processing by the component of the processor (110). For example, the communication circuit (160) may include one or more communication circuits. The sensor interface (119) (or sensing data processing circuit, sensor hub) may be configured to process data regarding the state of the electronic device (100) and / or the state around the electronic device (100), obtained through the sensor (170), into a format suitable for the component of the processor (110).

[0045] Memory (120) may include one or more storage media (or one or more storage devices). For example, memory (120) may include a memory assembly comprising one or more storage media. For example, the one or more storage media may include a hard drive, a permanent memory such as flash memory, read-only memory (ROM) (e.g., non-volatile memory (122)), a semi-permanent memory such as random access memory (RAM) (e.g., volatile memory (121)), any other suitable type of storage (or storage assembly), or any combination thereof. Memory (120) may include a cache memory, which is one or more different types of memory used to temporarily store data for a function or feature of the electronic device (100). As an example not limited to, the cache memory may be included within the processor (110). The memory (120) may be fixedly embedded within the electronic device (100) or incorporated into one or more suitable types of components (e.g., a SIM (subscriber identity module) card and / or an SD (secure digital) card) that can be repeatedly inserted into and removed from the electronic device (100).

[0046] For example, memory (120) may store one or more software applications, such as operating system (or system) software applications, firmware software applications, driver software applications, plugin (e.g., add-in, add-on, and / or applet) software applications, and / or any other suitable software applications. For example, the one or more software applications may include instructions executable by the processor (110). For example, memory (120) may store instructions that can be called by an application programming interface (API). For example, memory (120) may store instructions within a library.

[0047] FIG. 3 is a block diagram of an electronic device according to one embodiment.

[0048] Referring to FIG. 3, the electronic device (300) may include at least one processor (310) (hereinafter referred to as processor (310)), at least one memory (320) (hereinafter referred to as memory (320)), and at least one communication circuit (330) (hereinafter referred to as communication circuit (330)). The electronic device (300) shown in FIG. 3 may be the electronic device (100) of FIG. 2. The processor (310), memory (320), and communication circuit (330) may each perform the same functions by being implemented substantially identically to the processor (110), memory (120), and communication circuit (160) of FIG. 2. In describing the components of the electronic device (300) of FIG. 3, details that overlap with those described in FIG. 1 and FIG. 2 will be omitted.

[0049] The processor (310) can control the overall operations of the electronic device (300). For example, the processor (310) can control the overall operations of the electronic device (300) described below by executing one or more instructions stored in memory (320) individually or collectively. There may be one or more processors (310). The processor (310) may include processing circuits. For example, the processor (310) may include any processing circuit that is operational for controlling the execution and operations of memory (320) and / or communication circuits (330).

[0050] The processor (310) may be composed of at least one of, for example, a central processing unit, a microprocessor, a graphic processing unit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), an application processor, a communication processor, a neural processing unit, or an AI-dedicated processor designed with a hardware structure specialized for processing AI models, but is not limited thereto.

[0051] The processor (310) can control one or any combination of other components of the electronic device (300) and can perform operations or data processing related to communication. The processor (310) can execute one or more programs or instructions stored in memory (320). For example, the processor (310) can perform a method according to an embodiment of the present disclosure by executing one or more instructions stored in memory (320).

[0052] When a method according to one embodiment includes a plurality of operations, the plurality of operations may be performed by a single processor or by a plurality of processors. For example, when a first operation, a second operation, and a third operation are performed by a method according to an embodiment, the first operation, the second operation, and the third operation may all be performed by a first processor, or the first operation and the second operation may be performed by a first processor (e.g., a general-purpose processor) and the third operation may be performed by a second processor (e.g., an artificial intelligence dedicated processor).

[0053] The processor (310) may be implemented as a single-core processor including one core, or as one or more multicore processors including multiple cores (e.g., homogeneous multicore or heterogeneous multicore). When the processor (310) is implemented as a multicore processor, each of the multiple cores included in the multicore processor may include internal processor memory such as cache memory or on-chip memory, and a common cache shared by the multiple cores may be included in the multicore processor. Additionally, each of the multiple cores included in the multicore processor (or some of the multiple cores) may independently read and execute program instructions for implementing the method according to the embodiment of the present disclosure, or all (or some) of the multiple cores may be linked together to read and execute program instructions for implementing the method according to the embodiment of the present disclosure.

[0054] When a method according to one embodiment includes a plurality of operations, the plurality of operations may be performed by one of the plurality of cores included in a multi-core processor, or may be performed by a plurality of cores. For example, when a first operation, a second operation, and a third operation are performed by a method according to an embodiment, the first operation, the second operation, and the third operation may all be performed by a first core included in a multi-core processor, or the first operation and the second operation may be performed by a first core included in a multi-core processor and the third operation may be performed by a second core included in a multi-core processor.

[0055] According to one embodiment, the processor (310) may mean a system-on-chip (SoC) in which the processor and other electronic components are integrated, a single-core processor, a multi-core processor, or a core included in a single-core processor or a multi-core processor, wherein the core may be implemented as a CPU, GPU, APU, MIC, DSP, NPU, hardware accelerator, or machine learning accelerator, but the embodiments of the present disclosure are not limited thereto.

[0056] The communication circuit (330) can perform data communication with an external electronic device (e.g., the wearable device (200) of FIG. 1) under the control of the processor (310). For example, the communication circuit (330) can communicate with the external electronic device through a network. The network may include, for example, the Internet or a computer network. Additionally, the network may include a cellular network. For example, the processor (310) can receive data from the external electronic device through the communication circuit (330) and transmit data to the external electronic device through the communication circuit (330).

[0057] The communication circuit (330) may include hardware components to support the transmission and / or reception of electrical signals between the electronic device (300) and an external electronic device. For example, the communication circuit (330) may include at least one of a modem (modulator and demodulator), an antenna, and an O / E (optic / electronic) converter. The communication circuit (330) may support the transmission and / or reception of electrical signals based on various types of protocols such as Ethernet, LAN (local area network), WAN (wide area network), Wi-Fi (wireless fidelity), Bluetooth, BLE (bluetooth low energy), ZigBee, LTE (long term evolution), 5G NR (new radio), and / or 6G.

[0058] FIG. 4 is a block diagram illustrating the configuration of a wearable device according to one embodiment.

[0059] Referring to FIG. 4, the electronic device (300) can detect whether the user of the wearable device (200) has sleep apnea based on data received from the wearable device (200). A method for the electronic device (300) to detect whether the user has sleep apnea based on data received from the wearable device (200) is described in detail below in the drawings.

[0060] According to one embodiment, a wearable device (200) may include at least one processor (210, hereinafter referred to as processor (210)), at least one memory (220, hereinafter referred to as memory (220)), at least one communication circuit (230, hereinafter referred to as communication circuit (230)), and at least one biometric sensor (240).

[0061] According to one embodiment, the processor (210) may be implemented as a digital signal processor (DSP) that processes digital signals, a microprocessor, or a timing controller (TCON). However, it is not limited thereto, and may include or be defined by one or more of a central processing unit (CPU), microcontroller unit (MCU), microprocessing unit (MPU), controller, application processor (AP), communication processor (CP), ARM (advanced reduced instruction set computer machine) processor, or artificial intelligence (AI) processor. Additionally, the processor (210) may be implemented as a system on chip (SoC) or large scale integration (LSI) with a built-in processing algorithm, or may be implemented in the form of a field programmable gate array (FPGA). The processor (210) can perform various functions by executing computer executable instructions stored in memory.

[0062] According to one embodiment, the processor (210) may include one or more of a CPU (central processing unit), GPU (graphics processing unit), APU (accelerated processing unit), MIC (many integrated core), DSP (digital signal processor), NPU (neural processing unit), hardware accelerator, or machine learning accelerator. The processor (210) may control one or any combination of other components of an electronic device and may perform operations or data processing related to communication. The processor (210) may execute one or more programs or instructions stored in memory. For example, the processor (210) may perform a method according to an embodiment of the present disclosure by executing one or more instructions stored in memory.

[0063] According to one embodiment, when a method includes a plurality of operations, the plurality of operations may be performed by a single processor or by a plurality of processors. For example, when a first operation, a second operation, and a third operation are performed by a method according to an embodiment, the first operation, the second operation, and the third operation may all be performed by a first processor, or the first operation and the second operation may be performed by a first processor (e.g., a general-purpose processor) and the third operation may be performed by a second processor (e.g., an artificial intelligence dedicated processor).

