Wearable device, method, and storage medium for identifying state of user

Abstract

According to one embodiment, a method performed by a wearable device comprises an operation of identifying a geographic area in which the wearable device is located using a communication circuit of the wearable device. The method comprises an operation of identifying data related to a motion of the wearable device using at least one sensor of the wearable device. The method comprises an operation of inputting the data related to the motion and data related to the state of the geographic area into a trained model in the wearable device. The method comprises an operation of determining the state of a user wearing the wearable device using the trained model to which the data related to the motion and the data related to the state of the geographic area have been input. The method comprises an operation of performing one or more functions, allocated for an emergency state, on the basis of a determination that the user is in the emergency state.

Description

Wearable device, method, and storage medium for identifying a user's status

[0001] The descriptions below relate to a wearable device, method, and storage medium for identifying a user's status.

[0002] A variety of services are provided through wearable devices. Wearable devices can be worn on a part of the user's body and operate. While worn, wearable devices can identify the user's biometric information and provide services based on that information.

[0003] The above information may be provided as background art to aid in understanding the present disclosure. No claim or determination is made as to whether any of the above is applicable as prior art related to the present disclosure.

[0004] According to one embodiment, a wearable device may include at least one sensor, a communication circuit, a memory storing instructions and including one or more storage media, and at least one processor including a processing circuit. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify a geographic area in which the wearable device is located using the communication circuit. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify data regarding a motion of the wearable device using the at least one sensor. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to input the data regarding the motion and the data regarding a state of the geographic area into a trained model within the wearable device. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to determine a condition of a user wearing the wearable device using the trained model input with the data regarding the motion and the data regarding the condition of the geographic area. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to perform one or more functions assigned for the emergency condition based on a determination that the user is in an emergency condition.

[0005] According to one embodiment, a method performed by a wearable device may include an operation of identifying a geographic area in which the wearable device is located using a communication circuit of the wearable device. The method may include an operation of identifying data regarding a motion of the wearable device using at least one sensor of the wearable device. The method may include an operation of inputting the data regarding the motion and the data regarding a state of the geographic area into a trained model within the wearable device. The method may include an operation of determining a state of a user wearing the wearable device using the trained model into which the data regarding the motion and the data regarding the state of the geographic area have been input. The method may include an operation of performing one or more functions assigned for the emergency state based on a determination that the user is in an emergency state.

[0006] According to one embodiment, a non-transitory computer-readable storage medium may store one or more programs. The one or more programs may include instructions that, when executed by at least one processor of a wearable device having at least one sensor and communication circuitry, cause the wearable device to identify a geographic area in which the wearable device is located using the communication circuitry. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to identify data regarding motion of the wearable device using the at least one sensor. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to input data regarding the motion and data regarding a state of the geographic area into a trained model within the wearable device. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to determine a state of a user wearing the wearable device using the trained model input with the data regarding the motion and the data regarding the state of the geographic area. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to perform one or more functions assigned for the emergency state based on a determination that the user is in an emergency state.

[0007] FIG. 1 is a block diagram of an electronic device within a network environment according to one embodiment.

[0008] FIGS. 2A and 2B illustrate perspective views of an electronic device according to an exemplary embodiment.

[0009] FIG. 3 illustrates an exploded perspective view of an exemplary electronic device according to one embodiment.

[0010] FIG. 4 illustrates an example of a wearable device for identifying accident risks and accident situations, according to one embodiment.

[0011] FIG. 5 is a simplified block diagram of a wearable device according to one embodiment.

[0012] FIG. 6 illustrates an example of the operation of a wearable device for identifying an accident risk and / or an accident situation using an artificial intelligence model, according to one embodiment.

[0013] Figure 7a illustrates input data of an artificial intelligence model according to one embodiment.

[0014] Figure 7b illustrates output data of an artificial intelligence model according to one embodiment.

[0015] Figure 8 illustrates an example of an artificial intelligence model according to one embodiment.

[0016] FIG. 9A illustrates an example of operation of a wearable device according to one embodiment.

[0017] FIG. 9b illustrates an example of operation of a wearable device according to one embodiment.

[0018] FIG. 9c illustrates an example of operation of a wearable device according to one embodiment.

[0019] FIG. 10 illustrates an example of the operation of a wearable device for obtaining a query and obtaining response data, according to one embodiment.

[0020] FIG. 11A illustrates an example of operation of a wearable device for providing notifications according to an accident situation, according to one embodiment.

[0021] FIG. 11b illustrates an example of operation of a wearable device for providing an injury risk assessment, according to one embodiment.

[0022] FIG. 12 illustrates an example of operation of a wearable device for a structure request, according to one embodiment.

[0023] FIG. 13 illustrates an example of a screen displayed on an external electronic device based on a signal broadcast from a wearable device, according to one embodiment.

[0024] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings so that those skilled in the art can easily implement the present disclosure. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein. In connection with the description of the drawings, the same or similar reference numerals may be used for identical or similar components. Furthermore, in the drawings and related descriptions, descriptions of well-known functions and configurations may be omitted for clarity and conciseness.

[0025] FIG. 1 is a block diagram of an electronic device within a network environment according to one embodiment.

[0026] Referring to FIG. 1, in a network environment (100), an electronic device (101) may communicate with an electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with at least one of an electronic device (104) or a server (108) via a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include a processor (120), a memory (130), an input module (150), an audio 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 (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)).

[0027] The processor (120) may control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) by executing, for example, software (e.g., a program (140)), and may perform various data processing or calculations. According to one embodiment, as at least a part of the data processing or calculation, the processor (120) may store a command or data received from another component (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the command or data stored in the volatile memory (132), and store the resulting data in a non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or a secondary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or together therewith. For example, if the electronic device (101) includes a main processor (121) and a secondary processor (123), the secondary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a specified function. The secondary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0028] The auxiliary processor (123) may control at least a part of functions or states associated with at least one component (e.g., a display module (160), a sensor module (176), or a communication module (190)) of the electronic device (101), for example, on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). In one embodiment, the auxiliary processor (123) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, on the electronic device (101) itself where the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (108)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model can include multiple artificial neural network layers.The artificial neural network may be one of 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 of the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0029] The number of processors (120) may be one or more. For example, the processor (210) may have a multi-core processor structure such as a dual core, quad core, or hexa core.

[0030] The processor (120) can control the operations of the electronic device (101) by executing instructions stored in the memory (130). For example, the processor (120) can correspond to a plurality of processors that collectively perform a plurality of operations by dividing them among the processors.

[0031] The memory (130) can store various data used by at least one component (e.g., processor (120) or sensor module (176)) of the electronic device (101). The data can include, for example, software (e.g., program (140)) and input data or output data for commands related thereto. The memory (130) can include volatile memory (132) or non-volatile memory (134).

[0032] The program (140) may be stored as software in the memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0033] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0034] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0035] The display module (160) can visually provide information to an external party (e.g., a user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0036] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), output sound through the sound output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0037] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0038] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). In one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0039] The connection terminal (178) may include a connector through which the electronic device (101) may be physically connected to an external electronic device (e.g., electronic device (102)). According to one 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).

[0040] A haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. According to one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0041] The camera module (180) can capture still images and videos. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0042] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

[0043] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0044] The communication module (190) may support the establishment of 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 the performance of communication through the established communication channel. The communication module (190) may operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one 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 module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (104) via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0045] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) may support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC realization.

[0046] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). According to one embodiment, the antenna module (197) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). According to one 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 method used in a communication network, such as the first network (198) or the second network (199), may be selected from the plurality of antennas, for example, by the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device through the selected at least one antenna. According to some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0047] According to various embodiments, the antenna module (197) may form a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high-frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high-frequency band.

[0048] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0049] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service by itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0050] FIGS. 2A and 2B illustrate perspective views of an electronic device according to an exemplary embodiment.

[0051] Referring to FIGS. 2A and 2B , an electronic device (200) according to one embodiment (e.g., the electronic device (101) of FIG. 1 ) may include a housing (210) including a first side (or front side) (210A), a second side (or back side) (210B), and a side surface (210C) surrounding a space between the first side (210A) and the second side (210B), and a fastening member (250, 260) connected to at least a portion of the housing (210) and configured to releasably fasten the electronic device (200) to a body part (e.g., a wrist or an ankle) of a user. In another embodiment (not shown), the housing may also refer to a structure forming a portion of the first side (210A), the second side (210B), and the side surface (210C) of FIGS. 2A and 2B . In one embodiment, the first side (210A) may be formed by a front plate (201) that is at least partially substantially transparent (e.g., a glass plate or a polymer plate comprising various coating layers). The second side (210B) may be formed by a substantially opaque back plate (207). The back plate (207) may be formed of, for example, coated or colored glass, ceramic, polymer, metal (e.g., aluminum, stainless steel (STS), or magnesium), or a combination of at least two of the foregoing materials. The side surface (210C) may be formed by a side bezel structure (or “side member”) (206) that is coupled to the front plate (201) and the back plate (207) and comprises a metal and / or a polymer. In some embodiments, the back plate (207) and the side bezel structure (206) may be formed integrally and comprise the same material (e.g., a metal material such as aluminum). The above-mentioned fastening member (250, 260) may be formed of various materials and shapes. The integral and multiple unit links may be formed to be mutually movable by a combination of at least two of the above-mentioned materials, such as woven fabric, leather, rubber, urethane, metal, ceramic, or a combination of the above-mentioned materials.

[0052] According to one embodiment, the electronic device (200) may include at least one of a display (220, see FIG. 3), an audio module (205, 208), a sensor module (211), a key input device (202, 203, 204), and a connector hole (209). In some embodiments, the electronic device (200) may omit at least one of the components (e.g., the key input device (202, 203, 204), the connector hole (209), or the sensor module (211)) or may additionally include other components.

[0053] The display (220) may be visually exposed, for example, through a significant portion of the front plate (201). The shape of the display (220) may correspond to the shape of the front plate (201), and may have various shapes such as a circle, an oval, or a polygon. The display (220) may be combined with or disposed adjacent to a touch detection circuit, a pressure sensor capable of measuring the intensity (pressure) of a touch, and / or a fingerprint sensor.

[0054] The audio module (205, 208) may include a microphone hole (205) and a speaker hole (208). The microphone hole (205) may have a microphone positioned therein for acquiring external sounds, and in some embodiments, multiple microphones may be positioned therein to detect the direction of sounds. 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 a single hole, or a speaker may be included without the speaker hole (208) (e.g., a piezo speaker).

[0055] The sensor module (211) can generate an electric signal or data value corresponding to an internal operating state of the electronic device (200) or an external environmental state. The sensor module (211) can 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) can further include at least one of a sensor module not shown, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0056] The sensor module (211) may include electrode areas (213, 214) forming a portion of the surface of the electronic device (200) and a biosignal detection circuit (not shown) electrically connected to the electrode areas (213, 214). For example, the electrode areas (213, 214) may include a first electrode area (213) and a second electrode area (214) arranged on a second surface (210B) of the housing (210). The sensor module (211) may be configured such that the electrode areas (213, 214) obtain an electrical signal from a portion of the user's body, and the biosignal detection circuit detects the user's bioinformation based on the electrical signal.

