Wearable device and method for measuring status of user

Abstract

The wearable device includes a communication circuit, at least one sensor, and a processor configured to: identify a distance between the wearable device and an external electronic device upon identifying that a user wearing the wearable device is sleeping; based at least in part on identifying that the distance is less than a reference distance, comparing data obtained by the at least one sensor with first reference data for identifying a first state of the user while the user is sleeping; and based at least in part on identifying that the distance is greater than or equal to the reference distance, comparing the data with second reference data different from the first reference data and used to identify a second state of the user when the user is awake.

Description

Technical Field

[0001] Certain example embodiments relate to wearable devices and / or methods for identifying a user's state. Background Art

[0002] Various services are provided through wearable devices. The wearable device can be worn and operated on a part of the user's body. The wearable device can identify the user's biometric information when worn on a part of the user's body and provide services based on the user's biometric information. For example, the wearable device can identify the user's biometric information by using multiple sensors. The wearable device can identify various activity states of the user based on the identified user's biometric information. Summary of the invention

[0003] Technical Solutions

[0004] According to an example embodiment, a wearable device may include a communication circuit, at least one sensor, and at least one processor operably coupled directly or indirectly to the communication circuit and the at least one sensor. The processor may be configured to identify the distance between the wearable device and the external electronic device through the communication circuit when recognizing that the user wearing the wearable device is sleeping. The processor may be configured to compare data obtained through the at least one sensor with first reference data for identifying a first state of the user when the user is sleeping, based at least in part on recognizing that the distance is less than a reference distance, and perform a predetermined first function based on the comparison between the data and the first reference data. The processor may be configured to compare the data with second reference data different from the first reference data for identifying a second state of the user when the user is awake, based at least in part on recognizing that the distance is greater than or equal to the reference distance, and perform a predetermined second function based on the comparison between the data and the second reference data.

[0005] According to an example embodiment, a method of a wearable device may include: upon identifying that a user wearing the wearable device is sleeping, identifying, through a communication circuit of the wearable device, a distance between the wearable device and an external electronic device. The method may include: at least in part based on identifying that the distance is less than a reference distance, comparing data obtained by at least one sensor of the wearable device with first reference data for identifying a first state of the user when the user is sleeping, and performing a predetermined first function based on the comparison between the data and the first reference data. The method may include: at least in part based on identifying that the distance is greater than or equal to the reference distance, comparing the data with second reference data different from the first reference data for identifying a second state of the user when the user is awake, and performing a predetermined second function based on the comparison between the data and the second reference data. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The above and other aspects, features and advantages of certain embodiments of the present disclosure will become more apparent through the following description in conjunction with the accompanying drawings, in which:

[0007] Figure 1 is a block diagram of an electronic device in a network environment according to an example embodiment.

[0008] Figure 2a and Figure 2b is a perspective view of an electronic device according to an example embodiment.

[0009] Figure 3 is an exploded perspective view of an electronic device according to an example embodiment.

[0010] Figure 4 is a simplified block diagram of a wearable device according to an example embodiment.

[0011] Figure 5 An example of a first module and a second module included in a processor according to example embodiments is shown.

[0012] Figure 6 is a flow chart illustrating an exemplary operation of a wearable device according to an example embodiment.

[0013] Figure 7 is a flow chart illustrating an exemplary operation of a wearable device according to an example embodiment.

[0014] Figure 8a is a flow chart illustrating an exemplary operation of a wearable device according to an example embodiment.

[0015] Figure 8b is an example of a graph representing a change in acceleration recognized in a wearable device over time according to example embodiments.

[0016] Figure 8c is an example of a graph representing a change in air pressure recognized in a wearable device over time according to an example embodiment.

[0017] Figure 8d is an example of a graph representing changes in a heart rate recognized in a wearable device over time according to an example embodiment.

[0018] Figure 9a is a flow chart illustrating an exemplary operation of a wearable device according to an example embodiment.

[0019] Figure 9b is an example of a graph representing a change in acceleration recognized in a wearable device over time according to example embodiments.

[0020] Fig.9c is an example of a graph representing a change in air pressure recognized in a wearable device over time according to an example embodiment.

[0021] Figure 9d is an example of a graph representing changes in a heart rate recognized in a wearable device over time according to an example embodiment.

[0022] Fig.10 is a flow chart illustrating an exemplary operation of a wearable device according to an example embodiment.

[0023] Fig.11 is a flow chart illustrating an exemplary operation of a wearable device according to an example embodiment.

[0024] Fig.12 An example of the operation of a wearable device according to example embodiments is shown.

[0025] Fig.13 An example of the operation of a wearable device according to an example embodiment is shown.

[0026] Fig.14 is a flow chart illustrating an exemplary operation of a wearable device according to an example embodiment.

[0027] Fig.15a An example of the operation of a wearable device according to example embodiments is shown.

[0028] Fig.15b An example of the operation of a wearable device according to an example embodiment is shown.

[0029] Fig.16 is a flow chart illustrating an exemplary operation of a wearable device according to an example embodiment.

[0030] Fig.17 An example of the operation of a wearable device according to example embodiments is shown.

[0031] Fig.18 is a flow chart illustrating an exemplary operation of a wearable device according to an example embodiment. DETAILED DESCRIPTION

[0032] Figure 1 is a block diagram illustrating an electronic device 101 in a network environment 100 according to various embodiments.

[0033] refer to Figure 1, the electronic device 101 in the network environment 100 may communicate with the electronic device 102 via the first network 198 (e.g., a short-range wireless communication network), or communicate with at least one of the electronic device 104 or the server 108 via the second network 199 (e.g., a long-range wireless communication network). According to an embodiment, the electronic device 101 may communicate with the electronic device 104 via the server 108. According to an embodiment, the electronic device 101 may include a processor 120, a memory 130, an input module 150, a sound output module 155, a display module 160, an audio module 170, a sensor module 176, an interface 177, a connection terminal 178, a haptic module 179, a camera module 180, a power management module 188, a battery 189, a communication module 190, a subscriber identification module (SIM) 196, or an antenna module 197. In some embodiments, at least one of the components (e.g., the connection terminal 178) may be omitted from the electronic device 101, or one or more other components may be added in the electronic device 101. In some embodiments, some of the components (eg, sensor module 176, camera module 180, or antenna module 197) may be implemented as a single component (eg, display module 160).

[0034] The processor 120 may execute, for example, software (e.g., program 140) to control at least one other component (e.g., hardware or software component) of the electronic device 101 coupled to the processor 120, and may perform various data processing or calculations. According to one embodiment, as at least part of the data processing or calculation, the processor 120 may store a command or data received from another component (e.g., sensor module 176 or communication module 190) in the volatile memory 132, process the command or data stored in the volatile memory 132, and store the resulting data in the non-volatile memory 134. According to an embodiment, the processor 120 may include a main processor 121 (e.g., a central processing unit (CPU) or an application processor (AP)) or an auxiliary processor 123 (e.g., a graphics processing unit (GPU), a neural processing unit (NPU), an image signal processor (ISP), a sensor hub processor, or a communication processor (CP)) that may operate independently of the main processor 121 or in conjunction with the main processor 121. For example, when the electronic device 101 includes a main processor 121 and an auxiliary processor 123, the auxiliary processor 123 may be adapted to consume less power than the main processor 121 or be dedicated to a specified function. The auxiliary processor 123 may be implemented separately from the main processor 121 or as part of the main processor 121.

[0035] The auxiliary processor 123 may replace the main processor 121 when the main processor 121 is in an inactive (e.g., sleep) state, or control at least some of the functions or states related to at least one of the components of the electronic device 101 (e.g., display module 160, sensor module 176, or communication module 190) together with the main processor 121 when the main processor 121 is in an active state (e.g., executing an application). According to an embodiment, the auxiliary processor 123 (e.g., an image signal processor or a communication processor) may be implemented as a part of another component (e.g., camera module 180 or communication module 190) that is functionally related to the auxiliary processor 123. According to an embodiment, the auxiliary processor 123 (e.g., a neural processing unit) may include a hardware structure specified for artificial intelligence model processing. The artificial intelligence model may be generated by machine learning. Such learning may be performed, for example, by the electronic device 101 that performs artificial intelligence or via a separate server (e.g., server 108). The learning algorithm may include, but is not limited to, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. The artificial intelligence model may include multiple artificial neural network layers. The artificial neural network may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q network or a combination of two or more thereof, but is not limited thereto. Additionally or alternatively, the artificial intelligence model may include a software structure in addition to a hardware structure.

[0036] The memory 130 may store various data used by at least one component of the electronic device 101 (e.g., the processor 120 or the sensor module 176). The various data may include, for example, software (e.g., the program 140) and input data or output data for commands related thereto. The memory 130 may include a volatile memory 132 or a nonvolatile memory 134.

[0037] The program 140 may be stored as software in the memory 130 , and may include, for example, an operating system (OS) 142 , middleware 144 , or applications 146 .

[0038] The input module 150 may receive a command or data to be used by another component (e.g., the processor 120) of the electronic device 101 from outside (e.g., a user) of the electronic device 101. The input module 150 may include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus).

[0039] The sound output module 155 can output sound signals to the outside of the electronic device 101. The sound output module 155 may include, for example, a speaker or a receiver. The speaker may be used for general purposes, such as playing multimedia or playing recordings. The receiver may be used to receive incoming calls. Depending on the embodiment, the receiver may be implemented as a part of the speaker or as a part of the speaker.

[0040] The display module 160 can visually provide information to the outside of the electronic device 101 (e.g., a user). The display module 160 may include, for example, a display, a hologram device, or a projector, and a control circuit for controlling a corresponding one of the display, the hologram device, and the projector. According to an embodiment, the display module 160 may include a touch sensor adapted to detect a touch or a pressure sensor adapted to measure the strength of a force caused by a touch.

[0041] The audio module 170 can convert sound into an electrical signal, and vice versa. According to an embodiment, the audio module 170 can obtain sound via the input module 150, or output sound via the sound output module 155 or an earphone of an external electronic device (e.g., electronic device 102) directly (e.g., wired) or wirelessly coupled to the electronic device 101.

[0042] The sensor module 176 can detect an operating state (e.g., power or temperature) of the electronic device 101 or an environmental state (e.g., a user's state) outside the electronic device 101, and then generate an electrical signal or data value corresponding to the detected state. According to an embodiment, the sensor module 176 may include, for example, a gesture sensor, a gyro sensor, an atmospheric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an infrared (IR) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illumination sensor.

[0043] The interface 177 may support one or more designated protocols for the electronic device 101 to be directly (e.g., wired) or wirelessly coupled to an external electronic device (e.g., the electronic device 102). According to an embodiment, the interface 177 may include, for example, a high-definition multimedia interface (HDMI), a universal serial bus (USB) interface, a secure digital (SD) card interface, or an audio interface.

[0044] The connection terminal 178 may include a connector, via which the electronic device 101 may be physically connected to an external electronic device (e.g., the electronic device 102). According to an embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0045] The haptic module 179 may convert the electric signal into mechanical stimulation (eg, vibration or movement) or electric stimulation that can be recognized by the user via his tactile or kinesthetic sense. According to an embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electric stimulator.

[0046] The camera module 180 may capture still images or moving images. According to an embodiment, the camera module 180 may include one or more lenses, an image sensor, an image signal processor, or a flash.

[0047] The power management module 188 may manage power supplied to the electronic device 101. According to an embodiment, the power management module 188 may be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

[0048] The battery 189 may supply power to at least one component of the electronic device 101. According to an embodiment, the battery 189 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0049] The communication module 190 can support establishing a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device 101 and an external electronic device (e.g., electronic device 102, electronic device 104, or server 108), and perform communication via the established communication channel. The communication module 190 may include one or more communication processors that can operate independently of the processor 120 (e.g., an application processor (AP)) and support direct (e.g., wired) communication or wireless communication. According to an embodiment, the communication module 190 may include a wireless communication module 192 (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (e.g., a local area network (LAN) communication module or a power line communication (PLC) module). A corresponding one of these communication modules can communicate via a first network 198 (e.g., a short-range communication network such as Bluetooth). TM The wireless communication module 192 can communicate with an external electronic device in a wireless communication network (e.g., wireless fidelity (Wi-Fi) direct connection or infrared data association (IrDA)) or a second network 199 (e.g., a long-range communication network such as a traditional cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or a wide area network (WAN))). These various types of communication modules can be implemented as a single component (e.g., a single chip), or can be implemented as multiple components separated from each other (e.g., multiple chips). The wireless communication module 192 can use the subscriber information (e.g., international mobile subscriber identity (IMSI)) stored in the subscriber identification module 196 to identify and authenticate the electronic device 101 in a communication network (such as the first network 198 or the second network 199).

[0050] The wireless communication module 192 can support 5G networks and next-generation communication technologies (e.g., new radio (NR) access technologies) after 4G networks. NR access technologies can support enhanced mobile broadband (eMBB), massive machine type communications (mMTC), or ultra-reliable and low-latency communications (URLLC). The wireless communication module 192 can support high-frequency bands (e.g., mmWave bands) to achieve, for example, high data transmission rates. The wireless communication module 192 can support various technologies for ensuring performance on high-frequency bands, such as, for example, beamforming, massive multiple-input multiple-output (massive MIMO), full-dimensional MIMO (FD-MIMO), array antennas, analog beamforming, or massive antennas. The wireless communication module 192 can support various requirements specified in the electronic device 101, an external electronic device (e.g., an electronic device 104), or a network system (e.g., a second network 199). In accordance with an embodiment, the wireless communication module 192 may support a peak data rate for implementing eMBB (e.g., 20 Gbps or greater), loss coverage for implementing mMTC (e.g., 164 dB or less), or U-plane delay for implementing URLLC (e.g., 0.5 ms or less for each of the downlink (DL) and uplink (UL), or 1 ms or less round trip).

[0051] The antenna module 197 may transmit or receive a signal or power to or from the outside of the electronic device 101 (e.g., an external electronic device). According to an embodiment, the antenna module 197 may include an antenna including a radiation element formed of a conductive material or a conductive pattern formed in or on a substrate (e.g., a printed circuit board (PCB)). According to an embodiment, the antenna module 197 may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication scheme used in a communication network such as the first network 198 or the second network 199 may be selected from the plurality of antennas by, for example, the communication module 190 (e.g., the wireless communication module 192). Then, a signal or power may be transmitted or received between the communication module 190 and the external electronic device via the selected at least one antenna. According to an embodiment, another component other than the radiation element (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module 197.

[0052] According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to an embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent to a first surface (e.g., bottom surface) of the printed circuit board and capable of supporting a specified high frequency band (e.g., mmWave frequency band), and a plurality of antennas (e.g., array antennas) disposed on or adjacent to a second surface (e.g., top surface or side surface) of the printed circuit board and capable of transmitting or receiving signals in the specified high frequency band.

[0053] At least some of the above components may be coupled to each other and transmit signals (e.g., commands or data) therebetween via an inter-peripheral communication scheme (e.g., a bus, general purpose input and output (GPIO), a serial peripheral interface (SPI), or a mobile industry processor interface (MIPI)).

[0054] According to an embodiment, a command or data may be sent or received between the electronic device 101 and the external electronic device 104 via a server 108 coupled to the second network 199. Each of the electronic devices 102 or 104 may be a device of the same type or a different type as the electronic device 101. According to an embodiment, all or some of the operations to be performed at the electronic device 101 may be performed at one or more of the external electronic devices 102, 104, or 108. For example, if the electronic device 101 should automatically or in response to a request from a user or another device to perform a function or service, the electronic device 101 may request one or more external electronic devices to perform at least a portion of the function or service instead of performing the function or service, or in addition to performing the function or service. The one or more external electronic devices receiving the request may perform at least a portion of the requested function or service, or an additional function or additional service related to the request, and transmit the result of the execution to the electronic device 101. The electronic device 101 may provide the result as at least a part of the reply to the request with or without further processing of the result. To this end, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device 101 can provide ultra-low latency services using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an Internet of Things (IoT) device. The server 108 may be an intelligent server using machine learning and / or neural networks. According to an embodiment, the external electronic device 104 or the server 108 may be included in the second network 199. The electronic device 101 may be applied to intelligent services (e.g., smart homes, smart cities, smart cars, or healthcare) based on 5G communication technology or IoT-related technologies.

[0055] Figure 2a and Figure 2b is a perspective view of an electronic device according to various embodiments.

[0056] refer to Figure 2a and Figure 2b According to an embodiment, the electronic device 200 (eg, Figure 1 The electronic device 101 may include a housing 210 and binding members 250 and 260, the housing 210 including a first surface (or front surface) 210A, a second surface (or rear surface) 210B, and a side surface 210C surrounding a space between the first surface 210A and the second surface 210B, the binding members 250 and 260 are directly or indirectly connected to at least a portion of the housing 210 and detachably connect the electronic device 200 to a part of the user's body (e.g., a wrist, an ankle, etc.). In another embodiment (not shown), the housing may refer to a portion of the housing 210 that forms a portion of the user's body (e.g., a wrist, an ankle, etc.). Figure 2a and Figure 2b The structure of some of the first surface 210A, the second surface 210B and the side surface 210C. According to an embodiment, at least a portion of the first surface 210A may be formed by a substantially transparent front plate 201 (e.g., a glass plate including various coatings, or a polymer plate). The second surface 210B may be formed by a substantially opaque rear plate 207. For example, the rear plate 207 may be formed by coating or tinting glass, ceramic, polymer, metal (e.g., aluminum, stainless steel (STS) or magnesium) or a combination of at least two of these materials. The side surface 210C may be formed by a side frame structure (or "side member") 206 that is directly or indirectly coupled to the front plate 201 and the rear plate 207 and includes metal and / or polymer. In some embodiments, the rear plate 207 and the side frame structure 206 may be formed integrally and may include the same material (e.g., a metal material such as aluminum). Binding members 250 and 260 may be formed by various materials and shapes. The integrated unit connection member and the plurality of unit connection members may be formed by woven fabric, leather, rubber, urethane, metal, ceramic, or a combination of at least two of these materials to flow with each other.