[0064] According to one embodiment, the processor (210) may be implemented as a single-core processor including one core, or as one or more multicore processors including a plurality of cores (e.g., homogeneous multicore or heterogeneous multicore). When the processor (210) is implemented as a multicore processor, each of the plurality of cores included in the multicore processor may include internal processor memory such as cache memory or on-chip memory, and a common cache shared by the plurality of cores may be included in the multicore processor. Additionally, each of the plurality of cores included in the multicore processor (or some of the plurality of cores) may independently read and execute program instructions for implementing the method according to the embodiment of the present disclosure, or all (or some) of the plurality of cores may be linked together to read and execute program instructions for implementing the method according to the embodiment of the present disclosure.

[0065] When a method according to one embodiment includes a plurality of operations, the plurality of operations may be performed by one of the plurality of cores included in a multi-core processor, or may be performed by a plurality of cores. For example, when a first operation, a second operation, and a third operation are performed by a method according to an embodiment, the first operation, the second operation, and the third operation may all be performed by a first core included in a multi-core processor, or the first operation and the second operation may be performed by a first core included in a multi-core processor and the third operation may be performed by a second core included in a multi-core processor.

[0066] According to one embodiment, a processor may mean a system-on-chip (SoC) in which a processor and other electronic components are integrated, a single-core processor, a multi-core processor, or a core included in a single-core processor or a multi-core processor, wherein the core may be implemented as a CPU, GPU, APU, MIC, DSP, NPU, hardware accelerator, or machine learning accelerator, but the embodiments of the present disclosure are not limited thereto.

[0067] According to one embodiment, the memory (220) may include one or more storage media (or one or more storage devices). For example, the memory (220) may include a memory assembly comprising one or more storage media. For example, the one or more storage media may include a hard disk drive, a flash memory, a permanent memory (e.g., non-volatile memory) such as ROM (read-only memory), a semi-permanent memory (e.g., volatile memory) such as RAM (random access memory), any other suitable type of storage (or storage assembly), or any combination thereof. The memory (220) may include a cache memory, which is one or more different types of memory used to temporarily store data for a function or feature of the wearable device (200). As an example not limited to, the cache memory may be included within the processor (210). The memory (220) may be fixedly embedded within the wearable device (200) or incorporated into one or more suitable types of components (e.g., a SIM (subscriber identity module) card and / or an SD (secure digital) card) that can be repeatedly inserted into and removed from the wearable device (200).

[0068] For example, memory (220) may store one or more software applications, such as operating system (or system) software applications, firmware software applications, driver software applications, plugin (e.g., add-in, add-on, and / or applet) software applications, and / or any other suitable software applications. For example, the one or more software applications may include instructions executable by the processor (210). For example, memory (220) may store instructions that can be called by an application programming interface (API). For example, memory (220) may store instructions within a library.

[0069] According to one embodiment, the communication circuit (230) can perform data communication with other electronic devices under the control of the processor (210). For example, the communication circuit (230) can transmit and receive control commands or data with other electronic devices. For example, the communication circuit (230) can support the transmission and / or reception of electrical signals based on various types of protocols, such as Ethernet, LAN (local area network), WAN (wide area network), WiFi (wireless fidelity), Bluetooth, BLE (bluetooth low energy), ZigBee, NFC (near field communication), ANT+, Cellular (LTE, 5G, 6G, NB-IoT), RFID (radio-frequency identification), UWB (ultra wide band), GNSS (global navigation satellite system), or RF communication.

[0070] According to one embodiment, the biometric sensor (240) can measure the user's biometric data based on received light under the control of the processor (210).

[0071] According to one embodiment, the biometric sensor (240) may include a sensor controller, a plurality of emitters that output light signals, and a plurality of receivers that receive light signals. The emitters may include light-emitting elements that emit various wavelengths or colors (e.g., G (green), R (red)) to measure biometric signals. The emitters may be formed as at least one of a light-emitting diode (LED), a semiconductor laser (LD), an infrared (IR) diode, and a vertical cavity surface emitting laser (VCSEL). The receivers may be formed as a photodiode (PD) or a complementary metal-oxide-semiconductor (CMOS) camera. The receivers may convert the received light signals through an analog-to-digital converter (ADC) and store them in a processor (210) or memory (220). The sensor controller may control the emitters and receivers.

[0072] For example, the biometric sensor (240) may include at least one of a transmissive sensor or a reflective sensor. A transmissive sensor may refer to a sensor that outputs a light signal from a light-emitting part and receives at least some light signals that have passed through the user's body at a light-receiving part. A reflective sensor may refer to a sensor that outputs a light signal from a light-emitting part and receives at least some light signals reflected from the surface of the user's body at a light-receiving part.

[0073] FIG. 5 is a flowchart illustrating an example of an operation for determining a user's breathing state during sleep according to one embodiment.

[0074] In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0075] In the following embodiments, each operation may be performed in a wearable device (200) or an electronic device (300). According to one embodiment, a processor (210, 310) of a wearable device (200) or an electronic device (300) may perform at least one of the operations of FIG. 5. When instructions stored in the memory (220, 320) of a wearable device (200) or an electronic device (300) are executed by the processor (210, 310) of a wearable device (200) or an electronic device (300), the wearable device (200) or an electronic device (300) may be made to perform the operations of FIG. 5.

[0076] In operation 510, according to one embodiment, while the user is in a sleeping state, the wearable device (200) can emit a first light corresponding to a first band and a second light corresponding to a second band different from the first band towards the user's body by using at least one of a plurality of light-emitting parts included in the wearable device worn on the user's body.

[0077] According to one embodiment, a wearable device (200) can identify whether a user is in a sleep state based on at least one of the user's biometric data (e.g., heart rate, heart rate variability, oxygen saturation, skin temperature), movement information (e.g., acceleration information, location information), time, or ambient environment information (e.g., ambient sound, light intensity).

[0078] According to one embodiment, a wearable device (200) may emit light corresponding to each of a plurality of bands using a plurality of light-emitting parts. The plurality of light-emitting parts may be included in at least one biometric sensor (240). The plurality of bands may include, for example, a green band, a red band, a blue band, or an infrared band.

[0079] The light corresponding to each of the plurality of bands may include light included in the wavelength range of each of the plurality of bands. For example, the wavelength range of the green band may include a first range (e.g., about 510 nm or more and less than about 525 nm), the wavelength range of the red band may include a second range (e.g., about 525 nm or more and less than about 805 nm), the wavelength range of the blue band may include a third range (e.g., about 400 nm or more and less than about 510 nm), and the wavelength range of the infrared band may include a fourth range (e.g., about 805 nm or more and less than about 1000 nm). Meanwhile, the wavelength range corresponding to each of the plurality of bands is not limited to the examples described above.

[0080] According to one embodiment, a wearable device (200) may emit a first light corresponding to a first band (e.g., red band) and a second light corresponding to a second band (e.g., infrared band) using at least one of a plurality of light-emitting parts. Meanwhile, one embodiment of the present disclosure is described based on the first light corresponding to the red band and the second light corresponding to the infrared band, but is not limited thereto. For example, the light emitted from the light-emitting part of the wearable device (200) may include any one of the light corresponding to the red band, green band, blue band, or infrared band.

[0081] In operation 520, according to one embodiment, the wearable device (200) can receive a first light and a second light through the body using a light receiving part included in at least one biometric sensor (240). According to one embodiment, receiving light through the body may include cases where light output from a light emitting part is reflected by the user's body and received by at least a portion of the light receiving part, or cases where light output from a light emitting part passes through the user's body and is received by at least a portion of the light receiving part.

[0082] According to one embodiment, a wearable device (200) can acquire first sensor data for a first light and second sensor data for a second light using at least one biometric sensor (240). According to one embodiment, the wearable device (200) can acquire first sensor data and second sensor data based on a preset sampling frequency. For example, the preset sampling frequency may be determined at a frequency of about 0.5 Hz or higher. However, the preset sampling frequency is not limited to the example described above and may be determined by considering the battery of the wearable device (200) and a minimum frequency for detecting sleep apnea (e.g., about 0.5 Hz). Meanwhile, the description of the first sensor data for the first light and the second sensor data for the second light is described in detail in FIG. 6.