[0057] The key input devices (202, 203, 204) may include a wheel key (202) disposed on a first side (210A) of the housing (210) and rotatable in at least one direction, and / or a side key button (203, 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 other embodiments, the electronic device (200) may not include some or all of the above-mentioned key input devices (202, 203, 204), and the key input devices (202, 203, 204) that are not included may be implemented in another form, such as a soft key, on the display (220). The connector hole (209) may include another connector hole (not shown) that may accommodate a connector (e.g., a USB connector) for transmitting and receiving power and / or data with an external electronic device, and may accommodate a connector for transmitting and receiving audio signals with the external electronic device. The electronic device (200) may further include, for example, a connector cover (not shown) that covers at least a portion of the connector hole (209) and blocks the inflow of external foreign substances into the connector hole.

[0058] The fastening member (250, 260) can be detachably fastened to at least a portion of the housing (210) using a locking member (251, 261). The fastening member (250, 260) can include one or more of a fixing member (252), a fixing member fastening hole (253), a band guide member (254), and a band fastening ring (255).

[0059] The fixing member (252) can be configured to fix the housing (210) and the fastening members (250, 260) to a part of the user's body (e.g., a wrist or an ankle). The fastening member fastening hole (253) can fix the housing (210) and the fastening members (250, 260) to a part of the user's body in response to the fastening member (252). The band guide member (254) is configured to limit the range of motion of the fastening member (252) when the fastening member (252) is fastened to the fastening member fastening hole (253), thereby allowing the fastening members (250, 260) to be fastened in close contact with a part of the user's body. The band fixing ring (255) can limit the range of motion of the fastening members (250, 260) when the fastening member (252) and the fastening member fastening hole (253) are fastened.

[0060] FIG. 3 illustrates an exploded perspective view of an exemplary electronic device according to one embodiment.

[0061] Referring to FIG. 3, an electronic device (300) (e.g., the electronic device (101) of FIG. 1 or the electronic device (200) of FIGS. 2A to 2B) may include a side bezel structure (310), a wheel key (320) (e.g., the wheel key (202) of FIG. 2A), a front plate (201), a display (220), a first antenna (350), a second antenna (355), a support member (360) (e.g., a bracket), a battery (370), a printed circuit board (380), a sealing member (390), a rear plate (393) (e.g., the rear plate (207) of FIG. 2B), and fastening members (395, 397) (e.g., the fastening members (250, 260) of FIG. 2B). At least one of the components of the electronic device (300) may be identical or similar to at least one of the components of the electronic device (200) of FIG. 1 or FIGS. 2A to 2B, and any overlapping descriptions will be omitted below. The support member (360) may be disposed inside the electronic device (300) and connected to the side bezel structure (310), or may be formed integrally with the side bezel structure (310). The support member (360) may be formed of, for example, a metal material and / or a non-metallic (e.g., a polymer) material. The support member (360) may have a display (220) coupled to one surface and a printed circuit board (380) coupled to the other surface. A processor, a memory, and / or an 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 GPU (graphics processing unit), an application processor, a sensor processor, or a communication processor.

[0062] The memory may include, for example, volatile memory or non-volatile 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. The interface may electrically or physically connect the electronic device (300) to an external electronic device, for example, and may include a USB connector, an SD card / MMC connector, or an audio connector.

[0063] The battery (370) is a device for supplying power to at least one component of the electronic device (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 substantially on the same plane as, for example, the printed circuit board (380). The battery (370) may be disposed integrally within the electronic device (200), or may be disposed detachably from the electronic device (200).

[0064] The first antenna (350) may be positioned 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. The first antenna (350) may, for example, perform short-range communication with an external device, wirelessly transmit and receive power required for charging, and transmit a magnetic-based signal including a short-range communication signal or payment data. In another embodiment, the antenna structure may be formed by a portion or a combination of the side bezel structure (310) and / or the support member (360).

[0065] The second antenna (355) may be positioned between the printed circuit board (380) and the back plate (393). The second antenna (355) may include, for example, a near field communication (NFC) antenna, a wireless charging antenna, and / or a magnetic secure transmission (MST) antenna. The second antenna (355) may, for example, perform short-range communication with an external device, wirelessly transmit and receive power required for charging, and transmit a magnetic-based signal including a short-range communication signal or payment data. In another embodiment, the antenna structure may be formed by a portion or a combination of the side bezel structure (310) and / or the back plate (393).

[0066] A sealing member (390) may be positioned between the side bezel structure (310) and the rear plate (393). The sealing member (390) may be configured to block moisture and foreign substances from entering the space surrounded by the side bezel structure (310) and the rear plate (393) from the outside.

[0067] According to one embodiment, a wearable device (e.g., electronic device 101 of FIG. 1, electronic device 200 illustrated in FIGS. 2A and 2B, or electronic device 300 illustrated in FIG. 3) may be worn by a user and may operate. For example, the wearable device may be worn on a part of the user's body (e.g., wrist, finger, or face). According to one embodiment, the wearable device may be used to identify whether an accident (e.g., fall, collision) has occurred to the user of the wearable device. For example, the wearable device may use an artificial intelligence model to predict the risk of an accident and determine the user's condition. If the user is in an emergency state, the wearable device may perform one or more functions assigned for the emergency state. The wearable device may predict the risk of an accident and provide notifications according to the risk of an accident. If an accident occurs, the wearable device may perform the assigned functions according to the user's condition.

[0068] The operation of the wearable device according to the above-described embodiment may be described below. The wearable device described below may correspond to the electronic device (101) of FIG. 1, the electronic device (200) of FIGS. 2A and 2B, and / or the electronic device (300) of FIG. 3.

[0069] FIG. 4 illustrates an example of a wearable device for identifying accident risks and accident situations, according to one embodiment.

[0070] Referring to FIG. 4, a wearable device (400) can be used to identify an accident risk and / or an accident situation of a user wearing the wearable device (400). For example, the wearable device (400) can be implemented in various forms that can be worn by a user, such as a smart watch, a smart band, a smart ring, wireless earphones, or smart glasses. In the following specification, for the convenience of explanation, an example in which the wearable device (400) is formed in the form of a watch will be described. For example, the wearable device (400) can correspond to the electronic device (101) of FIG. 1, the electronic device (200) of FIGS. 2A and 2B, and / or the electronic device (300) of FIG. 3. However, the present invention is not limited thereto. The wearable device (400) can have various structures and can be worn on various body parts of the user (e.g., eyes, fingers, head, or ears).

[0071] According to one embodiment, the wearable device (400) may be connected to and operate with an electronic device (401) connected to the wearable device (400). For example, at least some of the operations of the wearable device (400) described below may be performed by the electronic device (401) connected to the wearable device (400).

[0072] According to one embodiment, the wearable device (400) may perform one or more functions according to the user's accident risk (e.g., fall risk) and / or accident situation. For example, the wearable device (400) may identify the user's accident risk and / or accident situation based on data regarding the motion of the wearable device (400), data regarding the user of the wearable device (400), and / or data regarding the geographic area in which the wearable device (400) is located. The wearable device (400) may perform one or more functions according to the user's accident risk and / or accident situation.

[0073] For example, the wearable device (400) can obtain information about the user's accident risk and / or accident situation using an artificial intelligence model (e.g., a generative AI model). The input data of the artificial intelligence model can be set in various ways. For example, data obtained from the wearable device (400), data obtained from another wearable device (402) connected to the wearable device (400), and / or data obtained from a server (403) connected to the wearable device (400) can be set as the input data of the artificial intelligence model. For example, the wearable device (400) can obtain at least one of a query for identifying the user's status, an accident risk, and / or an accident situation based on the output of the artificial intelligence model. Specific examples of the above-described artificial intelligence model will be described later with reference to FIGS. 6 to 8.

[0074] According to one embodiment, instructions for an artificial intelligence model may be stored in the memory of a wearable device (400). According to one embodiment, the wearable device (400) may have a structure for being worn by a user. It may be difficult to perform operations according to the artificial intelligence model on the wearable device (400). Therefore, at least some or all of the operations according to the artificial intelligence model may be performed on an external device (e.g., an electronic device (401), another wearable device (402), or a server (403)) connected to the wearable device (400).

[0075] According to one embodiment, the wearable device (400) can identify the severity of the user's injury when an accident occurs to the user. Based on identifying that the severity of the user's injury is outside a threshold range, the wearable device (400) can transmit a signal to an external electronic device (e.g., the external electronic device (404)) located within a reference distance from the wearable device (400) to notify the user of an emergency condition of the wearable device (400). For example, the external electronic device (e.g., the external electronic device (404)) can provide a notification to the user of the external electronic device based on the received signal. For example, the signal transmitted to the external electronic device can cause the external electronic device to display information indicating the emergency condition of the user of the wearable device (400). For example, the signal transmitted to the external electronic device can cause the external electronic device to display information indicating a movement path from the location of the external electronic device to the location of the wearable device (400).

[0076] According to one embodiment, the wearable device (400) can track the user's physical activity. The wearable device (400) can identify (or detect) the user's abnormal movement and / or sudden change in the position of the wearable device (400). Based on the abnormal movement of the user and / or sudden change in the position of the wearable device (400), the wearable device (400) can identify (or detect) an accident situation of the user (e.g., a fall situation) and provide a notification to the user. For example, the wearable device (400) can provide a notification to the user (or another user) of an external electronic device about an accident situation of the user of the wearable device (400).

[0077] For example, the wearable device (400) can identify the user's accident situation using at least one sensor (e.g., an acceleration sensor, a gyro sensor, a barometric pressure sensor, and / or a heart rate sensor). The wearable device (400) can identify the location of the wearable device (400) (or the user) using a communication circuit (e.g., a global positioning system (GPS) circuit). The wearable device (400) can establish a connection (e.g., a call connection) with an external electronic device using an emergency contact set in the wearable device (e.g., a designated contact, a contact for accident rescue (e.g., 911)).

[0078] According to one embodiment, the wearable device (400) can obtain information about the user's thoughts by using data obtained from an external electronic device (e.g., an electronic device (401), another wearable device (402), or a server (403)) as well as data (or information) obtained from the wearable device (400).

[0079] For example, the wearable device (400) may input at least one of the user's metadata (e.g., the user's profile, medical data, whether the user is taking medication, whether the user is drinking), data about the surrounding environment (e.g., ground conditions, terrain, ambient brightness), and / or accident history data of other users (e.g., accident location, accident type, accident frequency) into an artificial intelligence model (e.g., a large multimodal model (LLM)). In order to obtain information about the user's accident, not only the data obtained from the wearable device (400) but also data obtained from an external electronic device may be used together. Therefore, when an accident (e.g., a fall) occurs to the user, the wearable device (400) may identify a more accurate fall situation and identify the accident type, injury site, and / or injury severity.

[0080] For example, the wearable device (400) can predict personalized accident risks and provide notifications based on the user's metadata and data about the surrounding environment (or data about the status of the geographic area where the wearable device (400) is located).

