Head-wearable electronic device for checking gesture of user by using other wearable electronic device, and operating method thereof

Abstract

According to one embodiment, a head-wearable electronic device comprises a camera, a communication circuitry, a display, at least one processor, and a memory for storing instructions, and can be instructed to: use an artificial intelligence model to identify a gesture corresponding to a first movement of the hand of a user on the basis of images captured by the camera; on the basis of identifying that an identification reliability of the gesture is lower than a first threshold value, display, on the display, guide information for identifying the gesture, and a notification indicating that an identification mode of the wearable electronic device acquired using a generative AI model is switched to a first identification mode using a first wearable electronic device worn on the hand; receive, from the first wearable electronic device, first feature information about a second movement of the hand of the user sensed by the first wearable electronic device; and use the artificial intelligence model to identify a gesture corresponding to the second movement of the hand of the user on the basis of the first feature information and the images captured by the camera.

Description

A head-worn wearable electronic device that verifies a user's gesture using another wearable electronic device, and a method of operation thereof

[0001] The present disclosure relates to a wearable electronic device that can be worn on the head to verify a user's gesture using another wearable electronic device, and a method of operating the same.

[0002] As communication technology advances, wearable electronic devices are becoming smaller and lighter enough to be used without significant discomfort even when worn on the user's body. For example, wearable electronic devices such as head-mounted display devices (HMDs), smartwatches (or bands), contact lens-type devices, ring-type devices, glove-type devices, shoe-type devices, or clothing-type devices are being commercialized. Since wearable electronic devices are worn directly on the body, portability and user accessibility can be improved.

[0003] Various services and additional functions provided through wearable electronic devices, such as AR glasses (augmented reality glasses), VST (video see-through) devices, and VR (virtual reality) devices, are gradually increasing. To enhance the utility value of these electronic devices and satisfy the needs of diverse users, telecommunications service providers or electronic device manufacturers are competitively developing devices to offer various functions and differentiate themselves from competitors. Accordingly, the various functions provided through wearable electronic devices are also becoming increasingly sophisticated.

[0004] Augmented reality glasses, video see-through (VST) devices, and virtual reality (VR) devices can provide users with a realistic experience by displaying virtual images while worn on the user's body. Augmented reality glasses, video see-through (VST) devices, and virtual reality (VR) devices can replace the usability of smartphones in various fields such as game entertainment, education, or social networking services (SNS). Users can receive content similar to reality through augmented reality glasses, video see-through (VST) devices, or virtual reality (VR) devices, and can feel as if they are staying in a virtual world through interaction.

[0005] Recently, in line with consumer trends that prioritize design, the convenience of use of wearable electronic devices is being given important consideration along with the external design of wearable electronic devices in the development of wearable electronic devices.

[0006] For example, in the case of a ring-shaped wearable electronic device that can be worn on a user's finger, it is small in size so it can be worn at all times, and thus can provide various services to manage the user's health or check their health status by measuring various biosignals.

[0007] The information described above may be provided as related art for the purpose of aiding understanding of the present disclosure. No claim or determination is made as to whether any of the foregoing may be applied as prior art related to the present disclosure.

[0008] According to one embodiment, a wearable electronic device that can be worn on the head may include a camera, a communication circuit, a display, at least one processor, and a memory for storing instructions. According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to identify a gesture corresponding to a first movement of a user's hand based on images captured through the camera using an artificial intelligence model. According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to display through the display a notification indicating that the identification mode of the wearable electronic device, obtained using a generative AI model, is switched to a first identification mode using a first wearable electronic device worn on the hand, and guide information for identifying the gesture, based on confirming that the identification reliability of the gesture is lower than a first threshold. According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may cause the wearable electronic device to receive first feature information regarding a second movement of the user's hand sensed by the first wearable electronic device from the first wearable electronic device. According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may cause the wearable electronic device to identify a gesture corresponding to the second movement of the user's hand based on the first feature information and images captured through the camera using the artificial intelligence model.

[0009] According to one embodiment, a method of operation for a wearable electronic device that can be worn on the head may include an action of identifying a gesture corresponding to a first movement of a user's hand based on images captured through a camera included in the wearable electronic device using an artificial intelligence model. According to one embodiment, a method of operation for the wearable electronic device may include an action of displaying a notification indicating that the identification mode of the wearable electronic device, obtained using a generative AI model, is switched to a first identification mode using a first wearable electronic device worn on the hand, and a guide information for identifying the gesture, based on confirming that the identification reliability of the gesture is lower than a first threshold value. According to one embodiment, a method of operation for the wearable electronic device may include an action of receiving first feature information regarding a second movement of the user's hand, which is sensed by the first wearable electronic device, from the first wearable electronic device. According to one embodiment, the method of operating the wearable electronic device may include the operation of confirming a gesture corresponding to the second movement of the user's hand based on the first feature information and images captured through the camera using the artificial intelligence model.

[0010] According to one embodiment, in a computer-readable non-transient storage medium storing instructions, the instructions, when executed individually or collectively by at least one processor, may cause a wearable electronic device to identify a gesture corresponding to a first movement of a user's hand based on images captured through a camera included in the wearable electronic device using an artificial intelligence model, and to identify the gesture based on the identification reliability of the gesture being lower than a first threshold value, and to display a notification indicating that the identification mode of the wearable electronic device is switched to a first identification mode using a first wearable electronic device worn on the hand, and guide information for identifying the gesture, and to receive first feature information regarding a second movement of the user's hand sensed by the first wearable electronic device from the first wearable electronic device, and to identify a gesture corresponding to the second movement of the user's hand based on the first feature information and images captured through the camera using the artificial intelligence model.

[0011] FIG. 1 is a block diagram of an electronic device in a network environment according to various embodiments.

[0012] FIG. 2 is a perspective view for explaining the internal configuration of a wearable electronic device according to one embodiment of the present disclosure.

[0013] FIGS. 3a and FIGS. 3b are drawings showing the front and rear of a wearable electronic device according to one embodiment.

[0014] FIG. 4a is a perspective view showing a first wearable electronic device according to one embodiment.

[0015] FIG. 4b is a cross-sectional view of a first wearable electronic device according to one embodiment.

[0016] FIG. 5a is a drawing of a wearable electronic device according to one embodiment, an external first wearable electronic device, and an external second wearable electronic device.

[0017] FIG. 5b is a block diagram of the configurations of a wearable electronic device according to one embodiment, an external first wearable electronic device, and an external second wearable electronic device.

[0018] FIG. 6 is a flowchart illustrating a method for a wearable electronic device to identify a user's gesture according to one embodiment.

[0019] FIG. 7a is a diagram illustrating a method for a wearable electronic device according to one embodiment to identify a gesture using an artificial intelligence model.

[0020] FIG. 7b is a diagram illustrating a method for an external first wearable electronic device according to one embodiment to acquire feature information about the movement of a user's hand.

[0021] FIGS. 8a, FIGS. 8b, and FIGS. 8c are drawings for explaining a method in which an external first wearable electronic device according to one embodiment acquires feature information regarding the movement of a user's hand.

[0022] FIG. 9 is a flowchart illustrating a method for a wearable electronic device according to one embodiment to switch an identification mode to a first identification mode using a first wearable electronic device.

[0023] FIG. 10 is a data flow diagram illustrating a method for a wearable electronic device according to one embodiment to identify a user's hand gesture by receiving feature information sensed by a first wearable electronic device.

[0024] FIG. 11 is a flowchart illustrating a method for a wearable electronic device to switch identification modes according to one embodiment.

[0025] FIG. 12 is a drawing for explaining a method of displaying information that guides a wearable electronic device according to one embodiment to form a communication connection with an external first wearable electronic device.

[0026] FIG. 13a is a diagram illustrating a method for a wearable electronic device according to one embodiment to generate notification and guide information using a generative artificial intelligence model.

[0027] FIG. 13b is a drawing illustrating a prompt for a wearable electronic device to generate notification and guide information according to one embodiment.

[0028] FIGS. 14a, FIGS. 14b, and FIGS. 14c are drawings of notification and guide information displayed by a wearable electronic device according to various embodiments.

[0029] FIGS. 15a and FIGS. 15b are drawings for explaining a method of identifying a user's gesture by further considering feature information regarding the movement of a user's hand received from an external first wearable electronic device according to one embodiment.

[0030] FIGS. 16a and FIGS. 16b are drawings for explaining a method in which a wearable electronic device according to one embodiment identifies a user's gesture using feature information regarding the movement of a user's hand received from an external first wearable electronic device.

[0031] FIG. 1 is a block diagram of an electronic device (101) in a network environment (100) according to various embodiments. Referring to FIG. 1, in the network environment (100), the electronic device (101) may communicate with an electronic device (102) through 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) through 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) through a server (108). According to one embodiment, the electronic device (101) may include a processor (120), memory (130), input module (150), sound output module (155), display module (160), audio module (170), sensor module (176), interface (177), connection terminal (178), haptic module (179), camera module (180), power management module (188), battery (189), communication module (190), subscriber identification module (196), or antenna module (197). In some embodiments, at least one of these components (e.g., connection terminal (178)) may be omitted from the electronic device (101), or one or more other components may be added. In some embodiments, some of these components (e.g., sensor module (176), camera module (180), or antenna module (197)) may be integrated into a single component (e.g., display module (160)).

[0032] The processor (120) can control at least one other component (e.g., hardware or software component) of the electronic device (101) connected to the processor (120) by executing software (e.g., program (140)), for example, and can perform various data processing or operations. According to one embodiment, as at least part of the data processing or operations, the processor (120) can store commands or data received from other components (e.g., sensor module (176) or communication module (190)) in volatile memory (132), process the commands or data stored in volatile memory (132), and store the resulting data in non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., central processing unit or application processor) or an auxiliary processor (123) that can operate independently or together with it (e.g., graphics processing unit, neural processing unit (NPU), image signal processor, sensor hub processor, or communication processor). For example, if the electronic device (101) includes a main processor (121) and an auxiliary processor (123), the auxiliary processor (123) may be configured to use lower power than the main processor (121) or to be specialized for a designated function. The auxiliary processor (123) may be implemented separately from the main processor (121) or as part thereof.