[0083] In operation 530, according to one embodiment, the wearable device (200) can determine the breathing state of the user during sleep based on at least a portion of at least one component separated from sensor data. According to one embodiment, the wearable device (200) can identify the breathing state of the user during sleep based on at least a portion of a first component extracted from first sensor data and a second component extracted from second sensor data.

[0084] According to one embodiment, sensor data for light may include an AC (alternating current) component and a DC component. In this case, the sensor data for light may include sensor data measured through a biometric sensor (240) (e.g., a PPG sensor) of a wearable device (200).

[0085] According to one embodiment, the first component may include a first DC component of the first sensor data, and the second component may include a second DC component of the second sensor data. Hereinafter, the first component may be referred to as the first DC component and the second component may be referred to as the second DC component.

[0086] In the present disclosure, the DC component (or base component) may include a component extracted through low-pass filtering of the sensor data. That is, the DC component can identify a gradual change (or trend) in the sensor data. For example, the DC component may include a component extracted from the sensor data using a low-pass filter with a first filtering frequency (e.g., about 0.5 Hz). Meanwhile, the filtering frequency of the low-pass filter is not limited to the example described above and may be determined within a range where the trend of the sensor data can be identified.

[0087] In the present disclosure, the AC component (or pulsation component) may refer to a component obtained by subtracting the DC component from the sensor data. According to one embodiment, the AC component may be extracted from the sensor data through bandpass filtering. For example, the AC component may include a component extracted from the sensor data using a bandpass filter of a first frequency band (e.g., approximately 0.5 Hz or higher and less than 5 Hz). Meanwhile, the first frequency band is not limited to the examples described above and may be determined based on a frequency range resulting from the user's heart rate. Information such as the user's heart rate may be obtained through the AC component.

[0088] Meanwhile, AC components or DC components may be referred to by various expressions representing the same or similar concepts. For example, the AC component may be replaced with expressions such as "high-frequency signal," "high-frequency signal component," "alternating component," "variable component," "dynamic component," "frequency component," "high-frequency component," and "noise component," but is not limited to the examples mentioned above. The DC component may be replaced with expressions such as, for example, "low-pass signal," "low-pass signal component," "direct current component," "mean value," "static component," or "low-frequency component," but is not limited to the examples described above. According to one embodiment, a wearable device (200) can extract a DC component from sensor data regarding light. The DC component may represent the average value (or trend) of the sensor data. For example, the wearable device (200) can extract a DC component from the sensor data through a low-pass filter or a simple moving average. The description of the first DC component of the first light and the second DC component of the second light is described in detail in FIG. 7.

[0089] According to one embodiment, the wearable device (200) can determine the breathing state of a user during sleep based on at least a portion of the first DC component of the first light and the second DC component of the second light. According to one embodiment, the wearable device (200) can obtain a ratio between the first DC component and the second DC component. For example, the wearable device (200) can obtain a ratio in which the value of the first DC component is the denominator and the value of the second DC component is the numerator.

[0090] According to one embodiment, the wearable device (200) can normalize the ratio between the first DC component and the second DC component. Normalizing the ratio between the first DC component and the second DC component may mean adjusting the magnitude (or amplitude) of the ratio between the first DC component and the second DC component to a specific range. For example, the wearable device (200) can normalize the ratio between the first DC component and the second DC component based on the intensity of light output through the light-emitting part of at least one biometric sensor (240). A description of the ratio between the first DC component and the second DC component and the method of normalizing the ratio is described in detail in FIG. 8.

[0091] According to one embodiment, the wearable device (200) can transmit information regarding the ratio (or normalized ratio) between the first DC component and the second DC component to the electronic device (300) through the communication circuit (230). For example, when the user wakes up, the wearable device (200) can transmit information regarding the ratio between the first DC component and the second DC component obtained over the entire period of the user's sleep to the electronic device (300) in batches. For example, the wearable device (200) can transmit information regarding the ratio between the first DC component and the second DC component to the electronic device (300) periodically or non-periodically.

[0092] According to one embodiment, the electronic device (300) can determine the user's breathing state based on a change in the ratio between a first DC component and a second DC component during a specified period of time. According to one embodiment, the specified period of time may be determined based on a ratio interval necessary to determine the breathing state during sleep. For example, since apnea during sleep is medically diagnosed when breathing is interrupted for 10 seconds or more, the specified period of time may be set to 10 seconds or more. As an example, the specified period of time may be set to 10 seconds or more and 1 hour or less, but is not limited thereto. As an example, the specified period of time may be set to 10 seconds or more and 5 minutes or less.

[0093] According to one embodiment, the electronic device (300) can determine the user's breathing state based on the amount of change in the ratio between the first DC component and the second DC component. For example, a section in which the amount of change in the ratio between the first DC component and the second DC component is relatively large during a specified time interval can be identified as a section in which the user's breathing state during sleep is apnea, and a section in which the amount of change in the ratio between the first DC component and the second DC component is relatively small during a specified time interval can be identified as a section in which the user's breathing state during sleep is normal breathing.

[0094] According to one embodiment, an electronic device (300) can identify a user's breathing state by using artificial intelligence (AI) processing for a first DC component or a second DC component during a specified time interval. For example, the electronic device (300) can identify a user's breathing state by inputting the ratio between the first DC component and the second DC component during a specified time interval into an artificial intelligence model. In one embodiment, the electronic device (300) can identify a user's breathing state by inputting the ratio between the first DC component and the second DC component during a specified time interval into a data-based artificial intelligence model. For example, the artificial intelligence model may include a machine learning or deep learning-based model. The electronic device (300) can identify a user's breathing state based on data (e.g., first sensor data, second sensor data) or components of data (e.g., first component, second component) using an artificial intelligence model. The artificial intelligence model may also be referred to as a "classifier model." The artificial intelligence model can take the ratio during a pre-set interval as input data and the user's breathing state (e.g., apnea, hypopnea, normal breathing state), the intensity and duration of each breathing state as output data. The intensity of the breathing state may refer to a probability value for each breathing state. The duration may refer to the time during which the breathing state is sustained. For example, the electronic device (300) can input the ratio between the first DC component and the second DC component into the artificial intelligence model to output a state (e.g., apnea, normal breathing state), the intensity of each breathing state (e.g., apnea: 0.3, normal breathing state: 0.7), and a duration (e.g., 10s).

[0095] The artificial intelligence model may be a model trained through the ratio between the first DC component and the second DC component, and the measurement results of polysomnography (e.g., apnea, hypopnea, normal breathing). Polysomnography may refer to a test that precisely diagnoses sleep disorders (e.g., breathing status during sleep) by measuring brain waves, respiration, and electrocardiograms during sleep. The measurement results of polysomnography may be acquired simultaneously with the acquisition of the first sensor data and the second sensor data.

[0096] Meanwhile, the artificial intelligence-related functions according to the present disclosure may be operated through a processor (310) and a memory (320). The processor (310) may be composed of one or more processors. In this case, the one or more processors may be a general-purpose processor such as a CPU, AP, or DSP, a graphics-dedicated processor such as a GPU or VPU (vision processing unit), or an artificial intelligence-dedicated processor such as an NPU.

[0097] One or more processors control the processing of input data according to predefined operation rules or artificial intelligence models stored in memory (320). Alternatively, if one or more processors are dedicated artificial intelligence processors, the dedicated artificial intelligence processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model. The predefined operation rules or artificial intelligence models are characterized by being created through learning.

[0098] Here, "created through learning" means that a basic artificial intelligence model is trained using multiple learning data by a learning algorithm, thereby creating a predefined rule of operation or an artificial intelligence model configured to perform a desired characteristic (or objective). Such learning may be performed on the device itself where the artificial intelligence according to the present disclosure is executed, or it may be performed through a separate server and / or system. Examples of learning algorithms include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0099] An artificial intelligence model can be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values ​​and performs neural network operations through calculations between the results of previous layers and the multiple weights. The multiple weights possessed by the multiple neural network layers can be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights can be updated during the learning process so that the loss or cost values ​​obtained by the artificial intelligence model are reduced or minimized.