[0057] According to an embodiment, the electronic device 200 may include a display 220 (refer to Figure 3 ), audio modules 205 and 208, a sensor module 211 including at least one sensor, key input devices 202, 203, and 204, and a connector hole 209. In some embodiments, the electronic device 200 may omit at least one of the components (e.g., key input devices 202, 203, and 204, connector hole 209, or sensor module 211) or may additionally include another component.

[0058] The display 220 may be visibly exposed, for example, through a large portion of the front plate 201. The shape of the display 220 may be a shape corresponding to the shape of the front plate 201, and may have various shapes, such as a circle, an ellipse, or a polygon. The display 220 may be coupled to a touch detection circuit, a pressure sensor capable of measuring the strength (pressure) of a touch, and / or a fingerprint sensor, or may be disposed adjacent to the touch detection circuit, the pressure sensor capable of measuring the strength (pressure) of a touch, and / or a fingerprint sensor.

[0059] The audio modules 205 and 208 may include a microphone hole 205 and a speaker hole 208. In the microphone hole 205, a microphone for obtaining external sound may be provided inside, and in some embodiments, a plurality of microphones may be provided to detect the direction of the sound. The speaker hole 208 may be used as an external speaker and a receiver for calls. In some embodiments, the speaker hole 208 and the microphone hole 205 may be implemented as one hole, or a speaker may be included without the speaker hole 208 (e.g., a piezoelectric speaker).

[0060] The sensor module 211 may generate an electrical signal or data value corresponding to an internal operating state or an external environmental state of the electronic device 200. The sensor module 211 may include, for example, a biometric sensor module 211 (e.g., an HRM sensor) disposed on the second surface 210B of the housing 210. The electronic device 200 may further include at least one of sensor modules not shown (e.g., a gesture sensor, a gyro sensor, an atmospheric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a color sensor, an infrared (IR) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illumination sensor).

[0061] The sensor module 211 may include electrode regions 213 and 214 forming a portion of the surface of the electronic device 200 and a biosignal detection circuit (not shown) directly or indirectly electrically connected to the electrode regions 213 and 214. For example, the electrode regions 213 and 214 may include a first electrode region 213 and a second electrode region 214 provided on the second surface 210B of the housing 210. The sensor module 211 may be configured such that the electrode regions 213 and 214 obtain an electrical signal from a portion of the user's body, and the biosignal detection circuit detects the user's bio-information based on the electrical signal.

[0062] The key input devices 202, 203, and 204 may include a wheel key 202 disposed on a first surface 210A of the housing 210 and rotatable in at least one direction, and / or side key buttons 203 and 204 disposed on a side surface 210C of the housing 210. The wheel key may have a shape corresponding to the shape of the front plate 201. In another embodiment, the electronic device 200 may not include some or all of the above-mentioned key input devices 202, 203, and 204, and the key input devices 202, 203, and 204 not included may be implemented in other forms such as soft keys on the display 220. The connector hole 209 may accommodate a connector (e.g., a USB connector) for transmitting and receiving power and / or data to and from an external electronic device, and includes another connector hole (not shown) capable of accommodating a connector for transmitting and receiving audio signals to and from an external electronic device. For example, the electronic device 200 may further include a connector cover (not shown) that covers at least a portion of the connector hole 209 and blocks external foreign matter from flowing into the connector hole.

[0063] The binding members 250 and 260 may be detachably attached to at least a portion of the housing 210 using locking members 251 and 261. The binding members 250 and 260 may include one or more of a fixing member 252, a fixing member fastening hole 253, a belt guide member 254, and a belt fixing ring 255.

[0064] The fixing member 252 may be configured to fix the housing 210 and the binding members 250 and 260 to a part of the user's body (e.g., a wrist, an ankle, etc.). Corresponding to the fixing member 252, the fixing member fastening hole 253 may fix the housing 210 and the binding members 250 and 260 to a part of the user's body. The belt guide member 254 may be configured to limit the range of movement of the fixing member 252 when the fixing member 252 is fastened to the fixing member fastening hole 253, so that the binding members 250 and 260 can be tightly connected to a part of the user's body. In a state where the fixing member 252 and the fixing member fastening hole 253 are fastened, the belt fixing ring 255 may limit the range of movement of the binding members 250 and 260.

[0065] Figure 3 is an exploded perspective view of an electronic device according to various embodiments.

[0066] refer to Figure 3 , the electronic device 300 (for example, Figure 1 Electronic equipment 101 or Figure 2a to Figure 2bThe electronic device 200 may include a side frame structure 310, a wheel key 320, a front panel 201, a display 220, a first antenna 350, a second antenna 355 and a support member 360 (e.g., a bracket), a battery 370, a printed circuit board 380, a sealing member 390, a back panel 393, and binding members 395 and 397. At least one of the components of the electronic device 300 may be connected to Figure 1 and / or Figure 2a to Figure 2b At least one of the components of the electronic device 300 is the same or similar, and a repeated description thereof will be omitted. The support member 360 may be disposed inside the electronic device 300 to be directly or indirectly connected to the side frame structure 310, or may be integrally formed with the side frame structure 310. The support member 360 may be formed of, for example, a metal material and / or a non-metallic (e.g., polymer) material. In the support member 360, the display 220 may be directly or indirectly coupled to one surface, and the printed circuit board 380 may be coupled to another surface. The processor, memory, and / or interface may be mounted on the printed circuit board 380. The processor may include, for example, one or more of a central processing unit, a graphics processing unit (GPU), an application processor, a sensor processor, or a communication processor.

[0067] The memory may include, for example, a volatile memory or a nonvolatile memory. The interface may include, for example, a high-definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, and / or an audio interface. For example, the interface may electrically or physically connect the electronic device 300 to an external electronic device, and may include a USB connector, an SD card / MMC connector, or an audio connector.

[0068] The battery 370 is a device for supplying power to at least one component of the electronic device 200 / 300, and may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell. At least a portion of the battery 370 may be disposed on substantially the same plane as, for example, the printed circuit board 380. The battery 370 may be integrally disposed inside the electronic device 200 / 300, or may be detachably disposed from the electronic device 200 / 300.

[0069] The first antenna 350 may be disposed between the display 220 and the support member 360. The first antenna 350 may include, for example, a near field communication (NFC) antenna, a wireless charging antenna, and / or a magnetic secure transmission (MST) antenna. For example, the first antenna 350 may perform short-range communication with an external device, wirelessly send and receive power required for charging, and may send a short-range communication signal including payment data or a self-based signal. In another embodiment, the antenna structure may be formed by a portion of the side frame structure 310 and / or the support member 360, or a combination thereof.

[0070] The second antenna 355 may be disposed between the printed circuit board 380 and the back plate 393. For example, the second antenna 355 may include a near field communication (NFC) antenna, a wireless charging antenna, and / or a magnetic secure transmission (MST) antenna. For example, the second antenna 355 may perform short-range communication with an external device, wirelessly send and receive power required for charging, and may send a short-range communication signal including payment data or a signal based on itself. In another embodiment, the antenna structure may be formed by a portion of the side frame structure 310 and / or the back plate 393, or a combination thereof.

[0071] The sealing member 390 may be located between the side frame structure 310 and the rear plate 393. The sealing member 390 may be configured to block moisture and foreign matter from flowing into a space surrounded by the side frame structure 310 and the rear plate 393 from the outside.

[0072] According to an embodiment, a wearable device (e.g., Figure 1 Electronic devices 101, Figure 2a and Figure 2b The electronic device 200 or Figure 3 The electronic device 300 shown may be worn and operated by a user. For example, the wearable device may be worn on a part of the user's body (e.g., a wrist, a finger, or a face). According to an embodiment, the wearable device may be used to identify whether a fall has occurred to the user of the wearable device. For example, the wearable device may be used to identify whether a fall has occurred while the user is sleeping. For example, the wearable device may be used to identify whether a fall has occurred while the user is awake.

[0073] The operation of the wearable device according to the above-mentioned embodiment may be described below. The wearable device described below may correspond to Figure 1 Electronic devices 101, Figure 2a and Figure 2b The electronic device 200 and / or Figure 3 An electronic device 300.

[0074] Figure 4 is a simplified block diagram of a wearable device according to an embodiment.

[0075] refer to Figure 4 The wearable device 400 may be implemented in various forms. For example, the wearable device 400 may be implemented in various forms that can be worn by a user, such as a smart watch, a smart wristband, a smart ring, a wireless headset, or smart glasses. For example, the wearable device 400 may correspond to Figure 1 Electronic devices 101, Figure 2a and Figure 2b The electronic device 200 and / or Figure 3 An electronic device 300.

[0076] According to an embodiment, the wearable device 400 may include a processor 410, a communication circuit 420, a sensor 430, a memory 440, and / or a microphone 450. According to an embodiment, the wearable device 400 may include at least one of the processor 410, the communication circuit 420, the sensor 430, the memory 440, or the microphone 450. For example, according to an embodiment, at least a portion of the processor 410, the communication circuit 420, the sensor 430, the memory 440, and the microphone 450 may be omitted.

[0077] According to an embodiment, the processor 410 may correspond to Figure 1 Processor 410 may be directly or indirectly operably coupled to (or directly or indirectly connected to) communication circuit 420, sensor 430, memory 440, and microphone 450. For example, when processor 410 is directly or indirectly operably coupled to other components, it may indicate that processor 410 may control other components. Processor 410 may control communication circuit 420, sensor 430, memory 440, and microphone 450. For example, processor 410 may determine the operating moment of sensor 430. Processor 410 may control the operation of sensor 430. Processor 410 may activate or deactivate sensor 430. Processor 410 may process information obtained from sensor 430. Processor 410 herein includes processing circuitry.

[0078] According to an embodiment, the processor 410 may be configured as at least one processor. The processor 410 may include at least one processor. According to an embodiment, the processor 410 may include a hardware component for processing data based on one or more instructions. For example, the hardware component for processing data may include an arithmetic and logic unit (ALU), a field programmable gate array (FPGA) and / or a central processing unit (CPU).

[0079] According to an embodiment, the wearable device 400 may include a communication circuit 420. For example, the communication circuit 420 may correspond to Figure 1 At least a portion of a communication module 190 (eg, a communication module 190 including a communication circuit).

[0080] For example, the communication circuit 420 may be used for various radio access technologies (RATs). For example, the communication circuit 420 may be used to perform Bluetooth communication, wireless local area network (WLAN) communication, or ultra-wideband (UWB) communication. For example, the communication circuit 420 may be used to perform cellular communication.

[0081] For example, the processor 410 may establish a connection with an external electronic device (eg, the electronic device 102, the electronic device 104, and / or the server 108) through the communication circuit 420. For example, the processor 410 may establish a connection with a server through the communication circuit 420.

[0082] According to an embodiment, the wearable device 400 may include a sensor 430. The sensor 430 may be used to obtain various external information. For example, the sensor 430 may be used to obtain data about the user's body. For example, the sensor 430 may be used to obtain data about the user's state, data about the user's movement, and / or data about the user's heart rate. For example, the sensor 430 may correspond to Figure 1 Sensor module 176.

[0083] According to an embodiment, the sensor 430 may include at least one sensor. For example, the sensor 430 may include at least one of an acceleration sensor 431 , a gyro sensor 432 , a photoplethysmography (PPG) sensor 433 , an atmospheric pressure sensor 434 , or an electrode sensor 435 .

[0084] For example, the acceleration sensor 431 can identify (or measure or detect) the acceleration of the wearable device 400 in three directions of the x-axis, y-axis, and z-axis. For example, the gyroscope sensor 432 can identify (or measure or detect) the angular velocity of the wearable device 400 in three directions (x-axis, y-axis, and z-axis). According to an embodiment, the wearable device 400 may include a motion sensor including the acceleration sensor 431 and the gyroscope sensor 432.

[0085] For example, the PPG sensor 433 may be used to measure the pulse (or the change in the amount of blood in the blood vessel) by identifying the change in the amount of photosensitivity of light according to the change in the volume of the blood vessel. For example, the processor 410 may identify the sleeping state or the non-sleeping state (or the active state) of the user based on the biometric data obtained through the PPG sensor 433. For example, the PPG sensor 433 may be used to identify information about changes in the user's heart rate, information about the user's stress based on heart rate variability (HRV), information about the user's sleep stage, information about the user's breathing rate, and information about the user's blood pressure.

[0086] For example, the PPG sensor 433 may include one or more photodiodes (PD), one or more light emitting diodes (LED), or one or more laser diodes (LD). The LED may convert electrical energy into light energy. The PD may convert light energy into electrical energy. When light is transmitted from the LED to the skin, the light is partially absorbed into the skin, and at least a portion of the remaining reflected light may be detected by the PD. For example, the LED may emit light having one or more wavelengths. For example, the light emitted by the LED may include infrared radiation (IR) and visible light.

[0087] For example, the atmospheric pressure sensor 434 may identify (or measure or detect) the atmospheric pressure around the wearable device 400. The processor 410 may identify the height (or altitude) of the wearable device 400 from the ground based on data about the air pressure around the wearable device 400 identified using the atmospheric pressure sensor 434.

[0088] For example, the sensor 430 may include an electrode sensor 435. The processor 410 may identify (or measure) electrodermal activity (EDA) through the electrode sensor 435. The processor 410 may identify information about the tension level of the skin based on the EDA.

[0089] Although not shown, the sensor 430 may further include a sensor for obtaining (or identifying, or measuring, or detecting) various data about the user.

[0090] For example, the sensor 430 may include a body temperature sensor. The processor 410 may measure the skin temperature of a part of the user's body through the body temperature sensor. The processor 410 may obtain the user's body temperature based on the skin temperature of the part of the user's body.

[0091] For example, the sensor 430 may include a heart rate variability (HRV) sensor. The processor 410 may measure the regularity or variability of the heart rate through the HRV sensor. The processor 410 may obtain information about the regularity or variability of the heart rate through the HRV sensor.

[0092] For example, the sensor 430 may include a blood sugar sensor. The processor 410 may identify the blood sugar level of the user by identifying (or measuring) a current generated by causing an electrochemical reaction with blood sugar in the blood.

[0093] According to an embodiment, the wearable device 400 may include a memory 440. The memory 440 may be used to store information or data. For example, the memory 440 may be used to store data obtained from a user. For example, the memory 440 may correspond to Figure 140 may be one or more volatile memory units. For example, memory 440 may be one or more non-volatile memory units. For example, memory 440 may be another type of computer-readable medium, such as a magnetic disk or an optical disk. For example, memory 440 may store data obtained based on operations performed by processor 410 (e.g., algorithm execution operations). For example, memory 440 may store data obtained from sensor 430 (e.g., data about a user's heart rate).

[0094] According to an embodiment, the wearable device 400 may include a microphone 450 (eg, Figure 1 The processor 410 may use the microphone 450 to identify a sound generated from a user (e.g., snoring). The processor 410 may use the sound generated from the user to identify (or determine) the sleeping state of the user.

[0095] According to an embodiment, the wearable device 400 may include various components other than the illustrated components.

[0096] For example, the wearable device 400 may include a display. The display may be used to display various screens. For example, the display may be used to output content, data, or signals through the screen. For example, the display may correspond to Figure 1 The display module 160 is provided.

[0097] For example, the wearable device 400 may further include an actuator. The actuator may correspond to Figure 1 The haptic module 179 of the wearable device 400 may include a touch sensor 179. For example, the actuator may be used to generate vibration of the wearable device 400.

[0098] For example, the wearable device 400 may further include a global positioning system (GPS) circuit. The GPS circuit may be used to identify the location of the wearable device 400.

[0099] Figure 5 An example of a first module and a second module included in a processor according to an embodiment is shown.

[0100] refer to Figure 5 , processor 410 (e.g., Figure 4The processor 410 of the embodiment may include a first module 411 and a second module 412. For example, the first module 411 may be used to identify whether a user falls while the user is sleeping using a first model 501 indicated by a plurality of first parameters. For example, the first module 411 may be referred to as a bed fall detection module. For example, the second module 412 may be used to identify whether a user falls while the user is awake using a second model 502 indicated by a plurality of second parameters. For example, the second module may be referred to as a general fall detection module (or a hard fall detection module).

[0101] According to an embodiment, the first model 501 and the second model 502 may be indicated by a plurality of parameters related to a neural network (or machine learning). For example, the first model 501 of the processor 410 may be indicated by a plurality of first parameters related to a neural network. For example, the second model 502 may be indicated by a plurality of second parameters related to a neural network.

[0102] For example, each of the first model 501 and the second model 502 may include a set of parameters related to the neural network. For example, the plurality of first parameters and / or the plurality of second parameters may represent the assigned weights of the plurality of nodes and / or the connections between the plurality of nodes included in the neural network.

[0103] According to an embodiment, the processor 410 may use the first module 411 to identify whether the user has fallen while the user is sleeping. For example, the processor 410 may use the first model 501 of the first module 411 to identify whether the user has fallen while the user is sleeping. Figure 4 The processor 410 may compare the data obtained by the sensor 430 with the first reference data to identify whether the user has fallen while the user is sleeping. For example, the processor 410 may use the first module 411 to identify whether the user has fallen from the bed while sleeping. According to an embodiment, after the user falls, the processor 410 may use the first module 411 to identify that the user has impacted, and identify (or determine) whether the user is conscious.

[0104] According to an embodiment, the processor 410 may identify whether the user has fallen while the user is awake by using the second module 412. For example, the processor 410 may identify whether the user has fallen while the user is awake by using the second model 502 of the second module 412 and comparing the data obtained by the sensor 430 with the second reference data. For example, the processor 410 may use the second module 412 to identify the type of fall that occurred to the user. For example, the processor 410 may use the second module 412 to identify the type of fall that occurred to the user as at least one of falling while walking, falling while running, falling on a slope (e.g., a ramp or stairs), falling from a height, falling from a bicycle, and falling while climbing. According to an embodiment, after the user falls, the processor 410 may use the second module 412 to identify that the user has hit, and may identify (or determine) whether the user is conscious. According to an embodiment, after the user falls, the processor 410 may bypass (or omit) identifying that the user has hit, and may identify (or determine) whether the user is conscious. According to an embodiment, after a user falls, the processor 410 may avoid recognizing that the user has been hit, and may recognize (or determine) whether the user is conscious.