[0033] The auxiliary processor (123) may control at least some of the functions or states associated with at least one component of the electronic device (101) (e.g., display module (160), sensor module (176), or communication module (190)) 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. According to one embodiment, the auxiliary processor (123) (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module (180) or communication module (190)). According to one embodiment, the auxiliary processor (123) (e.g., neural network processing unit) may include a hardware structure specialized for processing an artificial intelligence model. The artificial intelligence model may be generated through machine learning. Such learning may be performed, for example, on the electronic device (101) itself where the artificial intelligence is performed, or through a separate server (e.g., server (108)). The learning algorithm may 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 may include a plurality of artificial neural network layers.An artificial neural network may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to the hardware structure, the artificial intelligence model may include a software structure, either additionally or substantially.

[0034] The memory (130) can store various data used by at least one component of the electronic device (101) (e.g., processor (120) or sensor module (176)). The data may include, for example, input data or output data for software (e.g., program (140)) and related commands. The memory (130) may include volatile memory (132) or non-volatile memory (134).

[0035] The program (140) may be stored as software in memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0036] The input module (150) can receive commands or data to be used for a component of the electronic device (101) (e.g., processor (120)) from outside the electronic device (101) (e.g., user). The input module (150) may include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0037] The sound output module (155) can output a sound signal to the outside of the electronic device (101). The sound output module (155) may include, for example, a speaker or a receiver. The speaker may be used for general purposes, such as multimedia playback or recording playback. The receiver may be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part thereof.

[0038] The display module (160) can visually provide information to an external (e.g., 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 said 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 the force generated by said touch.

[0039] The audio module (170) can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150) or output sound through the sound output module (155) or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphones) connected directly or wirelessly to the electronic device (101).

[0040] The sensor module (176) can detect the operating state of the electronic device (101) (e.g., power or temperature) or the external environmental state (e.g., user state) and generate an electrical signal or data value corresponding to the detected state. According to one embodiment, the sensor module (176) may include, for example, a gesture sensor, a gyroscope sensor, a barometric pressure sensor, a magnetic sensor, an accelerometer sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biosensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0041] The interface (177) may support one or more specified protocols that can be used for the electronic device (101) to be connected directly or wirelessly to an external electronic device (e.g., electronic device (102)). According to 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.

[0042] The connection terminal (178) may include a connector through which the electronic device (101) can 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).

[0043] The haptic module (179) can convert an electrical signal into a mechanical stimulus (e.g., vibration or movement) or an electrical stimulus that the user can perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module (179) may include, for example, a motor, a piezoelectric element, or an electric stimulation device.

[0044] The camera module (180) can capture still images and video. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0045] The power management module (188) can manage the power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented, for example, as at least part of a power management integrated circuit (PMIC).

[0046] The battery (189) can supply power to at least one component of the electronic device (101). According to one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0047] The communication module (190) can support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between an 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 include one or more communication processors that operate independently of the processor (120) (e.g., application processor) and 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., cellular communication module, short-range wireless communication module, or GNSS (global navigation satellite system) communication module) or a wired communication module (194) (e.g., LAN (local area network) communication module, or power line communication module). The corresponding communication module among these communication modules can communicate with an external electronic device (104) through a first network (198) (e.g., a short-range communication network such as Bluetooth, WiFi (wireless fidelity) direct, or IrDA (infrared data association)) or a second network (199) (e.g., 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 may 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 identify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) using subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module (196).

[0048] The wireless communication module (192) can support 5G networks and next-generation communication technologies following 4G networks, for example, new radio access technology. NR access technology can support high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and connection of multiple terminals (massive machine type communications (mMTC)), or high reliability and low latency (ultra-reliable and low-latency communications (URLLC)). The wireless communication module (192) can support a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate, for example. The wireless communication module (192) can support various technologies for securing performance in the high-frequency band, such as beamforming, massive MIMO (multiple-input and multiple-output), 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), external electronic device (e.g., electronic device (104)), or network system (e.g., second network (199)). According to one embodiment, the wireless communication module (192) can support a Peak data rate (e.g., 20 Gbps or more) for realizing eMBB, loss coverage (e.g., 164 dB or less) for realizing mMTC, or U-plane latency (e.g., downlink (DL) and uplink (UL) each 0.5 ms or less, or round trip 1 ms or less) for realizing URLLC.

[0049] An antenna module (197) can transmit a signal or power to or from an external source (e.g., an external electronic device). According to one embodiment, the antenna module (197) may include an antenna comprising a radiator made 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 a first network (198) or a second network (199), may be selected from the plurality of antennas, for example, by a 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, other components (e.g., a radio frequency integrated circuit (RFIC)) may be additionally created as part of the antenna module (197).

[0050] According to one embodiment, the antenna module (197) can create a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent to a first surface (e.g., bottom surface) of the printed circuit board and capable of supporting a specified high frequency band (e.g., mmWave band), and a plurality of antennas (e.g., array antennas) disposed on or adjacent to a second surface (e.g., top surface or side surface) of the printed circuit board and capable of transmitting or receiving a signal of the specified high frequency band.

[0051] At least some of the above components can be connected to each other via a communication method between peripheral devices (e.g., bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)) and exchange signals (e.g., commands or data) with each other.

[0052] According to one embodiment, commands or data may be transmitted or received between an electronic device (101) and an external electronic device (104) through 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 performed on the electronic device (101) may be performed on one or more of the external electronic devices (102, 104, or 108). For example, if the electronic device (101) needs to perform a function or service automatically or in response to a request from a user or another device, the electronic device (101) may request one or more external electronic devices to perform at least part of the function or service instead of performing the function or service itself or additionally. One or more external electronic devices that receive the above request may execute at least part of the requested function or service, or 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 provide the result as is or additionally processed as at least part of the response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device (101) may provide ultra-low latency services using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server using machine learning and / or neural networks. According to one embodiment, the external electronic device (104) or the server (108) may be included within a 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.

[0053] FIG. 2 is a perspective view for explaining the internal configuration of a wearable electronic device according to one embodiment of the present disclosure.

[0054] Referring to FIG. 2, a wearable electronic device (200) according to one embodiment of the present disclosure may include at least one of a light output module (211), a display member (201), and a camera module (250).

[0055] According to one embodiment of the present disclosure, the light output module (211) may include a light source capable of outputting an image and a lens that guides the image to a display member (201). According to one embodiment of the present disclosure, the light output module (211) may include at least one of a liquid crystal display (LCD), a digital mirror device (DMD), a liquid crystal on silicon (LCoS), an organic light emitting diode (OLED), or a micro light emitting diode (micro LED).

[0056] According to one embodiment of the present disclosure, the display member (201) may include an optical waveguide (e.g., a waveguide). According to one embodiment of the present disclosure, an output image of an optical output module (211) incident on one end of the optical waveguide may propagate within the optical waveguide and be provided to a user. According to one embodiment of the present disclosure, the optical waveguide may include at least one diffractive element (e.g., a Diffractive Optical Element (DOE), a Holographic Optical Element (HOE)) or a reflective element (e.g., a reflective mirror). For example, the optical waveguide may guide the output image of the optical output module (211) to the user's eye using at least one diffractive element or reflective element.

[0057] According to one embodiment of the present disclosure, the camera module (250) can capture still images and / or video. According to one embodiment, the camera module (250) may be placed within a lens frame and around a display member (201).

[0058] According to one embodiment of the present disclosure, the first camera module (251) can capture and / or recognize the trajectory of the user's eye (e.g., pupil, iris) or gaze. According to one embodiment of the present disclosure, the first camera module (251) can periodically or non-periodically transmit information related to the trajectory of the user's eye or gaze (e.g., trajectory information) to a processor (e.g., processor (120) of FIG. 1).

[0059] According to one embodiment of the present disclosure, the second camera module (253) can capture an external image.

[0060] According to one embodiment of the present disclosure, a third camera module (255) may be used for hand detection and tracking and recognition of user gestures (e.g., hand movements). According to one embodiment of the present disclosure, a third camera module (255) may be used for 3 degrees of freedom (3DoF), 6DoF head tracking, location (space, environment) recognition, and / or movement recognition. According to one embodiment of the present disclosure, a second camera module (253) may be used for hand detection and tracking and recognition of user gestures. According to one embodiment of the present disclosure, at least one of the first camera module (251) to the third camera module (255) may be replaced with a sensor module (e.g., a LiDAR sensor). For example, the sensor module may include at least one of a vertical cavity surface emitting laser (VCSEL), an infrared sensor, and / or a photodiode.

[0061] FIGS. 3A and FIGS. 3B are drawings showing the front and rear of a wearable electronic device according to one embodiment.

[0062] Referring to FIG. 3a and FIG. 3b, in one embodiment, camera modules (311, 312, 313, 314, 315, 316) and / or a depth sensor (317) for acquiring information related to the surrounding environment of the wearable electronic device (300) may be disposed on the first surface (310) of the housing.

[0063] In one embodiment, camera modules (311, 312) can acquire images related to the surrounding environment of a wearable electronic device.

[0064] In one embodiment, camera modules (313, 314, 315, 316) can acquire images while the wearable electronic device is worn by a user. Camera modules (313, 314, 315, 316) can be used for hand detection, tracking, and user gesture (e.g., hand movements) recognition. Camera modules (313, 314, 315, 316) can be used for 3DoF, 6DoF head tracking, position (space, environment) recognition, and / or movement recognition. In one embodiment, camera modules (311, 312) may be used for hand detection and tracking and user gestures.

[0065] In one embodiment, the depth sensor (317) may be configured to transmit a signal and receive a signal reflected from the subject, and may be used for determining the distance to the object, such as time of flight (TOF). In place of or additionally to the depth sensor (317), camera modules (313, 314, 315, 316) may determine the distance to the object.

[0066] According to one embodiment, a face recognition camera module (325, 326,) and / or a display (321) (and / or a lens) may be disposed on the second surface (320) of the housing.