[0081] For example, the wearable device (400) can obtain (or generate) a query for identifying the user's status using an artificial intelligence model (or a trained model). For example, the query for identifying the user's status may include a query regarding a situation that may cause an emergency. For example, the wearable device (400) can obtain a query regarding whether an accident has occurred using the artificial intelligence model. The wearable device (400) can obtain response data for at least one of the above-described queries. The wearable device (400) can feed back the obtained response data to an artificial intelligence model (e.g., LLM). The wearable device (400) can set the response data (or feedback) as input data of the artificial intelligence model (600) (e.g., the artificial intelligence model (600) of FIG. 6). According to an embodiment, the wearable device (400) can train the artificial intelligence model (600) using the response data. The wearable device (400) can more accurately predict accident risks and / or accident situations using an artificial intelligence model based on response data (or feedback).

[0082] For example, based on the user's emergency status, the wearable device (400) may broadcast (or transmit) a signal to an external electronic device located within a reference distance from the wearable device (400) to notify the user of the emergency status of the wearable device (400). For example, the wearable device (400) may identify an external electronic device registered in relation to the user's emergency status in the wearable device (400). The wearable device (400) may determine whether to broadcast a signal to notify the user of the emergency status of the wearable device (400) based on whether it is connected to the external electronic device and the time until the user of the external electronic device arrives at the user of the wearable device (400). An external electronic device that receives the signal may provide, based on the signal, the location of the wearable device (400) (or the user of the wearable device (400)), the movement path to the wearable device (400), and / or an emergency treatment guide according to the injured area.

[0083] Below, the specific operation of the above-described wearable device (400) will be described.

[0084] FIG. 5 is a simplified block diagram of a wearable device according to one embodiment.

[0085] Referring to FIG. 5, the wearable device (400) may correspond to the electronic device (101) of FIG. 1, the electronic device (200) of FIGS. 2A and 2B, and / or the electronic device (300) of FIG. 3. The wearable device (400) may include at least some or all of the components of the electronic device (101) of FIG. 1, the electronic device (200) of FIGS. 2A and 2B, and / or the electronic device (300) of FIG. 3.

[0086] According to one embodiment, the wearable device (400) may include a processor (510), a communication circuit (520), a sensor (530), a memory (540), a display (550), a camera (560), and / or a microphone (570). Depending on the embodiment, the wearable device (400) may include at least one of the processor (510), the communication circuit (520), the sensor (530), the memory (540), the display (550), the camera (560), and / or the microphone (570). For example, at least some of the processor (510), the communication circuit (520), the sensor (530), the memory (540), the display (550), the camera (560), and the microphone (570) may be omitted depending on the embodiment.

[0087] According to one embodiment, the processor (510) may correspond to the processor (120) of FIG. 1. The processor (510) may be operatively or operably coupled with or connected with a communication circuit (520), a sensor (530), a memory (540), a display (550), a camera (560), and a microphone (570). For example, operatively coupling the processor (510) with another component may mean that the processor (510) can control the other component. The processor (510) may control the communication circuit (520), the sensor (530), the memory (540), the display (550), the camera (560), and the microphone (570). For example, the processor (510) may determine an operating point of the sensor (530). The processor (510) may control the operation of the sensor (530). The processor (510) can activate or deactivate the sensor (530). The processor (510) can process information obtained from the sensor (530).

[0088] According to one embodiment, the processor (510) may be composed of at least one processor. The processor (510) may include at least one processor. According to one embodiment, the processor (510) may include a hardware component for processing data based on one or more instructions. The hardware component for processing data may include, for example, an arithmetic and logic unit (ALU), a field programmable gate array (FPGA), and / or a central processing unit (CPU).

[0089] For example, the processor (510) may include an NPU and / or a GPU for operating an artificial intelligence model (e.g., LLM, generative AI model). The processor (510) may include an MCU (micro controller unit) for processing sensing data acquired through a sensor (530).

[0090] According to one embodiment, the wearable device (400) may include a communication circuit (520). For example, the communication circuit (520) may correspond to at least a portion of the communication module (190) of FIG. 1.

[0091] For example, the communication circuit (520) can be used for various radio access technologies (RATs). For example, the communication circuit (520) can be used to perform Bluetooth communication, Bluetooth low energy (BLE) communication, wireless local area network (WLAN) communication, or ultra wideband (UWB) communication. For example, the communication circuit (520) can be used to perform cellular communication.

[0092] For example, the processor (510) can establish a connection with an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)) through the communication circuit (520). For example, the processor (510) can establish a connection with a server (e.g., server (403)) through the communication circuit (520). For example, the processor (510) can be connected to another wearable device (e.g., another wearable device (402)) through the communication circuit (520). For example, the wearable device (400) can obtain at least one of weather information, environmental information, terrain information, and / or ground condition information for a geographic area where the wearable device (400) is located from an external electronic device (e.g., electronic device (401) or server (403)) through the communication circuit (520). For example, the wearable device (400) can use the communication circuit (520) to broadcast (or transmit) a signal to notify an emergency state of the user to an external electronic device located within a standard distance from the wearable device (400).

[0093] For example, the processor (510) may identify (or measure) the location of the wearable device (400) based on a wireless signal (e.g., a global positioning system (GPS) signal, a global navigation satellite system (GNSS) signal) received or transmitted by the communication circuit (520). For example, the wearable device (400) may include a circuit (or module) for at least one of the global positioning system (GPS), the global navigation satellite system (GNSS), the global navigation satellite system (GLONASS), the Beidou Navigation Satellite System (hereinafter, "Beidou"), the quasi-zenith satellite system (QZSS), the Indian reginal satellite system (IRNSS), and / or the European global satellite-based navigation system (Galileo), depending on the usage area or bandwidth. According to an embodiment, the communication circuit (520) may be configured to be integrated with the processor (510).

[0094] According to one embodiment, the wearable device (400) may include a sensor (530). The sensor (530) may be used to acquire various external information. For example, the sensor (530) may be used to acquire data regarding the user's body. For example, the sensor (530) may be used to acquire data regarding the user's condition, data regarding the user's movement, and / or data regarding the user's heart rate. For example, the sensor (530) may correspond to the sensor module (176) of FIG. 1.

[0095] According to one embodiment, the sensor (530) may be composed of at least one sensor. For example, the sensor (530) may include at least one of an acceleration sensor (531), a gyro sensor (532), an HR sensor (533), a barometric pressure sensor (534), and an ambient light sensor (535).

[0096] For example, the acceleration sensor (531) can identify (or measure, detect) the acceleration of the wearable device (400) in three directions of the x-axis, the y-axis, and the z-axis. For example, the gyro sensor (532) can identify (or measure, detect) the angular velocity of the wearable device (400) in three directions of the x-axis, the y-axis, and the z-axis. According to an embodiment, the wearable device (400) may include an inertial sensor composed of an acceleration sensor (531) and a gyro sensor (532).

[0097] For example, it may include a heart rate (HR) sensor (533) (or heart rate variability (HRV) sensor, electrode sensor). The processor (510) may measure the regularity or variability of the heartbeat through the HR sensor (533). The processor (510) may obtain information about the regularity or variability of the heartbeat through the HR sensor (533).

[0098] For example, the barometric pressure sensor (534) can identify (or measure, detect) the barometric pressure around the wearable device (400). The processor (510) can identify the height (or altitude) at which the wearable device (400) is located above the ground based on data regarding the barometric pressure around the wearable device (400) identified using the barometric pressure sensor (534).

[0099] For example, the light sensor (535) can identify (or measure, detect) the brightness of the surroundings of the wearable device (400). For example, the light sensor (535) can be placed under the display (550).

[0100] Although not shown, the sensor (530) may further include a sensor for obtaining (or identifying, measuring, detecting) various data about the user.

[0101] For example, the sensor (530) may include a body temperature sensor. The processor (510) may measure the skin temperature of a part of the user's body through the body temperature sensor. The processor (510) may obtain the user's body temperature based on the skin temperature of the part of the user's body.

[0102] For example, the sensor (530) may include a photoplethysmography (PPG) sensor. The PPG sensor may be used to measure pulse (or changes in blood volume within a blood vessel) by identifying changes in light sensitivity due to changes in blood vessel volume. For example, the PPG sensor may be used to identify information about changes in a user's heart rate, information about a user's stress level based on HRV, information about a user's sleep stage, information about a user's breathing rate, and information about a user's blood pressure.

[0103] For example, the sensor (530) may include a blood sugar sensor. The processor (510) may identify the user's blood sugar level by identifying (or measuring) the current generated by an electrochemical reaction with blood sugar in the blood.

[0104] According to one embodiment, the wearable device (400) may include a memory (540). The memory (540) may be used to store information or data. For example, the memory (540) may be used to store data obtained from a user. For example, the memory (540) may correspond to the memory (130) of FIG. 1. For example, the memory (540) may be a volatile memory unit or units. For example, the memory (540) may be a non-volatile memory unit or units. For example, the memory (540) may be another form of computer-readable media, such as a magnetic or optical disk. For example, the memory (540) may store data obtained based on operations performed by the processor (510) (e.g., algorithm execution operations). For example, the memory (540) may store data obtained from a sensor (530) (e.g., data regarding a user's heart rate).

[0105] For example, the memory (540) may include an artificial intelligence model (e.g., a generative AI model, a large multimodal model (LMM), a large language model (LLM)) as a program.

[0106] For example, the display (550) can be used to display various screens. The display (550) can be controlled by a processor (510) including a circuit such as a GPU (graphics processing unit) to output visualized information to a user. The display (550) can be used to output content, data, or signals through the screen. For example, the display (550) can correspond to the display module (160) of FIG. 1.

[0107] For example, the display (550) may be used to display a user interface obtained based on the output of the artificial intelligence model. For example, the display (550) may be used to display a user interface indicating a notification regarding an accident risk and / or an accident detection. For example, the display (550) may be used to receive feedback on a query obtained based on the output of the artificial intelligence model. As an example, the wearable device (400) may display a screen indicating the obtained query through the display (550). The wearable device (400) may obtain response data based on an input to the screen (e.g., a touch input).

[0108] For example, the camera (560) may include one or more optical sensors (e.g., a charged coupled device (CCD) sensor, a complementary metal oxide semiconductor (CMOS) sensor) that generate electrical signals representing the color and / or brightness of light. The plurality of optical sensors included in the camera (560) may be arranged in the form of a two-dimensional array. For example, the camera (560) may correspond to the camera module (180) of FIG. 1.

[0109] For example, the camera (560) may be used to obtain an image (or video) of the surrounding environment of the wearable device (400). For example, the camera (560) may be used to obtain an image (or video) representing a user of the wearable device (400). For example, the camera (560) may be used to identify ambient light and / or brightness.

[0110] For example, the microphone (570) can acquire voice data of a user of the wearable device (400) and / or audio data of the surroundings of the wearable device (400). For example, the microphone (570) can be composed of at least one microphone. If the microphone (570) is composed of multiple microphones, the user's voice data can be acquired even in a noisy environment through beamforming.