[0105] According to an embodiment, the processor 410 may activate one of the first module 411 and the second module 412 according to whether the user is sleeping. For example, the processor 410 may activate the first module 411 of the first module 411 and the second module 412 based on recognizing that the user is sleeping. For example, the processor 410 may activate the second module 412 of the first module 411 and the second module 412 based on recognizing that the user is awake.

[0106] Figure 6 is a flowchart illustrating an exemplary operation of a wearable device according to an embodiment.

[0107] In the following embodiments, each operation may be performed sequentially, but not necessarily sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0108] According to an embodiment, operations 610 to 660 may be understood as to be performed in an electronic device (eg, Figure 4 A processor (eg, Figure 4 410).

[0109] refer to Figure 6In operation 610, the processor 410 may identify the distance between the wearable device 400 and the external electronic device (e.g., the electronic device 102, the electronic device 104, and / or the server 108). For example, the processor 410 may detect the distance between the wearable device 400 and the external electronic device (e.g., the electronic device 102, the electronic device 104, and / or the server 108) when recognizing that the user wearing the wearable device 400 is sleeping. Figure 4 The communication circuit 420) identifies the distance between the wearable device 400 and the external electronic device.

[0110] According to an embodiment, the processor 410 may recognize that the user wearing the wearable device 400 is sleeping. For example, the processor 410 may detect that the user wearing the wearable device 400 is sleeping based on the use of the sensor 430 (e.g., Figure 4 For example, the processor 410 may use the information obtained by the acceleration sensor 431 (e.g., Figure 4 431), gyroscope sensor 432 (for example, Figure 4 gyro sensor 432) and / or PPG sensor 433 (e.g., Figure 4 The processor 410 may recognize that the user is sleeping based on recognizing that a value indicating user movement obtained by at least one of the acceleration sensor 431 and / or the gyro sensor 432 is lower than a first threshold and the user's heart rate obtained by the PPG sensor 433 is lower than a threshold.

[0111] For example, the processor 410 may use a global positioning system (GPS) circuit of the wearable device 400 to identify the location of a user wearing the wearable device 400. The processor 410 may use at least one of the user's location, the user's posture, and / or the altitude of the wearable device 400 to identify that the user is lying in bed. The processor 410 may obtain data using a sensor 430 (e.g., an acceleration sensor 431, a gyroscope sensor 432, and / or a PPG sensor 433) during a first sleep identification interval (e.g., about 30 minutes). The processor 410 may obtain data by monitoring the user using the sensor 430 during a first sleep identification interval (e.g., about 30 minutes). The processor 410 may identify that the user is sleeping based on the data obtained during the first sleep identification interval. For example, when the user temporarily wakes up, the processor 410 may identify whether the user falls asleep again based on the data obtained during the second sleep identification interval that is shorter than the first sleep identification interval.

[0112] According to an embodiment, the processor 410 may use the PPG sensor 433 or the electrocardiogram sensor to identify the user's sleep state. For example, the sleep state may include a light sleep state, a deep sleep state, or a rapid eye movement sleep (REM) state (or REM sleep state).

[0113] According to an embodiment, the wearable device 400 can establish a connection with an external electronic device. For example, the wearable device 400 can operate in conjunction with an external electronic device. For example, the processor 410 of the wearable device 400 can control the external electronic device by sending a signal to the external electronic device. For example, the processor 410 can perform an operation based on a signal received from an external electronic device. For example, the wearable device 400 can be connected to an external electronic device with any communication method that supports a time difference of arrival (TDoA) method. For example, the communication method may include Bluetooth communication, Bluetooth low energy (BLE) communication, Wi-Fi communication, near field communication (NFC) communication, and / or ultra-wideband (UWB) communication, but is not limited thereto.

[0114] According to an embodiment, the processor 410 may identify the distance between the wearable device 400 and the external electronic device using the communication circuit 420. For example, the processor 410 may identify the distance between the wearable device 400 and the external electronic device using a UWB signal.

[0115] For example, the processor 410 may send a UWB signal to an external electronic device. The processor 410 may receive a reflected signal of the UWB signal caused by the external electronic device. The processor 410 may identify the time when the UWB signal is sent and the time when the reflected signal is received. The processor 410 may identify the distance between the wearable device 400 and the external electronic device based on the time when the UWB signal is sent and the time when the reflected signal is received.

[0116] For example, the processor 410 may send a UWB signal to an external electronic device. The processor 410 may receive another UWB signal (or a response signal) for the UWB signal from the external electronic device. The processor 410 may identify the moment of sending a UWB signal, the moment of receiving another UWB signal, the moment of receiving a UWB signal from an external electronic device, and the moment of sending another UWB signal from an external electronic device. The processor 410 may identify the distance between the wearable device 400 and the external electronic device based on the moment of sending a UWB signal, the moment of receiving another UWB signal, the moment of receiving a UWB signal from an external electronic device, and the moment of sending another UWB signal from an external electronic device.

[0117] According to an embodiment, the processor 410 may use the sensor 430 (or at least one sensor) to identify the movement of the user. The processor 410 may identify the distance between the wearable device 400 and the external electronic device based on the identified movement of the user. For example, the processor 410 may identify the movement of a sleeping user. The processor 410 may identify the distance between the wearable device 400 and the external electronic device based on the identified movement of the user to identify whether the user is sleeping.

[0118] For example, the processor 410 may identify multiple steps of the user. The processor 410 may identify that the number of the multiple steps of the user is greater than or equal to a threshold number of steps. The processor 410 may identify the movement of the user based on identifying that the number of the multiple steps of the user is greater than or equal to the threshold number of steps. The processor 410 may identify the distance between the wearable device 400 and the external electronic device based on identifying that the number of the multiple steps of the user is greater than or equal to the threshold number of steps.

[0119] According to an embodiment, a plurality of steps may be recognized by the user's movement on the bed through the wearable device 400. The processor 410 may recognize the distance between the wearable device 400 and the external electronic device to recognize that the user has moved after waking up. For example, the processor 410 may recognize the distance between the wearable device 400 and the external electronic device to recognize that the user has left the bed after waking up.

[0120] In operation 620, the processor 410 may identify whether the distance between the wearable device 400 and the external electronic device is less than a reference distance. For example, the processor 410 may identify whether the distance between the wearable device 400 and the external electronic device is less than a reference distance to identify whether the user is sleeping.

[0121] According to an embodiment, when the user is sleeping, the external electronic device may be located at a fixed position. For example, the external electronic device may be located in a bedside space and located at a fixed position when the user is sleeping. For example, when the user is sleeping, the external electronic device may be located at a fixed position around the user. For example, the processor 410 may identify whether the distance between the external electronic device located at a fixed position and the wearable device 400 worn by the user when the user is sleeping is less than a reference distance.

[0122] In operation 630, when the distance between the wearable device 400 and the external electronic device is less than the reference distance, the processor 410 may compare the data obtained by the sensor 430 with the first reference data. For example, the processor 410 may compare the data obtained by the sensor 430 (or at least one sensor) with the first reference data for identifying the first state of the user when the user is sleeping based at least in part on recognizing that the distance between the wearable device 400 and the external electronic device is less than the reference distance.

[0123] According to an embodiment, the processor 410 may recognize that the distance between the wearable device 400 and the external electronic device is less than the reference distance. The processor 410 may recognize that the user maintains a sleeping state based on recognizing that the distance between the wearable device 400 and the external electronic device is less than the reference distance.

[0124] According to an embodiment, the processor 410 may obtain data (e.g., biometric data of the user or data about the user) through the sensor 430. For example, the processor 410 may obtain data through the sensor 430 while the user is sleeping. For example, the processor 410 may obtain data through the sensor 430 to identify whether the user is in a first state. The first state may include a state in which the user falls while sleeping.

[0125] For example, the processor 410 may obtain data through the acceleration sensor 431, the gyroscope sensor 432, the atmospheric pressure sensor 434, and / or the PPG sensor 433 when the user is sleeping. The processor 410 may obtain data through the acceleration sensor 431, the gyroscope sensor 432, the atmospheric pressure sensor 434, and / or the PPG sensor 433 to identify whether the user is in a first state (e.g., a state in which the user falls while sleeping).

[0126] According to an embodiment, the processor 410 may activate the first module 411 (eg, Figure 5 411) and a second module 412 (e.g., Figure 5 The processor 410 may compare the data obtained by the sensor 430 with the first reference data for identifying the first state of the user when the user is sleeping by activating the first module 411. For example, the processor 410 may use the first model 501 (e.g., Figure 5 The processor 410 can identify whether the user falls while the user is sleeping by comparing the data obtained by the sensor 430 with the first reference data using the first model 501 indicated by the plurality of first parameters.

[0127] For example, the first reference data may be used to identify the first state of the user. The processor 410 may identify that the user is in the first state based on identifying that the data obtained by the sensor 430 corresponds to the first reference data.

[0128] For example, the processor 410 may use the acceleration sensor 431 to detect an acceleration greater than or equal to a predefined first magnitude. The processor 410 may use the atmospheric pressure sensor 434 to identify a change in air pressure within a time interval including the moment when the acceleration greater than or equal to the predefined first magnitude is detected. The processor 410 may identify that the user is in the first state based on identifying that the rate of change of air pressure and the amount of change of air pressure meet a predefined first condition. The processor 410 may identify that the user has fallen during sleep based on identifying that the rate of change of air pressure and the amount of change of air pressure meet a predefined first condition.

[0129] Later on Figures 8a to 8d An operation of identifying whether a user falls while the user is sleeping by comparing data obtained using the sensor 430 with first reference data is described in FIG.

[0130] In operation 640, the processor 410 may identify / execute a predetermined first function. For example, the processor 410 may execute the predetermined first function according to a comparison between data obtained by the sensor 430 and first reference data.

[0131] According to an embodiment, the processor 410 may perform a predetermined first function based on a comparison between the data obtained by the sensor 430 and the first reference data, and the predetermined first function is a function for notifying the user that an emergency situation is in progress. For example, the processor 410 may perform a predetermined first function based on a comparison between the data obtained by the sensor 430 and the first reference data to notify the user that a fall has occurred while the user is sleeping.

[0132] For example, the processor 410 may use an external electronic device to send a signal to a predetermined user notifying the user that the user is in an emergency. The processor 410 may send a signal to the external electronic device to call a designated contact (eg, an SOS call or an emergency contact).

[0133] According to an embodiment, the processor 410 may provide a first notification to indicate that the user has fallen while the user is sleeping based on a comparison between the data obtained by the sensor 430 and the first reference data. For example, the first notification may be provided through a display (e.g., Figure 1 For example, the first notification may be displayed by a speaker (e.g., Figure 1 The sound output module 155) outputs the first notification.

[0134] According to an embodiment, the processor 410 may identify that the sleeping user has fallen based on comparing the data obtained by the sensor 430 with the first reference data. In response to identifying that the sleeping user has fallen, the processor 410 may identify the magnitude of the impact applied to the user. The processor 410 may identify the magnitude of the impact applied to the user by using the sensor 430 (e.g., the acceleration sensor 431, the PPG sensor 433, and / or the atmospheric pressure sensor 434).

[0135] For example, the processor 410 may execute a predetermined first function in response to recognizing that the magnitude of the impact applied to the user is greater than or equal to a reference magnitude. The processor 410 may execute a predetermined first function in response to recognizing that the magnitude of the impact applied to the user is greater than or equal to a reference magnitude, and the predetermined first function is a function for notifying the user that an emergency situation is in progress.

[0136] According to an embodiment, the processor 410 may identify whether the user has lost consciousness in response to recognizing that the magnitude of the impact applied to the user is less than a reference magnitude. For example, the processor 410 may identify the movement of the user using the acceleration sensor 431, the gyroscope sensor 432, and / or the atmospheric pressure sensor 434. The processor 410 may identify that the user has lost consciousness based on not recognizing the movement of the user within a specified time and / or the change in altitude or air pressure being less than or equal to a specific magnitude.

[0137] The processor 410 may execute a predetermined first function in response to recognizing that the user has lost consciousness. The processor 410 may execute a predetermined first function in response to recognizing that the user has lost consciousness, even when the magnitude of the impact applied to the user is less than a reference magnitude, the predetermined first function being a function for notifying the user that the user is in an emergency.

[0138] In operation 650, when the distance between the wearable device 400 and the external electronic device is greater than or equal to the reference distance, the processor 410 may compare the data obtained by the sensor 430 (or at least one sensor) with the second reference data. For example, the processor 410 may compare the data obtained by the sensor 430 (or at least one sensor) with the second reference data for identifying the second state of the user when the user is awake based at least in part on recognizing that the distance between the wearable device 400 and the external electronic device is greater than or equal to the reference distance.

[0139] According to an embodiment, the processor 410 may recognize that the distance between the wearable device 400 and the external electronic device is greater than or equal to the reference distance. The processor 410 may recognize that the user is awake based on recognizing that the distance between the wearable device 400 and the external electronic device is greater than or equal to the reference distance.

[0140] According to an embodiment, the processor 410 may obtain data through the sensor 430. For example, the processor 410 may obtain data through the sensor 430 when the user is awake. For example, the processor 410 may obtain data through the sensor 430 to identify whether the user is in the second state. The second state may include a state in which the user falls while the user is awake.

[0141] For example, the processor 410 may obtain data through the acceleration sensor 431 when the user is awake. The processor 410 may obtain data through the acceleration sensor 431 to identify whether the user is in the second state (eg, a state in which the user falls while awake).

[0142] According to an embodiment, the processor 410 may activate the second module 412 of the first module 411 and the second module 412 to identify a fall based at least in part on recognizing that the distance between the wearable device 400 and the external electronic device is greater than or equal to the reference distance. The processor 410 may compare the data obtained by the sensor 430 with the second reference data for identifying the second state of the user when the user is awake by activating the second module 412. For example, the processor 410 may use the second model 502 (e.g., Figure 5 The processor 410 can identify whether the user falls while the user is awake by comparing the data obtained by the sensor 430 with the second reference data using the second model 502 indicated by the plurality of second parameters.

[0143] For example, the second reference data may be used to identify the second state of the user. The processor 410 may identify that the user is in the second state based on identifying that the data obtained by the sensor 430 corresponds to the second reference data.

[0144] For example, the processor 410 may use the acceleration sensor 431 to detect an acceleration greater than or equal to a predefined second magnitude. The processor 410 may use the atmospheric pressure sensor 434 to identify a change in air pressure within a time interval including the moment when the acceleration greater than or equal to the predefined second magnitude is detected. The processor 410 may identify that the user is in the second state based on identifying that the rate of change of air pressure and the amount of change of air pressure meet the predefined second condition. The processor 410 may identify that a fall has occurred while the user is awake based on identifying that the rate of change of air pressure and the amount of change of air pressure meet the predefined second condition.

[0145] Later on Figures 9a to 9d An operation of identifying whether a user falls while the user is awake by comparing data obtained using the sensor 430 with second reference data is described in FIG.

[0146] In operation 660, the processor 410 may identify / execute a predetermined second function. For example, the processor 410 may execute the predetermined second function according to a comparison between the data obtained by the sensor 430 and the second reference data.

[0147] According to an embodiment, the processor 410 may perform a predetermined second function based on a comparison between the data obtained by the sensor 430 and the second reference data, and the predetermined second function is a function for notifying the user that an emergency situation is in progress. For example, the processor 410 may perform a predetermined second function based on a comparison between the data obtained by the sensor 430 and the second reference data to notify the user that a fall has occurred while the user is awake.

[0148] According to an embodiment, the processor 410 may identify the type of fall that occurred to the user using the second module 412. For example, the processor 410 may identify the type of fall that occurred to the user as at least one of falling while walking, falling while running, falling on a slope (e.g., a ramp or stairs), falling from a height, falling from a bicycle, and falling while climbing by using the second module 412.

[0149] Processor 410 may perform a predetermined second function based on the type of fall that has occurred to the user to notify that a fall has occurred. For example, processor 410 may identify that the user has fallen while climbing. Processor 410 may notify that a fall has occurred by sending a rescue signal including user location information and user status.

[0150] According to an embodiment, the processor 410 may provide a second notification to indicate that the user has fallen while the user is awake based on a comparison between the data obtained by the sensor 430 and the second reference data. For example, the second notification may be displayed through a display of the wearable device 400. For example, the second notification may be output through a speaker of the wearable device 400.

[0151] According to an embodiment, the processor 410 may identify that the awake user has fallen based on comparing the data obtained by the sensor 430 with the second reference data. In response to identifying that the awake user has fallen, the processor 410 may identify the magnitude of the impact applied to the user. The processor 410 may identify the magnitude of the impact applied to the user using the sensor 430 (e.g., the acceleration sensor 431, the PPG sensor 433, and / or the atmospheric pressure sensor 434).

[0152] For example, the processor 410 may execute a predetermined second function in response to recognizing that the magnitude of the impact applied to the user is greater than or equal to a reference magnitude. The processor 410 may execute a predetermined second function in response to recognizing that the magnitude of the impact applied to the user is greater than or equal to a reference magnitude, and the predetermined second function is a function for notifying the user that an emergency situation is in progress.

[0153] For example, the processor 410 may identify whether the user has lost consciousness in response to identifying that the magnitude of the impact applied to the user is less than a reference magnitude. The processor 410 may identify the movement of the user using the acceleration sensor 431, the gyroscope sensor 432, and / or the atmospheric pressure sensor 434. The processor 410 may identify that the user has lost consciousness based on not identifying the movement of the user within a specified time or the change in altitude or air pressure being less than or equal to a specific size.

[0154] The processor 410 may execute a predetermined second function in response to recognizing that the user has lost consciousness. The processor 410 may execute a predetermined second function in response to recognizing that the user has lost consciousness, even when the magnitude of the impact applied to the user is less than the reference magnitude, which is a function for notifying the user that the user is in an emergency.