[0067] In one embodiment, a face recognition camera module (325, 326) adjacent to the display may be used to recognize the user's face or to recognize and / or track both of the user's eyes.

[0068] In one embodiment, the display (321) (and / or lens) may be disposed on a second surface (320) of the wearable electronic device (300). In one embodiment, the wearable electronic device (300) may not include camera modules (315, 316) among a plurality of camera modules (313, 314, 315, 316). Although not illustrated in FIG. 3a and 3b, the wearable electronic device (300) may further include at least one of the configurations illustrated in FIG. 2.

[0069] As described above, according to one embodiment, the wearable electronic device (300) may have a form factor for being worn on a user's head. The wearable electronic device (300) may further include a strap and / or a wearing member for being secured on a part of the user's body. The wearable electronic device (300) may provide a user experience based on augmented reality, virtual reality, and / or mixed reality while being worn on the user's head.

[0070] FIG. 4a is a perspective view showing a wearable electronic device according to one embodiment.

[0071] Referring to FIG. 4a, the wearable electronic device (402) may include a housing (405). The housing (405) may form the overall appearance of the wearable electronic device (402).

[0072] According to one embodiment, the housing (405) may be in the shape of a ring. The housing (405) may include an opening configured to receive a user's finger. For example, the opening may be defined as a hole formed in the housing (405).

[0073] According to one embodiment, the housing (405) may include an outer housing portion (406) or an inner housing portion (407). The inner housing portion (407) may be coupled to the outer housing portion (406). According to one embodiment, the outer housing portion (406) and the inner housing portion (407) may be manufactured separately and assembled, or formed integrally.

[0074] According to one embodiment, the outer housing portion (406) may include a material capable of withstanding external impact and / or scratches and enabling the implementation of design features. For example, the outer housing portion (406) may include at least one of titanium, stainless steel, or ceramic. The outer housing portion (406) may be colored or coated to enable the implementation of the design.

[0075] According to one embodiment, the inner housing portion (407) may be a portion that comes into contact with the user's finger when the user wears the wearable electronic device (402). The inner housing portion (407) may be made of a material such as a molding material for sensing, transparent plastic, or glass. For example, the inner housing portion (407) may be configured to be at least partially transparent. For example, the inner housing portion (407) may include a material through which light for measuring biometric information is transmitted. At least a portion of the inner housing portion (407) may be made of a material substantially the same or similar to the outer housing portion (406). Additionally, at least a portion of the inner housing portion (407) may include a metallic material for measuring biometric information.

[0076] According to one embodiment, an outer housing portion (406) and an inner housing portion (407) may be combined to provide an internal space of the housing (405). Various electrical / electronic components of the wearable electronic device (402) may be placed and / or mounted in the internal space of the housing (405). For example, the housing (405) may accommodate various electrical / electronic components. Refer to FIG. 4b to examine the internal space of the housing (405) in detail.

[0077] FIG. 4b is a cross-sectional view of a wearable electronic device according to one embodiment.

[0078] Meanwhile, the arrangement of the components of the wearable electronic device (402) in FIG. 4b is merely an example. The components of the wearable electronic device (402) may be arranged differently from FIG. 4b.

[0079] Referring to FIG. 4b, according to one embodiment, a wearable electronic device (402) may include a housing (405) (e.g., the housing (405) of FIG. 4a).

[0080] According to one embodiment, the wearable electronic device (402) may include a processor (e.g., 420). For example, the processor (420) may be a microcontroller unit (MCU). Additionally, the processor (420) may be an application processor (AP), a supplementary processor (SP, e.g., a sensor hub), a central processor unit (CPU), a neural processor unit (NPU), a graphic processor unit (GPU), or an Internet of Things (IoT) processor.

[0081] According to one embodiment, the wearable electronic device (402) may include a communication module (e.g., 410).

[0082] According to one embodiment, the wearable electronic device (402) may include an antenna (e.g., 413). The antenna (413) may be an antenna for wireless communication. The antenna (413) may include a single or multiple segmented antennas. Referring to FIG. 4b, a portion of the housing (405) of the wearable electronic device (402) may be utilized as the antenna (413).

[0083] According to one embodiment, the wearable electronic device (402) may include a memory (e.g., 430). Referring to FIG. 4b, the wearable electronic device (402) may store data (e.g., sensing data, communication data) in the memory (430). Depending on the implementation, the memory (430) may be integrated with the processor (420).

[0084] According to one embodiment, the wearable electronic device (402) may include a photoplethysmography (PPG) sensor (e.g., 441, 442, 443). The PPG sensor (e.g., 441, 442, 443) may be a sensor that receives light absorbed, scattered, or reflected by irradiating light onto a living organism. The wearable electronic device (402) can verify a biological signal by using the PPG sensor (e.g., 441, 442, 443). Referring to FIG. 4b, one or more light-emitting parts (441) of the PPG sensor emit light of various bands and may be composed of elements such as an LED (light-emitting diode), a laser, or a VCSEL (vertical cavity surface-emitting laser). The bands of the light-emitting part (441) may include Green, Red, and IR (Infrared). One or more light receiving units (442) of the PPG sensor can receive light that is reflected and / or transmitted from the light emitting unit (441). A signal (e.g., light) obtained through the light receiving unit (442) can be converted through an analog-to-digital converter (ADC) and stored in memory (430) or a sensor buffer. The light receiving unit (442) can be composed of a photodiode (PD) or a complementary metal oxide semiconductor (CMOS). The control unit (443) of the PPG sensor can be an integrated circuit (IC) or an analog front end (AFE), and can control the light emitting unit (441) and the light receiving unit (442), process the received data, and transmit it to a processor (420) or store it in memory (430).

[0085] According to one embodiment, the wearable electronic device (402) may include an inertial sensor (e.g., 451). The inertial sensor (e.g., 451) may be a sensor that detects inertia, such as an accelerometer or a gyroscope. Referring to FIG. 4b, the inertial sensor (451) may include only an accelerometer (e.g., a 3-axis sensor) or may include both an accelerometer and a gyroscope (e.g., a 6-axis sensor). The wearable electronic device (402) may detect (or sense) gestures, motions, impacts, postures, and activities (sedentary, moving, sports) of the wearable electronic device (402) by using the inertial sensor (451).

[0086] According to one embodiment, the wearable electronic device (402) may include a temperature sensor (e.g., 452). The temperature sensor (e.g., 452) may be a sensor that measures the temperature of a body or a part. Depending on the method, the temperature sensor (e.g., 452) may be contact-type or non-contact-type. A temperature value measured by the temperature sensor (e.g., 452) may be stored in memory (430) or transmitted to a processor (420). By using the temperature sensor (e.g., 452), the wearable electronic device (402) (e.g., processor (420)) may estimate the temperature of a body, estimate the temperature of the wearable electronic device (402), or perceive the surrounding conditions of the wearable electronic device (402).

[0087] According to one embodiment, the wearable electronic device (402) may include a battery (e.g., 460). The battery (460) may be a device that converts and stores chemical energy into electricity to supply power to the wearable electronic device (402). The battery (460) (e.g., a secondary battery) is charged and discharged and may be configured in various ways depending on the material, such as lithium-ion, mercury, or dry cell. Referring to FIG. 4b, the battery (460) may include a flexible battery pack to correspond to the housing (405). The battery (460) may include a plurality of non-flexible battery packs. The battery (460) may include a flexible battery pack and a non-flexible battery pack.

[0088] According to one embodiment, the wearable electronic device (402) may include a charging circuit (e.g., 470). The charging circuit (470) may be configured to support wired charging (e.g., terminal, pogo pin) and / or wireless charging (e.g., WPC, NFC) methods for charging the wearable electronic device (402) (e.g., battery (460)). The wearable electronic device (402) may charge the battery (460) through the charging circuit (470).

[0089] According to one embodiment, the wearable electronic device (402) may include a power management module (e.g., 480). The power management module (480) may be a module that manages the power of the wearable electronic device (402). The wearable electronic device (402) (e.g., processor (420)) may distribute and control power appropriately to the processor (420), memory (430), and sensors (e.g., 441, 442, 443, 451, 452) through the power management module (e.g., 480).

[0090] According to one embodiment, the wearable electronic device (402) may include a substrate (e.g., 490). For example, the substrate (e.g., 490) may be a flexible printed circuit board (FPCB). Referring to FIG. 4b, various components such as a communication module (410), a processor (420), a memory (430), sensors (e.g., 441, 442, 443, 451, 452), a battery (460), and a power management module (480) may be placed on the substrate (490). Various components placed on the substrate (490) may be electrically connected.

[0091] According to one embodiment, the wearable electronic device (402) may include a sensor module (e.g., 476). According to one embodiment, the sensor module (476) may include a touch circuit, and the touch circuit may include a touch sensor and a touch sensor IC for controlling the same. The touch sensor IC may control the touch sensor to detect a touch input for a specific location on the surface of an external housing, for example. For example, the touch sensor IC may detect a touch input by measuring a change in a signal (e.g., voltage, light intensity, resistance, or charge) for a specific location on the surface. The touch sensor IC may provide information regarding the detected touch input (e.g., location, area, pressure, or time) to a processor (420).

[0092] According to one embodiment, the sensor module (476) may further include a pressure sensor capable of measuring the intensity (pressure) of the touch.

[0093] According to one embodiment, at least a portion of the housing (405) of the wearable electronic device (402) (e.g., the outer housing (406) of FIG. 4a) may include a display module. If the wearable electronic device (402) includes a display module, the display module may include a touch circuit. Additionally, the display module may further include at least one sensor (e.g., a pressure sensor) of the sensor module (476) or a control circuit for the same. In this case, the at least one sensor or the control circuit for the same may be embedded in a portion of the display module or a portion of the touch circuit. For example, if the sensor module (476) embedded in the display module includes a pressure sensor, the pressure sensor may acquire pressure information associated with a touch input through a portion or the entire area of ​​the housing (405) (or display). According to one embodiment, the sensor module (476) including the touch sensor may be placed between pixels of the pixel layer of the display, or above or below the pixel layer.