[0100] Artificial neural networks may include deep neural networks (DNNs), such as, but are not limited to, convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), generative adversarial networks (GANs), or deep Q-networks. Meanwhile, personal information such as sleep stages, user's sleeping posture, body mass index (BMI), weight, and age can be used as input values ​​for the personalization of artificial intelligence models.

[0101] In operation 540, according to one embodiment, the electronic device (100) may provide an indication indicating a breathing state to a user through the electronic device (100), a wearable device (200), or an external electronic device functionally connected to the wearable device (200). Meanwhile, the indication may be referred to by being replaced by various expressions representing the same or similar concept. The indication may be replaced by expressions such as, for example, an icon, a user interface, a graphical interface, or a notification, but is not limited to the examples described above. An operation of providing an indication indicating a breathing state to a user is described in FIGS. 11a through 12b.

[0102] FIG. 6 is a diagram illustrating sensor data for light according to one embodiment.

[0103] Referring to FIG. 6, a first graph (610) corresponding to sensor data for a first light and a second graph (620) corresponding to sensor data for a second light are shown. The first graph (610) may represent sensor data for a first light detected through a biometric sensor of a wearable device (200). The first light is output through a light-emitting part of the biometric sensor, reflected or transmitted through the user's body, and may be detected through a light-receiving part of the biometric sensor. The second graph (620) may represent sensor data for a second light detected through a biometric sensor of a wearable device (200). The second light is output through a light-emitting part of the biometric sensor, reflected or transmitted through the user's body, and may be detected through a light-receiving part of the biometric sensor.

[0104] The x-axis of the graph represents time, and the y-axis represents the output value of the ADC (analog-to-digital converter). The unit of the x-axis is seconds (s), and the y-axis is arbitrary units (AU).

[0105] According to one embodiment, a wearable device (200) can acquire sensor data (e.g., first sensor data and second sensor data) for light (e.g., first light and second light) using at least one biometric sensor (240). In this case, the first light may be light corresponding to the red band, and the second light may be light corresponding to the infrared band.

[0106] The light receiving portion of at least one biosensor (240) can measure sensor data as a voltage value and then convert it into a digital value through an ADC. The wearable device (200) can identify the digital value as sensor data. At this time, the sensor data may include an AC component and a DC component. The AC component is a component that changes due to changes in blood flow (or pulse), and the DC component may be a component that changes due to pigment (melanin concentration) or the color of the blood. The color of the blood is the oxygen saturation (SpO₂) of hemoglobin (Hb). 2) It may vary depending on the. For example, if the blood contains a relatively large amount of oxygen, the color of the blood may be a relatively bright red (or crimson), and if the blood contains a relatively small amount of oxygen, the color of the blood may be a relatively dark red (or blackish-red).

[0107] FIG. 7 is a diagram illustrating the DC component of light according to one embodiment.

[0108] Referring to FIG. 7, a first graph (710) corresponding to a first DC component and a second graph (720) corresponding to a second DC component are shown. The x-axis of the graph represents time, and the y-axis represents the output value of an analog-to-digital converter (ADC). The unit of the x-axis is seconds (s), and the y-axis is arbitrary units (AU).

[0109] According to one embodiment, a wearable device (200) can extract a DC component of sensor data (e.g., the first sensor data and the second sensor data of FIG. 6). Since the method for extracting a DC component from sensor data has been described above, redundant details are omitted.

[0110] At this time, the graph (710) for the first DC component is a graph from which the DC component is extracted from the graph for the first light (e.g., the first light of FIG. 6) (e.g., the first graph (610) of FIG. 6), and the graph (720) for the second DC component is a graph from which the DC component is extracted from the graph for the second light (e.g., the second light of FIG. 6) (e.g., the second graph (620) of FIG. 6).

[0111] Referring to the graph for the first DC component, it can be observed that the DC component value is measured to be higher when the blood color is bright red than when the blood color is dark red. Additionally, it can be seen that there is a large deviation between the DC component value when the blood color is bright red and the DC component value when the blood color is dark red. On the other hand, referring to the graph for the second DC component, it can be observed that the deviation of the DC component value according to blood color is smaller than that of the graph for the first light.

[0112] FIG. 8 is a diagram illustrating the ratio between a first DC component and a second DC component according to one embodiment.

[0113] The graph shown in FIG. 8 represents the ratio between the normalized first DC component and the second DC component over time. The x-axis of the graph represents time, and the y-axis represents the ratio between the normalized first DC component and the second DC component. The unit of the x-axis is seconds (s), and the y-axis is arbitrary units (AU).

[0114] According to one embodiment, the wearable device (200) can obtain a ratio between a first DC component and a second DC component. For example, the wearable device (200) can obtain a ratio in which the value of the first DC component is the denominator and the value of the second DC component is the numerator.

[0115] According to one embodiment, the wearable device (200) can normalize the ratio between a first DC component and a second DC component. According to one embodiment, the wearable device (200) can normalize the ratio based on a first intensity of light output toward a user's body or a second intensity of light output toward a user's body through at least one biometric sensor (240). For example, the wearable device (200) can obtain a value obtained by dividing the first DC component value by the intensity of the first light output through the light-emitting unit (hereinafter referred to as the first value) and a value obtained by dividing the second DC component value by the intensity of the second light output through the light-emitting unit (hereinafter referred to as the second value). The wearable device (200) can obtain a normalized ratio in which the first value is the denominator and the second value is the numerator.

[0116] At this time, the intensity of the first light or the second light output toward the user's body may be determined based on the skin color or tone of the user's body. For example, the wearable device (200) may emit a stronger light when the user's body skin tone is dark than when the user's body skin tone is light.

[0117] According to one embodiment, a wearable device (200) can identify a user's body skin tone based on the ratio between the intensity of light output through a light-emitting unit and the intensity of light received through a light-receiving unit (hereinafter referred to as light reflectance). For example, the wearable device (200) can identify the user's body skin tone as a dark tone when the light reflectance is low, and identify the user's body skin tone as a light tone when the light reflectance is high. Meanwhile, the reflectance may vary depending on the wavelength of light emitted through the light-emitting unit or the user's body skin tone.

[0118] Meanwhile, the example of normalizing the ratio between the first DC component and the second DC component based on the intensity of the first or second light described above is merely one example of a method for normalizing the ratio, and the ratio between the first DC component and the second DC component can be normalized by various methods such as a method of adjusting all values ​​to a range between 0 and 1 (e.g., Min-Max normalization), or a method of making the mean 0 and the standard deviation 1 (e.g., Z-score normalization).

[0119] FIG. 9 is a diagram for comparing a graph of a user's oxygen saturation and a graph of the ratio between a first DC component and a second DC component, according to one embodiment.

[0120] The graphs illustrated in FIG. 9 are an oxygen saturation graph (910) and a ratio graph (920). The oxygen saturation graph (910) is a graph showing the user's blood oxygen saturation over time. The x-axis of the oxygen saturation graph (910) represents time, and the y-axis represents oxygen saturation. The unit of the x-axis is seconds (s), and the y-axis is percentage (%).

[0121] The ratio graph (920) is a graph representing the ratio between the first DC component and the second DC component over time. At this time, the ratio between the first DC component and the second DC component may include a normalized ratio. The x-axis of the ratio graph (920) represents time, and the y-axis represents the ratio between the first DC component and the second DC component. The unit of the x-axis is seconds (s), and the y-axis is AU (arbitrary unit).

[0122] Oxygen saturation is an indicator representing the amount of oxygen in the user's blood. At this time, if the oxygen saturation drops to about 90% or less and occurs continuously for about 10 seconds, the user's sleep state may be in a sleep apnea state. Referring to the oxygen saturation graph (910), the first section of the oxygen saturation graph (910) is the section where the user's breathing state is in an apnea state, and the second section is the section where the user's breathing state is in a normal breathing state. At this time, the first section of the oxygen saturation graph (910) corresponds to the first section of the ratio graph (920), and the second section of the oxygen saturation graph (910) may correspond to the second section of the ratio graph (920). That is, the first section of the ratio graph (920) is the section where the user's breathing state is in an apnea state, and the second section is the section where the user's breathing state is in a normal breathing state. By referring to the ratio graph (920), the pattern of change in the ratio between the first DC component and the second DC component according to the user's breathing state can be confirmed. When comparing each section of the oxygen saturation graph (910) (e.g., first section, second section) with each section of the corresponding ratio graph (920) (e.g., first section, second section), it can be seen that the ratio changes in a shape similar to the change in oxygen saturation caused by apnea.