[0111] Although FIG. 5 illustrates examples of components included in a wearable device (400), another wearable device (402) of FIG. 4 may also include at least some or all of the components illustrated in FIG. 5. For example, the other wearable device (402) may be worn on another part of the user's body (e.g., an ear). The other wearable device (402) may include an acceleration sensor (531), a gyro sensor (532), and / or a barometric pressure sensor (534).

[0112] FIG. 6 illustrates an example of the operation of a wearable device for identifying an accident risk and / or an accident situation using an artificial intelligence model, according to one embodiment.

[0113] Figure 7a illustrates input data of an artificial intelligence model according to one embodiment.

[0114] Figure 7b illustrates output data of an artificial intelligence model according to one embodiment.

[0115] Referring to FIGS. 6, 7a, and 7b, the wearable device (400) may include an artificial intelligence model (600). For example, the artificial intelligence model (600) may be referred to as a trained model in that it is constructed based on learning.

[0116] According to one embodiment, the artificial intelligence model (600) may be configured based on a large multimodal model (LMM), which is an example of a generative artificial intelligence model. The artificial intelligence model (600) configured based on the LMM may generate a new type of data based on user input information. An example of the specific operation of the artificial intelligence model (600) will be described later with reference to FIG. 8. Depending on the embodiment, the artificial intelligence model (600) may be configured in various ways. For example, the artificial intelligence model (600) may be configured based on parameters for driving a neural network such as a large language model (LLM), a convolutional neural network (CNN), a recurrent neural network (RNN), a feedforward neural network (FNN), and / or a long short-term memory (LSTM).

[0117] According to one embodiment, the wearable device (400) may perform a query generation operation (621) for feedback on the user's status using the artificial intelligence model (600). For example, the wearable device (400) may use the artificial intelligence model (600) to generate a query regarding a situation that may cause an emergency. For example, the wearable device (400) may use the artificial intelligence model (600) to generate a query for feedback on whether the user is drinking. For example, the wearable device (400) may use the artificial intelligence model (600) to obtain a query regarding whether the user has been in an accident. For example, the wearable device (400) may use the artificial intelligence model (600) to generate a query for feedback on whether an accident has actually occurred.

[0118] According to one embodiment, the wearable device (400) can perform an operation (622) to predict an accident risk (e.g., a fall risk) using an artificial intelligence model (600). The wearable device (400) can predict an accident risk (e.g., a fall risk) using the artificial intelligence model (600). For example, the wearable device (400) can identify one of a plurality of stages indicating an accident risk using the artificial intelligence model (600). For example, the wearable device (400) can identify an accident risk as one of a normal stage, a dangerous stage, and a very dangerous stage using the artificial intelligence model (600). For example, the wearable device (400) can identify an accident risk as one of stages 0 to 10 using the artificial intelligence model (600).

[0119] For example, the wearable device (400) may perform one or more functions assigned based on the predicted accident risk. As an example, the wearable device (400) may provide a user interface indicating the predicted accident risk through the display (550).

[0120] According to one embodiment, the wearable device (400) may perform an accident situation identification operation (623) using an artificial intelligence model (600). For example, the wearable device (400) may use the artificial intelligence model (600) to identify a body part predicted to be injured due to an accident, an accident type, and / or an accident severity level.

[0121] According to one embodiment, the artificial intelligence model (600) may be used to perform at least one of a query generation operation (621) for feedback on a user's status, an accident risk (e.g., fall risk) prediction operation (622), and an accident situation identification operation (623). For example, the artificial intelligence model (600) may include a plurality of models for each operation. For example, the artificial intelligence model (600) may include a first artificial intelligence model (600-1) for performing a query generation operation (621) for feedback on a user's status. The artificial intelligence model (600) may include a second artificial intelligence model (600-2) for performing a accident risk (e.g., fall risk) prediction operation (622). The artificial intelligence model (600) may include a third artificial intelligence model (600-3) for performing an accident situation identification operation (623).

[0122] For example, the wearable device (400) can determine one of the first artificial intelligence model (600-1), the second artificial intelligence model (600-2), and the third artificial intelligence model (600-3) based on input data of the artificial intelligence model (600).

[0123] For example, the wearable device (400) can identify the need for feedback regarding the user's status based on input data from the artificial intelligence model (600). The wearable device (400) can input input data to the first artificial intelligence model (600-1) and obtain output data to generate a query for feedback regarding the user's status.

[0124] For example, the wearable device (400) may identify the need for prediction of an accident risk (e.g., a fall risk) based on input data of the artificial intelligence model (600). To predict the accident risk (e.g., a fall risk), the wearable device (400) may input input data to the second artificial intelligence model (600-2) and obtain output data.

[0125] For example, the wearable device (400) can identify the need for identification of an accident situation based on input data of the artificial intelligence model (600). To identify the accident situation, the wearable device (400) can input input data to the third artificial intelligence model (600-3) and obtain output data.

[0126] According to one embodiment, the wearable device (400) can obtain output data of an artificial intelligence model (600) for at least one of a query generation operation (621) for feedback on a user's status, an operation (622) for predicting an accident risk (e.g., a fall risk), and / or an operation (623) for identifying an accident situation. An example (720) of FIG. 7B illustrates an example of output data of the artificial intelligence model (600). The wearable device (400) can obtain at least some or all of the data shown in the example (720) as output data of the artificial intelligence model (600).

[0127] According to one embodiment, the input data of the artificial intelligence model (600) may be configured in various ways. For example, the wearable device (400) may obtain motion data (601) using an acceleration sensor (531) and / or a gyro sensor (532). The wearable device (400) may obtain barometric data (602) using a barometric pressure sensor (534). The wearable device (400) may obtain biometric data (603) using an HR sensor (533) (or a PPG sensor, a body temperature sensor). The wearable device (400) may obtain audio data (604) regarding the wearable device (400) using a microphone (570). The wearable device (400) may obtain location data (605) regarding the wearable device (400) using a communication circuit (520). The wearable device (400) can obtain environmental data (606) surrounding the wearable device (400) using a camera (560). The wearable device (400) can obtain environmental data (606) surrounding the wearable device (400) from an external electronic device (e.g., electronic device (401), server (403)). The wearable device (400) can obtain accident history data (607) from an external electronic device (e.g., server (403)) using a communication circuit (520). The wearable device (400) can obtain metadata (608) about the user. According to one embodiment, the wearable device (400) can obtain data (609) of another wearable device obtained from another wearable device (e.g., another wearable device (402)). For example, data (609) of another wearable device may include motion data and / or air pressure data acquired from another wearable device.

[0128] The wearable device (400) can set the above-described data (601 to 609) as input data of the artificial intelligence model (600). For example, the example (710) of FIG. 7A shows an example of input data of the artificial intelligence model (600). The wearable device (400) can set at least some or all of the data shown in the example (710) as input data of the artificial intelligence model (600).

[0129] According to one embodiment, the wearable device (400) can obtain additional information using the data (601 to 609).

[0130] For example, the wearable device (400) can obtain impact pattern information (611) based on at least one of motion data (601), air pressure data (602), and / or biometric data (603). The wearable device (400) can set the impact pattern information (611) as input data of the artificial intelligence model (600).

[0131] For example, the wearable device (400) may obtain activity information (612) about the user based on at least one of motion data (601), air pressure data (602), and / or biometric data (603). The wearable device (400) may set the activity information (612) about the user as input data of the artificial intelligence model (600). The activity information (612) about the user may include information about an activity performed by the user (e.g., type of exercise, climbing stairs, showering).

[0132] For example, the wearable device (400) can obtain wearing information (613) about the wearable device (400) based on at least one of motion data (601), air pressure data (602), and / or biometric data (603). The wearable device (400) can obtain wearing information (613) indicating a body part on which the wearable device (400) is worn and / or whether the wearable device (400) is worn based on at least one of motion data (601), air pressure data (602), and / or biometric data (603). The wearable device (400) can set the wearing information (613) as input data of the artificial intelligence model (600).

[0133] For example, the wearable device (400) can obtain surrounding environment information (614) based on at least one of audio data (604), location data (605), environmental data (606), and / or accident history data (607). The wearable device (400) can set the surrounding environment information (614) as input data of the artificial intelligence model (600).

[0134] As described above, the wearable device (400) not only sets data (601 to 608) as input data of the artificial intelligence model (600), but also sets information acquired using the data (601 to 608) as input data of the artificial intelligence model (600).

[0135] According to one embodiment, the wearable device (400) can set data received from not only the wearable device (400) but also external electronic devices (e.g., electronic device (401), another wearable device (402), server (403)) as input data of the artificial intelligence model (600) (e.g., LMM).

[0136] For example, an artificial intelligence model (600) (e.g., LMM) can predict the user's accident risk based on metadata (608) about the user and surrounding environment information (614), and if the accident risk is outside a critical range, the wearable device (400) can provide a notification about the accident risk. For example, if the accident risk exceeds a critical range, the wearable device (400) can provide a notification about the accident risk. In addition to a notification about the accident risk, the wearable device (400) can provide a response plan to reduce the accident risk.

[0137] For example, meta-information about a user may include information about the user's condition and / or circumstances that may lead to an accident, such as the user's profile, medical information, medication information, and whether the user is intoxicated. For example, surrounding environment information (614) may include ground conditions (e.g., rain, snow), terrain (e.g., mountainous, flat), and / or ambient brightness.

[0138] For example, if the user of the wearable device (400) is intoxicated and exhibits a walking pattern (or an unstable walking pattern) that differs from the user's usual walking pattern, the risk of an accident may be identified as high. For example, if it is snowing and / or raining, if the surroundings are dark at night, and / or if the user is hiking, the risk of an accident may be identified as high.

[0139] According to one embodiment, the wearable device (400) can identify (or predict) the type of accident, the location of the injury, and / or the severity of the injury using the artificial intelligence model (600). Depending on the severity of the injury, the wearable device (400) can transmit a signal for requesting rescue to a first external electronic device registered in relation to the user's emergency condition. For example, depending on the severity of the injury, the wearable device (400) can transmit a signal for requesting rescue to a first external electronic device corresponding to an emergency contact.

[0140] For example, the wearable device (400) can identify whether a connection is established with a first external electronic device and / or the time until the user of the first external electronic device arrives at the user of the wearable device (400). Based on whether a connection is established with the first external electronic device and / or the time until the user of the first external electronic device arrives at the user of the wearable device (400), the wearable device (400) can search for an external electronic device located within a reference distance from the wearable device (400) and broadcast (or transmit) a signal to notify the searched external electronic device of the user's emergency condition.

[0141] For example, when the wearable device (400) identifies (or detects) that an accident has occurred for the user, the wearable device (400) may provide a notification to check the user's condition. The wearable device (400) may determine that no additional assistance is required based on receiving an input from the user indicating that the injury severity is not high. The wearable device (400) may establish a connection with a first external electronic device registered in relation to the user's emergency condition based on receiving an input indicating that the injury severity is high, and transmit a signal for the user's rescue request to the first external electronic device. The wearable device (400) may establish a connection with a first external electronic device registered in relation to the user's emergency condition based on predicting an injured area on the head or identifying that the user is unconscious, and transmit a signal for the user's rescue request to the first external electronic device. According to an embodiment, the wearable device (400) may broadcast (or transmit) a signal to notify an external electronic device located within a reference distance of the wearable device (400) of an emergency condition of the user based on a failure in connection with the first external electronic device or a time until the user of the first external electronic device reaches the user of the wearable device (400) exceeding a threshold time. According to an embodiment, the wearable device (400) may broadcast (or transmit) a signal to notify an external electronic device located within a reference distance of the wearable device (400) of an emergency condition of the user based on identifying that the severity of the injury is outside a threshold range.