[0094] According to one embodiment, the sensor module (476) can detect touch input on the entire area or a part of the curved shape of the outer housing.

[0095] According to one embodiment, the sensor module (476) can detect touch input at a first part (or first touch area) (476a) of a curved outer housing and a second part (or second touch area) (476b) spaced apart from the first part (476a) at a certain distance. For example, the first part (476a) and the second part (476b) may correspond to a position where the finger and the fingers adjacent to it touch when the wearable electronic device (402) is worn on the user's finger.

[0096] According to one embodiment, the sensor module (476) may include a third part (or third touch area) (476c) that is different from the first part (476a) and the second part (476b). Additionally, pressure sensors may be placed in some areas (476a, 476b, 476c) to measure the intensity of the force generated by the touch. According to one embodiment, the pressure sensors may include a plurality of pressure sensors. Additionally, the pressure sensors may be spaced apart at predetermined intervals along the curved shape of the housing so that pressure can be detected by touch input at specific locations on the surface of the entire area or a portion of the area surrounding the outer housing.

[0097] The functions related to artificial intelligence according to the present disclosure are operated through a processor and memory. The processor may be composed of one or more processors. In this case, the one or more processors may be general-purpose processors such as CPUs, APs, and DSPs (Digital Signal Processors), graphics-dedicated processors such as GPUs and VPUs (Vision Processing Units), or artificial intelligence-dedicated processors such as NPUs. The one or more processors control the processing of input data according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, if the one or more processors are artificial intelligence-dedicated processors, the artificial intelligence-dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0098] The predefined rules of operation or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that a predefined rules of operation or artificial intelligence models configured to perform desired characteristics (or objectives) are created by a basic artificial intelligence model being trained using multiple learning data by a learning algorithm. Such learning may be performed on the device itself where the artificial intelligence according to the present disclosure is executed, or it may be performed through a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

[0099] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values ​​and performs neural network operations through operations between the results of previous layers and the multiple weights. The multiple weights possessed by the multiple neural network layers can be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model during the learning process is reduced or minimized. Artificial neural networks may include deep neural networks (DNNs), such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), Bidirectional Recurrent Deep Neural Networks (BRDNNs), or Deep Q-Networks, but are not limited to the examples mentioned above.

[0100] Meanwhile, terms related to 'identify' in the present disclosure may be replaced with 'detect', 'recognize', 'determine', and / or 'sense'.

[0101] FIG. 5a is a drawing of a wearable electronic device according to one embodiment, an external first wearable electronic device, and an external second wearable electronic device.

[0102] Referring to FIG. 5a, according to one embodiment, a wearable electronic device (501) (e.g., the electronic device (101) of FIG. 1, the electronic device (200) of FIG. 2, or the electronic device (300) of FIG. 3) can wirelessly transmit and receive data with respect to the wearable electronic device (501) with respect to an external first wearable electronic device (502) (e.g., the electronic device (102) of FIG. 1 or the electronic device (402) of FIG. 4) and a second wearable electronic device (503) (e.g., the electronic device (104) of FIG. 1). For example, the wearable electronic device (501) can form a communication connection with at least one of the first wearable electronic device (502) and the second wearable electronic device (503) using short-range communication (e.g., BLE communication) technology. For example, the wearable electronic device (501) may include a wearable electronic device that can be worn on a user's head (e.g., a VST device, a VR device, an XR device, an MR device, or an AR device). For example, the first wearable electronic device (502) may include a wearable electronic device that can be worn on a user's hand (or finger) (e.g., a ring-shaped wearable electronic device or a smart ring). For example, the second wearable electronic device (503) may include a wearable electronic device that can be worn on a user's hand (or wrist) (e.g., a watch-shaped wearable electronic device or a smartwatch).

[0103] According to one embodiment, a wearable electronic device (501) can identify a user's gesture by analyzing images captured using a camera (e.g., camera (510) of FIG. 5b). Based on the identified gesture, the wearable electronic device (501) can perform a function or a control action assigned to the corresponding gesture.

[0104] According to one embodiment, a wearable electronic device (501) may support a plurality of identification modes for identifying gestures. For example, the plurality of identification modes may include a low-power identification mode (or high-efficiency identification mode) that identifies gestures with low power and a high-precision identification mode that identifies gestures with high power. For example, the low-power identification mode may be a mode that identifies gestures by tracking a rough skeleton skeleton without tracking the detailed joints of the user's finger. Accordingly, the low-power identification mode may operate at low power but may have relatively low precision. For example, the high-precision identification mode may be a mode that identifies gestures by tracking down to the detailed joints of the user's finger. Accordingly, the high-precision identification mode may provide high precision and may consume relatively high power due to a large amount of computation.

[0105] According to one embodiment, the wearable electronic device (501) may operate in a low-power identification mode by default to identify a user's gesture. The wearable electronic device (501) may operate in a high-precision identification mode when it is necessary to precisely identify the movement of the user's hand.

[0106] Conventional wearable electronic devices could operate in a high-precision identification mode when a user's gesture was not accurately identified in a low-power identification mode. However, this increased the power consumption of the wearable electronic device and, by processing a large amount of computation, could lead to processing time delays and slow response speeds.

[0107] In addition, existing wearable electronic devices identified user gestures by analyzing images captured using a camera, so they could not accurately identify user gestures when the user's hand was out of the camera's field of view.

[0108] According to one embodiment, when a user’s gesture is not accurately identified in a low-power identification mode, the wearable electronic device (501) may switch to a first identification mode using an external wearable electronic device (e.g., 502 and / or 503) without immediately operating in a high-precision identification mode. For example, the first identification mode may mean a mode for identifying a user’s gesture by further considering feature information about the user’s movement sensed by an external wearable electronic device (e.g., 502 and / or 503). For example, in the first identification mode, the wearable electronic device (501) may operate by considering information (e.g., feature information) sensed by an external wearable electronic device (e.g., 502 and / or 503) while maintaining tracking of a rough skeleton skeleton without tracking the detailed joints of the user’s fingers. Therefore, the first identification mode is driven at relatively low power compared to the high-precision identification mode, and can identify the user's gesture with relatively high precision compared to the low-power identification mode.

[0109] According to one embodiment, the wearable electronic device (501) can identify or recognize the user's gesture more accurately with low power by identifying the user's gesture in a first identification mode. Additionally, the wearable electronic device (501) can identify the gesture even if the user's hand is out of the camera's field of view. Additionally, the wearable electronic device (501) can identify the user's gesture even in low-light situations or when the user's hand is away from the camera. Through this, the wearable electronic device (501) can provide improved gesture interaction compared to the conventional one.

[0110] FIG. 5b is a block diagram of the configurations of a wearable electronic device according to one embodiment, an external first wearable electronic device, and an external second wearable electronic device.

[0111] Referring to FIG. 5b, the wearable electronic device (501) may include a camera (510) (e.g., camera module (180) of FIG. 1), a processor (520) (e.g., processor (120) of FIG. 1), a memory (530) (e.g., memory (130) of FIG. 1), a display (560) (e.g., display module (160) of FIG. 1), and a communication circuit (570) (e.g., communication module (190)). According to one embodiment, the wearable electronic device (501) may omit at least one of the components or additionally include other components.

[0112] According to one embodiment, the processor (520) can control the overall operation of the wearable electronic device (501). For example, the processor (520) may be implemented identically or similarly to the processor (120) of FIG. 1. According to one embodiment, the processor (520) can execute software (e.g., the program (140) of FIG. 1) to control at least one other component (e.g., a hardware or software component) of the wearable electronic device (501) connected to the processor (520), and can perform data processing or operations based on instructions. According to one embodiment, the instructions may include instructions composed of machine language that can be processed by the wearable electronic device (501) or the processor (520). For example, the instructions may include instructions corresponding to operation instructions used in the program.

[0113] Meanwhile, although FIG. 5b illustrates that the wearable electronic device (501) includes one processor (520), this is exemplary and the technical concept of the present invention may not be limited thereto. For example, the wearable electronic device (501) may include at least one processor. For example, the processor (520) may be implemented as at least one processor.

[0114] According to one embodiment, a memory (530) included in a wearable electronic device (501) (e.g., memory (130) of FIG. 1) may store at least one instruction (or command) that causes at least one operation of the wearable electronic device (501). When the at least one instruction is executed collectively or individually by a processor (520), it may cause the wearable electronic device (501) to perform the corresponding operation.

[0115] According to one embodiment, the processor (520) can identify a gesture based on a first movement of the user's hand included in images captured through the camera (510) using an artificial intelligence model. For example, the processor (520) can identify a gesture based on a first movement of the user's hand in a low-power identification mode. For example, the first movement may represent the movement of the user's hand at a first point in time. For example, the processor (520) can preprocess the images and use an object detection model (e.g., R-CNN (region proposals with CNN)) to detect a hand region corresponding to the hand included in the image. The processor (520) can analyze the detected hand region in a designated identification mode to extract image feature information about the hand region and provide the extracted image feature information to the artificial intelligence model to obtain a gesture identification result. For example, the artificial intelligence model may be a model trained to identify the user's gesture based on feature information about the movement of the user's hand. For example, the artificial intelligence model may be stored in memory (530) or stored in an external electronic device (e.g., a server).

[0116] According to one embodiment, the processor (520) can identify a gesture based on a first movement of the user's hand using an artificial intelligence model and obtain an identification reliability of the identified gesture. For example, the identification reliability may be a value representing the identification accuracy of the gesture in the range of 0 to 100%.

[0117] According to one embodiment, the processor (520) can determine whether the identification reliability of the identified gesture is lower than a first threshold. For example, the first threshold may be a reference value for switching the identification mode of the wearable electronic device (501) from a low-power identification mode to a first identification mode using the first wearable electronic device (502).