[0123] For example, the first section corresponds to a section where the phase change of the oxygen saturation graph (910) is relatively large. At this time, it can be confirmed that a relatively large phase change with an opposite phase is observed in the first section of the ratio graph (920). The second section corresponds to a section where the phase change of the oxygen saturation graph (910) is relatively small. At this time, it can be confirmed that a relatively small phase change is observed in the first section of the ratio graph (920).

[0124] FIG. 10 is a diagram illustrating an operation to identify a user's breathing state during sleep based on a ratio graph according to one embodiment.

[0125] Referring to FIG. 10, according to one embodiment, an electronic device (300) can identify the breathing state of a user during sleep based on the ratio (or normalized ratio) between a first DC component and a second DC component.

[0126] For example, the electronic device (300) can identify the breathing state of the user during sleep by inputting the ratio between the first DC component and the second DC component (hereinafter referred to as the ratio) into an artificial intelligence model. Since the description of the artificial intelligence model has been described above, redundant content is omitted.

[0127] According to one embodiment, the electronic device (300) can obtain the breathing state of a user during sleep by inputting information about the ratio of a specified time interval into an artificial intelligence model. The information about the ratio of a specified time interval may include ratio values ​​during a specified time. At this time, the specified time (t1) may include a time interval of about 10 seconds or more.

[0128] For example, the electronic device (300) can input a ratio for the first interval (1010-1) into an artificial intelligence model to identify the user's breathing state during sleep (e.g., apnea) in the first interval (1010-1), input a ratio for the second interval (1010-2) into an artificial intelligence model to identify the user's breathing state during sleep (e.g., apnea) in the second interval (1010-2), input a ratio for the third interval (1010-3) into an artificial intelligence model to identify the user's breathing state during sleep (e.g., apnea) in the third interval (1010-3), and input a ratio for the nth interval (1010-n) into an artificial intelligence model to identify the user's breathing state during sleep (e.g., normal breathing) in the nth interval (1010-n).

[0129] According to one embodiment, the electronic device (300) may input time intervals overlapping to the artificial intelligence model to increase temporal resolution. For example, the electronic device (300) may input a ratio for the first interval (1010-1) into the artificial intelligence model to identify the user's breathing state during sleep (e.g., apnea) in the first interval (1010-1), input a ratio for the middle point of the second interval (1010-2) from the middle point of the first interval (1010-1) into the artificial intelligence model to identify the user's breathing state during sleep (e.g., apnea), and input a ratio for the second interval (1010-2) into the artificial intelligence model to identify the user's breathing state during sleep (e.g., apnea).

[0130] Meanwhile, according to one embodiment, the wearable device (200) may further include a motion sensor. The motion sensor may include a sensor for acquiring user movement information (e.g., acceleration information, position information). The motion sensor may include, for example, an accelerometer or a gyroscope. According to one embodiment, the wearable device (200) may acquire the motion state of the wearable device (200) using the motion sensor. The wearable device (200) may transmit the motion state of the wearable device (200) to an electronic device (300). The electronic device (300) may identify the user's breathing state during sleep based on the motion state of the wearable device (200). For example, the electronic device (200) may identify the user's breathing state during sleep by inputting the ratio between the first DC component and the second DC component and the motion state into an artificial intelligence model.

[0131] FIGS. 11a and FIGS. 11b illustrate an example of an operation that provides an indication of a breathing state when a user's breathing state during sleep is identified as sleep apnea, according to one embodiment.

[0132] FIG. 11a is a diagram showing an example of an indication of a breathing state displayed on a wearable device (200) when the breathing state of a user during sleep is identified as sleep apnea according to one embodiment.

[0133] According to one embodiment, if the user's breathing state during sleep is identified as sleep apnea, the electronic device (300) may transmit information about the user's breathing state to a wearable device (200). The information about the user's breathing state may include an indication (1105) indicating the user's breathing state. The wearable device (200) may display the indication (1105) indicating the user's breathing state (e.g., "Signs of severe obstructive sleep apnea detected. Consult a doctor.") on a display.

[0134] FIG. 11b is a diagram showing an example of an indication of a breathing state displayed on an electronic device (300) when the breathing state of a user during sleep is identified as sleep apnea according to one embodiment.

[0135] In 1111, according to one embodiment, if the user's breathing state during sleep is identified as sleep apnea, the electronic device (300) may display an indication (1110) indicating the user's breathing state (e.g., "Signs of severe obstructive sleep apnea have been detected. Your wearable device has detected signs of sleep apnea. Consult a doctor.") on a display.

[0136] In 1112, according to one embodiment, the electronic device (300) may display an indication (1115) on a display that includes information regarding the detection of a user's breathing state during sleep. For example, the electronic device (300) may display an indication (1115) on a display that includes information regarding whether the user's breathing state detection function during sleep is activated, a method for activating the breathing state detection function during sleep, and information regarding the breathing state detection function during sleep (e.g., the user's pre-sleep behavior, when the breathing state during sleep is not being used, the operating principle of the breathing state during sleep).

[0137] FIGS. 12a and FIGS. 12b illustrate an example of an operation that provides an indication of a breathing state when a user's breathing state during sleep is identified as a normal breathing state, according to one embodiment.

[0138] FIG. 12a is a diagram showing an example of an indication of a breathing state displayed on a wearable device when the breathing state of a user during sleep is identified as normal, according to one embodiment.

[0139] According to one embodiment, if the user's breathing state during sleep is identified as normal, the electronic device (300) may transmit information about the user's breathing state to the wearable device (200). The information about the user's breathing state may include an indication (1205) indicating the user's breathing state. The wearable device (200) may display the indication (1205) indicating the user's breathing state (e.g., "No signs of severe obstructive sleep apnea detected.") on a display.

[0140] FIG. 12b is a drawing showing an example of an indication of a breathing state displayed on an electronic device (300) when the breathing state of a user during sleep is identified as normal according to one embodiment.

[0141] According to one embodiment, if the user's breathing state during sleep is identified as normal, the electronic device (300) may display an indication (1210) indicating the user's breathing state (e.g., "No signs of severe obstructive sleep apnea detected. No signs of sleep apnea detected on your wearable device. However, if you have symptoms, consult a doctor.") on the display.

[0142] FIGS. 13a and FIGS. 13b are drawings illustrating an example of an indication provided to a user when a sleep apnea detection function is activated, according to one embodiment.

[0143] According to one embodiment, the electronic device (300) can determine the breathing state of the user during sleep based on information obtained while the user sleeps multiple times. For example, if the sleep apnea detection function is activated, the electronic device (300) can determine the breathing state of the user during sleep based on ratio data obtained for at least one day (e.g., two days).

[0144] FIG. 13a is a diagram showing an example of an indication displayed on a wearable device when a sleep apnea detection function is activated, according to one embodiment.

[0145] Referring to FIG. 13a, the wearable device (200) may display on the display an indication (1305) indicating whether the sleep apnea detection function is activated, an indication (1310) for indicating the progress of the sleep apnea detection function, and an indication (1315) for requesting the wearing of the wearable device. The wearable device (200) may identify the user's breathing state during sleep at least once (or at least one day). The progress of the sleep apnea detection function may include information on the number of times the user's breathing state during sleep was identified out of the total number of times.

[0146] According to one embodiment, if the wearing condition of the wearable device (200) is poor, the wearable device (200) may display an indication (1315) to request a proper wearing condition (e.g., "Wear the wearable device snugly on your wrist while sleeping") on the display.

[0147] FIG. 13b is a diagram showing an example of an indication displayed on an electronic device when a sleep apnea detection function is activated, according to one embodiment.

[0148] Referring to FIG. 13b, the electronic device (300) may display on the display an indication (1320) indicating whether the sleep apnea detection function is activated, an indication (1325) for requesting the wearing of a wearable device, an indication (1330) for indicating the progress of the sleep apnea detection function, and an indication (1335) including information on whether the user's sleep breathing state detection function is activated, a method for activating the sleep breathing state detection function, and information about the sleep breathing state detection function (e.g., user's pre-sleep behavior, when the sleep breathing state is not being used, operating principle of the sleep breathing state).