[0142] Figure 8 illustrates an example of an artificial intelligence model according to one embodiment.

[0143] Referring to FIG. 8, the artificial intelligence model (600) may be configured based on a generative adversarial network (GAN) and / or a variational autoencoder (VAE), which are models for generating images. For example, the artificial intelligence model (600) may use VAE and transformer structures, and may include a diffusion-based generative artificial intelligence model. For example, the artificial intelligence model (600) may include a model for generating language. The model for generating language may be trained to output the most appropriate output data based on input data.

[0144] According to one embodiment, the artificial intelligence model (600) may be configured based on an LMM. For example, the LMM may support various forms of input data including text, images, and voice. The LMM may include an LVM (large vision model) in which image data is set as input data and / or an LLM (large language model) in which language data is set as input data. Therefore, the LMM may support various modalities including image data and language data as input data. For example, the wearable device (400) may obtain sensor data using the sensor (530) of the wearable device (400). Data of a different modality, distinct from the sensor data, may be obtained from an external electronic device (e.g., an electronic device (401), another wearable device (402), a server (403)) connected to the wearable device (400). The sensor data and data of the different modalities may be set as input data of the artificial intelligence model (600). The artificial intelligence model (600) can evaluate the performance status of the artificial intelligence model (600) based on input data, or generate content including voice, text, images, and video. The artificial intelligence model (600) can generate control signals for controlling a wearable device (400) or an external electronic device and / or data related to the control based on the input data.

[0145] According to one embodiment, an artificial intelligence model (600) configured based on LLM may include a feature extraction unit (810), an encoding unit (820), and a dense layer (830). In FIG. 8, an example in which the feature extraction unit (810) is included in the artificial intelligence model (600) is described, but is not limited thereto. The artificial intelligence model (600) may also include an encoding unit (820) and a dense layer (830).

[0146] For example, the feature point extraction unit (810) may be configured to extract feature points for each data. The feature point extraction unit (810) may extract feature points according to the type of input data. For example, in the feature point extraction unit (810), feature point(s) for motion data, feature point(s) for biometric data, feature point(s) for location / environmental data, feature point(s) for user metadata, and / or feature point(s) for audio data may be acquired. The input data illustrated in FIG. 8 is exemplary and is not limited thereto. For example, the input data may be set as in the example (710) illustrated in FIG. 7A.

[0147] For example, feature points acquired through the feature point extraction unit (810) may be input to the encoding unit (820). The feature points may be input to the encoder (or transformer) of the encoding unit (820). The encoding unit (820) may output data based on the number of input nodes of the dense layer (830). The dense layer (830) may output data based on the data acquired from the encoding unit (820). The data output through the dense layer (830) may correspond to the output data of the artificial intelligence model (600). For example, the dense layer (830) may be referred to as a fully-connected layer.

[0148] FIG. 9A illustrates an example of operation of a wearable device according to one embodiment.

[0149] FIG. 9b illustrates an example of operation of a wearable device according to one embodiment.

[0150] FIG. 9c illustrates an example of operation of a wearable device according to one embodiment.

[0151] In the following examples, the operations may be performed sequentially, but are not necessarily sequential. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0152] Referring to FIG. 9a, operations 901 to 905 may be related to the query generation operation (621) for feedback on the user's status of FIG. 6.

[0153] According to one embodiment, in operation 901, the wearable device (400) (or the processor (510) of the wearable device (400)) may obtain input data for the artificial intelligence model (600) through the wearable device (400) and another wearable device (402).

[0154] For example, the wearable device (400) can obtain motion data (e.g., motion data (601) of FIG. 6), barometric data (e.g., barometric data (602) of FIG. 6), biometric data (e.g., biometric data (603) of FIG. 6), audio data (e.g., audio data (604) of FIG. 6), and / or location data (e.g., location data (605) of FIG. 6), environmental data (e.g., environmental data (606) of FIG. 6).

[0155] For example, the wearable device (400) can obtain the user's metadata. For example, the wearable device (400) can obtain the user's profile information (e.g., height, weight, gender), medical information, information regarding medication use, and / or information regarding alcohol consumption.

[0156] For example, the wearable device (400) can obtain input data for the artificial intelligence model (600) (e.g., data (609) of the other wearable device in FIG. 6) from another wearable device (402) worn by the user (e.g., earbuds, earphones, TWS (true wireless stereo) device). The wearable device (400) can receive input data for the artificial intelligence model (600) from the other wearable device (402) using the communication circuit (520). As an example, the wearable device (400) can receive input data for the artificial intelligence model (600) from the other wearable device (402) through the electronic device (401). For example, a wearable device (400) can obtain motion data and / or air pressure data obtained from another wearable device (402) through another wearable device (402).

[0157] For example, another wearable device (402) may be worn on the user's ear. The other wearable device (402) may include an inertial sensor and a barometric sensor. The other wearable device (402) may acquire motion data and / or barometric data. The other wearable device (402) may transmit the motion data and / or barometric data. The wearable device (400) may identify whether the user's head is injured using the motion data and / or barometric data acquired from the other wearable device (402). The wearable device (400) may identify the amount and / or time of impact identified by the other wearable device (402). The wearable device (400) may predict the severity of an injury of a user wearing the other wearable device (402).

[0158] According to one embodiment, in operation 902, the wearable device (400) may obtain accident history data from the server (403). For example, the wearable device (400) may obtain accident history data regarding an external electronic device in a geographical area where the wearable device (400) is located. For example, the server (403) may obtain accident history data regarding an accident that occurred to another user of the external electronic device. The server (403) may obtain accident information from the external electronic device and, based on the obtained accident information, obtain accident history data. The wearable device (400) may request the server (403) for accident history data regarding an external electronic device in a geographical area where the wearable device (400) is located. Based on the request, the wearable device (400) may receive accident history data regarding the external electronic device from the server (403). According to an embodiment, the wearable device (400) may obtain accident history data regarding an external electronic device directly from the external electronic device, without receiving accident history data regarding the external electronic device through the server (403). For example, an external electronic device located within a reference distance from the location of the wearable device (400) may broadcast accident history data. The wearable device (400) may obtain accident history data from the external electronic device.

[0159] According to one embodiment, in operation 903, the wearable device (400) may input acquired data into the artificial intelligence model (600). For example, the wearable device (400) may input at least one of data acquired through a sensor (530) of the wearable device (400), data acquired from another wearable device (402), and / or accident history data regarding an external electronic device acquired from a server (403) into the artificial intelligence model (600).

[0160] According to one embodiment, the wearable device (400) may acquire (or extract) feature point(s) for data regarding the user's condition related to an accident and / or data regarding the condition of a geographical area where the wearable device (400) is located. The wearable device (400) may identify a pattern of an impact identified in the wearable device (400) using an acceleration sensor (531) and / or a gyro sensor (532). The wearable device (400) may set the identified pattern as input data of an artificial intelligence model (600). The wearable device (400) may identify information about the user's activity before and after the impact identified in the wearable device (400), whether the wearable device (400) is worn, the wearing position of the wearable device (400), and / or the surrounding environment. The wearable device (400) can set information about the user's activity before and after an impact occurs, whether the wearable device (400) is worn, the wearing position of the wearable device (400), and / or information about the surrounding environment as input data for the artificial intelligence model (600).

[0161] According to one embodiment, in operation 904, the wearable device (400) may obtain a query for identifying the user's status and / or a query for determining whether an accident has occurred. For example, the wearable device (400) may obtain a query for identifying the user's status and / or a query for determining whether an accident has occurred based on the output of the artificial intelligence model (600).

[0162] For example, the wearable device (400) may obtain a query regarding a situation that may cause an emergency. For example, the wearable device (400) may obtain a query regarding whether the user is drinking. For example, the wearable device (400) may obtain a query regarding whether the user is passing by. For example, the wearable device (400) may obtain a query regarding whether the user is showering. For example, the wearable device (400) may obtain a query regarding whether the user is alone. For example, the wearable device (400) may obtain a query regarding whether the user is currently in a situation where walking is difficult. For example, the wearable device (400) may obtain a query regarding whether the user has any pain.

[0163] For example, the wearable device (400) can obtain a query regarding whether an accident has occurred. The wearable device (400) can obtain a query to confirm whether an actual accident has occurred to the user.

[0164] According to one embodiment, the wearable device (400) may display a user interface indicating the acquired query through the display (550). According to one embodiment, the wearable device (400) may output a voice signal indicating the acquired query through a speaker. According to one embodiment, the wearable device (400) may provide a vibration together with the user interface indicating the acquired query.

[0165] According to one embodiment, in operation 905, the wearable device (400) may obtain response data from the user and train the artificial intelligence model (600) based on the response data. For example, the wearable device (400) may obtain response data from the user based on a query.

[0166] According to one embodiment, the wearable device (400) can generate (or obtain) a query to check the user's condition. The wearable device (400) can provide the generated (or obtained) query to the user. For example, even if the wearable device (400) determines that an accident has occurred to the user, the wearable device (400) can provide the user with a query to determine whether the accident actually occurred and whether the user is in good condition after the accident. The wearable device (400) can provide the query through a user interface. The wearable device (400) can obtain response data to the query based on an input to the user interface provided by the wearable device (400). The wearable device (400) can obtain response data to the query based on a voice input obtained from the wearable device (400).

[0167] For example, response data acquired from a wearable device (400) may be reflected in an artificial intelligence model (600). As the response data is reflected in the artificial intelligence model (600), a personalized output for the user may be provided through the artificial intelligence model (600).

[0168] Referring to FIG. 9B, operations 911 to 917 may be related to the prediction operation (622) of an accident risk (e.g., a fall risk) of FIG. 6. Operations 911 to 913 may correspond to operations 901 to 903 of FIG. 9A.

[0169] According to one embodiment, in operation 914, the wearable device (400) can identify (or predict) an accident risk. The wearable device (400) can identify (or predict) the accident risk based on output data of the artificial intelligence model (600).

[0170] For example, the AI ​​model (600) can identify an accident risk based on the user's metadata and surrounding environmental information. For example, the wearable device (400) can identify an accident risk as one of three levels: normal, dangerous, and very dangerous. For example, the wearable device (400) can use the AI ​​model (600) to identify an accident risk as one of three levels: 0 to 10.

[0171] According to one embodiment, in operation 915, the wearable device (400) may identify whether the risk of an accident is outside a threshold range. For example, the wearable device (400) may identify whether the risk of an accident is outside a threshold range to determine whether to provide a notification. For example, the wearable device (400) may identify whether the risk of an accident exceeds a threshold range.