[0118] According to one embodiment, the processor (520) may switch the identification mode of the wearable electronic device (501) from the low-power identification mode to the first identification mode based on confirming that the identification reliability of the identified gesture is lower than the first threshold. For example, the processor (520) may request feature information regarding the movement of the user's hand from the first wearable electronic device (502) worn on the user's hand via the communication circuit (570) based on confirming that the identification reliability of the identified gesture is lower than the first threshold. For example, the processor (520) may transmit information indicating that the identification mode of the wearable electronic device (501) is switched to the first identification mode based on confirming that the identification reliability of the identified gesture is lower than the first threshold.

[0119] According to one embodiment, the processor (520) may display guide information for identifying a notification and / or gesture indicating that the identification mode of the wearable electronic device (501) is switched to the first identification mode through the display (560), based on switching the identification mode of the wearable electronic device (501) to the first identification mode. For example, the processor (520) may generate guide information for identifying a notification and gesture indicating that the identification mode of the wearable electronic device (501) is switched to the first identification mode using a generative AI model (e.g., LLM). The processor (520) may generate a prompt for generating the notification and guide information. For example, the processor (520) may obtain the notification and guide information based on providing a prompt to a generative AI model that includes tracking information for the user's hand, information about the identification mode identifying the user's hand, information about the type of gesture, and identification reliability. For example, the notification may further include information about the reason for switching the identification mode of the wearable electronic device (501).

[0120] According to one embodiment, the processor (520) may receive first feature information regarding a second movement of a user's hand sensed by the first wearable electronic device (502) from the first wearable electronic device (502). For example, the first feature information may be feature information (e.g., feature) regarding the second movement of the user's hand and may be extracted by the first wearable electronic device (502). For example, the second movement may represent the movement of the user's hand at a second point in time. For example, the second point in time may be after the first point in time.

[0121] According to one embodiment, the processor (520) can identify a gesture based on a second movement of the user's hand by analyzing first feature information and images captured through the camera (510) using an artificial intelligence model.

[0122] According to one embodiment, the first wearable electronic device (502) may include a sensor (581) (e.g., the sensor module (176) of FIG. 1 or the sensor module (476) of FIG. 4b), a processor (583) (e.g., the processor (120) of FIG. 1 or the processor (420) of FIG. 4b), and / or a communication circuit (585) (e.g., the communication module (190) of FIG. 1 or the communication module (410) of FIG. 4b). According to one embodiment, the first wearable electronic device (502) may omit at least one of the components or additionally include other components. For example, the first wearable electronic device (502) may further include a memory (not shown) for storing data. For example, the first wearable electronic device (502) may use the memory as a buffer.

[0123] According to one embodiment, the processor (583) can control the overall operation of the first wearable electronic device (502). The processor (583) can obtain feature information about the user's movement by analyzing a signal about the user's movement sensed through the sensor (581). For example, the sensor (581) may include an accelerometer and / or a gyroscope. The processor (583) can store the obtained feature information in memory. Additionally, when a request is confirmed from the wearable electronic device (501), the processor (583) can transmit the feature information to the wearable electronic device (501) through the communication circuit (585). Depending on the implementation, when the processor (583) receives a request from the wearable electronic device (501) to transmit feature information sensed from a specific point in time, it can transmit the feature information obtained from that point in time to the wearable electronic device (501). Subsequently, the processor (583) may transmit feature information acquired in real-time or periodically to the wearable electronic device (501) until a request to stop transmission is received from the wearable electronic device (501). However, the embodiment of the present invention may not be limited to transmitting feature information. For example, the processor (583) may transmit information about user movement (e.g., sensed raw data) or the result of analyzing a sensed signal (e.g., feature signal) to the wearable electronic device (501).

[0124] According to one embodiment, the second wearable electronic device (503) may include a sensor (591) (e.g., sensor module (176) of FIG. 1), a processor (593) (e.g., processor (120) of FIG. 1), and / or a communication circuit (595) (e.g., communication module (190) of FIG. 1). According to one embodiment, the second wearable electronic device (503) may omit at least one of the components or additionally include other components. For example, the second wearable electronic device (503) may further include a memory (not shown) for storing data. For example, the second wearable electronic device (503) may use the memory as a buffer. Meanwhile, the second wearable electronic device (503) may be a wearable electronic device of the same type or a different type from the first wearable electronic device (502).

[0125] According to one embodiment, the processor (593) can control the overall operation of the second wearable electronic device (503). The processor (593) can obtain characteristic information about the user's movement by analyzing a signal about the user's movement sensed through the sensor (591). For example, the sensor (591) may include an accelerometer and / or a gyroscope. Depending on the implementation, the sensor (591) may include a PPG sensor. For example, when the user's movement can be identified by analyzing a signal sensed through the PPG sensor, the processor (593) may obtain characteristic information about the user's movement by analyzing the PPG signal. The processor (593) may store the obtained characteristic information in memory. Additionally, when a request is confirmed from the wearable electronic device (501), the processor (593) may transmit the characteristic information to the wearable electronic device (501) through the communication circuit (595). For example, when the processor (593) receives a request from the wearable electronic device (501) to transmit feature information sensed from a specific point in time, it can transmit the feature information acquired from that point in time to the wearable electronic device (501). Subsequently, the processor (593) can transmit feature information acquired in real time or periodically to the wearable electronic device (501) until a request to stop transmission is received from the wearable electronic device (501).

[0126] Meanwhile, the number or type of the first wearable electronic device (502) and the second wearable electronic device (503) shown in FIG. 5a and FIG. 5b are exemplary, and the technical features of the present invention may not be limited thereto.

[0127] At least some of the operations of the wearable electronic device (501) described in the drawings below may be performed or controlled by a processor (520). However, for convenience of explanation, the operations performed by the processor (520) will be described as being performed by the wearable electronic device (501).

[0128] FIG. 6 is a flowchart illustrating a method for a wearable electronic device to identify a user's gesture according to one embodiment.

[0129] Referring to FIG. 6, according to one embodiment, in operation 601, a wearable electronic device (e.g., the wearable electronic device (501) of FIG. 5a) can identify a gesture corresponding to a first movement of a user's hand based on images captured through a camera using an artificial intelligence model in a low-power identification mode. For example, the first movement may be a user's movement at a first point in time.

[0130] According to one embodiment, in operation 603, the wearable electronic device (501) can check whether the identification reliability of the gesture is lower than a first threshold.

[0131] According to one embodiment, if it is confirmed that the identification reliability of the gesture is not lower than the first threshold (No of operation 603), the wearable electronic device (501) can identify the gesture corresponding to the movement of the user's hand in low-power identification mode without switching the identification mode.

[0132] According to one embodiment, if it is confirmed that the identification reliability of a gesture is lower than a first threshold (e.g., operation 603), in operation 605, the wearable electronic device (501) may display a notification and / or guide information for identifying a gesture through a display (e.g., display (560) in FIG. 5b) indicating that the identification mode of the wearable electronic device obtained using a generative AI model is switched to a first identification mode using the first wearable electronic device while requesting feature information about the movement of the hand from the first wearable electronic device (502) worn on the user's hand. For example, the wearable electronic device (501) may display the notification and / or guide information while switching the identification mode to the first identification mode. For example, the first identification mode may be a low-power and high-precision identification mode. Depending on the implementation, the wearable electronic device (501) may request feature information regarding the movement of the hand from the second wearable electronic device (503) instead of the first wearable electronic device (502). However, the explanation regarding this will be omitted to avoid redundancy.

[0133] According to one embodiment, in operation 607, the wearable electronic device (501) may receive first feature information regarding a second movement of a user's hand sensed by the first wearable electronic device (502) from the first wearable electronic device (502). For example, the second movement may be a movement of the user at a second point in time (e.g., a point in time after the first point in time).

[0134] According to one embodiment, in operation 609, the wearable electronic device (501) can identify a gesture corresponding to a second movement of the user's hand based on first feature information and images captured through a camera using an artificial intelligence model.

[0135] According to the method described above, the wearable electronic device (501) can identify or recognize the user's gesture more accurately with low power by identifying the user's gesture in the first identification mode. Through this, the wearable electronic device (501) can provide improved gesture interaction compared to the existing one.

[0136] FIG. 7a is a diagram illustrating a method for a wearable electronic device according to one embodiment to identify a gesture using an artificial intelligence model.

[0137] Referring to FIG. 7a, according to one embodiment, the artificial intelligence model (720) may be a model trained to identify a user's gesture based on feature information regarding the movement of the user's hand. For example, the artificial intelligence model (720) may be stored in memory (e.g., memory (530) in FIG. 5b) or stored in an external electronic device (e.g., a server).

[0138] According to one embodiment, a processor (e.g., the processor (520) of FIG. 5b) may provide image feature information that analyzes the movement of a user's hand included in images to an artificial intelligence model (720) in a low-power identification mode. For example, the image feature information may be processed into a form suitable for the input data of the artificial intelligence model (720) by performing preprocessing by a vector embedding module (711). For example, the vector embedding module (711) may be included in a wearable electronic device (501). The artificial intelligence model (720) may output a gesture identification result (e.g., identified gesture and identification confidence) based on the image feature information (e.g., preprocessed image feature information) in a low-power identification mode.

[0139] According to one embodiment, the processor (520) may provide image feature information and sensor feature information that analyzes the movement of a user's hand sensed by the first wearable electronic device (502) of FIG. 5A to an artificial intelligence model (720) in a first identification mode using the first wearable electronic device (e.g., the first wearable electronic device (502) of FIG. 5A). For example, the sensor feature information may be processed or transformed into a form suitable for the input data of the artificial intelligence model (720) by performing preprocessing by a vector embedding module (712). For example, the vector embedding module (711) may be included in the wearable electronic device (501) or the first wearable electronic device (502). In the first identification mode, the artificial intelligence model (720) may output a gesture identification result (e.g., identified gesture and identification reliability) based on image feature information (e.g., preprocessed image feature information) and sensor feature information (e.g., preprocessed sensor feature information).