[0149] FIG. 14 is a drawing for explaining a module for identifying a user's breathing state during sleep according to one embodiment.

[0150] According to one embodiment, the wearable device (200) may include a first light receiver (1410), a second light receiver (1420), and a ratio calculation unit (1430). According to one embodiment, the electronic device (300) may include a classifier (1440) and a result output unit (1450). However, it is not limited thereto, and some of the modules for identifying the breathing state of a user during sleep (e.g., the first light receiver (1410), the second light receiver (1420), the ratio calculation unit (1430), the classifier (1440), and the result output unit (1450)) may be included in the wearable device (200) and the remaining parts may be included in the electronic device (300).

[0151] According to one embodiment, the first light receiver (1410) may be a module for detecting a first light signal. In this case, the first light may be a light signal corresponding to a wavelength band in which the difference in absorbance between oxidized hemoglobin and non-oxidized hemoglobin is relatively large. The first light receiver (1410) may include a light emitting unit (e.g., LED, Laser) and a light receiving unit (e.g., PD (photo detector)). The first light receiver (1410) may acquire first sensor data for the first light signal.

[0152] According to one embodiment, the second light receiver (1420) may be a module for detecting a second light signal. In this case, the second light may be a light signal corresponding to a wavelength band where the difference in absorbance between oxidized hemoglobin and non-oxidized hemoglobin is relatively large. In this case, the second light receiver (1420) may include a light emitting unit (e.g., LED, Laser) and a light receiving unit (e.g., PD (photo detector)). The second light receiver (1420) may acquire second sensor data for the second light signal. Meanwhile, the first light receiver (1410) and the second light receiver (1420) may detect the light signal through the same light receiving unit or different light receiving units.

[0153] According to one embodiment, the ratio calculation unit (1430) may be a module that calculates the ratio of sensor data obtained from the first light receiving unit (1410) and the second light receiving unit (1420). For example, the ratio calculation unit (1430) may extract the DC component of the first sensor data and the DC component of the second sensor data obtained from the first light receiving unit (1410) and the second light receiving unit (1420). The ratio calculation unit (1430) may obtain a normalized ratio between the DC component of the first sensor data and the DC component of the second sensor data.

[0154] According to one embodiment, the classifier (1440) may be a module for identifying whether a sleep apnea has occurred based on a ratio measured by the ratio calculation unit (1430). For example, the classifier (1440) may use the ratio of a pre-set interval from the ratio calculation unit (1430) as input data and the user's breathing state, the intensity and duration of each breathing state as output data. The classifier (1440) may operate as an artificial intelligence model or a rule-based model. The classifier (1440) may be learned based on the measurement results of a polysomnography measured while acquiring first sensor data and second sensor data.

[0155] According to one embodiment, the result output unit (1450) may be a module for providing the user with the user's breathing status during sleep. For example, the result output unit (1450) may provide the user with the number of occurrences of apnea events and whether an apnea event occurred in real time. The result output unit (1450) may provide information regarding the user's breathing status during sleep in real time or after waking up.

[0156] FIG. 15 is a flowchart illustrating an operation to provide an indication of a breathing state to a user based on information about the user's breathing state obtained from a wearable device, according to one embodiment.

[0157] In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0158] The processor (210, 310) of each device can perform at least one operation of each device among the operations of FIG. 15. Instructions stored in the memory (220, 320) of each device (200, 300) can cause each device (200, 300) to perform the operations of FIG. 15 when executed by the processor (210, 310) of each device (200, 300).

[0159] In operation 1510, according to one embodiment, the wearable device (200) can emit a first light corresponding to a first band and a second light corresponding to a second band different from the first band by using at least one of a plurality of light-emitting parts included in the wearable device (200) worn on the user's body while the user is in a sleeping state.

[0160] In operation 1520, according to one embodiment, the wearable device (200) can acquire first sensor data corresponding to the received first light and second sensor data corresponding to the received second light based on the fact that the first light and the second light are received by a light receiving part included in the wearable device (200) through the body (via).

[0161] In operation 1530, according to one embodiment, the wearable device (200) can identify the breathing state of a user during sleep based on at least a portion of the first DC (direct current) component of the first sensor data and the second DC component of the second sensor data.

[0162] In operation 1540, according to one embodiment, the wearable device (200) may transmit information regarding the identified breathing state to the electronic device (300) via the communication circuit (230). In operation 1550, according to one embodiment, the wearable device (200) may provide an indication regarding the breathing state to the user. In operation 1560, according to one embodiment, the electronic device (300) may provide an indication regarding the breathing state to the user based on information regarding the breathing state received from the wearable device (200). As the details regarding operations 1510 to 1560 are described in detail in FIGS. 5 to 14, duplicate details are omitted.

[0163] FIG. 16 is a flowchart illustrating an operation to provide an indication of breathing status to a user based on information about a ratio obtained from a wearable device, according to one embodiment.

[0164] In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0165] The processor (210, 310) of each device can perform at least one operation of each device among the operations of FIG. 16. Instructions stored in the memory (220, 320) of each device (200, 300) can cause each device (200, 300) to perform the operations of FIG. 16 when executed by the processor (210, 310) of each device (200, 300).

[0166] In operation 1610, according to one embodiment, the wearable device (200) can emit a first light corresponding to a first band and a second light corresponding to a second band different from the first band by using at least one of a plurality of light-emitting parts included in the wearable device worn on the user's body while the user is in a sleeping state.

[0167] In operation 1620, according to one embodiment, the wearable device (200) can acquire first sensor data corresponding to the received first light and second sensor data corresponding to the received second light based on the fact that the first light and the second light are received by a light receiving part included in the wearable device via the body.

[0168] In operation 1630, according to one embodiment, the wearable device (200) can identify the ratio between the first DC component of the first sensor data and the second DC component of the second sensor data.

[0169] In operation 1640, according to one embodiment, the wearable device (200) can transmit information regarding the ratio between the first DC component and the second DC component to the electronic device (300) via the communication circuit (230). In operation 1650, according to one embodiment, the electronic device (300) can identify the breathing state of the user during sleep based on the information regarding the ratio between the first DC component and the second DC component. In operation 1660, according to one embodiment, the electronic device (300) can transmit information regarding the breathing state to the wearable device (200). In operation 1670, according to one embodiment, the electronic device (300) can provide an indication regarding the breathing state to the user. In operation 1680, according to one embodiment, the wearable device (200) can provide an indication regarding the breathing state to the user based on the information regarding the breathing state received from the electronic device (300).

[0170] FIG. 17 is a drawing illustrating an example of a wearable device according to one embodiment.

[0171] Referring to FIG. 17, according to one embodiment, an accessory electronic device (1701) that interacts with an electronic device (300) supports biometric information sensing functions, touch functions, and wireless communication functions, and may refer to an electronic device that can be worn on a user's body. The accessory electronic device (1701) may also be referred to as a wearable electronic device or a wearable device. The accessory electronic device (1701) shown in FIG. 17 may be the wearable device (200) of FIG. 17.

[0172] The accessory electronic device (1701) illustrated in FIG. 17 is illustrated as a ring type (e.g., smart ring) that is worn on the user's finger, but is not limited thereto and may be implemented as other types of accessory electronic devices such as a watch type (e.g., smart watch) or a band type (e.g., smart band).

[0173] An accessory electronic device (1701) according to one embodiment may include an annular first housing (1702) (e.g., an outer ring housing, a first ring housing, or a first housing portion) and an annular second housing (1703) (e.g., an inner ring housing, a second ring housing, or a second housing portion) coupled to the first housing (1702) and including an opening. The opening may be formed to a size in which a user's finger can be inserted. For example, the first housing (1702) may be formed of a material that withstands external impact or scratches, such as a metal material, ceramic, or stainless steel. The first housing (1702) may undergo a separate fixing or coating process for color implementation. The second housing (1703) may be formed of the same material as the first housing (1702) or may be formed of a material such as a molding material for sensing, plastic, or glass. The second housing (1703) may be formed such that a metallic material for biometric measurement is composed of at least a portion thereof.