[0172] For example, if the risk of an accident is higher than a critical range, the wearable device (400) may identify that the risk of an accident is outside the critical range. For example, if the risk of an accident is identified as one of levels 0 to 10, and the risk of an accident is higher than level 2, the wearable device (400) may identify that the risk of an accident is outside the critical range. In some embodiments, the lower the level indicating the risk of an accident, the more dangerous the user may be instructed to be. If the risk of an accident is identified as one of levels 0 to 10, and the risk of an accident is lower than level 8, the wearable device (400) may identify that the risk of an accident is outside the critical range.

[0173] According to one embodiment, in operation 916, if the risk of an accident is outside the threshold range, the wearable device (400) may provide a notification of the risk of an accident. For example, the wearable device (400) may provide a notification of the risk of an accident of the wearable device (400) based on identifying that the risk of an accident is outside the threshold range. For example, the wearable device (400) may provide a notification of the risk of an accident of the wearable device (400) based on identifying that the risk of an accident is above the threshold range.

[0174] For example, a wearable device (400) can predict the user's current accident risk based on the user's metadata and surrounding environmental information, and provide the user with the predicted accident risk. For example, if the accident risk falls outside a critical range, the wearable device (400) can provide a notification of the accident risk and provide the user with countermeasures to address the accident risk.

[0175] For example, the wearable device (400) may provide a fall warning notification to the user when the user enters the restroom, if the user has a history of frequent falls in the restroom. For example, if the ground is slippery due to heavy snowfall, the wearable device (400) may provide a fall warning notification to the user and provide countermeasures (e.g., guidance on wearing crampons, guidance on detours). For example, the wearable device (400) may provide a fall warning notification to the user when accidents (e.g., falls) frequently occur within the geographic area where the wearable device (400) is located.

[0176] According to one embodiment, in operation 917, if the risk of an accident is within a critical range, the wearable device (400) may refrain from providing a notification regarding the risk of an accident. For example, the wearable device (400) may refrain from providing a notification regarding the risk of an accident of the wearable device (400) based on identifying that the risk of an accident is within a critical range. For example, the wearable device (400) may refrain from providing a notification regarding the risk of an accident of the wearable device (400) based on identifying that the risk of an accident is below a critical range.

[0177] Referring to FIG. 9c, operations 921 to 927 may be related to the accident situation identification operation (623) of FIG. 6. Operations 921 to 923 may correspond to operations 901 to 903 of FIG. 9a.

[0178] According to one embodiment, in operation 924, the wearable device (400) can identify an accident situation. The wearable device (400) can identify the accident situation based on output data of the artificial intelligence model (600).

[0179] For example, the wearable device (400) can predict an injury site based on identifying an accident situation. The wearable device (400) can identify that an injury is expected in at least one body part among the head, hip joint, and / or wrist. The wearable device (400) can provide the user with information about the predicted injury site. For example, the wearable device (400) can provide the user with information about the probability that an injury has occurred in a major body part (e.g., the head, hip joint, wrist, or ankle).

[0180] For example, the wearable device (400) may identify an accident type based on identifying the accident situation. The wearable device (400) may identify that at least one of a trip, a slip, a fall, a fall from a bicycle / ramp / bed, and / or an obstacle / vehicle collision has occurred to the user.

[0181] For example, the wearable device (400) can identify the severity of an injury based on the identification of the accident situation. For example, the wearable device (400) can identify the severity of an injury for a predicted area of ​​injury. The wearable device (400) can identify the severity of an injury based on the intensity, direction, and / or surrounding environment of the impact.

[0182] According to one embodiment, in operation 925, the wearable device (400) can identify whether the user is in an emergency state. For example, the wearable device (400) can identify whether the user is in an emergency state to determine whether to request rescue. For example, whether the user is in an emergency state can be identified based on at least one of the user's motion, the user's heart rate, the user's body temperature, the force of the impact, the direction, the severity of the injury, and / or the surrounding environment.

[0183] According to one embodiment, in operation 926, if the user is in an emergency state, the wearable device (400) may perform a function for requesting rescue from the user. For example, the wearable device (400) may perform a function for requesting rescue from the user based on identifying that the user is in an emergency state. For example, if the severity of the user's injury is above a reference range, the wearable device (400) may transmit a notification of the user's accident and a rescue request to a first external electronic device registered in relation to the user's emergency state (e.g., an electronic device corresponding to an emergency contact).

[0184] According to one embodiment, the wearable device (400) can identify that the severity of the user's injury is outside a threshold range. Based on identifying that the severity of the user's injury is outside the threshold range, the wearable device (400) can broadcast (or transmit) a signal to notify an emergency state of the user of the wearable device (400) to an external electronic device located within a reference distance from the wearable device (400). For example, the wearable device (400) can identify that the severity of the user's injury is outside a threshold range. Based on identifying that the severity of the user's injury is outside a threshold range, the wearable device (400) can broadcast (or transmit) a signal to notify an emergency state of the user of the wearable device (400) to an external electronic device located within a reference distance from the wearable device (400). The specific operation of the wearable device (400) to broadcast a signal to notify an external electronic device within a reference distance of the user's emergency condition will be described later in FIG. 13.

[0185] According to one embodiment, in operation 927, the wearable device (400) may identify that the user's injury severity is within a threshold range. Based on identifying that the user's injury severity is within the threshold range, the wearable device (400) may refrain from performing a function for requesting rescue from the user. For example, the wearable device (400) may identify that the user's injury severity is below a threshold range. Based on identifying that the user's injury severity is below a threshold range, the wearable device (400) may refrain from performing a function for requesting rescue from the user. Based on identifying that the user's injury severity is below a threshold range, the wearable device (400) may not perform a function for requesting rescue from the user.

[0186] FIG. 10 illustrates an example of the operation of a wearable device for obtaining a query and obtaining response data, according to one embodiment.

[0187] Referring to FIG. 10, the wearable device (400) can obtain a query to identify the user's status using the artificial intelligence model (600). For example, the wearable device (400) can generate a query to determine the user's status and provide the generated query to the user. For example, the wearable device (400) can obtain a query regarding whether the user is drinking.

[0188] According to one embodiment, the wearable device (400) can identify that the user is drinking liquid based on motion data and / or biometric data. The wearable device (400) can identify that the user is located in a location where drinking is possible (e.g., a restaurant, home, bar) based on the user's location data. The wearable device (400) can identify that the user's heart rate has changed (or increased) using the biometric data. The wearable device (400) can identify that there is a high probability that the user is drinking. The wearable device (400) can predict that the user is drinking. The wearable device (400) can generate a query regarding whether the user is drinking.

[0189] For example, the wearable device (400) may display a user interface according to one of examples (1010) and (1020) through the display (550) based on the generated query.

[0190] The wearable device (400) may display a user interface (1011) according to example (1010) when the accuracy of the prediction that the user is drinking exceeds a defined accuracy. For example, the user interface (1011) may include a query (1012) regarding a specific user status, such as "Are you drinking now?" The user interface (1011) may include an object (1013) and an object (1014) for obtaining a response to the query. The object (1013) may indicate a positive response to the query displayed through the user interface (101). The object (1014) may indicate a negative response to the query displayed through the user interface (1011).

[0191] If the accuracy of the prediction that the user is drinking is below a defined accuracy, the wearable device (400) may display a user interface (1021) according to example (1020). For example, the wearable device (400) may provide predicted results regarding the user's status or activity in the form of tags. For example, the user interface (1021) may include a query (1012) such as "What are you drinking now?" and selectable objects (1023). The wearable device (400) may improve usability by displaying objects corresponding to predicted results regarding the user's status or activity.

[0192] The wearable device (400) can obtain response data based on the user interface according to example (1010) or example (1020). For example, in example (1010), the wearable device (400) can obtain response data based on inputs to objects (1013) and objects (1014). For example, in example (1020), the wearable device (400) can obtain response data based on inputs to one of selectable objects (1023).

[0193] In example (1030), the wearable device (400) can identify that the user is drinking based on the response data. Based on the identification that the user is drinking, the wearable device (400) can display a user interface (1031) on the display (550) to provide a notification requesting attention to an accident. According to an embodiment, the wearable device (400) can provide a notification requesting attention to an accident through a voice signal using a speaker.

[0194] In FIG. 10, an example of generating a query regarding whether a user is drinking and obtaining response data regarding whether the user is drinking is illustrated, but is not limited thereto. The wearable device (400) may obtain a query regarding a situation that may cause an emergency state for the user and provide the obtained query to the user.

[0195] FIG. 11A illustrates an example of operation of a wearable device for providing notifications according to an accident situation, according to one embodiment.

[0196] FIG. 11b illustrates an example of operation of a wearable device for providing an injury risk assessment, according to one embodiment.

[0197] Referring to FIG. 11A, in example (1110), the wearable device (400) may display a user interface (1111) through the display (550) based on identifying an accident situation of the user (e.g., a fall situation). For example, the user interface (1111) may include text indicating that an accident (e.g., a fall) has been detected, an object (1112) for stopping a notification, and an object (1113) for connection with a registered external electronic device. As an example, the wearable device (400) may attempt to connect with a registered external electronic device related to the user's emergency condition based on an input to the object (1113).

[0198] According to one embodiment, the wearable device (400) may change the user interface displayed on the display (550) of the wearable device (400) from the user interface (1111) according to the example (1110) to the user interface (1121) according to the example (1120) based on the input to the object (1113).

[0199] According to one embodiment, the wearable device (400) may change the user interface displayed on the display (550) of the wearable device (400) from the user interface (1111) according to the example (1110) to the user interface (1121) according to the example (1120) based on the absence of a user response or the identification of a user motion for a reference time period.

[0200] In example (1120), the wearable device (400) may transmit a call connection request to an external electronic device registered in relation to the user's emergency condition. The wearable device (400) may display a user interface (1121) indicating that a call is being connected to the external electronic device registered in relation to the user's emergency condition through the display (550). For example, the user interface (1121) may include text (1122) indicating that a call is being connected, an object (1123) for activating a speakerphone function, an object (1124) for activating a mute function, an object (1125) for displaying additional functions, and an object (1126) for terminating a call connection.

[0201] According to one embodiment, the wearable device (400) may broadcast a signal to an external electronic device (404) surrounding the wearable device (400) to notify the user of an emergency condition of the wearable device (400) when the severity of the user's injury exceeds a threshold range. For example, the signal broadcast by the wearable device (400) may cause the external electronic device (404) that receives the signal broadcast from the wearable device (400) to display information indicating the user's emergency condition.

[0202] Example (1130) illustrates an example of an operation of an external electronic device (404) that receives a signal to notify an emergency state of a user. In example (1130), the external electronic device (404) may display a user interface (1131) based on a signal received from a wearable device (400). The user interface (1131) may include text (1132) indicating that the user of the wearable device (400) is in an emergency state, an object (1133) for stopping a notification, and an object (1134) providing a function for performing a rescue operation of the user of the wearable device (400). Based on an input to the object (1134), the wearable device (400) may provide a status of the user of the wearable device (400) or a movement path to a location of the user of the wearable device (400).