[0140] According to one embodiment, the coupling module (715) may provide sensor feature information (e.g., preprocessed sensor feature information) to the artificial intelligence model (720) based on an input weight (W). For example, in a low-power identification mode, the coupling module (715) may not provide sensor feature information (e.g., preprocessed sensor feature information) to the artificial intelligence model (720). In this case, the weight (W) may be 0. For example, in a first identification mode, the coupling module (715) may provide sensor feature information (e.g., preprocessed sensor feature information) to the artificial intelligence model (720). In this case, the weight (W) may be 1. That is, the coupling module (715) may provide sensor feature information (e.g., preprocessed sensor feature information) to the artificial intelligence model (720) in the first identification mode, and may block the provision of sensor feature information (e.g., preprocessed sensor feature information) in the low-power identification mode.

[0141] FIG. 7b is a diagram illustrating a method for an external first wearable electronic device according to one embodiment to acquire feature information about the movement of a user's hand.

[0142] Referring to FIG. 7b, according to one embodiment, the motion feature identification model (750) may be stored in a first wearable electronic device (e.g., the first wearable electronic device (502) of FIG. 5b) or in an external electronic device (e.g., a server). The first wearable electronic device (502) may obtain feature information regarding the movement of a user's hand using the motion feature identification model (750). The motion feature identification model (750) may receive a signal regarding the user's movement sensed from a sensor (e.g., the sensor (581) of FIG. 5b) (e.g., an accelerometer and / or a gyroscope) included in the first wearable electronic device (502). The motion feature identification model (750) may output feature information regarding the movement of the user's hand based on the received signal.

[0143] According to one embodiment, the motion feature identification model (750) may include a preprocessing operation unit (751), a segmentation unit (753), a feature extraction unit (755), and a feature selection unit (757). For example, at least some of the preprocessing operation unit (751), the segmentation unit (753), the feature extraction unit (755), or the feature selection unit (757) may be implemented as an artificial intelligence model.

[0144] According to one embodiment, the preprocessing operation unit (751) can preprocess (e.g., process or modify) the signal regarding the user's movement sensed from the sensor (581) (e.g., accelerometer and / or gyroscope) included in the first wearable electronic device (502) into a form that can be processed. For example, in the preprocessing operation, noise included in the signal may be removed, and various noise removal techniques (e.g., filtering techniques and scaling techniques through normalization) may be used. The segmentation unit (753) can detect a section corresponding to the gesture in the entire preprocessed signal and extract the signal included in that section. To do this, the segmentation unit (753) may use various techniques (e.g., adapting windowing and / or DTW (dynamic time wrapping)). The feature extraction unit (755) can extract features in the time domain and frequency domain for the signal included in that section. The feature selection unit (757) can select features for the user's hand movements (e.g., hand motion) from among the various features extracted by the feature extraction unit (757). For example, the feature selection unit (757) can determine the importance of features using a dimensionality reduction technique (e.g., PCA, LDA) or an information gain technique, and determine and output feature information for the user's hand movements from among the various features based on the determined importance. Depending on the implementation, the feature selection unit (757) may operate optionally.

[0145] FIGS. 8a, 8b, and 8c are drawings illustrating a method for an external first wearable electronic device according to one embodiment to acquire feature information regarding the movement of a user's hand.

[0146] Referring to FIG. 8a, according to one embodiment, a wearable electronic device (e.g., the wearable electronic device (501) of FIG. 5b) can identify various forms of gestures. For example, information regarding corresponding gestures may be stored or registered in advance in the wearable electronic device (501). The wearable electronic device (501) can identify a corresponding gesture by checking whether the movement of the user's hand matches or corresponds to information regarding a gesture that is stored or registered in advance.

[0147] For example, various forms of gestures may include pinch gestures, swipe gestures, and knock gestures. In addition, various forms of gestures may further include click gestures, double-click gestures, double-pinch gestures, and double-knock gestures, and the forms or types thereof may not be limited by the examples described above.

[0148] Referring to FIGS. 8b and 8c, according to one embodiment, a first wearable electronic device (502) can acquire a signal indicating a specific movement of a user's hand through a sensor (510) (e.g., an accelerometer and a gyroscope). The first wearable electronic device (502) can acquire feature information about the movement of the user's hand by using a movement feature identification model (e.g., the movement feature identification model (750) of FIG. 7b).

[0149] According to one embodiment, the graphs shown above in FIGS. 8b and 8c may be signals sensed from an accelerometer. The graphs shown below in FIGS. 8b and 8c may be signals sensed from a gyroscope. For example, the horizontal axis of the graph may represent time, and the vertical axis of the graph may represent signal magnitude.

[0150] For example, the first wearable electronic device (502) can identify an up-and-down swipe gesture based on information about a first section (810) from signals obtained from an accelerometer and a gyroscope, as shown in FIG. 8b. The first wearable electronic device (502) can identify a left-and-right swipe gesture based on information about a second section (820) from signals obtained from an accelerometer and a gyroscope, as shown in FIG. 8b. Alternatively, the first wearable electronic device (502) can identify a pinch gesture based on information about a third section (830) from signals obtained from an accelerometer and a gyroscope, as shown in FIG. 8c.

[0151] As described above, the first wearable electronic device (502) can obtain feature information about the movement of the hand by analyzing signal characteristics indicating the movement of the hand as in the example above.

[0152] Meanwhile, the signals illustrated in FIGS. 8b and FIGS. 8c are exemplary, and the technical features of the present invention may not be limited thereto.

[0153] FIG. 9 is a flowchart illustrating a method for a wearable electronic device according to one embodiment to switch an identification mode to a first identification mode using a first wearable electronic device.

[0154] Referring to FIG. 9, according to one embodiment, in operation 901, a wearable electronic device (e.g., the wearable electronic device (501) of FIG. 5b) can identify or confirm a user's hand detection and gesture based on images captured using a camera (e.g., the camera (510) of FIG. 5b) in a low-power identification mode.

[0155] According to one embodiment, in operation 903, the wearable electronic device (501) can determine the type of identified or confirmed gesture. For example, the wearable electronic device (501) can determine whether the identified or confirmed gesture is a pre-registered gesture (e.g., pinch gesture, swipe gesture, knock gesture, or click gesture). Additionally, in operation 905, the wearable electronic device (501) can determine whether the identified or confirmed gesture is a gesture not registered to the wearable electronic device (501).

[0156] According to one embodiment, if it is confirmed that the identified gesture is an unregistered gesture (e.g., operation 905), the wearable electronic device (501) can again identify or confirm the user's hand detection and gesture without switching the identification mode.

[0157] According to one embodiment, if the identified gesture is not confirmed to be an unregistered gesture (or if the identified gesture is confirmed to be a pre-registered gesture) (No in operation 905), in operation 907, the wearable electronic device (501) can check whether the identification reliability of the gesture is lower than a first threshold.

[0158] According to one embodiment, if it is confirmed that the identification reliability of a gesture is lower than a first threshold (e.g., operation 907), in operation 915, the wearable electronic device (501) may switch the identification mode to a first identification mode using an external first wearable electronic device (502).

[0159] According to one embodiment, if it is confirmed that the identification reliability of the gesture is not lower than the first threshold (No in operation 907), in operation 909, the wearable electronic device (501) can determine whether the identified gesture involves a positional shift. For example, a gesture involving a positional shift may mean that the position of the hand, as well as the movement of the finger, is moved to a different location. For example, a gesture involving a positional shift may include a gesture that moves a selected object to a different location.

[0160] According to one embodiment, if it is confirmed that the identified gesture does not involve a positional movement (No of operation 909), the wearable electronic device (501) can again identify or confirm the user's hand detection and gesture without switching the identification mode.

[0161] According to one embodiment, if it is confirmed that an identified gesture involves a positional movement (e.g., operation 909), in operation 911, the wearable electronic device (501) can identify or predict the probability that the gesture will go out of the camera's field of view (FOV). For example, the wearable electronic device (501) can identify or predict the probability that the user's hand will go out of the camera's field of view (FOV) based on at least one of the position of the hand, the direction of movement of the hand, or the speed of movement of the hand. The wearable electronic device (501) can determine that if the probability that the hand will go out of the camera's field of view (FOV) is high, the probability that the gesture will go out of the camera's field of view is high.

[0162] According to one embodiment, in operation 913, the wearable electronic device (501) can determine whether the predicted probability is higher than a specified value. For example, the specified value may represent a value that serves as a criterion for determining that the gesture is likely to be out of the camera's field of view.

[0163] According to one embodiment, if the predicted probability is not found to be higher than a specified value (No in operation 913), the wearable electronic device (501) can again identify or confirm the user's hand detection and gesture without switching the identification mode.

[0164] According to one embodiment, if it is confirmed that the predicted probability is higher than a specified value (e.g., operation 913), in operation 915, the wearable electronic device (501) may switch the identification mode to a first identification mode using the first wearable electronic device.

[0165] Considering the method described above, the wearable electronic device (501) can determine a situation where switching to an identification mode is necessary. Accordingly, the wearable electronic device (501) can switch to an identification mode only when necessary, thereby minimizing power consumption.

[0166] FIG. 10 is a data flow diagram illustrating a method for a wearable electronic device according to one embodiment to identify a user's hand gesture by receiving feature information sensed by a first wearable electronic device.

[0167] Referring to FIG. 10, according to one embodiment, in operation 1001, a wearable electronic device (e.g., the wearable electronic device (501) of FIG. 5b) can identify a gesture corresponding to the movement of a user's hand based on images captured using a camera (e.g., the camera (510) of FIG. 5b) in a low-power identification mode.

[0168] According to one embodiment, in operation 1003, a first wearable electronic device (502) outside of the wearable electronic device (501) (e.g., the first wearable electronic device (502) of FIG. 5b) can sense the movement of a user's hand using a sensor (e.g., the sensor (581) of FIG. 5b) and obtain feature information about the movement of the user's hand. The first wearable electronic device (501) can store the obtained feature information in a buffer.

[0169] According to one embodiment, in operation 1005, the wearable electronic device (501) may decide to switch the identification mode of the wearable electronic device (501) to a first identification mode (e.g., an identification mode using the first wearable electronic device (502)).