[0174] An accessory electronic device (1701) according to one embodiment may include a processor (1710), memory (1720), communication module (1730), antenna (1725), battery (1740), charging interface (17175), at least one biometric sensor (1750), touch sensor (1760), inertial sensor (1770), temperature sensor (1780), and power management integrated circuit (PMIC) (1790) disposed in the space between a first housing (1702) and a second housing (1703). Some components may be disposed on a substrate (1795) (e.g., FPCB, flexible printed circuit board) having flexibility to correspond to the curvature of the accessory electronic device (1701).

[0175] According to some embodiments, the accessory electronic device (1701) may include additional components (e.g., a display, an ultrasonic sensor, an audio output device) in addition to the illustrated components.

[0176] A communication module (1730) according to one embodiment may include various hardware and / or software configurations to support wireless communication with an external electronic device (hereinafter, the electronic device (100) of FIG. 1). The wearable electronic device (1701) may receive various data or control commands from the electronic device (100) via wired or wireless means through the communication module (1730). In one embodiment, the communication module may support short-range wireless communication. Short-range wireless communication includes, but is not limited to, at least one of Bluetooth, BLE (Bluetooth Low Energy), ZigBee, ANT+, Wi-Fi, Cellular (LTE, 5G, 6G, NB-IoT), NFC (near field communication), RFID (radio frequency identification), UWB (ultra wide band), GNSS (global navigation satellite system), or / and MST (magnetic secure transmission). According to some embodiments, the communication module (1730) may be implemented in a form integrated with the processor (1710).

[0177] An antenna (1725) according to one embodiment may be connected to a communication module (1730) through a substrate (1795). A wearable electronic device (1701) may transmit or receive communication signals / data to or from the outside through the antenna (1725). The antenna (1725) may include a single or multiple antennas. In some embodiments, a part of the first housing (1702) (e.g., a metal member) may be designed to be used as an antenna (1725).

[0178] A battery (1740) according to one embodiment may be formed in a curved shape to have a curvature corresponding to the curvature of the space formed between the first housing (1702) and the second housing (1703). A plurality of battery packs may be arranged separately in the battery (1740). The battery (1740) may be connected to a charging interface (1745).

[0179] A charging interface (1745) according to one embodiment may be electrically connected to a PMIC (1790) mounted on a substrate (1795) through a substrate (1795). The charging interface (1745) may support wired charging (terminal) or wireless charging (WPC, NFC) methods for charging.

[0180] According to one embodiment, at least one biometric sensor (1750) can acquire various biometric information of a user using an optical signal. For example, the biometric sensor (1750) may be a photoplethysmogram (PPG) sensor or an optical sensor capable of acquiring various biometric information such as heart rate and blood circulation by measuring a plethysmogram according to an optical signal, but is not limited thereto. The biometric sensor (1750) may acquire biometric information such as heart rate (HR), blood pressure, saturation of percutaneous oxygen (SpO2), galvanic skin response (GSR), electrocardiography (ECG), blood flow velocity, and bioelectrical impedance, but is not limited thereto.

[0181] According to some embodiments, the biometric sensor (1750) may include a fingerprint sensor.

[0182] A biometric sensor (1750) according to one embodiment may include a sensor controller (1750a), a plurality of light-emitting units (1750b) that output a light signal, and a plurality of light-receiving units (1750c) that receive a light signal. For example, the biometric sensor (1750) may output visible light (e.g., green light, red light, or blue light) or infrared light to the user's skin, and when the output light is reflected by blood vessels and received, the amount of light reflected or absorbed based on the received light may be measured to obtain sensor data.

[0183] A touch sensor (1760) according to one embodiment can detect a touch signal from a user touching a wearable electronic device (1701). The touch sensor (1760) may be formed in at least one of, for example, pressure, capacitive, optical, or ultrasonic methods.

[0184] According to some embodiments, the touch sensor (1760) may be omitted.

[0185] An inertial sensor (1770) according to one embodiment can acquire movement information of a wearable electronic device (1701). For example, the inertial sensor (1770) can detect motion, gesture, impact, posture and / or activity (e.g., sedentary, moving, sports). The inertial sensor (1770) may be formed as a 3-axis accelerometer, but is not limited thereto, and may be formed as a 6-axis sensor including an accelerometer and a gyroscope.

[0186] A temperature sensor (1780) according to one embodiment can measure the temperature of a user's body or a component (e.g., an electronic component) included in a wearable electronic device (1701). The temperature sensor (1780) may be formed in a contact or non-contact manner and may vary depending on the design. The wearable electronic device (1701) may use the temperature information recorded through the temperature sensor (1780) to measure the user's body temperature, estimate skin temperature, or estimate situational awareness, by recording the temperature information in memory or under processor control.

[0187] According to one embodiment, the PMIC (1790) can manage power delivered from the battery (170) to each component of the wearable electronic device (1701).

[0188] A memory (1720) according to one embodiment can store various instructions that can be executed by a processor (1710). Such instructions may include control commands such as arithmetic and logical operations, data movement, or input / output that can be recognized by the processor (1710).

[0189] A processor (1710) according to one embodiment is configured to perform operations or data processing regarding the control and / or communication of each component of an accessory electronic device (1700), and may be composed of one or more processors. There is no limitation to the operations and data processing functions that the processor (1710) can implement on the accessory electronic device (1701), but in this document, it may process various operations to support the user's sleep apnea in conjunction with the electronic device (100).

[0190] Meanwhile, the technical problems to be solved in this disclosure are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which this disclosure belongs.

[0191] As described above, conventional methods have detected sleep apnea using biosignals such as oxygen saturation and heart rate; however, these methods were significantly affected by signal quality. In particular, when the AC signal of sensor data (e.g., user's pulse) was weak, the detection accuracy of methods using biosignals such as oxygen saturation and heart rate to detect sleep apnea decreased. Additionally, methods utilizing minute movements measured at the wrist due to breathing were proposed, but there was a limitation in that it was difficult to detect sleep apnea when the minute movements were minimal. In the case of the present invention, by detecting the breathing state during sleep through the DC signal, the user's breathing state during sleep can be identified with high accuracy regardless of the quality of the AC signal (e.g., user's pulse). Furthermore, the present invention can reliably identify apnea periods without utilizing blood oxygen saturation. Moreover, since the present invention measures the DC component, the user's breathing state during sleep can be identified even without being in close contact with the user's body.

[0192] Meanwhile, although the present invention has been described based on the case where the first light is red light and the second light is infrared light, it is not limited to the examples described above. For example, the first light or the second light may be determined to be any one of green light, red light, blue light, or infrared light.

[0193] In addition, the present invention discloses a method using two lights, but is not limited thereto. For example, the breathing state of a user during sleep can be identified based on a DC component obtained using one light (e.g., red light) that significantly changes the oxidative-non-oxidative level of hemoglobin.

[0194] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs.

[0195] A method according to one embodiment of the present disclosure may include, while a user is in a sleep state, using at least one of a plurality of light-emitting parts included in a wearable device worn on the user's body, emitting a first light corresponding to a first band and a second light corresponding to a second band different from the first band toward the user's body; acquiring first sensor data corresponding to the received first light and second sensor data corresponding to the received second light based on the fact that the first light and the second light are received by a light-receiving part included in the wearable device via the body; identifying the user's breathing state during sleep based on at least a portion of a first component extracted from the first sensor data and a second component extracted from the second sensor data; and providing an indication indicating the breathing state to the user through the wearable device or an external electronic device functionally connected to the wearable device.

[0196] For example, the first light and the second light emitted toward the user's body may be reflected or transmitted through the body and received by the light receiving part.

[0197] For example, the first component may include a first DC component of the first sensor data, and the second component may include a second DC component of the second sensor data.

[0198] For example, the operation of identifying the breathing state may include an operation of identifying the user's breathing state during sleep based on the ratio between the first DC component and the second DC component.

[0199] For example, the operation of identifying the breathing state may include an operation of identifying the user's breathing state during sleep based on a change in rate during a specified period of time.

[0200] For example, the specified time interval above may be a time interval of 10 seconds or more and 5 minutes or less.

[0201] For example, the operation of identifying the breathing state may include the operation of normalizing the ratio based on the first intensity of a first light output toward the user's body or the second intensity of a second light output toward the user's body, and the operation of identifying the breathing state of the user during sleep based on the normalized ratio.

[0202] For example, the intensity of the first light or second light output toward the user's body may be based on the skin color or tone of the user's body.