[0203] Referring to FIG. 11b, the wearable device (400) may provide the user with an area where the user's injury is predicted. In example (1140), the wearable device (400) may display a user interface (1141) via the display (550) to indicate the area where the user's injury is predicted.

[0204] For example, after the user interface (1141) is displayed and a specified period of time has elapsed, the wearable device (400) may display the user interface (1151) of the example (1150). For example, the user interface (1151) may include an object (1155) representing the user's body. The wearable device (400) may display objects (1152, 1153, 1154) overlapping the object (1155) to highlight an area of ​​the user's body where injury is predicted.

[0205] After the user interface (1151) is displayed and a specified period of time has elapsed, the wearable device (400) may display the user interface (1161) of the example (1160). For example, the user interface (1161) may include text (1162) indicating probability information about the predicted injury site of the user provided in the user interface (1151).

[0206] Figure 12 illustrates an example of the operation of a wearable device for a structure request, according to one embodiment. In the following embodiments, the operations may be performed sequentially, but are not necessarily sequential. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0207] Referring to FIG. 12, in operation 1210, the wearable device (400) can identify an accident situation (e.g., a fall situation) and the severity of the injury. For example, the wearable device (400) can use an artificial intelligence model (600) to identify the accident situation and predict the severity of the injury resulting from the accident.

[0208] According to one embodiment, in operation 1220, the wearable device (400) may identify whether the injury severity falls outside a threshold range. For example, the wearable device (400) may identify an accident situation (e.g., a fall situation) and, based on the injury severity identification, determine whether the injury severity falls outside a threshold range. For example, the wearable device (400) may determine whether the injury severity exceeds a threshold range.

[0209] For example, the wearable device (400) can identify that the severity of the injury is outside the critical range when the severity of the injury is very high, such as when the user has fainted.

[0210] According to one embodiment, in operation 1230, if the severity of the injury is outside the threshold range, the wearable device (400) may broadcast a signal to notify an external electronic device located within a reference distance from the wearable device (400) of an emergency condition of the user of the wearable device (400). For example, if the severity of the injury exceeds the threshold range, the wearable device (400) may broadcast a signal to notify an external electronic device located within a reference distance from the wearable device (400) of an emergency condition of the user of the wearable device (400). For example, the broadcasted signal may cause an external electronic device that receives the broadcasted signal from the wearable device (400) to display an emergency condition. For example, the broadcasted signal may cause the external electronic device to display information indicating a movement path from a location of the external electronic device to a location of the wearable device (400).

[0211] According to one embodiment, the wearable device (400) may broadcast a signal so that users of external electronic devices around the wearable device (400) can recognize the accident of the user of the wearable device (400) when the user is in a state where it is difficult to respond due to an accident.

[0212] According to an embodiment, the wearable device (400) may determine external electronic devices to which to transmit a signal based on the severity of the injury, whether the user responds to a notification, and / or the number of external electronic devices located within a reference distance from the wearable device (400). The wearable device (400) may efficiently perform a rescue request in an emergency situation by transmitting a signal only to some of the external electronic devices, rather than transmitting a signal to all external electronic devices located within the reference distance. According to an embodiment, in a situation where the severity of the injury is very high and requires rapid emergency treatment, the wearable device (400) may transmit a signal to all external electronic devices within the reference distance to notify the user of an emergency condition.

[0213] According to one embodiment, in operation 1240, if the injury severity is not outside the threshold range, the wearable device (400) may provide a notification regarding the identification of an accident situation in the wearable device (400). For example, the wearable device (400) may provide a notification regarding the identification of an accident situation as in example (1110) of FIG. 11A. If the injury severity is below the threshold range, the wearable device (400) may provide a notification regarding the identification of an accident situation in the wearable device (400).

[0214] In one embodiment, at step 1250, the wearable device (400) may attempt to establish a connection (e.g., establish a call connection) with a first external electronic device registered in connection with the emergency situation. For example, the first external electronic device may be associated with a contact designated in connection with the emergency situation (e.g., an emergency contact, an emergency reporting number (e.g., 911 or 112)).

[0215] According to one embodiment, in operation 1260, the wearable device (400) can identify whether the connection with the registered first external electronic device has failed. The wearable device (400) can identify whether the connection with the registered first external electronic device performed for the rescue request has failed.

[0216] According to one embodiment, when the connection with the registered first external electronic device fails, the wearable device (400) may perform operation 1230. For example, the wearable device (400) may broadcast a signal to notify an emergency state of the user of the wearable device (400) to an external electronic device located within a reference distance from the wearable device (400) based on the connection failure with the registered first external electronic device. Since the connection with the first external electronic device has failed, the wearable device (400) may broadcast a signal to notify an emergency state of the user of the wearable device (400) to an external electronic device located within a reference distance from the wearable device (400) to request rescue.

[0217] According to one embodiment, in operation 1270, if the connection with the registered first external electronic device does not fail, the wearable device (400) can identify whether the time until the user of the first external electronic device arrives at the user of the wearable device (400) is longer than a threshold time. The wearable device (400) can identify whether the time until the user of the first external electronic device arrives at the user of the wearable device (400) is longer than the threshold time based on the connection with the registered first external electronic device. For example, the wearable device (400) can identify the location of the first external electronic device based on the connection with the registered first external electronic device. The wearable device (400) can identify the time until the user of the first external electronic device arrives at the user of the wearable device (400) based on the location of the wearable device (400). The wearable device (400) can identify whether the time until the user of the first external electronic device arrives at the user of the wearable device (400) is longer than a threshold time.

[0218] According to one embodiment, the wearable device (400) may receive information (e.g., latitude information and / or longitude information) about the location of the first external electronic device (or the location of the user of the first external electronic device). Based on the information about the location of the wearable device (400) and the location of the first external electronic device, the wearable device (400) may use an application for identifying travel time (e.g., a navigation application) to identify the time required to rescue the user of the wearable device (400). Accordingly, the time required for the user of the first external electronic device to move to the location of the wearable device (400) may vary depending on the complexity of the travel route. The wearable device (400) may determine the time until the user of the first external electronic device arrives at the user of the wearable device (400) based on real-time traffic conditions.

[0219] According to one embodiment, if the time until the user of the first external electronic device reaches the user of the wearable device (400) is longer than the threshold time, the wearable device (400) may perform operation 1230. For example, based on the time until the user of the first external electronic device reaches the user of the wearable device (400) being longer than the threshold time, the wearable device (400) may perform operation 1230. For example, if the time required for the user of the first external electronic device to rescue the user of the wearable device (400) is too long, the wearable device (400) may broadcast a signal to notify an emergency state of the user of the wearable device (400) to external electronic devices located within a reference distance from the wearable device (400) for a rescue request.

[0220] According to one embodiment, the wearable device (400) may transmit to the first external electronic device the type of accident that occurred to the user of the wearable device (400), the location of the injury (or the location where the injury is predicted), and / or the severity of the injury.

[0221] In operation 1280, if the time until the user of the first external electronic device reaches the user of the wearable device (400) is less than or equal to a threshold time, the wearable device (400) may wait for rescue by the first external electronic device. For example, based on identifying the time until the user of the first external electronic device reaches the user of the wearable device (400) as less than or equal to a threshold time, the wearable device (400) may refrain from broadcasting a signal to notify an emergency state of the user of the wearable device (400) to external electronic devices located within a reference distance, and may wait for rescue by the first external electronic device.

[0222] According to one embodiment, when the microphone (570) is composed of multiple microphones, the wearable device (400) can acquire user voice data even in a noisy environment through beamforming. For example, the wearable device (400) can estimate (or identify) the user's (or speaker's) speech location using beamforming. The wearable device (400) can generate a query and acquire a response to the generated query using voice data. Since the wearable device (400) acquires user voice data even in a noisy environment through beamforming, the usability of the wearable device (400) can be increased. For example, if an accident occurs to the user of the wearable device (400), even if the user is conscious, the user may not be able to respond loudly to a query of the wearable device (400) and may only groan. The wearable device (400) can identify the location of the user's moaning sound by using beamforming through multiple microphones, and identify (or estimate) the user's condition accordingly.

[0223] FIG. 13 illustrates an example of a screen displayed on an external electronic device based on a signal broadcast from a wearable device, according to one embodiment.

[0224] Referring to FIG. 13, as in operation 1230, the wearable device (400) can broadcast a signal to notify the user of an emergency condition of the wearable device (400). The wearable device (400) can broadcast a signal to notify the user of an emergency condition of the wearable device (400) to an external electronic device located within a reference range.

[0225] In example (1310), the external electronic device (1300) may receive a signal broadcast from the wearable device (400). For example, the external electronic device (1300) may display a user interface (1311) based on the signal. For example, the user interface (1311) may include text (1312) indicating that the user is in an emergency state, an object (1313) for rejecting a rescue request, and / or an object (1314) for accepting a rescue request.

[0226] According to one embodiment, based on identifying an input for an object (1313) for refusing a rescue request, the external electronic device (1300) may transmit a signal to another external electronic device within a reference distance from the external electronic device (1300) to notify an emergency state of the user of the wearable device (400). According to the above embodiment, as signals for notifying an emergency state of the user of the wearable device (400) are transmitted in a chain, the probability that rescue of the user will proceed quickly may increase.

[0227] In example (1320), the external electronic device (1300) may display a user interface (1321) based on an input to an object (1314) of the user interface (1311). For example, the user interface (1321) may include text (1322) indicating a status of a user of the wearable device (400).

[0228] In example (1330), the external electronic device (1300) may display a user interface (1331) for indicating a path from the external electronic device (1300) to the wearable device (400) based on an input to the user interface (1321) (or identifying that a reference time has elapsed since the user interface was displayed). For example, the wearable device (400) may receive location information of the wearable device (400) (or a user of the wearable device (400)) and, based on the location information of the wearable device (400), execute an application (e.g., a navigation application, a map application) for indicating a movement path to a location of the wearable device (400). The wearable device (400) may receive location information of the wearable device (400) (or a user of the wearable device (400)) and, based on the location information of the wearable device (400), display a user interface (1331) for indicating a path from an external electronic device (1300) to the wearable device (400). For example, the user interface may include an object (1333) indicating location information of the wearable device (400) (or a user of the wearable device (400)) and an object (1332) indicating the location of the external electronic device (1300).

[0229] According to one embodiment, a wearable device may include at least one sensor, a communication circuit, a memory storing instructions and including one or more storage media, and at least one processor including a processing circuit. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify a geographic area in which the wearable device is located using the communication circuit. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify data regarding a motion of the wearable device using the at least one sensor. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to input the data regarding the motion and the data regarding a state of the geographic area into a trained model within the wearable device. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to determine a condition of a user wearing the wearable device using the trained model input with the data regarding the motion and the data regarding the condition of the geographic area. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to perform one or more functions assigned for the emergency condition based on a determination that the user is in an emergency condition.

[0230] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify, using the communication circuitry, a location of a first external electronic device registered in connection with the emergency condition based on the determination that the user is in the emergency condition. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to determine, based on the location of the first external electronic device, a time until the user of the first external electronic device arrives at the user of the wearable device. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to broadcast, through the communication circuitry, a signal to notify an external electronic device located within a reference distance from the wearable device of the emergency condition of the user of the wearable device based on the time being longer than a threshold time.