[0170] According to one embodiment, in operation 1007, the wearable electronic device (501) may request (or transmit a request command) feature information regarding the movement of the user's hand to the first wearable electronic device (502). For example, the wearable electronic device (501) may request the transmission of feature information acquired from a specified time. To this end, the wearable electronic device (501) may transmit a timestamp indicating a specified time to the first wearable electronic device (502). For example, the specified time may include a time before or after the time when the wearable electronic device (201) requested the feature information.

[0171] According to one embodiment, the first wearable electronic device (502) can sense the user's movement more precisely in response to a request received from the wearable electronic device (501). For example, the first wearable electronic device (502) can acquire feature information in an idle state before receiving a request from the wearable electronic device (501). The first wearable electronic device (502) can acquire feature information in a state that uses relatively more power than the idle state after receiving a request from the wearable electronic device (501).

[0172] According to one embodiment, in operation 1009, the first wearable electronic device (502) may transmit feature information previously stored in a buffer to the wearable electronic device (501). For example, the first wearable electronic device (502) may transmit feature information acquired a certain time prior to the point in time when the request of the wearable electronic device (501) is confirmed (or received). Alternatively, the first wearable electronic device (502) may transmit feature information acquired from a specified point in time requested by the wearable electronic device (501) up to the present point in time. Depending on the implementation, operation 1009 may be omitted.

[0173] According to one embodiment, in operation 1011, the first wearable electronic device (502) can transmit sensed feature information to the wearable electronic device (501) in real time. For example, the first wearable electronic device (502) can transmit the sensed feature information until it is requested by the wearable electronic device (501) to stop transmitting the feature information. According to another embodiment, the first wearable electronic device (502) can be operated in a batch manner to transmit sensed feature information for a certain period of time at once for low-power operation. For example, the first wearable electronic device (501) can transmit the feature information collected for a certain period of time to the wearable electronic device (501) at a certain period according to the low-power operation method, without transmitting the sensed feature information in real time.

[0174] According to one embodiment, in operation 1013, the wearable electronic device (501) can synchronize images acquired through a camera with feature information received from the first wearable electronic device (502). For example, the wearable electronic device (501) can synchronize the time between the images and the feature information.

[0175] According to one embodiment, in operation 1015, the wearable electronic device (501) can identify a gesture corresponding to the movement of the user's hand based on images and feature information. In operation 1017, when the gesture identification operation is completed, the wearable electronic device (501) may request the first wearable electronic device (502) to stop transmitting feature information. For example, the wearable electronic device (501) may switch the identification mode back to a low-power identification mode while requesting the stop transmission of feature information.

[0176] According to the method described above, the wearable electronic device (501) can identify or recognize the user's gesture more accurately with low power by identifying the user's gesture in the first identification mode. Through this, the wearable electronic device (501) can provide improved gesture interaction compared to the existing one.

[0177] FIG. 11 is a flowchart illustrating a method for a wearable electronic device to switch identification modes according to one embodiment.

[0178] Referring to FIG. 11, according to one embodiment, in operation 1101, a wearable electronic device (e.g., the wearable electronic device (501) of FIG. 5b) can detect a user's hand based on images captured using a camera (e.g., the camera (510) of FIG. 5b) in a low-power identification mode.

[0179] According to one embodiment, in operation 1103, the wearable electronic device (501) can identify a gesture corresponding to the movement of the user's hand and verify the reliability of the identification.

[0180] According to one embodiment, in operation 1105, the wearable electronic device (501) can determine whether a switching of the identification mode is required. For example, the wearable electronic device (501) can decide to switch the low-power identification mode to the first identification mode based on determining that the identification reliability is lower than the first threshold.

[0181] According to one embodiment, if it is determined that switching the identification mode is not necessary (No of operation 1105), the wearable electronic device (501) can detect the user's hand in a low-power identification mode without switching the identification mode.

[0182] According to one embodiment, if it is determined that a switching of the identification mode is necessary (e.g., operation 1105), in operation 1107, the wearable electronic device (501) switches the identification mode to a first identification mode using the first wearable electronic device, and can identify a gesture using the user's hand in the first identification mode. Additionally, the wearable electronic device can verify the identification reliability of the gesture identified in the first identification mode.

[0183] According to one embodiment, in operation 1109, the wearable electronic device (501) can determine whether the identification reliability is less than a second threshold. For example, the second threshold may be equal to or lower than the first threshold.

[0184] According to one embodiment, if it is confirmed that the identification reliability is not less than the second threshold (No of operation 1109), the wearable electronic device (501) can detect the user's hand and identify the gesture in the first identification mode without switching the identification mode.

[0185] According to one embodiment, when a user's hand is identified within the camera's field of view (FOV) and the identification reliability is found to be less than a second threshold (e.g., in operation 1109), in operation 1111, the wearable electronic device (501) may switch the identification mode to a high-precision identification mode. Additionally, the wearable electronic device (501) may identify the user's gesture in the high-precision identification mode. For example, the high-precision identification mode may represent a mode that identifies the user's hand more precisely. For example, the high-precision identification mode may consume relatively more power and process a larger amount of processing than the low-power identification mode and the first identification mode.

[0186] According to one embodiment, in operation 1113, the wearable electronic device (501) can check whether the movement of the hand for the user's gesture is not detected. If the movement of the hand for the user's gesture is not detected (yes in operation 1113), the wearable electronic device (501) can switch the identification mode back to the low-power identification mode. For example, when the user's gesture identification is completed, the wearable electronic device (501) can switch the identification mode back to the low-power identification mode to minimize power consumption. If the movement of the hand for the user's gesture is detected (no in operation 1113), the wearable electronic device (501) can identify the user's gesture in the high-precision identification mode.

[0187] Considering the method described above, the wearable electronic device (501) can determine a situation where switching to an identification mode is necessary. Accordingly, the wearable electronic device (501) can switch to an identification mode only when necessary, thereby minimizing power consumption.

[0188] FIG. 12 is a drawing for explaining a method of displaying information that guides a wearable electronic device according to one embodiment to form a communication connection with an external first wearable electronic device.

[0189] Referring to FIG. 12, according to one embodiment, a wearable electronic device (e.g., the wearable electronic device (501) of FIG. 5b) may form a communication connection with an external first wearable electronic device (e.g., the first wearable electronic device (501) of FIG. 5b) in order to switch the identification mode to a first identification mode. When the wearable electronic device (501) has not formed a communication connection with the first wearable electronic device (503), the wearable electronic device (501) may display information (1210 or 1220) guiding it to form the communication connection through a display (1201) (e.g., the display (560) of FIG. 5b).

[0190] According to one embodiment, when the wearable electronic device (501) decides to switch to a first identification mode, it may guide the formation of a communication connection with the first wearable electronic device (502). For example, referring to FIG. 12 (a), the wearable electronic device (501) may display first information (1210) through the display (1201) when the first wearable electronic device (502) is not found in the vicinity. For example, referring to FIG. 12 (b), the wearable electronic device (501) may display second information (1220) through the display (1201) when the first wearable electronic device (502) is found in the vicinity.

[0191] Through the method described above, the wearable electronic device (501) can induce a switch in the identification mode when a switch in the identification mode is required. Through this, the wearable electronic device (501) can provide a gesture interaction that is more enhanced than before.

[0192] FIG. 13a is a diagram illustrating a method for a wearable electronic device according to one embodiment to generate notification and guide information using a generative artificial intelligence model. FIG. 13b is a diagram illustrating a prompt for a wearable electronic device according to one embodiment to generate notification and guide information.

[0193] Referring to FIG. 13a, according to one embodiment, a wearable electronic device (e.g., the wearable electronic device (501) of FIG. 5b) can generate guide information for the accurate identification of notifications and gestures indicating the switching of identification modes using a generative artificial intelligence (AI) model (e.g., LLM) (1310). For example, the generative AI model (1310) may be stored in memory (e.g., the memory (530) of FIG. 5b) or in an external electronic device (e.g., a server).

[0194] According to one embodiment, the wearable electronic device (501) can generate notification and guide information based on providing at least one of tracking information about the user's hand, information about an identification mode for identifying the user's hand, information about the type of gesture, or identification reliability to a generative AI model (1310).

[0195] Referring to FIG. 13a and FIG. 13b, according to one embodiment, a prompt (1320) for generating the notification and guide information can be generated using a generative AI model (1310). For example, the prompt (1320) may include at least one of tracking information for the user's hand (hereinafter, hand tracking information), information about an identification mode for identifying the user's hand (hereinafter, hand identification mode), information about the type of gesture, or information about identification reliability.

[0196] Referring to FIG. 13b, according to one embodiment, a prompt (1320) may include first prompt data (1330) (e.g., gesture-related information), second prompt data (1340) (e.g., guide information), and output rules (1350). For example, the prompt (1320) may include corresponding texts. For example, the first prompt data (1330) may include information regarding hand tracking information, hand identification mode, gesture type, and identification reliability. The second prompt data (1340) may include content guiding a method to more accurately identify a user's gesture based on the gesture identification status, the type of gesture, and identification reliability. The output rules (1350) may include information regarding rules (e.g., language and amount) for which guidance and instructions are displayed.

[0197] According to one embodiment, a wearable electronic device (501) can generate notification and guidance information based on providing a prompt (1320) to a generative AI model (1310). Specific examples of notification and guidance information will be described in FIGS. 14a, 14b, and 14c.

[0198] FIGS. 14a, 14b, and 14c are drawings of notification and guide information displayed by a wearable electronic device according to various embodiments.

[0199] Referring to FIGS. 14a, 14b, and 14c, according to one embodiment, a wearable electronic device (e.g., the wearable electronic device (501) of FIG. 5b) may display guidance information for accurate identification of a notification and / or gesture indicating the switching of the identification mode based on switching the identification mode to a first identification mode through a display (e.g., the display (560) of FIG. 5b).