[0203] For example, the first band may be approximately 525 nm or more and less than 805 nm, and the first light corresponding to the first band may include light having a wavelength included in the first band, and the second band may be approximately 805 nm or more and less than 1000 nm, and the second light corresponding to the second band may include light having a wavelength included in the second band.

[0204] For example, the above method further includes an operation of acquiring a motion state of the wearable device using a motion sensor included in the wearable device, and the operation of identifying the breathing state may further include an operation of identifying the breathing state based on the motion state of the wearable device.

[0205] For example, the operation of identifying the breathing state may include the operation of identifying the breathing state without the operation of obtaining the user's blood oxygen saturation.

[0206] For example, the operation of acquiring the above breathing state may include an operation using artificial intelligence (AI) processing for the first DC component or the second DC component.

[0207] An electronic device according to one embodiment of the present disclosure comprises at least one processor including a communication circuit, a memory for storing instructions, and a processing circuitry, wherein when the instructions are executed individually or collectively by the at least one processor, the electronic device may receive a ratio between a first component of first sensor data and a second component of second sensor data obtained through a biometric sensor of a wearable device worn on the body of a user through the communication circuit from the wearable device, obtain a breathing state of the user during sleep based on the ratio, and provide an indication indicating the breathing state to the user through the electronic device or the wearable device.

[0208] For example, the first sensor data may include data obtained based on the reception of a first light corresponding to a first band emitted through the biometric sensor by the user's body, and the second sensor data may include data obtained based on the reception of a second light corresponding to a second band emitted through the biometric sensor by the user's body.

[0209] For example, when the above instructions are executed individually or collectively by the at least one processor, the electronic device may acquire the user's breathing state during sleep based on the change in the rate during a specified period of time.

[0210] For example, the specified time interval may be 10 seconds or more and 1 hour or less.

[0211] For example, the above ratio may be information normalized based on at least a portion of the first intensity of the first light or the second intensity of the first light output from the biometric sensor of the wearable device.

[0212] For example, the intensity of the first light or second light output toward the user's body may be based on the skin color or tone of the user's body.

[0213] For example, when the above instructions are executed individually or collectively by the at least one processor, the electronic device may input the ratio into an artificial intelligence model to obtain information about the user's breathing state during sleep.

[0214] For example, the first band may be approximately 525 nm or more and less than 805 nm, and the first light corresponding to the first band may include light having a wavelength included in the first band, and the second band may be approximately 805 nm or more and less than 1000 nm, and the second light corresponding to the second band may include light having a wavelength included in the second band.

[0215] For example, the first component may include a first DC component extracted from the first sensor data, and the second component may include a second DC component extracted from the second sensor data.

[0216] Although various embodiments have been described above, each embodiment is not necessarily implemented individually, and may be combined with at least one other embodiment, either wholly or partially, to be implemented together in a single product.

[0217] Meanwhile, embodiments of the present disclosure may also be implemented in the form of a storage medium comprising computer-executable instructions, such as program modules executed by a computer. A computer-readable medium may be any available medium accessible by a computer and includes both volatile and non-volatile media, and both removable and non-removable media. Additionally, a computer-readable medium may include computer storage media and communication media. A computer storage medium includes both volatile and non-volatile, removable and non-removable media implemented by any method or technique for storing information, such as computer-readable instructions, data structures, program modules, or other data. A communication medium may typically include other data of modulated data signals, such as computer-readable instructions, data structures, or program modules.

[0218] Additionally, computer-readable storage media may be provided in the form of non-transitory storage media. Here, 'non-transitory storage media' simply means that it is a tangible device and does not contain a signal (e.g., electromagnetic waves), and this term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily. For example, 'non-transitory storage media' may include a buffer in which data is stored temporarily.

[0219] According to one embodiment, the method according to the various embodiments disclosed herein may be provided by being included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application 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., downloadable app) may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0220] The foregoing description of the present disclosure is for illustrative purposes only, and those skilled in the art will understand that other specific forms can be easily modified without altering the technical spirit or essential features of the present disclosure. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single unit may be implemented in a distributed manner, and components described as distributed may likewise be implemented in a combined form.

[0221] The scope of the present disclosure is defined by the claims set forth below rather than by the detailed description above, and all modifications or variations derived from the meaning and scope of the claims and equivalent concepts thereof should be interpreted as being included within the scope of the present disclosure.

Claims

1. In a method for processing biosignals, While the user is in a sleeping state, the operation (510) of emitting a first light corresponding to a first band and a second light corresponding to a second band different from the first band towards the user's body using at least one of a plurality of light-emitting parts included in a wearable device (200) worn on the user's body; An operation (520) of acquiring first sensor data corresponding to the received first light and second sensor data corresponding to the received second light based on the fact that the first light and the second light are received by a light receiving part included in the wearable device (200) through the body; An operation (530) for identifying the breathing state of the user during sleep based on at least a portion of the first component extracted from the first sensor data and the second component extracted from the second sensor data; and A method comprising: an operation (540) of providing the user with an indication indicating the breathing state through the wearable device (200) or an external electronic device (300) functionally connected to the wearable device (200).

2. In Paragraph 1, A method in which the first light and the second light emitted toward the body of the user are reflected or transmitted through the body and received by the light receiving part.

3. In Paragraph 1, The first component above includes the first DC component of the first sensor data, and A method in which the second component comprises a second DC component of the second sensor data.

4. In Paragraph 3, The action of identifying the above breathing state is, A method comprising: identifying the breathing state of the user during sleep based on the ratio between the first DC component and the second DC component.

5. In Paragraph 4, The action of identifying the above breathing state is, A method comprising: identifying the breathing state of the user during sleep based on a change in the ratio during a specified period of time.

6. In Paragraph 5, The above-mentioned specified time interval is a method in which the interval is from 10 seconds to 1 hour.

7. In Paragraph 4, The action of identifying the above breathing state is, An operation to normalize the ratio based on the first intensity of the first light output toward the body of the user or the second intensity of the second light output toward the body of the user; and A method comprising: identifying the breathing state of the user during sleep based on the normalized ratio above.

8. In Paragraph 7, A method in which the intensity of a first light or a second light output toward the body of the user is based on the skin color or tone of the user's body.

9. In Paragraph 1, The first band is 525 nm or larger and less than 805 nm, and the first light corresponding to the first band includes light having a wavelength included in the first band, A method wherein the second band is 805 nm or more and less than 1000 nm, and the second light corresponding to the second band comprises light having a wavelength included in the second band.

10. In Paragraph 1, The above method is, The method further includes the operation of acquiring the motion state of the wearable device (200) using a motion sensor included in the wearable device (200), and A method comprising: an operation to identify the breathing state, wherein the operation to identify the breathing state further comprises an operation to identify the breathing state based on the motion state of the wearable device (200).

11. In Paragraph 1, A method comprising: an operation to identify the above breathing state, wherein the operation to identify the above breathing state without an operation to obtain the user's blood oxygen saturation.

12. In Paragraph 1, A method comprising: an operation for identifying the above breathing state, wherein the operation utilizes artificial intelligence (AI) processing for the first component or the second component.

13. In the electronic device (300), Communication circuit (330); Memory (320) for storing instructions; and It includes at least one processor (310) including a processing circuitry; and When the above instructions are executed individually or collectively by the at least one processor (310), the electronic device (300), The ratio between the first component of the first sensor data and the second component of the second sensor data obtained through the biometric sensor of the wearable device (200) worn on the user's body is received from the wearable device (200) through the communication circuit (330), and Based on the above ratio, the breathing state of the user during sleep is obtained, and An indication indicating the above breathing state is provided to the user through the electronic device (300) or the wearable device (200), and The first sensor data above includes data obtained based on the reception of a first light corresponding to a first band emitted through the biometric sensor through the user's body, and The electronic device (300) comprises data obtained based on the second sensor data being received through the user's body, wherein the second light corresponding to the second band emitted through the biometric sensor is received.

14. In Paragraph 13, When the above instructions are executed individually or collectively by the at least one processor (310), the electronic device (300), An electronic device (300) for obtaining the user's breathing state during sleep based on the change in the ratio during a specified period of time.

15. In Paragraph 14, The electronic device (300), wherein the specified time interval is a period of 10 seconds or more and 1 hour or less.

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