[0231] In one embodiment, the signal may cause a second external electronic device that receives the signal broadcast from the wearable device to display information indicating the emergency condition.

[0232] In one embodiment, the signal may cause the second external electronic device to display information indicating a movement path from a location of the second external electronic device to a location of the wearable device.

[0233] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to establish a connection with the first external electronic device based on the determination that the user is in the emergency state. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to receive information about the location of the first external electronic device based on the connection with the first external electronic device.

[0234] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify, using the trained model, an injury severity of the user within the emergency condition. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device, in response to identifying that the injury severity is outside a threshold range, to broadcast, through the communication circuitry, a signal to notify an external electronic device located within the reference distance from the wearable device of the emergency condition of the user of the wearable device.

[0235] In one embodiment, a wearable device may include a display. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify a body part of the user in the emergency state, wherein the body part is predicted to be injured, using the trained model. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to display a screen representing the body part predicted to be injured, through the display.

[0236] According to one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to broadcast, through the communication circuitry, a signal to notify an external electronic device located within the reference distance from the wearable device of the emergency condition of the user of the wearable device in response to a connection failure with the first external electronic device.

[0237] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to obtain data regarding motion of another wearable device worn by the user from the other wearable device. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to obtain data regarding an incident history of another user associated with the geographic area from a server connected to the wearable device. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to input the data regarding the motion of the other wearable device and the data regarding the incident history of the other user, together with the data regarding the motion and the data regarding the state of the geographic area, into the trained model.

[0238] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to obtain a query regarding a situation that may cause the emergency condition using the trained model. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to obtain response data for the query based on the query. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to input the response data into the trained model.

[0239] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to obtain a query regarding whether the user is drinking using the trained model. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to obtain response data indicating whether the user is drinking based on the query.

[0240] In one embodiment, the wearable device may include a camera. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to obtain image data regarding the geographic area in which the wearable device is located using the camera. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to input the image data, together with the data regarding the motion and the data regarding the state of the geographic area, into the trained model.

[0241] According to one embodiment, the wearable device may include a display. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to generate, using the trained model, information on the probability of an accident occurring within the geographic area and a screen according to the geographic area. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to display the generated screen through the display.

[0242] According to one embodiment, a method performed by a wearable device may include an operation of identifying a geographic area in which the wearable device is located using a communication circuit of the wearable device. The method may include an operation of identifying data regarding a motion of the wearable device using at least one sensor of the wearable device. The method may include an operation of inputting the data regarding the motion and the data regarding a state of the geographic area into a trained model within the wearable device. The method may include an operation of determining a state of a user wearing the wearable device using the trained model into which the data regarding the motion and the data regarding the state of the geographic area have been input. The method may include an operation of performing one or more functions assigned for the emergency state based on a determination that the user is in an emergency state.

[0243] According to one embodiment, the method may include an operation of identifying a location of a first external electronic device registered in relation to the emergency state using the communication circuitry based on the determination that the user is in the emergency state. The method may include an operation of determining a time until the user of the first external electronic device arrives at the user of the wearable device based on the location of the first external electronic device. The method may include an operation of broadcasting a signal, via the communication circuitry, for notifying the emergency state of the user of the wearable device to an external electronic device located within a reference distance from the wearable device based on the time longer than a threshold time.

[0244] In one embodiment, the signal may cause a second external electronic device that receives the signal broadcast from the wearable device to display information indicating the emergency condition.

[0245] In one embodiment, the signal may cause the second external electronic device to display information indicating a movement path from a location of the second external electronic device to a location of the wearable device.

[0246] According to one embodiment, the method may include establishing a connection with the first external electronic device based on the determination that the user is in the emergency state. The method may include receiving information about the location of the first external electronic device based on the connection with the first external electronic device.

[0247] In one embodiment, the method may include an operation of identifying an injury severity of the user within the emergency state using the trained model. The method may include an operation of broadcasting a signal via the communication circuit to an external electronic device located within the reference distance from the wearable device to notify the user of the emergency state of the wearable device in response to identifying that the injury severity is outside a threshold range.

[0248] According to one embodiment, a non-transitory computer-readable storage medium may store one or more programs. The one or more programs may include instructions that, when executed by at least one processor of a wearable device having at least one sensor and communication circuitry, cause the wearable device to identify a geographic area in which the wearable device is located using the communication circuitry. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to identify data regarding motion of the wearable device using the at least one sensor. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to input data regarding the motion and data regarding a state of the geographic area into a trained model within the wearable device. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to determine a state of a user wearing the wearable device using the trained model input with the data regarding the motion and the data regarding the state of the geographic area. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to perform one or more functions assigned for the emergency state based on a determination that the user is in an emergency state.

[0249] Electronic devices according to embodiments disclosed herein may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to embodiments disclosed herein are not limited to the aforementioned devices.

[0250] The embodiments of this document and the terminology used herein are not intended to limit the technical features described in this document to specific embodiments, but should be understood to include various modifications, equivalents, or substitutes of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise. In this document, each of the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can include any one of the items listed together in the corresponding phrase among the phrases, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another component (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0251] In one embodiment of this document, the term "module" used may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0252] One embodiment of the present document may be implemented as software (e.g., a program (140)) including one or more instructions stored in a storage medium (e.g., an internal memory (136) or an external memory (138)) readable by a machine (e.g., an electronic device (101)). For example, a processor (e.g., a processor (120)) of the machine (e.g., an electronic device (101)) may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the machine to operate to perform at least one function according to the at least one called instruction. 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. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0253] According to one embodiment, the method according to one embodiment disclosed in the present document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disc read-only memory (CD-ROM)), or may be distributed online (e.g., by download or upload) through an application store (e.g., the Play Store) or directly between two user devices (e.g., smart phones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0254] According to one embodiment, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and arranged in other components. According to one embodiment, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to one embodiment, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Claims

In wearable devices, At least one sensor; communication circuit; A memory storing instructions and including one or more storage media; and At least one processor comprising a processing circuit, The above instructions, when individually or collectively executed by the at least one processor, Using the above communication circuit, identify the geographic area where the wearable device is located, Using at least one sensor, identifying data regarding motion of the wearable device, Inputting the data regarding the motion and the data regarding the state of the geographical area into the trained model within the wearable device, Using the trained model into which the data regarding the motion and the data regarding the state of the geographic area are input, the state of the user wearing the wearable device is determined, Based on a determination that the user is in an emergency state, perform one or more functions assigned for the emergency state. causing the above wearable device, Wearable devices. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor, Based on the determination that the user is in the emergency state, using the communication circuit, identifying the location of a first external electronic device registered in relation to the emergency state; Based on the location of the first external electronic device, determining the time until the user of the first external electronic device arrives at the user of the wearable device; Based on the above time longer than the threshold time, a signal for notifying the user of the wearable device of the emergency condition of the wearable device is broadcast through the communication circuit to an external electronic device located within a reference distance from the wearable device. causing the above wearable device, Wearable devices. In the second paragraph, the signal is, Causing a second external electronic device to receive the signal broadcast from the wearable device to display information indicating the emergency condition; Wearable devices. In the third paragraph, the signal is, Causing the second external electronic device to display information indicating a movement path from a location of the second external electronic device to a location of the wearable device; Wearable devices. In the second paragraph, when the instructions are individually or collectively executed by the at least one processor, Based on the determination that the user is in the emergency state, establishing a connection with the first external electronic device; Causing the wearable device to receive information about the location of the first external electronic device based on the connection with the first external electronic device; Wearable devices. In the second paragraph, when the instructions are individually or collectively executed by the at least one processor, Using the trained model, the injury severity of the user within the emergency condition is identified, In response to identifying that the injury severity is outside the threshold range, causing the wearable device to broadcast, through the communication circuit, a signal to notify the user of the wearable device of the emergency condition to an external electronic device located within the reference distance from the wearable device. Wearable devices. In the sixth paragraph, the wearable device, Includes a display, The above instructions, when individually or collectively executed by the at least one processor, Using the trained model, identify the body part of the user in the emergency state where the injury is predicted, Causing the wearable device to display a screen representing the body part where the injury is predicted through the display, Wearable devices. In the second paragraph, when the instructions are individually or collectively executed by the at least one processor, In response to a connection failure with the first external electronic device, causing the wearable device to broadcast, through the communication circuit, a signal for notifying the user of the wearable device of the emergency condition to an external electronic device located within the reference distance from the wearable device. Wearable devices. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor, Obtaining data about the motion of another wearable device worn by the user, Obtaining data on accident histories of other users related to the geographic area from a server connected to the wearable device; Causing the wearable device to input the data about the motion of the other wearable device and the data about the accident history of the other user, together with the data about the motion and the data about the state of the geographic area, into the trained model. Wearable devices. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor, Using the above trained model, obtain a query regarding a situation that may cause the above emergency condition, Based on the above query, obtain response data for the above query, causing the wearable device to input the above response data into the trained model; Wearable devices. In the 10th paragraph, when the instructions are individually or collectively executed by the at least one processor, Using the above trained model, a query is obtained as to whether the user is drinking, Causing the wearable device to obtain the response data indicating whether the user is drinking based on the query. Wearable devices. In the first paragraph, the wearable device, Includes a camera, The above instructions, when individually or collectively executed by the at least one processor, Using the above camera, image data regarding the geographical area where the wearable device is located is obtained, Causing the wearable device to input the image data, together with the data regarding the motion and the data regarding the state of the geographic area, into the trained model. Wearable devices. In the 12th paragraph, the wearable device, Includes a display, The above instructions, when individually or collectively executed by the at least one processor, Using the trained model, information on the probability of an accident occurring within the geographic area and a screen according to the geographic area are generated. Causing the wearable device to display the generated screen through the display; Wearable devices. In a method performed by a wearable device, An operation of identifying a geographic area in which the wearable device is located using a communication circuit of the wearable device; An operation of identifying data regarding motion of the wearable device using at least one sensor of the wearable device; An action of inputting data regarding said motion and data regarding the state of said geographical area into a trained model within said wearable device; An operation of determining a state of a user wearing the wearable device using the trained model into which the data regarding the motion and the data regarding the state of the geographic area are input; and Based on a determination that the user is in an emergency state, comprising an action of performing one or more functions assigned for the emergency state; method. In a non-transitory computer-readable storage medium storing one or more programs, the one or more programs, when executed by at least one processor of a wearable device having at least one sensor and communication circuit, Using the above communication circuit, identify the geographic area where the wearable device is located, Using at least one sensor, identifying data regarding motion of the wearable device, Inputting the data regarding the motion and the data regarding the state of the geographical area into the trained model within the wearable device, Using the trained model into which the data regarding the motion and the data regarding the state of the geographic area are input, the state of the user wearing the wearable device is determined, instructions for causing the wearable device to perform one or more functions assigned for the emergency condition based on a determination that the user is in an emergency condition; Non-transitory computer-readable storage medium.

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