[0155] Figure 7 is a flowchart illustrating an exemplary operation of a wearable device according to an embodiment.

[0156] In the following embodiments, each operation may be performed sequentially, but not necessarily sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0157] According to an embodiment, operations 710 to 740 may be understood as to be performed in an electronic device (eg, Figure 4 A processor (eg, Figure 4 410).

[0158] refer to Figure 7 , operations 710 to 740 can be performed with Figure 6 Operation 610 is related.

[0159] In operation 710, the processor 410 may recognize the movement of the user. For example, the processor 410 may use the sensor 430 (e.g., Figure 4 The processor 410 may use the accelerometer 431 (e.g., the sensor 430) (or at least one sensor) to identify the movement of the user. For example, the processor 410 may identify the movement of the user wearing the wearable device 400. For example, the processor 410 may use the accelerometer 431 (e.g., the sensor 430) (or at least one sensor) to identify the movement of the user. Figure 4 The acceleration sensor 431 and / or Figure 4 The gyroscope sensor 432 in the wearable device 400 is used to identify the movement of the user wearing the wearable device 400.

[0160] In operation 720, the processor 410 may identify whether the movement of the external electronic device corresponds to the movement of the user. For example, the processor 410 may identify whether the movement of the external electronic device corresponds to the movement of the user based on identifying the movement of the user.

[0161] According to an embodiment, the external electronic device (or the processor of the external electronic device) may recognize the movement of the external electronic device. The external electronic device may send information about the movement of the external electronic device to the wearable device 400. The processor 410 may receive information about the movement of the external electronic device from the external electronic device. The processor 410 may recognize whether the movement of the external electronic device corresponds to the movement of the user based on the information about the movement of the external electronic device.

[0162] In operation 730, when the movement of the external electronic device corresponds to the movement of the user, the processor 410 may recognize that the user is awake. For example, the processor 410 may recognize that the user is awake based on recognizing that the movement of the external electronic device corresponds to the movement of the user.

[0163] According to an embodiment, the processor 410 may recognize that the user is moving with the external electronic device based on recognizing that the movement of the external electronic device corresponds to the movement of the user. For example, the processor 410 may recognize that the user is moving while carrying the external electronic device.

[0164] For example, the processor 410 can recognize that the user is moving using the wearable device 400. The processor 410 can recognize that the speed and direction of the movement of the external electronic device corresponds to the speed and direction of the movement of the wearable device 400. The processor 410 can recognize that the movement of the external electronic device corresponds to the movement of the user based on recognizing that the speed and direction of the movement of the external electronic device corresponds to the speed and direction of the movement of the wearable device 400.

[0165] According to an embodiment, the processor 410 may identify that the user is awake based on identifying that the user is moving with the external electronic device. The processor 410 may identify the second state of the user based on identifying that the user is awake. For example, the processor 410 may identify whether the awake user has fallen.

[0166] In operation 740, when the movement of the external electronic device does not correspond to the movement of the user, the processor 410 may identify the distance between the wearable device 400 and the external electronic device. The processor 410 may identify the distance between the wearable device 400 and the external electronic device based on identifying that the movement of the external electronic device does not correspond to the movement of the user. For example, the processor 410 may perform Figure 6 Operation 610.

[0167] According to operations 710 to 740, the processor 410 may identify the movement of the user identified as sleeping. The processor 410 may identify that the user is awake based on that the movement of the external electronic device corresponds to the movement of the user. The processor 410 may identify a second state of the user when awake. The processor 410 may identify whether the user is awake based on the distance between the external electronic device and the wearable device 400 based on that the movement of the external electronic device does not correspond to the movement of the user.

[0168] Figure 8a is a flowchart illustrating an exemplary operation of a wearable device according to an embodiment.

[0169] Figure 8b is an example of a graph representing a change in acceleration recognized in a wearable device over time according to an embodiment.

[0170] Figure 8c is an example of a graph representing changes in air pressure recognized in a wearable device over time according to an embodiment.

[0171] Figure 8d is an example of a graph representing changes in a heart rate recognized in a wearable device over time according to an embodiment.

[0172] In the following embodiments, each operation may be performed sequentially, but not necessarily sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0173] According to an embodiment, operations 810 to 840 may be understood as to be performed in an electronic device (eg, Figure 4 A processor (eg, Figure 4 410).

[0174] refer to Figure 8a In operation 810, the processor 410 (eg, Figure 4 The processor 410 of the wearable device 400 can identify that the user is sleeping. For example, the processor 410 can identify that the user is sleeping based on identifying that the distance between the wearable device 400 and the external electronic device is less than the reference distance.

[0175] In operation 820, the processor 410 may identify whether the value indicating the movement of the wearable device 400 in the first time interval is greater than or equal to the first threshold. In order to identify whether the user is in the first state, the processor 410 may identify whether the value indicating the movement of the wearable device 400 in the first time interval is greater than or equal to the first threshold. For example, the processor 410 may identify whether the value indicating the movement of the wearable device 400 in the first time interval is greater than or equal to the first threshold to identify whether the user has fallen.

[0176] For example, the value indicating the movement of the wearable device 400 may be the magnitude of the acceleration of the wearable device 400. The processor 410 may use the acceleration sensor 431 (e.g., Figure 4 The processor 410 may use the acceleration sensor 431 to obtain (or identify) a value indicating the movement of the wearable device 400 (or the magnitude of the acceleration of the wearable device 400). The processor 410 may identify whether the value indicating the movement of the wearable device 400 (or the magnitude of the acceleration of the wearable device 400) obtained using the acceleration sensor 431 is greater than or equal to the first threshold.

[0177] According to an embodiment, when the value indicating the movement of the wearable device 400 within the first time interval is not greater than or equal to the first threshold, the processor 410 may monitor the movement of the wearable device 400.

[0178] According to an embodiment, when the value indicating the movement of the wearable device 400 in the first time interval is greater than or equal to the first threshold, the processor 410 may identify that the user is in the first state. The processor 410 may identify that the user is in the first state based on the value indicating the movement of the wearable device 400 in the first time interval being greater than or equal to the first threshold. For example, the processor 410 may identify that the user has fallen while the user is sleeping based on the value indicating the movement of the wearable device 400 in the first time interval being equal to or greater than the first threshold.

[0179] refer to Figure 8b , graph 801 shows the change of the magnitude of the acceleration of the wearable device 400 over time. The x-axis of graph 801 represents time. The unit of the x-axis is [milliseconds (ms)]. The y-axis of graph 801 represents the magnitude of the acceleration. The unit of the y-axis is [meters per second squared (m / s2)]. The processor 410 can use the acceleration sensor 431 to identify the acceleration of the wearable device 400 in three directions of the x-axis, y-axis, and z-axis. The processor 410 can perform vector addition based on the vectors representing the acceleration in the three directions of the x-axis, y-axis, and z-axis. The processor 410 can identify the magnitude of the synthetic vector determined according to the vector addition as the magnitude of the acceleration of the wearable device 400.

[0180] For example, in the first time interval 811, the processor 410 may identify that the magnitude of the acceleration identified using the acceleration sensor 431 is greater than or equal to a first threshold value (e.g., 100 m / s2). The processor 410 may identify that the user is in the first state based on identifying that the magnitude of the acceleration identified using the acceleration sensor 431 in the first time interval 811 is greater than or equal to the first threshold value. The processor 410 may identify that the user has fallen while the user is sleeping based on identifying that the magnitude of the acceleration identified using the acceleration sensor 431 in the first time interval 811 is greater than or equal to the first threshold value.

[0181] refer to Figure 8a , the processor 410 can identify the change of air pressure in the first time interval to identify that the user is in the first state. For example, the processor 410 can identify the change of the position of the wearable device 400 from the ground based on the change of air pressure in the first time interval.

[0182] For example, the processor 410 may identify that the user is in the first state based on the maximum change in air pressure within the first time interval being greater than or equal to the first reference change. The processor 410 may identify the minimum air pressure and the maximum air pressure within the first time interval. The processor 410 may identify the maximum change in air pressure based on the minimum air pressure and the maximum air pressure within the first time interval.

[0183] For example, the processor 410 may identify that the user is in the first state based on the air pressure change rate being greater than or equal to the first reference change rate within the first time interval. The processor 410 may identify the gradient of the air pressure within the first time interval. The processor 410 may identify the gradient of the air pressure by identifying the gradient of the air pressure within the first time interval. For example, the processor 410 may identify the gradient of the air pressure by differentiating the air pressure value identified within the first time interval with respect to time. The processor 410 may identify the air pressure change rate based on the identified air pressure change rate.

[0184] refer to Figure 8c , a graph 802 represents the change of the air pressure of the wearable device 400 over time. The x-axis of the graph 802 represents time. The unit of the x-axis is [milliseconds (ms)]. The y-axis of the graph 802 represents the amplitude of the air pressure. The unit of the y-axis is [hPa]].

[0185] For example, the processor 410 may identify the maximum change in air pressure within the first time interval 811. The processor 410 may identify that the user has fallen based on the maximum change in air pressure within the first time interval 811 being greater than or equal to a first reference change (e.g., 0.06 hPa). The processor 410 may identify the height from which the user has fallen (e.g., approximately 70 cm) based on the maximum change in air pressure. The processor 410 may identify the distance between the position from which the user lies on the bed and the ground based on the height from which the user has fallen.

[0186] For example, the processor 410 may identify a gradient of air pressure within the first time interval 811. The processor 410 may identify that the user has fallen while the user is sleeping based on the air pressure change rate within the first time interval 811 being greater than or equal to the first reference change rate.

[0187] refer to Figure 8a The processor 410 can identify that the user is in the first state based on at least one of whether a value indicating the movement of the wearable device 400 within the first time interval is greater than or equal to a first threshold, whether a maximum change in air pressure within the first time interval is greater than or equal to a first reference change, or whether a gradient of air pressure within the first time interval is greater than or equal to a first reference change rate.

[0188] For example, the processor 410 can identify that the user is in the first state based on identifying that the value indicating the movement of the wearable device 400 in the first time interval is greater than or equal to the first threshold, the maximum change in air pressure in the first time interval is greater than or equal to the first reference change, or the gradient of air pressure in the first time interval is greater than or equal to the first reference change rate. For example, the processor 410 can identify that the user has fallen while the user is sleeping based on identifying that the value indicating the movement of the wearable device 400 in the first time interval is greater than or equal to the first threshold, the maximum change in air pressure in the first time interval is greater than or equal to the first reference change, or the gradient of air pressure in the first time interval is greater than or equal to the first reference change rate.

[0189] In operation 830, when the value indicating the movement of the wearable device 400 in the first time interval is greater than or equal to the first threshold, the processor 410 may identify whether the difference between the maximum heart rate and the minimum heart rate in the second time interval is greater than or equal to the threshold heart rate. For example, the processor 410 may identify whether the difference between the maximum heart rate and the minimum heart rate in the second time interval is greater than or equal to the threshold heart rate based on the value indicating the movement of the wearable device 400 in the first time interval being greater than or equal to the first threshold. For example, the processor 410 may identify whether the difference between the maximum heart rate and the minimum heart rate in the second time interval is greater than or equal to the threshold heart rate to identify whether the magnitude of the impact applied to the user is greater than or equal to the reference magnitude.

[0190] Unlike a fall that occurs when the user is awake, when a fall occurs during sleep, the user is generally unable to reduce the impact on the body by reaching out. Therefore, when a fall occurs during sleep, a large impact is caused to the user's body, and therefore, the heart rate may increase after the fall occurs and then decrease again. The processor 410 can identify that the magnitude of the impact applied to the user is greater than or equal to the reference magnitude based on identifying that the heart rate increases and then decreases again after the fall occurs.

[0191] According to an embodiment, after recognizing that the value indicating the movement of the wearable device 400 in the first time interval is greater than or equal to the first threshold, the processor 410 may generate a signal based on the use of the PPG sensor 433 (eg, Figure 4 The PPG sensor 433 of the user is used to identify data about the user's heart rate, and identifies that the difference between the maximum heart rate and the minimum heart rate in a second time interval after the first time interval is greater than or equal to a threshold heart rate.

[0192] For example, the processor 410 may identify data about the user's heart rate within a second time interval after the first time interval in which the user falls. The processor 410 may identify that the difference between the maximum heart rate and the minimum heart rate within the second time interval is greater than or equal to the threshold heart rate. Based on the difference between the maximum heart rate and the minimum heart rate within the second time interval being greater than or equal to the threshold heart rate, it may be identified that the user's heart rate growth rate is greater than or equal to the reference growth rate. The processor 410 may identify that the amplitude of the impact applied to the user is greater than or equal to the reference amplitude based on the user's heart rate growth rate being greater than or equal to the reference growth rate. For example, the threshold heart rate and the reference growth rate may be changed based on the user's age and / or the user's physical activity ability. For example, the processor 410 may identify the user's representative heart rate before the second time interval. For example, the representative heart rate may be set to one of the maximum heart rate, the minimum heart rate, the intermediate heart rate, and the most frequent heart rate or the average heart rate. The threshold heart rate and the reference growth rate may be changed based on the user's representative heart rate.

[0193] According to an embodiment, the processor 410 may identify that the value indicating the movement of the user in the first time interval is greater than or equal to the first threshold value, and the value indicating the movement of the user in the second time interval is less than or equal to the reference value. The processor 410 may identify that the value indicating the movement of the user in the second time interval is less than or equal to the reference value (e.g., the first threshold value) to identify that the movement of the user did not occur after the fall occurred. For example, the processor 410 may identify that the difference between the maximum heart rate and the minimum heart rate in the second time interval is greater than or equal to the threshold heart rate based on identifying that the value indicating the movement of the user in the second time interval is less than or equal to the reference value.

[0194] According to an embodiment, the processor 410 may identify the heart rate within the second time interval to identify that the magnitude of the impact applied to the user is greater than or equal to the reference magnitude.

[0195] For example, the processor 410 may identify that the magnitude of the impact applied to the user is greater than or equal to the reference magnitude based on identifying that the heart rate increase rate within the second time interval is greater than or equal to the reference rate.

[0196] For example, the processor 410 may identify the maximum heart rate and the minimum heart rate in the second time interval. According to an embodiment, the processor 410 may identify the difference between the maximum heart rate and the minimum heart rate in the second time interval as the heart rate growth rate. The processor 410 may identify that the heart rate growth rate in the second time interval is greater than or equal to the reference rate by identifying that the difference between the maximum heart rate and the minimum heart rate is greater than or equal to the threshold heart rate.

[0197] For example, the processor 410 may identify the maximum heart rate in the second time interval and the representative heart rate before the second time interval. According to an embodiment, the processor 410 may identify the difference between the maximum heart rate and the representative heart rate as the heart rate growth rate. The processor 410 may identify that the heart rate growth rate in the second time interval is greater than or equal to the reference rate by identifying that the difference between the maximum heart rate and the representative heart rate is greater than or equal to the threshold heart rate.

[0198] For example, the processor 410 may identify that the magnitude of the impact applied to the user is greater than or equal to the reference magnitude based on identifying that the gradient of the heart rate in the second time interval is greater than or equal to the reference gradient. For example, the processor 410 may identify that the gradient of the heart rate in the second time interval is maintained above the reference gradient. The gradient of the heart rate may increase rapidly according to the wearing state of the wearable device 400 of the user and / or the error of the PPG sensor 433. Therefore, the processor 410 may identify that the magnitude of the impact applied to the user is greater than or equal to the reference magnitude by identifying that the gradient of the heart rate in the second time interval is maintained above the reference gradient.

[0199] refer to Figure 8d , the curve graph 803 represents the change of the user's heart rate over time. The x-axis of the curve graph 803 represents time. The unit of the x-axis is [milliseconds (ms)]. The y-axis of the curve graph 803 represents the heart rate. The unit of the y-axis is [beats per minute (bpm)].

[0200] For example, the processor 410 may identify that the magnitude of the impact applied to the user is greater than or equal to the reference magnitude based on identifying that the growth rate of the heart rate in the second time interval 832 after the first time interval 811 is greater than or equal to the reference growth rate. The processor 410 may identify the maximum heart rate (e.g., about 96) and the minimum heart rate (e.g., about 78) in the second time interval 832. The processor 410 may identify that the difference between the maximum heart rate and the minimum heart rate is greater than or equal to a threshold heart rate (e.g., 15). The processor 410 may identify that the heart rate growth rate in the second time interval 832 is greater than or equal to the reference rate by identifying that the difference between the maximum heart rate and the minimum heart rate is greater than or equal to the threshold heart rate. The processor 410 may identify that the magnitude of the impact applied to the user is greater than or equal to the reference magnitude based on identifying that the heart rate growth rate in the second time interval 832 is greater than or equal to the reference growth rate.

[0201] For example, the processor 410 may recognize that the heart rate increases and then decreases in the second time interval 832 after the first time interval 811. The second time interval 832 may include a heart rate increase interval 833 and a heart rate decrease interval 834. The processor 410 may recognize that the gradient of the heart rate in the heart rate increase interval 833 included in the second time interval 832 is maintained above the reference gradient. The processor 410 may recognize that the magnitude of the impact applied to the user is greater than or equal to the reference magnitude based on recognizing that the gradient of the heart rate in the heart rate increase interval 833 remains higher than the reference gradient.

[0202] refer to Figure 8a , the processor 410 can identify that the amplitude of the impact applied to the user is greater than or equal to the reference amplitude based on at least one of whether the heart rate growth rate in the second time interval is higher than the reference rate and whether the gradient of the heart rate in the second time interval is higher than the reference gradient.

[0203] For example, the processor 410 may identify that the magnitude of the impact applied to the user is greater than or equal to the reference magnitude based on the heart rate increase rate in the second time interval being higher than the reference rate and the heart rate gradient in the second time interval being higher than the reference gradient.

[0204] In operation 840, when the difference between the maximum heart rate and the minimum heart rate in the second time interval is greater than or equal to the threshold heart rate, the processor 410 may execute a predetermined first function. For example, the processor 410 may execute the predetermined first function based on recognizing that the difference between the maximum heart rate and the minimum heart rate in the second time interval is greater than or equal to the threshold heart rate. For example, the processor 410 may execute the predetermined first function to notify that the user has fallen while the user is sleeping. Operation 840 may correspond to Figure 6 Operation 640.

[0205] Figure 9ais a flowchart illustrating an exemplary operation of a wearable device according to an embodiment.

[0206] Figure 9b is an example of a graph representing a change in acceleration recognized in a wearable device over time according to an embodiment.

[0207] Fig.9c is an example of a graph representing changes in air pressure recognized in a wearable device over time according to an embodiment.

[0208] Figure 9d is an example of a graph representing changes in a heart rate recognized in a wearable device over time according to an embodiment.

[0209] In the following embodiments, each operation may be performed sequentially, but not necessarily sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0210] According to an embodiment, operations 910 to 930 may be understood as to be performed in an electronic device (eg, Figure 4 A processor (eg, Figure 4 410).

[0211] refer to Figure 9a In operation 910, the processor 410 may recognize that the user is awake. For example, the processor 410 may recognize that the user is awake based on recognizing that the distance between the wearable device 400 and the external electronic device is greater than or equal to a reference distance.

[0212] For example, in Figure 7 , the processor 410 may recognize that the user is awake by performing an operation corresponding to operation 730. The processor 410 may recognize that the user is awake based on recognizing that the movement of the external electronic device corresponds to the movement of the user.

[0213] For example, in Figure 7 , the processor 410 may recognize that the user is awake by performing an operation corresponding to operation 740. The processor 410 may recognize the distance between the wearable device 400 and the external electronic device, and recognize that the user is awake based on recognizing that the distance between the wearable device 400 and the external electronic device is greater than or equal to the reference distance.

[0214] In operation 920, the processor 410 may identify whether the value indicating the movement of the wearable device 400 in the third time interval is greater than or equal to the second threshold. The processor 410 may identify whether the value indicating the movement of the wearable device 400 in the third time interval is greater than or equal to the second threshold to identify whether the user is in the second state. For example, the processor 410 may identify whether the value indicating the movement of the wearable device 400 in the third time interval is greater than or equal to the second threshold to identify whether the user has fallen.

[0215] For example, the value indicating the movement of the wearable device 400 may be the magnitude of the acceleration of the wearable device 400. The processor 410 may identify the acceleration sensor 431 (eg, Figure 4 Whether the value indicating the movement of the wearable device 400 (or the magnitude of the acceleration of the wearable device 400) obtained by the acceleration sensor 431 is greater than or equal to the second threshold.

[0216] According to an embodiment, when the value indicating the movement of the wearable device 400 within the third time interval is not greater than or equal to the second threshold, the processor 410 may monitor the movement of the wearable device 400.

[0217] According to an embodiment, when the value indicating the movement of the wearable device 400 in the third time interval is greater than or equal to the second threshold, the processor 410 may identify that the user is in the second state. The processor 410 may identify that the user is in the second state based on the value indicating the movement of the wearable device 400 in the third time interval being greater than or equal to the second threshold. For example, the processor 410 may identify that the user has fallen while the user is awake based on the value indicating the movement of the wearable device 400 in the third time interval being greater than or equal to the second threshold.

[0218] refer to Figure 9b , graph 901 shows the change of the magnitude of the acceleration of the wearable device 400 over time. The x-axis of graph 901 represents time. The unit of the x-axis is [milliseconds (ms)]. The y-axis of graph 901 represents the magnitude of the acceleration. The unit of the y-axis is [meters per second squared (m / s2)]. The processor 410 can use the acceleration sensor 431 to identify the acceleration of the wearable device 400 in three directions of the x-axis, y-axis, and z-axis. The processor 410 can perform vector addition based on the vectors representing the acceleration in the three directions of the x-axis, y-axis, and z-axis. The processor 410 can identify the magnitude of the synthetic vector determined according to the vector addition as the magnitude of the acceleration of the wearable device 400.

[0219] For example, in the third time interval 911, the processor 410 may identify that the magnitude of the acceleration identified using the acceleration sensor 431 is greater than or equal to a second threshold value (e.g., 400 m / s2). The processor 410 may identify that the user is in the second state based on identifying that the magnitude of the acceleration identified using the acceleration sensor 431 in the third time interval 911 is greater than or equal to the second threshold value. The processor 410 may identify that the user has fallen while the user is awake based on identifying that the magnitude of the acceleration identified using the acceleration sensor 431 in the third time interval 911 is greater than or equal to the second threshold value.

[0220] For example, the second threshold can be Figure 8a The second threshold value may be set to be greater than the first threshold value. In the event that a fall occurs while the user is awake, the user may reduce the impact on the body by a part of the body (e.g., a hand) when falling. When the wearable device 400 is worn close to a part of the user, the magnitude of the impact applied to the wearable device 400 may be large. Therefore, the processor 410 may set the second threshold value to be greater than the first threshold value.

[0221] refer to Figure 9a , the processor 410 may identify a change in air pressure within a third time interval to identify that the user is in the second state. For example, the processor 410 may identify a change in the position of the wearable device 400 from the ground based on the change in air pressure within the third time interval.

[0222] For example, the processor 410 may identify that the user is in the second state based on the maximum change in air pressure within the third time interval being greater than or equal to the second reference change. The processor 410 may identify the minimum air pressure and the maximum air pressure within the third time interval. The processor 410 may identify the maximum change in air pressure based on the minimum air pressure and the maximum air pressure within the third time interval.

[0223] For example, the processor 410 may identify that the user is in the second state based on the air pressure change rate within the third time interval being greater than or equal to the second reference change rate. The processor 410 may identify the change rate of the air pressure within the third time interval. The processor 410 may identify the change rate of the air pressure by identifying the gradient of the air pressure within the third time interval.

[0224] refer to Fig.9c , a graph 902 shows the change of the air pressure of the wearable device 400 over time. The x-axis of the graph 902 represents time. The unit of the x-axis is [milliseconds (ms)]. The y-axis of the graph 902 represents the amplitude of the air pressure. The unit of the y-axis is [hPa]].

[0225] For example, the processor 410 may identify the maximum change in air pressure within the third time interval 911. The processor 410 may identify that the user has fallen based on the maximum change in air pressure within the third time interval 911 being greater than or equal to the second reference change (e.g., 0.1 hPa). The processor 410 may identify the height from which the user has fallen (e.g., approximately 120 cm) based on the maximum change in air pressure. The processor 410 may identify the height from the ground to the wearable device 400 worn on the user when the user is standing based on the height from which the user has fallen.

[0226] For example, the processor 410 may identify a change rate of the air pressure in the third time interval 911. The processor 410 may identify that the user has fallen while the user is awake based on the change rate of the air pressure in the third time interval 911 being greater than or equal to the second reference change rate.

[0227] refer to Figure 9a The processor 410 can identify that the user is in the second state based on at least one of whether the value indicating the movement of the wearable device 400 within the third time interval is greater than or equal to the second threshold, whether the maximum change in the air pressure within the third time interval is greater than or equal to the second reference change, or whether the change rate of the air pressure within the third time interval is greater than or equal to the second reference change rate.

[0228] For example, the processor 410 can identify that the user is in the second state based on identifying that the value indicating the movement of the wearable device 400 in the third time interval is greater than or equal to the second threshold, the maximum change in air pressure in the third time interval is greater than or equal to the second reference change, and the air pressure change rate in the third time interval is greater than or equal to the second reference change rate. For example, the processor 410 can identify that the user has fallen while the user is awake based on identifying that the value indicating the movement of the wearable device 400 in the third time interval is greater than or equal to the second threshold, the maximum change in air pressure in the third time interval is greater than or equal to the second reference change, and the air pressure change rate in the third time interval is greater than or equal to the second reference change rate.

[0229] In operation 930, when the value indicating the movement of the wearable device 400 in the third time interval is greater than or equal to the second threshold, the processor 410 may perform a predetermined second function. For example, the processor 410 may perform the predetermined second function based on the value indicating the movement of the wearable device 400 in the third time interval being greater than or equal to the second threshold. Operation 930 may correspond to Figure 6 Operation 660.

[0230] According to an embodiment, the processor 410 can avoid recognizing that the difference between the maximum heart rate of the user and the minimum heart rate of the user within the fourth time interval is greater than or equal to the threshold heart rate based on recognizing that the value indicating the movement of the wearable device 400 is greater than or equal to the second threshold, and performing a predetermined second function. For example, the fourth time interval can be set after the third time interval.

[0231] For example, when a user falls while the user is awake, the user's heart rate may not change after the fall. Therefore, processor 410 can avoid identifying that the difference between the user's maximum heart rate and the user's minimum heart rate during the fourth time interval is greater than or equal to the threshold heart rate.

[0232] For example, using a PPG sensor 433 (e.g., Figure 4 The data obtained by the PPG sensor 433) may not be used to identify whether the magnitude of the impact applied to the user is greater than or equal to the reference magnitude.

[0233] refer to Figure 9d , the curve graph 903 shows the change of the user's heart rate over time. The x-axis of the curve graph 903 represents time. The unit of the x-axis is [milliseconds (ms)]. The y-axis of the curve graph 903 represents the heart rate. The unit of the y-axis is [bpm (beats per minute)].

[0234] For example, the processor 410 may identify that the user has fallen while the user is awake during the third time interval 911. The user's heart rate may not change during the fourth time interval 932 after the third time interval 911. Therefore, the processor 410 may avoid or bypass identifying that the difference between the user's maximum heart rate and the user's minimum heart rate during the fourth time interval 932 is greater than or equal to the threshold heart rate.

[0235] Fig.10 is a flowchart illustrating an exemplary operation of a wearable device according to an embodiment.

[0236] In the following embodiments, each operation may be performed sequentially, but not necessarily sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0237] According to an embodiment, operations 1010 to 1020 may be understood as to be performed in an electronic device (eg, Figure 4 A processor (eg, Figure 4 410).

[0238] refer to Fig.10 In operation 1010, the processor 410 may identify physical information about the user. For example, the physical information about the user may include at least one of the user's gender, age, height, weight, and / or physical activity ability.

[0239] In operation 1020, the processor 410 may identify first and second reference data based on the user's body information. For example, the processor 410 may identify first and second reference data for identifying a first state of the user and a second state of the user based on the user's body information.

[0240] According to an embodiment, the data obtained by falling may be different according to at least one of the user's gender, age, height, weight and / or physical activity. Therefore, the processor 410 may change the parameter (e.g., the first threshold or the second threshold) for identifying a fall according to at least one of the user's gender, age, height, weight and / or physical activity.

[0241] For example, the physical activity capacity may be set based on information about the number of steps, distance moved, and calories burned during a specified time period. The processor 410 may be based on the use of sensors 430 (e.g., Figure 4 The processor 410 can identify the user's physical activity ability based on the data obtained by the sensor 430 of the user's mobile phone (the user's mobile phone sensor 430). The processor 410 can identify the user's physical activity ability based on the information about the number of steps, moving distance and calorie consumption during the specified time.

[0242] According to an embodiment, the processor 410 may set (or change) a threshold heart rate for identifying that the amplitude of the impact applied to the user is greater than or equal to the reference amplitude. The processor 410 may set the threshold heart rate based on the user's age and the user's physical activity ability. For example, as the user's age increases, the growth rate of the heart rate caused by falling may increase. For example, as the user's physical activity ability decreases, the growth rate of the heart rate caused by falling may increase. For example, the processor 410 may set the threshold heart rate as shown in Equation 1.

[0243] [Equation 1]

[0244]

[0245] Equation 1 is merely an example for understanding, is not limited thereto, and may be modified, applied, or expanded in various ways.

[0246] Referring to Equation 1, HR thrd is the threshold heart rate. A is the age of the user. P is a value indicating the user's physical activity ability. α(alpha), β(beta), and γ(gamma) are coefficients.

[0247] According to an embodiment, the processor 410 may increase the threshold heart rate as the user's age increases. The processor 410 may increase the threshold heart rate as the user's physical activity ability decreases. Therefore, the processor 410 may provide services for each user by setting different threshold heart rates according to the user.

[0248] Fig.11 is a flowchart illustrating an exemplary operation of a wearable device according to an embodiment.

[0249] In the following embodiments, each operation may be performed sequentially, but not necessarily sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0250] According to an embodiment, operations 1110 to 1150 may be understood as to be performed in an electronic device (eg, Figure 4 A processor (eg, Figure 4 410).

[0251] refer to Fig.11 In operation 1110, the processor 410 may identify that the user is sleeping based on the first information obtained within a first sleep identification interval (e.g., about 30 minutes). For example, the processor 410 may use the sensor 430 (e.g., Figure 4 The processor 410 may use the sensor 430 (or at least one sensor) to obtain the first information. The processor 410 may recognize that the user is sleeping based on the first information obtained using the sensor 430 during the first sleep recognition interval.

[0252] According to an embodiment, the processor 410 may identify the location of the user. For example, the processor 410 may identify that the user's location is in the bedroom. After identifying that the user's location is in the bedroom, the processor 410 may identify that the user is sleeping based on the first information obtained using the sensor 430 within the first sleep recognition interval.

[0253] According to an embodiment, the processor 410 may be based on using an acceleration sensor (eg, Figure 4 431), gyroscope sensor (e.g., Figure 4 gyro sensor 432 in ) and / or a PPG sensor (e.g., Figure 4 The processor 410 may identify that the user is sleeping based on the first information obtained during the first sleep identification interval by the PPG sensor 433 in the processor 410. For example, the processor 410 may identify that a value indicating the movement of the user obtained during the first sleep identification interval by at least one of the acceleration sensor 431 and / or the gyroscope sensor 432 is less than a first threshold, and identify that the user is sleeping based on identifying that the heart rate of the user obtained by the PPG sensor 433 is less than a threshold heart rate.

[0254] According to an embodiment, the processor 410 may activate the first module 411 (eg, Figure 5 411) and the second module 412 (e.g., Figure 5 The processor 410 may activate the first module 411 and deactivate the second module 412 based on recognizing that the user is sleeping. The processor 410 may use the first module 411 to identify whether the user falls while the user is sleeping. Each of the first module 411 and the second module 412 may include a circuit.

[0255] In operation 1120, the processor 410 may recognize that the user is awake. For example, the processor 410 may recognize that the user is awake based at least in part on recognizing that the distance between the wearable device 400 and the external electronic device is greater than or equal to a reference distance.

[0256] For example, processor 410 may recognize that the distance between wearable device 400 and the external electronic device is greater than or equal to a reference distance. Processor 410 may recognize that the user is awake based at least in part on recognizing that the distance between wearable device 400 and the external electronic device is greater than or equal to a reference distance.

[0257] According to an embodiment, the processor 410 may identify whether the distance between the wearable device 400 and the external electronic device is greater than or equal to a reference distance to identify whether the user has left the bed. The external electronic device may be placed in a fixed position when the user is sleeping. The processor 410 may identify whether the user has left the bed by identifying whether the distance between the wearable device 400 and the external electronic device placed in a fixed position when the user is sleeping is greater than or equal to a reference distance.

[0258] According to an embodiment, the processor 410 may identify multiple steps of the user using the sensor 430. The processor 410 may identify that the number of the multiple steps of the user is greater than or equal to the threshold number of steps. The processor 410 may identify the movement of the user based on identifying that the number of the multiple steps of the user is greater than or equal to the threshold number of steps. The processor 410 may identify that the distance between the wearable device 400 and the external electronic device is greater than or equal to the reference distance based on identifying that the number of the multiple steps of the user is greater than or equal to the threshold number of steps. After identifying that the number of the multiple steps of the user is greater than or equal to the threshold number of steps, the processor 410 may identify that the distance between the wearable device 400 and the external electronic device is greater than or equal to the reference distance.

[0259] According to an embodiment, the processor 410 may use a communication circuit (e.g., Figure 4The processor 410 may use the communication circuit 420 to identify the distance between the wearable device 400 and the external electronic device. For example, the processor 410 may use the communication circuit 420 to send a UWB signal to the external electronic device. The processor 410 may identify the distance between the wearable device 400 and the external electronic device based on the reflected signal that the transmitted UWB signal is reflected from the external electronic device and at least partially received. For example, the processor 410 may use the communication circuit 420 to send the UWB signal to an external identification electronic device. The processor 410 may receive a response signal to the UWB signal from the external electronic device. The processor 410 may identify the distance between the wearable device 400 and the external electronic device based on the UWB signal and the response signal. For example, the processor 410 may identify the received signal strength indicator (RSSI) of the Bluetooth signal received from the external electronic device. The processor 410 may identify the distance between the wearable device 400 and the external electronic device based on the received signal strength indicator (RSSI) of the Bluetooth signal received from the external electronic device.

[0260] According to an embodiment, the external electronic device may be carried by the user and moved with the user. The processor 410 may recognize that the movement of the external electronic device corresponds to the movement of the user. The processor 410 may recognize that the user is awake based on the movement of the external electronic device corresponding to the movement of the user.

[0261] For example, the processor 410 may identify a plurality of first steps of the user using the sensor 430. The processor 410 may identify that the number of the plurality of first steps of the user is greater than or equal to a first threshold number of steps. The processor 410 may identify that the number of the plurality of second steps of the user identified by the external electronic device is greater than or equal to a second threshold number of steps. The processor 410 may identify that the user is awake based on identifying that the number of the plurality of first steps is greater than or equal to the first threshold number of steps and the number of the plurality of second steps is greater than or equal to the second threshold number of steps.

[0262] For example, a plurality of first steps may be identified using the sensor 430 of the wearable device 400. A plurality of second steps may be identified using an external electronic device. Since the measuring devices are different, the number of the plurality of first steps and the number of the plurality of second steps may be identified differently. Therefore, the processor 410 may set the first threshold number of steps and the second threshold number of steps differently. The processor 410 may identify that the user wearing the wearable device 400 is moving with the external electronic device based on identifying that the number of the plurality of first steps is greater than or equal to the first threshold number of steps and the number of the plurality of second steps is greater than or equal to the second threshold number of steps. The processor 410 may identify that the user is awake based on identifying that the number of the plurality of first steps is greater than or equal to the first threshold number of steps and the number of the plurality of second steps is greater than or equal to the second threshold number of steps.

[0263] According to an embodiment, the processor 410 may use the atmospheric pressure sensor 434 (e.g., Figure 4 The processor 410 may recognize that the user is awake based on recognizing the change in altitude difference between the wearable device 400 and the external electronic device.

[0264] According to an embodiment, the processor 410 may activate the second module 412 of the first module 411 and the second module 412 to identify a fall based on recognizing that the user is awake. For example, the processor 410 may deactivate the first module 411 and activate the second module 412 based on recognizing that the user is awake. The processor 410 may use the second module 412 to identify whether the user falls while the user is awake.

[0265] In operation 1130, the processor 410 may identify whether the distance between the wearable device 400 and the external electronic device changes within a reference distance range. For example, the processor 410 may identify whether the distance between the wearable device 400 and the external electronic device changes within a reference distance to determine whether the user returns to the bed.

[0266] In operation 1140, when the processor 410 recognizes that the distance between the wearable device 400 and the external electronic device changes within the reference distance, the processor 410 may obtain the second information within the second sleep recognition interval (e.g., about 10 minutes). For example, the processor 410 may obtain the second information within the second sleep recognition interval based on recognizing that the distance between the wearable device 400 and the external electronic device changes within the reference distance.

[0267] In operation 1150, the processor 410 may recognize that the user is sleeping. For example, the processor 410 may recognize that the user is sleeping based on the second information obtained in the second sleep recognition interval.

[0268] For example, the second sleep recognition interval may be set to be shorter than the first sleep recognition interval. The user of the wearable device 400 may temporarily leave the bed while sleeping, then return to the bed and fall asleep. When the user temporarily leaves the bed while sleeping, then returns to the bed and falls asleep, the processor 410 may obtain second information using the sensor 430 (or at least one sensor) within the second sleep recognition interval that is set to be shorter than the first sleep recognition interval. The processor 410 may recognize that the user has fallen asleep again based on the second information obtained using the sensor 430 within the second sleep recognition interval.

[0269] For example, the processor 410 may identify that the user is sleeping based on identifying that the value indicating the user's movement within the second sleep identification interval is less than the first threshold. For example, the processor 410 may identify that the user is sleeping based on identifying that the user's heart rate obtained by the PPG sensor 433 within the second sleep identification interval is less than the threshold heart rate. For example, the processor 410 may identify that the altitude (or distance from the ground) of the wearable device 400 identified by the atmospheric pressure sensor 434 within the second sleep identification interval corresponds to the altitude of the user when sleeping. The processor 410 may identify that the user has fallen asleep again based on identifying that the altitude of the wearable device 400 identified by the atmospheric pressure sensor 434 within the second sleep identification interval corresponds to the altitude of the user when sleeping.

[0270] According to an embodiment, the second module 412 may be used to identify that a user falls while the user is awake, and to identify the type of the fall. Therefore, the power required for the operation of the second module 412 may be greater than the power required for the operation of the first module 411. The processor 410 may change the module for identifying a fall from the second module 412 to the first module 411 by determining whether the user falls asleep again during a second sleep identification interval that is shorter than the first sleep identification interval. By changing the module for identifying a fall from the second module 412 to the first module 411 by the processor 410, power consumption may be reduced and the accuracy for identifying a user fall may be improved.

[0271] In operation 1160, when the distance between the wearable device 400 and the external electronic device does not change within the reference distance, the processor 410 may recognize that the user remains awake. The processor 410 may recognize that the user remains awake based on recognizing that the distance between the wearable device 400 and the external electronic device does not change within the reference distance.

[0272] According to an embodiment, the processor 410 may maintain a module for recognizing a fall as the second module 412 based on recognizing that the user remains awake.

[0273] According to an embodiment, the processor 410 may recognize that the user is awake based on that the movement of the external electronic device corresponds to the movement of the user. After recognizing that the user is awake based on that the movement of the external electronic device corresponds to the movement of the user, the processor 410 may recognize that the user has fallen asleep again based on recognizing that the value indicating the movement of the user within the first sleep recognition interval is less than the first threshold.

[0274] For example, the processor 410 may identify that the user is awake based on the distance between the external electronic device placed in a fixed position and the wearable device 400 when the user is sleeping. The processor 410 may identify that the user has fallen asleep again based on identifying that a value indicating the user's movement within a second sleep identification interval is less than a first threshold. For example, the processor 410 may identify that the user is awake based on that the movement of the external electronic device corresponds to the movement of the user. The processor 410 may identify that the user has fallen asleep again based on identifying that a value indicating the user's movement within a first sleep identification interval longer than the second sleep identification interval is less than a first threshold.

[0275] Fig.12 An example of the operation of the wearable device according to the embodiment is shown.

[0276] refer to Fig.12 , the processor 410 (eg, Figure 4 The processor 410 can identify that the user is sleeping. For example, the processor 410 can use the sensor 430 (e.g., Figure 4 For example, the processor 410 may use the first information obtained by using the acceleration sensor 431 (e.g., Figure 4 The acceleration sensor 431 in the apparatus recognizes that the value indicating the user's movement is less than a first threshold to recognize that the user is sleeping.

[0277] The processor 410 may activate the first module 411 (eg, Figure 4 411) and a second module 412 (e.g., Figure 4 The processor 410 may deactivate the second module 412 and activate the first module 411 based on recognizing that the user is sleeping.

[0278] The processor 410 may identify the distance between the wearable device 400 and the external electronic device 1201. The processor 410 may identify that the user maintains a sleeping state based on identifying that the distance between the wearable device 400 and the external electronic device 1201 is less than a reference distance. The processor 410 may obtain data (e.g., biometric data) about the user through the sensor 430. For example, the data about the user may include at least one of data about the user's movement, data about the orientation of the wearable device 400, data about the user's heart rate, and / or data about the air pressure around the wearable device 400.

[0279] The processor 410 may compare the data about the user with the first reference data. Based on the identification that the data of the user corresponds to the first reference data, the processor 410 may identify that the sleeping user has fallen, and the amplitude of the impact applied to the user is greater than or equal to the reference amplitude. The processor 410 may perform a predetermined first function based on the comparison between the data about the user and the first reference data. For example, the processor 410 may send a signal for calling a specified contact (e.g., an SOS call or an emergency contact) to the external electronic device 1201. The processor 410 may control the external electronic device 1201 to call another user corresponding to the external electronic device 1202 through the external electronic device 1201. For example, the processor 410 may control the external electronic device 1201 to send a signal to the external electronic device 1202 operating as a server (e.g., a server for receiving an emergency signal) through the external electronic device 1201, thereby notifying the user that the user is in an emergency.

[0280] According to an embodiment, there is an effect of detecting a fall of a sleeping user or detecting a fall of an awake user even when no additional infrastructure is established in addition to the wearable device 400 and the external electronic device 1201. According to an embodiment, in a place where a user may have a fatal accident when falling from a bed (e.g., a nursing home or a hospital), there is an effect of quickly determining that the user has fallen and quickly notifying the external electronic devices 1201 and 1202 that the user has fallen during sleep.

[0281] Fig.13 An example of the operation of the wearable device according to the embodiment is shown.

[0282] refer to Fig.13 , the wearable device 400 may be directly or indirectly connected to the external electronic device 1310. The external electronic device 1310 may be directly or indirectly connected to the external electronic device 1320 as well as the wearable device 400. The external electronic device 1320 may be worn on a part of the body of another user different from the user of the wearable device 400. For example, the user and the other user may be located in the same space (e.g., a space corresponding to the size of a bed).

[0283] According to an embodiment, the first account registered in the wearable device 400 and the second account registered in the external electronic device 1320 may be configured as a group (e.g., a family group). The external electronic device 1310 may be used to manage the wearable device 400 according to the first account and to manage the external electronic device 1320 according to the second account. For example, the external electronic device 1310 may control the wearable device 400 and / or the external electronic device 1320.

[0284] According to an embodiment, the processor 410 of the wearable device 400 may identify the distance between the wearable device 400 and the external electronic device 1320. For example, the processor 410 may identify the distance between the wearable device 400 and the external electronic device 1310. The processor 410 may receive information about the distance between the external electronic device 1310 and the external electronic device 1320. The processor 410 may identify a first direction of a signal transmitted from the external electronic device 1310 to the wearable device 400. The processor 410 may identify a second direction of a signal transmitted from the external electronic device 1320 to the external electronic device 1310. The processor 410 may identify an angle between the first direction and the second direction based on the first direction and the second direction. The processor 410 may identify the distance between the wearable device 400 and the external electronic device 1310, the distance between the external electronic device 1310 and the external electronic device 1320, and the identified angle.

[0285] According to an embodiment, the processor 410 may identify whether the user of the wearable device 400 is sleeping based on the distance between the wearable device 400 and the external electronic device 1320. According to an embodiment, the processor 410 may identify whether another user of the external electronic device 1320 is sleeping based on the distance between the wearable device 400 and the external electronic device 1320.

[0286] For example, the processor 410 may recognize that the user is awake by recognizing whether the user of the wearable device 400 has left the bed based on the distance between the wearable device 400 and the external electronic device 1320.

[0287] and Fig.13 Differently, according to an embodiment, the wearable device 400 may be connected to a first electronic device. The external electronic device 1320 may be connected to a second electronic device. The wearable device 400 connected to the first electronic device may establish a connection with the external electronic device 1320 connected to the second electronic device. The wearable device 400 connected to the first electronic device may identify the distance to the external electronic device 1320 connected to the second electronic device, and identify whether the user of the wearable device 400 is sleeping based on the distance.

[0288] Fig.14 is a flowchart illustrating an exemplary operation of a wearable device according to an embodiment.

[0289] In the following embodiments, each operation may be performed sequentially, but not necessarily sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0290] According to an embodiment, operations 1410 to 1430 may be understood as to be performed in an electronic device (eg, Figure 4 A processor (eg, Figure 4 410).

[0291] refer to Fig.14 In operation 1410, the processor 410 may be configured to identify that the wearable device 400 is within a reference range. For example, the processor 410 may be configured to identify whether the wearable device 400 is within the reference range via at least the communication circuit 420. The processor 410 may be configured to identify that the wearable device 400 is within the reference range based on identifying the location of the wearable device 400.

[0292] According to an embodiment, the reference range may be set based on the distance between the wearable device 400 and the external electronic device connected to the wearable device 400. For example, the processor 410 may recognize that the wearable device 400 is outside the reference range based on the fact that the distance between the wearable device 400 and the external electronic device exceeds a threshold distance. For example, the processor 410 may recognize that the wearable device 400 is within the reference range based on the fact that the distance between the wearable device 400 and the external electronic device is less than or equal to the threshold distance.

[0293] According to an embodiment, the reference range may be set based on a specified distance from the wearable device 400. The processor 410 may set an area within the specified distance from the wearable device 400 as the reference range.

[0294] For example, the processor 410 may set a reference range based on a specified distance from a position where the state of the user is identified as a first state. For example, the first state may be referred to as a sleeping state. The second state may be referred to as an awake state. For example, the first state may be referred to as a driving state. The second state may be referred to as a non-driving state. For example, the first state may be referred to as a state in which the user of the wearable device 400 is located in a specified space (e.g., a house). The second state may be referred to as a state in which the user of the wearable device 400 is located outside the specified space (e.g., outside the house).

[0295] In operation 1420, when the wearable device 400 is located within the reference range, the processor 410 may perform a first function for processing data obtained through at least one sensor 430 (eg, at least one sensor) to identify a first type of accident.

[0296] For example, the processor 410 may recognize that the wearable device 400 is located within the reference range. The processor 410 may perform a first function to recognize a first type of accident based on the fact that the wearable device 400 is located within the reference range.

[0297] In operation 1430, when the wearable device 400 is located outside the reference range, the processor 410 may perform a second function for processing data obtained through the sensor 430 (eg, at least one sensor) to identify a second type of accident.

[0298] For example, the processor 410 may recognize that the wearable device 400 is located outside the reference range. The processor 410 may perform a second function to recognize a second type of accident based on the fact that the wearable device 400 is located outside the reference range.

[0299] For example, the processor 410 may activate a first number of sensors based on the execution of the first function. The processor 410 may activate a second number of sensors based on the execution of the second function. The second number may be set to be greater than or equal to the first number. For example, the processor 410 may activate the acceleration sensor 431 based on the execution of the first function. The processor 410 may activate the acceleration sensor 431 and the PPG sensor 433 based on the execution of the second function.

[0300] For example, the first power consumption when executing the first function may be less than the second power consumption when executing the second function. Since the first number of sensors are activated when executing the first function and the second number of sensors are activated when executing the second function, the first power consumption when executing the first function may be less than the second power consumption when executing the second function.

[0301] For example, the first type of accident may include an accident in which the user falls from a bed, and the second type of example accident may include an accident in which the user falls while walking. For example, the first type of accident may include a collision accident between cars, and the second type of example accident may include an accident in which the user falls while walking. For example, the first type of accident may include an accident in which the user falls within a designated space (e.g., home) of the user of the wearable device 400, and the second type of example accident may include an accident in which the user falls outside the designated space of the user of the wearable device 400.

[0302] According to an embodiment, the processor 410 may control to send a first signal indicating the occurrence of the first type of accident based on the occurrence of the first type of accident. For example, the processor 410 may send a first signal indicating the occurrence of the first type of accident in response to the first type of accident.

[0303] For example, the first signal and the second signal may be sent based on a broadcast technology. For example, the first signal and the second signal may include a notification message. For example, the first signal and the second signal may be set so that there is no target device of the signal.

[0304] For example, the processor 410 may control to transmit a first signal to a peripheral device of the wearable device 400 based on the occurrence of a first type of accident. The first signal may be set to cause the peripheral device of the wearable device 400 to provide a notification indicating that the first type of accident has occurred.

[0305] For example, the processor 410 may transmit a second signal to a peripheral device of the wearable device 400 based on the occurrence of the second type of accident. The second signal may be configured to cause the peripheral device of the wearable device 400 to provide a notification indicating that the second type of accident has occurred.

[0306] According to an embodiment, the processor 410 may identify data obtained by at least one component and the sensor 430. For example, the processor 410 may identify data obtained by the microphone 450. For example, the processor 410 may identify one of a first type of accident and a second type of accident based on the data obtained by the microphone 450 and the data obtained by the sensor 430. For example, the processor 410 may identify the occurrence of the first type of accident based on the fact that the loudness of the sound obtained by the microphone 450 is greater than or equal to the threshold loudness. For example, the processor 410 may identify the occurrence of the second type of accident based on the fact that the loudness of the sound obtained by the microphone 450 is less than the threshold loudness.

[0307] According to an embodiment, the processor 410 may identify an accident of a first type. After identifying the accident of the first type, the processor 410 may obtain data related to the accident of the second type through the sensor 430. The processor 410 may identify that an accident of the second type has occurred instead of the accident of the first type based on obtaining data related to the accident of the second type. However, it is not limited thereto. For example, after identifying the accident of the second type, the processor 410 may identify that an accident of the first type has occurred instead of the accident of the second type based on obtaining data related to the accident of the first type.

[0308] Fig.15a An example of the operation of the wearable device according to the embodiment is shown.

[0309] refer to Fig.15a , the processor 410 may set the reference range 1510. The processor 410 may identify at least one of the first type of accident and the second type of accident based on the reference range 1510.

[0310] According to an embodiment, the reference range 1510 may be set based on the distance between the wearable device 400 and the external electronic device 1501 connected to the wearable device 400. The reference range 1510 may be set based on the external electronic device 1501. For example, the processor 410 may recognize that the wearable device 400 is within the reference range based on the fact that the distance between the wearable device 400 and the external electronic device 1501 is less than or equal to a threshold distance. For example, the processor 410 may recognize that the wearable device 400 is outside the reference range based on the fact that the distance between the wearable device 400 and the external electronic device 1501 exceeds a threshold distance.

[0311] For example, the external electronic device 1501 may be in a fixed state. For example, the external electronic device 1501 may be located in a space next to a bed. When the user lies on the bed (or sleeps on the bed), the external electronic device 1501 may be located in a fixed position. The processor 410 may recognize that the position of the external electronic device 1501 remains unchanged. The position of the external electronic device 1501 may be maintained to charge a rechargeable battery of the external electronic device 1501. The processor 410 may recognize that the external electronic device 1501 is in a fixed state based on recognizing that the external electronic device 1501 is charging.

[0312] According to an embodiment, the reference range 1501 may be set based on the location of the wearable device 400. For example, the processor 410 may identify the location of the wearable device 400 based on signal exchange between the wearable device 400 and the peripheral electronic device. For example, the processor 410 may use a GPS circuit to identify information about the latitude and longitude of the wearable device 400. For example, the processor 410 may identify the location of the wearable device 400 within a designated space (e.g., home) of the user.

[0313] For example, the processor 410 may set the reference range 150. For example, the processor 410 may set the sleeping space of the user as the reference range 1501. Fig.15a An example is shown in which the user's sleeping space is set as the reference range 1501 , but is not limited thereto. For example, the processor 410 may set the user's designated space (eg, home) as the reference range 1501 .

[0314] According to an embodiment, the processor 410 may set the reference range based on a specified distance from a position where the state of the user of the wearable device 400 is identified as a first state. For example, the processor 410 may identify the state of the user of the wearable device 400 as a sleeping state. The processor 410 may set the reference range based on a specified distance from a position where the state of the user is identified as a sleeping state.

[0315] Fig.15bAn example of the operation of the wearable device according to the embodiment is shown.

[0316] refer to Fig.15b , the processor 410 may set the reference range 1530. The processor 410 may identify at least one of the first type of accident and the second type of accident based on the reference range 1530.

[0317] For example, the processor 410 may set the reference range 1530 based on the connection with the external electronic device 1550. The processor 410 may set the reference range 1530 based on the connection with the external electronic device 1550 included in the vehicle. The processor 410 may set the space in the vehicle as the reference range 1530. In an example, the reference range 1530 may not be an absolute position. For example, the reference range 1530 may change according to the movement of the vehicle. For example, the processor 410 may maintain the reference range 1530 as the space in the vehicle.

[0318] For example, the processor 410 may establish a connection with the external electronic device 1550 included in the vehicle. The processor 410 may recognize that the user is riding the vehicle based on the connection with the external electronic device 1550. The processor 410 may execute a first function to recognize a first type of accident when the user is riding the vehicle.

[0319] Fig.16 is a flowchart illustrating an exemplary operation of a wearable device according to an embodiment.

[0320] In the following embodiments, each operation may be performed sequentially, but not necessarily sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0321] According to an embodiment, operations 1601 to 1610 may be understood as to be performed via an electronic device (eg, Figure 4 The electronic device 400) includes at least one processor (eg, Figure 4 processor 410) to execute.

[0322] refer to Fig.16 In operation 1601, the processor 410 may identify an access point (AP) directly or indirectly connected to the wearable device 400. For example, the wearable device 400 may be connected via a wireless LAN.

[0323] For example, the wearable device 400 may be located within the coverage of the AP. The wearable device 400 may identify the AP based on at least one operation for network identification. The processor 410 may identify the AP based on active scanning and / or passive scanning. The processor 410 may establish a connection with the AP based on identifying the AP.

[0324] In operation 1602, the processor 410 may identify a plurality of external electronic devices configuring a basic service set (BSS) of an AP. For example, the AP may configure a BSS including the wearable device 400 and a plurality of external electronic devices. Each of the plurality of external electronic devices may be connected to the AP. The plurality of external electronic devices and the wearable device 400 may perform communication through the AP.

[0325] In operation 1603, the processor 410 may identify a first external electronic device for providing a notification from a plurality of external electronic devices. For example, the processor 410 may provide a notification through the wearable device 400 according to the occurrence of one of the first accident or the second accident. When there is no response from the user through the wearable device 400, the processor 410 may identify the first external electronic device for providing a notification.

[0326] For example, the processor 410 may identify a value indicating the priority of each of the plurality of external electronic devices. The processor 410 may identify a first external electronic device for outputting (or providing) a notification among the plurality of external electronic devices based on the value indicating the priority of each of the plurality of external electronic devices.

[0327] For example, a value indicating the priority of each of the plurality of external electronic devices may be identified based on the distance between the wearable device 400 and each of the plurality of external electronic devices. As the distance between each of the plurality of external electronic devices and the wearable device 400 is shorter, the value indicating the priority of each of the plurality of external electronic devices may be set higher. The processor 410 may identify a first external electronic device having the highest priority value among the plurality of external electronic devices.

[0328] According to an embodiment, the processor 410 may configure the BSS of the AP and identify multiple external electronic devices including a component (e.g., a speaker) capable of providing a sound output greater than or equal to a specified level. For example, the processor 410 may identify a first external electronic device including a component capable of providing a maximum sound output among the multiple external electronic devices.

[0329] In operation 1604, the processor 410 may identify whether a first type of accident has occurred. For example, the processor 410 may execute a first function for processing data obtained by at least one sensor to identify a first type of accident. The processor 410 may identify whether a first type of accident has occurred based on the execution of the first function.

[0330] In operation 1605, in the event of a first type of accident, the processor 410 may identify whether a specified time has passed since the time when the first type of accident occurred. For example, the processor 410 may identify whether a specified time has passed since the time when the first type of accident occurred based on the occurrence of the first type of accident.

[0331] According to an embodiment, the wearable device 400 may include a display and a speaker. The processor 410 may output a first notification by using at least one of the display and the speaker in response to identifying the first type of accident. The processor 410 may output a first notification for notifying the occurrence of the first type of accident. For example, the processor 410 may identify whether a specified time has passed / has passed since the time point when the first notification is output.

[0332] According to an embodiment, when the designated time has not elapsed, the processor 410 may perform operation 1605 .

[0333] In operation 1606, when the specified time has passed / has passed, the processor 410 may send a first signal. For example, the processor 410 may send the first signal based on recognizing that the specified time has passed / has passed. For example, the first signal may cause the first external electronic device to output a second notification. The first external electronic device may output a second notification for notifying the occurrence of the first type of accident.

[0334] In operation 1607, when the first type of accident does not occur, the processor may identify whether the second type of accident occurs. For example, the processor 410 may execute a second function for processing data obtained by at least one sensor to identify the second type of accident. The processor 410 may identify whether the second type of accident occurs based on the execution of the second function.

[0335] In operation 1608, when the second type of accident does not occur, the processor 410 may maintain the state of the wearable device. When the second type of accident does not occur, the processor 410 may monitor through the sensor 430 whether one of the first type of accident and the second type of accident occurs.

[0336] In operation 1609, in the case of a second type of accident, the processor 410 may identify whether a specified time has passed since the time when the second type of accident occurred. For example, the processor 410 may identify whether a specified time has passed based on the occurrence of the second type of accident.

[0337] According to an embodiment, the processor 410 may output a third notification by using at least one of a display and a speaker in response to identifying the second type of accident. The processor 410 may output a third notification for notifying the occurrence of the second type of accident. For example, the processor 410 may identify whether a specified time has passed / has passed since the time point when the third notification is output.

[0338] According to an embodiment, when the designated time has not elapsed, the processor 410 may perform operation 1609 .

[0339] In operation 1610, when the specified time has passed / has passed, the processor 410 may send a second signal. For example, the processor 410 may send the second signal based on recognizing that the specified time has passed / has passed. For example, the second signal may cause the first external electronic device to output a fourth notification. The first external electronic device may output a fourth notification for notifying the occurrence of the second type of accident.

[0340] Fig.17 An example of the operation of the wearable device according to the embodiment is shown.

[0341] refer to Fig.17 , the wearable device 400 may be connected to the AP 1700. The processor 410 may identify a plurality of external electronic devices 1710-1, 1710-2, 1710-3, and 1710-4 configuring the BSS of the AP 1700. The processor 410 may identify an external electronic device for providing a notification. For example, the processor 410 may identify an external electronic device 1710-1 including a component capable of providing a maximum sound output among a plurality of external electronic devices as an external electronic device for providing a notification. For example, the processor 410 may identify an external electronic device 1710-3 closest to the wearable device 400 as an external electronic device for providing a notification. For example, the processor 410 may identify an external electronic device 1710-2 worn by another user distinguished from a user of the wearable device 400 among a plurality of external electronic devices as an external electronic device for providing a notification.

[0342] For example, the processor 410 may output a first notification based on the occurrence of a first type of accident by using at least one of a display and a speaker. The processor 410 may identify whether a specified time has passed / has passed from the time point when the first notification is output. The processor 410 may identify whether an input (or movement) of a user of the wearable device 400 occurs during the specified time from the time point when the first notification is output. The processor 410 may send a first signal to an external electronic device for providing a notification based on the specified time having passed / has passed from the time point when the first notification is output. The first signal may cause the external electronic device for providing a notification to output a fourth notification.

[0343] For example, the processor 410 may output a third notification based on the occurrence of the second type of accident by using at least one of a display and a speaker. The processor 410 may identify whether a specified time has passed / has passed from the time point when the third notification is output. The processor 410 may identify whether an input (or movement) of the user of the wearable device 400 occurs during the specified time from the time point when the third notification is output. The processor 410 may send a second signal to an external electronic device for providing a notification based on the specified time having passed / has passed from the time point when the third notification is output. The second signal may cause the external electronic device for providing a notification to output a fourth notification.

[0344] According to an embodiment, the processor 410 may output a notification (e.g., a second notification or a fourth notification) through an external electronic device for providing a notification, and then send a signal for providing a notification through another external electronic device. The processor 410 may sequentially send a signal for providing a notification through multiple external electronic devices. The processor 410 may sequentially send a signal to multiple external electronic devices based on a value indicating a priority. According to an embodiment, the processor 410 may send a signal so that multiple external electronic devices output notifications simultaneously.

[0345] Fig.18 is a flowchart illustrating an exemplary operation of a wearable device according to an embodiment.

[0346] In the following embodiments, each operation may be performed sequentially, but not necessarily sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0347] According to an embodiment, operations 1810 to 1830 may be understood as to be performed in an electronic device (eg, Figure 4 A processor (eg, Figure 4 410).

[0348] In operation 1810, the processor 410 may obtain a value indicating a change in the position of the wearable device 400. For example, the processor 410 may obtain a value indicating a change in the position of the wearable device 400 by using the sensor 430. The processor 410 may recognize a change in the position of the wearable device 400 based on the value indicating a change in the position of the wearable device 400.

[0349] In operation 1820 , the processor 410 may transmit a signal notifying occurrence of a first type of accident in response to a change in a value indicating a change in the position of the wearable device 400 indicating a first reference state of the wearable device 400 .

[0350] According to an embodiment, the wearable device 400 may be in a first reference state. For example, the first reference state may include a state in which an accident occurs when the user of the wearable device 400 is in a car. For example, the first reference state may include a state in which a collision occurs when the user of the wearable device 400 is sleeping. For example, the first reference state may include a state in which a collision occurs when the wearable device 400 is within a reference range.

[0351] According to an embodiment, the processor 410 may send a signal notifying the occurrence of a first type of accident. For example, the processor 410 may send a first signal for calling a designated contact (e.g., an SOS call or an emergency contact). The processor 410 may notify the occurrence of a first type of accident by sending the first signal.

[0352] In operation 1830 , the processor 410 may transmit a signal notifying the occurrence of the second type of accident in response to the change in the value indicating the change in the position of the wearable device 400 indicating the second reference state of the wearable device 400 .

[0353] According to an embodiment, the wearable device 400 may be in a second reference state. For example, the second reference state may include a state in which a collision occurs when the user of the wearable device 400 is walking. For example, the second reference state may include a state in which a collision occurs when the user of the wearable device 400 is awake. For example, the second reference state may include a state in which a collision occurs when the wearable device 400 is located outside a reference range.

[0354] According to an embodiment, the processor 410 may send a signal notifying the occurrence of the second type of accident. For example, the processor 410 may send a second signal for calling a designated contact (e.g., an SOS call or an emergency contact). The processor 410 may notify the occurrence of the second type of accident by sending the second signal.

[0355] In an embodiment, a wearable device (e.g., 101, 200, 300, 400) may include a communication circuit (e.g., 420), at least one sensor (e.g., 430), and a processor (e.g., 410) operably coupled to the communication circuit (e.g., 420) and the at least one sensor (e.g., 430). The processor (e.g., 410) may be configured to identify the distance between the wearable device (e.g., 101, 200, 300, 400) and the external electronic device through the communication circuit (e.g., 420) when recognizing that the user wearing the wearable device (e.g., 101, 200, 300, 400) is sleeping. The processor may be configured to compare data obtained through at least one sensor (e.g., 430) with first reference data for identifying a first state of the user when the user is sleeping, and perform a predetermined (or predefined) first function according to (or based on) the comparison between the data and the first reference data, based at least in part on recognizing that the distance is less than a reference distance. The processor can be configured to compare the data with second reference data, different from the first reference data, for identifying a second state of the user when the user is awake, based at least in part on identifying that the distance is greater than or equal to a reference distance, and perform a predetermined (or predefined) operation according to (or based on) the comparison between the data and the second reference data.

[0356] According to an embodiment, the processor (e.g., 410) may be configured to recognize the movement of the user using at least one sensor (e.g., 430). The processor (e.g., 410) may be configured to recognize the distance between the wearable device (e.g., 101, 200, 300, 400) and the external electronic device based on the recognized movement of the user.

[0357] According to an embodiment, the processor (eg, 410) may be configured to recognize the distance between the wearable device (eg, 101, 200, 300, 400) and the external electronic device only when the user moves. Thus, power consumption is reduced.

[0358] According to an embodiment, the processor (e.g., 410) may be configured to use at least one sensor (e.g., 430) to identify the movement of the user. The processor (e.g., 410) may be configured to identify that the movement of the user corresponds to the movement of the external electronic device. The processor (e.g., 410) may be configured to identify that the user is awake based on the movement of the user corresponding to the movement of the external electronic device. The processor (e.g., 410) may be configured to perform a predetermined second function according to (or based on) a comparison between the data and the second reference data.

[0359] According to an embodiment, since the processor (eg, 410) identifies whether the movement of the external electronic device corresponds to the movement of the user, there is an effect of identifying whether the user moves while holding the external electronic device.

[0360] According to an embodiment, the first state may include a state in which the user falls while the user is sleeping. The second state may include a state in which the user falls while the user is awake.

[0361] According to an embodiment, at least one sensor may include a photoplethysmography (PPG) sensor (e.g., 433) and an acceleration sensor (e.g., 431). The processor may be configured to: based on data related to the acceleration of the wearable device identified using the acceleration sensor (e.g., 431), identify that when the distance is less than the reference distance, the value indicating the movement of the wearable device in the first time interval is greater than or equal to the first threshold.

[0362] The processor (e.g., 410) may be configured to, after identifying that the value representing the movement of the wearable device in the first time interval is greater than or equal to the first threshold, identify that the difference between the maximum heart rate and the minimum heart rate in a second time interval after the first time interval is greater than or equal to the threshold heart rate based on data related to the user's heart rate obtained (or identified) using the PPG sensor (e.g., 433). The processor (e.g., 410) may be configured to perform a first predetermined function based on identifying that the difference between the maximum heart rate and the minimum heart rate in the second time interval is greater than or equal to the threshold heart rate.

[0363] According to an embodiment, the processor (e.g., 410) may be configured to: based on data related to the acceleration of the wearable device (e.g., 101, 200, 300, 400) identified using an acceleration sensor (e.g., 431), identify that when the distance is greater than or equal to the reference distance, the value representing the movement of the wearable device in the third time interval is greater than or equal to the second threshold. The processor (e.g., 410) may be configured to perform a second predetermined function based on identifying that the value representing the movement of the wearable device in the third time interval is greater than or equal to the second threshold.

[0364] According to an embodiment, when a fall occurs to a conscious user, there is an effect of reducing power consumption by not recognizing the fall using a PPG sensor.

[0365] According to an embodiment, the processor (e.g., 410) can be configured to: based on identifying that the value representing the movement of the wearable device is greater than or equal to a second threshold, bypass identifying that the difference between the maximum heart rate and the minimum heart rate in a fourth time interval after the third time interval is greater than or equal to the threshold heart rate, and perform a second predetermined function.

[0366] According to an embodiment, the processor (eg, 410) may be configured to identify body information related to a user.The processor (eg, 410) may be configured to identify first reference data and second reference data based on the body information of the user.

[0367] According to an embodiment, the processor (e.g., 410) may be configured to compare the data with first reference data for identifying a first state of the user when the user is asleep by activating a first module (e.g., 411) of the first module (e.g., 411) and the second module (e.g., 412) for identifying a fall, based at least in part on recognizing that the distance is less than a reference distance. The processor (e.g., 410) may be configured to compare the data with second reference data for identifying a second state of the user when the user is awake, based at least in part on recognizing that the distance is greater than or equal to a reference distance, by activating a second module (e.g., 412) of the first module (e.g., 411) and the second module (e.g., 412) for identifying a fall.

[0368] According to an embodiment, a first module (e.g., 411) may be used to identify whether a user falls when the user is asleep using a first model (e.g., 501) indicated by a plurality of first parameters. A second module (e.g., 412) may be used to identify whether a user falls when the user is awake using a second model (e.g., 502) indicated by a plurality of second parameters.

[0369] According to an embodiment, when the user is awake, whether the user falls while the user is awake can be identified based on the second module 412. When the user is sleeping, whether the user falls while the user is sleeping can be identified based on the first module 411.

[0370] According to an embodiment, the processor (e.g., 410) may be configured to recognize that the user is sleeping in a first sleep recognition interval based on first information obtained using at least one sensor (e.g., 430). The processor (e.g., 410) may be configured to recognize that the user is awake based at least in part on recognizing that the distance is greater than or equal to a reference distance. The processor (e.g., 410) may be configured to obtain second information using at least one sensor (e.g., 430) in a second sleep recognition interval shorter than the first sleep recognition interval based on recognizing that the distance changes within the reference distance after recognizing that the user is awake. The processor (e.g., 410) may be configured to recognize that the user is sleeping based on the second information.

[0371] According to an embodiment, the processor (eg, 410 ) may be configured to execute a predetermined first function according to (or based on) a comparison between the data and first reference data, the predetermined first function being a function for notifying a user that an emergency situation is in progress.

[0372] According to an embodiment, the processor (eg, 410 ) may be configured to transmit a signal for notifying a predetermined user that the user is in an emergency situation using an external electronic device.

[0373] According to an embodiment, the processor (e.g., 410) may be configured to provide a first notification indicating that the user has fallen while the user is asleep according to (or based on) a comparison between the data and the first reference data. The processor (e.g., 410) may be configured to provide a second notification indicating that the user has fallen while the user is awake according to (or based on) a comparison between the data and the second reference data.

[0374] According to an embodiment, the processor (e.g., 410) may be configured to identify the distance between the wearable device (e.g., 101, 200, 300, 400) and another external electronic device. The processor (e.g., 410) may be configured to identify whether the user is sleeping based on the distance between the wearable device (e.g., 101, 200, 300, 400) and another external electronic device.

[0375] According to an embodiment, the processor (e.g., 410) may be configured to receive information related to the distance between the external electronic device and the other external electronic device from an external electronic device connected to the other external electronic device. The processor (e.g., 410) may be configured to identify the distance between the wearable device and the other external electronic device based on the distance between the wearable device and the external electronic device and the distance between the external electronic device and the other external electronic device.

[0376] According to an embodiment, a method of a wearable device (e.g., 101, 200, 300, 400) may include: after recognizing that a user wearing the wearable device (e.g., 101, 200, 300, 400) is sleeping, identifying the distance between the wearable device (e.g., 101, 200, 300, 400) and the external electronic device through a communication circuit (e.g., 420) of the wearable device (e.g., 101, 200, 300, 400). The method may include: at least partially based on recognizing that the distance is less than a reference distance, comparing data obtained by at least one sensor (e.g., 430) of the wearable device (e.g., 101, 200, 300, 400) with first reference data for identifying a first state of the user when the user is sleeping, and performing a predetermined first function according to (or based on) the comparison between the data and the first reference data. The method may include: at least in part based on identifying that the distance is greater than or equal to a reference distance, comparing the data with second reference data that is different from the first reference data and is used to identify a second state of the user when the user is awake, and performing a predetermined second function according to (or based on) the comparison between the data and the second reference data.

[0377] According to an embodiment, the method may include: based on data related to the acceleration of the wearable device (e.g., 101, 200, 300, 400) identified using an acceleration sensor (e.g., 431) in at least one sensor (e.g., 430), when the distance is less than a reference distance, identifying that a value indicating movement of the wearable device in a first time interval is greater than or equal to a first threshold. The method may include: after identifying that the value indicating movement of the wearable device in the first time interval is greater than or equal to the first threshold, based on data related to the user's heart rate identified using a PPG sensor (e.g., 433) in at least one sensor (e.g., 430), identifying that a difference between a maximum heart rate and a minimum heart rate in a second time interval after the first time interval is greater than or equal to a threshold heart rate. The method may include: based on identifying that the difference between a maximum heart rate and a minimum heart rate in the second time interval is greater than or equal to the threshold heart rate, executing a first predetermined function.

[0378] According to an embodiment, the method may include: based on data related to the acceleration of the wearable device (e.g., 101, 200, 300, 400) identified using an acceleration sensor (e.g., 431), when the distance is greater than or equal to the reference distance, identifying that the value representing the movement of the wearable device in the third time interval is greater than or equal to the second threshold. The method may include: based on identifying that the value representing the movement of the wearable device in the third time interval is greater than or equal to the second threshold, executing the second predetermined function.

[0379] According to an embodiment, the method may include: based on identifying that the value representing the movement of the wearable device is greater than or equal to a second threshold, bypassing identifying that the difference between the maximum heart rate and the minimum heart rate in a fourth time interval after the third time interval is greater than or equal to the threshold heart rate, and performing a second predetermined function.

[0380] According to an embodiment, there is an effect of identifying whether a user falls while the user is sleeping. According to an embodiment, there is an effect of accurately detecting a fall of a user according to the state of the user (e.g., a sleep state or a weather state) by identifying whether the user is leaving the bed (or sleeping) and whether the user is returning. According to an embodiment, the processor 410 identifies body information about the user and accordingly changes parameters (e.g., a threshold heart rate) for identifying a fall, thereby providing an enhanced user experience.

[0381] According to an embodiment, a wearable device (e.g., 101, 200, 300, 400) may include a communication circuit (e.g., 420), at least one sensor (e.g., 430), and at least one processor (e.g., 410) operably connected to the communication circuit and the at least one sensor. The at least one processor may be configured to identify that the wearable device is within a reference range via at least the communication circuit. The at least one processor may be configured to execute a first function for processing data obtained by at least one sensor when the wearable device is within the reference range to identify that a first type of accident has occurred. The at least one processor may be configured to execute a second function for processing the data when the wearable device is outside the reference range to identify that a second type of accident has occurred.

[0382] According to an embodiment, at least one processor may be configured to control to send a first signal indicating the occurrence of a first type of accident based on the occurrence of a first type of accident. At least one processor may be configured to control to send a second signal indicating the occurrence of a second type of accident based on the occurrence of a second type of accident. The first signal may be configured to cause a peripheral device of the wearable device to provide a notification indicating the occurrence of the first type of accident. The second signal may be configured to cause a peripheral device of the wearable device to provide a notification indicating the occurrence of the second type of accident.

[0383] According to an embodiment, the reference range may be set based on the distance between the external electronic device connected to the wearable device and the wearable device. At least one processor may be configured to identify that the wearable device is outside the reference range based on the distance between the wearable device and the external electronic device being greater than a threshold distance. At least one processor may be configured to identify that the wearable device is within the reference range based on the distance between the wearable device and the external electronic device being less than or equal to a threshold distance.

[0384] The at least one processor may be configured to set the reference range based on a specified distance from a location where a state of the user of the wearable device is identified as the first state.

[0385] The at least one processor may be configured to enable a first number of sensors based on execution of the first function. The at least one processor may be configured to enable a second number of sensors greater than or equal to the first number based on execution of the second function.

[0386] According to an embodiment, first power consumption when executing the first function may be less than second power consumption when executing the second function.

[0387] According to an embodiment, at least one processor may be configured to identify an access point (AP) connected to the wearable device. At least one processor may be configured to identify multiple external electronic devices that configure a basic service set (BSS) associated with the AP. At least one processor may be configured to identify a first external electronic device for providing a notification among the multiple external electronic devices.

[0388] According to an embodiment, the wearable device may include a display and a speaker. At least one processor may be configured to control to output a first notification via at least one of a display or a speaker in response to identifying the occurrence of a first type of accident. At least one processor may be configured to control to send a first signal for causing the first external electronic device to output a second notification based on the time that a specified time has passed since the time when the first notification was output.

[0389] According to an embodiment, at least one processor may be configured to control to output a third notification via at least one of a display or a speaker in response to identifying the occurrence of a second type of accident. At least one processor may be configured to control to send a second signal for causing the first external electronic device to output a fourth notification based on a specified time having passed / has passed since the time when the third notification was output.

[0390] The at least one processor may be configured to identify a value indicating a priority of each of the plurality of external electronic devices. The at least one processor may be configured to identify a first external electronic device for providing a notification among the plurality of external electronic devices based on the value indicating a priority of each of the plurality of external electronic devices.

[0391] According to an embodiment, at least one processor is configured so that a value indicating a priority of each of a plurality of external electronic devices can be identified based on a distance between the wearable device and each of the plurality of external electronic devices.

[0392] According to an embodiment, at least one processor may be configured to identify a plurality of external electronic devices including a component capable of providing a sound output above a designated level.

[0393] According to an embodiment, a method of a wearable device may include identifying that the wearable device is within a reference range via a communication circuit (e.g., 420) of the wearable device (e.g., 101, 200, 300, 400). The method may include: when the wearable device is within the reference range, executing a first function of processing data obtained by at least one sensor (e.g., 430) of the wearable device to identify that a first type of accident has occurred. The method may include: when the wearable device is outside the reference range, executing a second function of processing the data to identify that a second type of accident has occurred.

[0394] According to an embodiment, the method may include: based on the occurrence of the first type of accident, sending a first signal indicating the occurrence of the first type of accident. The method may include: based on the occurrence of the second type of accident, sending a second signal indicating the occurrence of the second type of accident. The first signal may be configured to cause the peripheral device to provide a notification indicating the occurrence of the first type of accident. The second signal may be configured to cause the peripheral device to provide a notification indicating the occurrence of the second type of accident.

[0395] According to an embodiment, the reference range may be set based on a distance between an external electronic device connected to the wearable device and the wearable device. The method may include identifying that the wearable device is outside the reference range based on the distance between the wearable device and the external electronic device being greater than a threshold distance. The method may include identifying that the wearable device is within the reference range based on the distance between the wearable device and the external electronic device being less than or equal to a threshold distance.

[0396] According to an embodiment, first power consumption when executing the first function may be less than second power consumption when executing the second function.

[0397] According to an embodiment, the method may include identifying an access point (AP) connected to the wearable device. The method may include identifying multiple external electronic devices configuring a basic service set (BSS) associated with the AP. The method may include identifying a first external electronic device for providing a notification among the multiple external electronic devices.

[0398] According to an embodiment, the method may include: in response to identifying the occurrence of the first type of accident, outputting a first notification using at least one of a display of the wearable device or a speaker of the wearable device. The method may include sending a first signal configured to cause the first external electronic device to output a second notification based on the elapse of a specified time from the time when the first notification was output.

[0399] According to an embodiment, the method may include: in response to identifying the occurrence of the second type of accident, outputting a third notification using at least one of a display or a speaker. The method may include sending a second signal configured to cause the first external electronic device to output a fourth notification based on the elapse of a specified time from the time when the third notification was output.

[0400] According to an embodiment, the method may include identifying a value indicating a priority of each of the plurality of external electronic devices. The method may include identifying a first external electronic device for providing a notification among the plurality of external electronic devices based on the value indicating the priority of each of the plurality of external electronic devices.

[0401] Each embodiment herein may be used in combination with any other embodiment described herein.The reference numerals herein are for illustrative purposes only and are not limiting.

[0402] In various embodiments of the present disclosure, "based on" as used herein encompasses being based at least in part on.

[0403] The electronic device according to various embodiments may be one of various types of electronic devices. The electronic device may include, for example, a portable communication device (e.g., a smart phone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a household appliance. According to an embodiment of the present disclosure, the electronic device is not limited to those described above.

[0404] It should be understood that the various embodiments of the present disclosure and the terms used therein are not intended to limit the technical features set forth herein to specific embodiments, and include various changes, equivalents or replacements for corresponding embodiments. With regard to the description of the accompanying drawings, similar reference numerals may be used to refer to similar or related elements. It should be understood that, unless otherwise clearly stated in the relevant context, the singular form of a noun corresponding to an item may include one or more things. As used herein, each of the 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 include any one or all possible combinations of the items listed together in a corresponding phrase. As used herein, terms such as "first" and "second" may be used to simply distinguish a corresponding component from another component, and do not limit the component in other aspects (e.g., importance or order). It should be understood that if an element (e.g., a first element) is referred to as being “coupled with another element (e.g., a second element)”, “coupled to another element (e.g., the second element)”, “connected with another element (e.g., the second element)” or “connected to another element (e.g., the second element)” with or without the term “operably” or “communicatively”, it means that the element can be coupled to the other element directly (e.g., by wire), wirelessly, or via at least a third element.

[0405] As used in conjunction with various embodiments of the present disclosure, the term "module" may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with other terms (e.g., "logic," "logic block," "portion," or "circuit"). A module may be a single integrated component or its smallest unit or portion suitable for performing one or more functions. For example, depending on the embodiment, a module may be implemented in the form of an application specific integrated circuit (ASIC). Therefore, each "module" herein may include a circuit.

[0406] The various embodiments described herein may be implemented as software (e.g., program 140) including one or more instructions stored in a storage medium (e.g., internal memory 136 or external memory 138) readable by a machine (e.g., electronic device 101). For example, a processor (e.g., processor 120) of a machine (e.g., electronic device 101) may call at least one of the one or more instructions stored in the storage medium and execute it with or without one or more other components under the control of the processor. This allows the machine to be operated to perform at least one function according to at least one instruction called. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Among them, the term "non-transitory" simply means that the storage medium is a tangible device and does not include signals (e.g., electromagnetic waves), but the term does not distinguish between a case where data is semi-permanently stored in the storage medium and a case where data is temporarily stored in the storage medium.

[0407] According to an embodiment, the method according to various embodiments of the present disclosure may be included and provided 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 machine-readable storage medium (e.g., a compact disc read-only memory (CD-ROM)), or via an application store (e.g., PlayStore). TM ) online distribution (e.g., download or upload), or directly between two user devices (e.g., smart phones) (e.g., download or upload). If distributed online, at least a portion of the computer program product may be temporarily generated or at least temporarily stored in a machine-readable storage medium (such as a memory of a manufacturer's server, a server of an application store, or a relay server).

[0408] According to various embodiments, each of the above-mentioned components (e.g., a module or a program) may include a single entity or multiple entities, and some of the multiple entities may be separately arranged in different components. According to various embodiments, one or more of the above-mentioned components may be omitted, or one or more other components may be added. Alternatively or additionally, multiple components (e.g., a module or a program) may be integrated into a single component. In this case, according to various embodiments, the integrated component may still perform one or more functions of each of the multiple components in the same or similar manner as performed by the corresponding components in the multiple components before integration. According to various embodiments, the operations performed by a module, a program or another component may be performed sequentially, in parallel, repeatedly or heuristically, or one or more of the operations may be performed or omitted in a different order, or one or more other operations may be added.

[0409] Although the present disclosure has been shown and described with reference to various embodiments, it should be understood that the various embodiments are intended to be illustrative rather than restrictive. It will be further understood by those skilled in the art that various changes may be made in form and detail without departing from the true spirit and full scope of the present disclosure (including the appended claims and their equivalents). It should also be understood that any embodiment described herein may be used in combination with any other embodiment described herein.

Claims

1. A wearable device (101; 200 ； 300; 400), including: Communication circuit (420); at least one sensor (430); and At least one processor (410) is operably coupled to the communication circuit (420) and the at least one sensor (430), wherein the at least one processor (410) is configured to: identifying, via at least the communication circuit (420), that the wearable device (101; 200; 300; 400) is within a reference range; When the wearable device (101; 200; 300; 400) is within the reference range, performing a first function for processing data obtained by the at least one sensor (430) to identify that a first type of accident has occurred; and When the wearable device (101; 200; 300; 400) is outside the reference range, a second function for processing the data obtained by the at least one sensor (430) is performed to identify that a second type of accident has occurred.

2. The wearable device (101; 200; 300; 400) according to claim 1, wherein the at least one processor is further configured to: Based on the occurrence of the first type of accident, sending a first signal indicating the occurrence of the first type of accident, Based on the occurrence of the second type of accident, sending a second signal indicating the occurrence of the second type of accident, and in, The first signal is configured to cause the peripheral device to provide a notification indicating the occurrence of a first type of incident, and The second signal is configured to cause the peripheral device to provide a notification indicating the occurrence of a second type of accident.

3. The wearable device (101; 200; 300; 400) according to claim 1, in, The reference range is set based on a distance between an external electronic device connected to the wearable device (101; 200; 300; 400) and the wearable device (101; 200; 300; 400), Wherein, the at least one processor is further configured to: Based on the distance between the wearable device (101; 200; 300; 400) and the external electronic device being greater than a threshold distance, identifying that the wearable device (101; 200; 300; 400) is outside the reference range, and Based on the distance between the wearable device (101; 200; 300; 400) and the external electronic device being less than or equal to the threshold distance, identifying the wearable device (101; 200; 300; 400) are within the reference range.

4. The wearable device (101; 200; 300; 400) according to claim 1, in, The at least one processor is further configured to: The reference range is set based on a specified distance from a position where a state of a user of the wearable device (101; 200; 300; 400) is identified as a first state.

5. The wearable device (101; 200; 300; 400) according to claim 1, in, The at least one processor is further configured to: Based on the execution of the first function, a first number of sensors are enabled, and Based on the execution of the second function, a second number of sensors is enabled, wherein the second number is greater than or equal to the first number.

6. The wearable device (101; 200; 300; 400) according to claim 5, in, The first power consumption when the first function is executed is less than the second power consumption when the second function is executed.

7. The wearable device (101; 200; 300; 400) according to claim 1, in, The at least one processor is further configured to: identifying an access point AP connected to the wearable device (101; 200; 300; 400), identifying a plurality of external electronic devices configured with a basic service set (BSS) associated with the AP, and A first external electronic device for providing a notification among the plurality of external electronic devices is identified.

8. The wearable device (101; 200; 300; 400) according to claim 7, further comprising: include: monitor; as well as speaker, Wherein, the at least one processor is further configured to: In response to identifying the occurrence of a first type of accident, outputting a first notification via at least one of the display or the speaker, and Based on the lapse of a specified time from the time when the first notification was output, a first signal for causing the first external electronic device to output a second notification is transmitted.

9. The wearable device (101; 200; 300; 400) according to claim 8, in, The at least one processor is further configured to: in response to identifying the occurrence of the second type of accident, outputting a third notification using at least one of the display or the speaker, and Based on the lapse of a specified time from the time when the third notification is output, a second signal for causing the first external electronic device to output a fourth notification is transmitted.

10. The wearable device (101; 200; 300; 400) according to claim 7, in, The at least one processor is further configured to: identifying a value indicating a priority of each of the plurality of external electronic devices, and A first external electronic device for providing a notification among the plurality of external electronic devices is identified based on a value indicating a priority of each of the plurality of external electronic devices.

11. The wearable device (101; 200; 300; 400) according to claim 10, in, A value indicating a priority of each of the plurality of external electronic devices is identified based on a distance between the wearable device (101; 200; 300; 400) and each of the plurality of external electronic devices.

12. The wearable device (101; 200; 300; 400) according to claim 7, in, The at least one processor is further configured to identify the plurality of external electronic devices configuring a BSS associated with the AP that include components capable of providing a sound output above a specified level.

13. A wearable device (101; 200 ； 300; 400), the method comprising: Identifying, via a communication circuit of the wearable device (101; 200; 300; 400), that the wearable device (101; 200; 300; 400) is within a reference range; When the wearable device (101; 200; 300; 400) is within the reference range, performing a first function of processing data obtained by at least one sensor of the wearable device (101; 200; 300; 400) to identify the occurrence of a first type of accident; and When the wearable device (101; 200; 300; 400) is outside the reference range, a second function of processing the data obtained by the at least one sensor (430) is performed to identify the occurrence of a second type of accident.

14. The method according to claim 13, further comprising: include: Based on the occurrence of the first type of accident, sending a first signal indicating the occurrence of the first type of accident, and Based on the occurrence of the second type of accident, sending a second signal indicating the occurrence of the second type of accident, wherein the first signal is configured to cause the peripheral device to provide a notification indicating the occurrence of a first type of incident, and The second signal is configured to cause the peripheral device to provide a notification indicating the occurrence of a second type of accident.

15. The method according to claim 13, in, The reference range is based on the external electronic device connected to the wearable device (101; 200; 300; 400) and the wearable device (101; 200; 300; 400), Wherein, the method further comprises: Based on the distance between the wearable device (101; 200; 300; 400) and the external electronic device being greater than a threshold distance, identifying that the wearable device (101; 200; 300; 400) is outside the reference range, and Based on the distance between the wearable device (101; 200; 300; 400) and the external electronic device being less than or equal to the threshold distance, identifying the wearable device (101; 200; 300; 400) are within the reference range.

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