[0200] Referring to FIG. 14a, according to one embodiment, a wearable electronic device (501) may display a notification (1410) and guide information (1420) when the user's hand moves out of the field of view of a camera (e.g., camera (510) in FIG. 5b) during gesture identification. At this time, the wearable electronic device (501) may switch the identification mode to a first identification mode using a first wearable electronic device (e.g., the first wearable electronic device (502) in FIG. 5b). For example, the notification (1410) may further include information regarding the reason for switching the identification mode of the wearable electronic device (501). For example, the wearable electronic device (501) may display a notification (1410) and guide information (1420) when it is confirmed that the user's hand moves out of the field of view of the camera due to the movement of the gesture. For example, notifications (1410) and guide information (1420) can be generated by providing the prompt (1320) of FIG. 13b to a generative AI model (e.g., the generative AI model (1310) of FIG. 13a).

[0201] Referring to FIG. 14b, according to one embodiment, a wearable electronic device (501) may display a notification (1430) and guide information (1440) when identifying a gesture while the user's hand movement is not large. At this time, the wearable electronic device (501) may switch the identification mode to a first identification mode using a first wearable electronic device (e.g., the first wearable electronic device (502) of FIG. 5b). For example, the notification (1430) may further include information regarding the reason for switching the identification mode of the wearable electronic device (501).

[0202] Referring to FIG. 14c, according to one embodiment, a wearable electronic device (501) may display a notification (1450) and guide information (1460) when identifying a gesture in a low-light condition of the surrounding environment or when the user's hand is too far from the camera (510). At this time, the wearable electronic device (501) may switch the identification mode to a first identification mode using a first wearable electronic device (e.g., the first wearable electronic device (502) of FIG. 5b). For example, the notification (1450) may further include information regarding the reason for switching the identification mode of the wearable electronic device (501).

[0203] Through the method described above, the wearable electronic device (501) can provide a notification of switching to an identification mode and a guide for more accurate gesture identification. Through this, the wearable electronic device (501) can provide improved gesture interaction compared to the existing one.

[0204] FIGS. 15a and FIGS. 15b are drawings for explaining a method of identifying a user's gesture by further considering feature information regarding the movement of a user's hand received from an external first wearable electronic device according to one embodiment.

[0205] Referring to FIG. 15a, according to one embodiment, both the first wearable electronic device (502) and the second wearable electronic device (503) can be worn on the user's hand.

[0206] In identifying a gesture using a user's hand, a wearable electronic device (e.g., the wearable electronic device (501) of FIG. 5b) may further consider feature information received from the first wearable electronic device (502) and the second wearable electronic device (503), respectively. For example, the wearable electronic device (501) may request feature information regarding hand movements from the first wearable electronic device (502) and the second wearable electronic device (503) worn on the user's hand via a communication circuit, based on switching the identification mode to a first identification mode that utilizes external wearable electronic devices.

[0207] Referring to FIG. 15b, according to one embodiment, in operation 1501, the wearable electronic device (501) may request characteristic information about the movement of the hand from the first wearable electronic device (502) as well as the second wearable electronic device (503) worn on the user's hand through a communication circuit (e.g., the communication circuit (570) of FIG. 5b) based on confirming that the identification reliability of the user's gesture is lower than a first threshold.

[0208] According to one embodiment, in operation 1503, the wearable electronic device (501) can identify a gesture corresponding to the second movement by further considering the second feature information regarding the second movement of the user's hand sensed by the second wearable electronic device (503) received from the second wearable electronic device (503). That is, the wearable electronic device (501) can identify the user's gesture by considering not only the first feature information received from the first wearable electronic device (502) but also the second feature information received from the second wearable electronic device (503).

[0209] According to another embodiment, the wearable electronic device (201) may receive sensed sensor data (e.g., raw data or sensing signal data) in real time instead of feature information from the first wearable electronic device (502) and / or the second wearable electronic device (503). In this case, the wearable electronic device (501) may extract feature information from the received sensing data and identify a user's gesture using the extracted feature information.

[0210] According to the method described above, the wearable electronic device (501) can identify or recognize the user's gesture more accurately with low power by identifying the user's gesture in the first identification mode. Through this, the wearable electronic device (501) can provide improved gesture interaction compared to the existing one.

[0211] FIGS. 16a and FIGS. 16b are drawings for explaining a method in which a wearable electronic device according to one embodiment identifies a user's gesture using feature information regarding the movement of a user's hand received from an external first wearable electronic device.

[0212] Referring to FIG. 16a (a), according to one embodiment, a second wearable electronic device (503) may be worn on a user's first hand (e.g., left hand). For example, when the movement of the user's hand can be identified by analyzing a signal sensed through a PPG sensor included in the second wearable electronic device (503), the second wearable electronic device (503) may obtain characteristic information about the movement of the user's hand by analyzing the PPG signal. Additionally, characteristic information about the movement of the user's hand may be obtained by analyzing a signal sensed through an accelerometer and / or gyroscope included in the second wearable electronic device (503).

[0213] According to one embodiment, a wearable electronic device (e.g., the wearable electronic device (501) of FIG. 5b) may request second feature information from a second wearable electronic device (503) without the first wearable electronic device (501) in response to an identification mode switch. The wearable electronic device (501) may identify a user's gesture based on images obtained from a camera (e.g., the camera (510) of FIG. 5b) and the second feature information.

[0214] Referring to (b) of FIG. 16a, according to one embodiment, a second wearable electronic device (503) may be worn on a user's first hand (e.g., left hand), and a first wearable electronic device (502) may be worn on a second hand (e.g., right hand). When a wearable electronic device (e.g., the wearable electronic device (501) of FIG. 5b) identifies a gesture using both hands of a user, feature information received from the first wearable electronic device (502) and the second wearable electronic device (503), respectively, may be further considered. At this time, the wearable electronic device (e.g., the wearable electronic device (501) of FIG. 5b) may request second feature information regarding the movement of the corresponding hand (e.g., left hand) from the second wearable electronic device (503) in response to an identification mode switch. The wearable electronic device (501) can identify a gesture for a corresponding hand (e.g., left hand) based on images obtained from a camera (e.g., camera (510) in FIG. 5b) and second feature information.

[0215] Referring to FIG. 16b, according to one embodiment, in operation 1601, the wearable electronic device (501) can identify or confirm a gesture corresponding to the movement of the user's hand based on images captured through a camera (510).

[0216] According to one embodiment, in operation 1603, the wearable electronic device (501) may switch the identification mode of the wearable electronic device (501) and request characteristic information regarding the movement of the corresponding hand (e.g., the hand on which the second wearable electronic device (503) is worn) to the second wearable electronic device (503) worn on the user's hand.

[0217] According to one embodiment, in operation 1605, the wearable electronic device (501) may receive third feature information regarding a third movement of the user's hand sensed by the second wearable electronic device (503) from the second wearable electronic device (503). For example, the third feature information may be feature information regarding a movement by the user's corresponding hand at a third time point.

[0218] According to one embodiment, in operation 1607, the wearable electronic device (501) can identify or confirm a gesture corresponding to a third movement of the user's hand based on third feature information and images captured through a camera using an artificial intelligence model. For example, the third movement may be a movement by the user's corresponding hand at a third point in time.

[0219] According to the method described above, the wearable electronic device (501) can identify or recognize the user's gesture more accurately with low power by identifying the user's gesture in the first identification mode. Through this, the wearable electronic device (501) can provide improved gesture interaction compared to the existing one.

[0220] According to one embodiment, a wearable electronic device (501) that can be worn on the head may include a camera (510), a communication circuit (570), a display (560), at least one processor (520), and a memory (530) that stores instructions. According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to identify a gesture corresponding to a first movement of the user's hand based on images captured through the camera using an artificial intelligence model (720). According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to display through the display a notification indicating that the identification mode of the wearable electronic device, obtained using a generative AI model (1310) based on confirming that the identification reliability of the gesture is lower than a first threshold, is switched to a first identification mode using a first wearable electronic device worn on the user's hand, and guide information for identifying the gesture. According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to receive first feature information regarding a second movement of the user's hand sensed by the first wearable electronic device from the first wearable electronic device. According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to identify a gesture corresponding to the second movement of the user's hand based on the first feature information and images captured through the camera using the artificial intelligence model.

[0221] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to obtain the notification and the guide information based on providing the generative AI model with tracking information about the hand, information about an identification mode for identifying the hand, information about the type of gesture, and the identification reliability.

[0222] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to generate a prompt including tracking information for the hand, information about an identification mode for identifying the hand, information about the type of gesture, and the identification reliability. According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to obtain the notification and the guide information based on providing the prompt to the generative AI model.

[0223] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to request characteristic information regarding the movement of the hand from the first wearable electronic device through the communication circuit, and to display the notification and the guide information through the display of the electronic device. According to one embodiment, the notification may further include information regarding the reason for switching the identification mode.

[0224] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to switch the identification mode from the second identification mode, which is set to identify a user's gesture at low power using images acquired through the camera, to the first identification mode based on confirming that the identification reliability is lower than the first threshold.

[0225] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to switch the identification mode to a third identification mode configured to accurately identify the user's gesture based on confirming that the identification reliability of the gesture corresponding to the second movement is lower than the second threshold when the hand is identified within the field of view (FOV) of the camera.

[0226] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may cause the wearable electronic device to determine the probability that the hand moves out of the field of view (FOV) of the camera by the first movement. According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may cause the wearable electronic device to change the identification mode to the first identification mode by requesting feature information about the hand from the first wearable electronic device worn on the hand through the communication circuit, based on determining that the probability is higher than a specified value.

[0227] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to determine the probability that the hand moves out of the field of view of the camera by the first movement, based on at least one of the position of the hand by the first movement, the speed of movement of the hand, or the direction of movement of the hand.

[0228] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to display the notification and the guide information when it is confirmed that the hand is out of the camera's field of view by the first movement.

[0229] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to request feature information regarding the movement of the hand from a second wearable electronic device (503) worn on the hand through the communication circuit, based on confirming that the identification reliability of the gesture is lower than a first threshold. According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device may be caused to confirm a gesture corresponding to the second movement by further considering the second feature information regarding the second movement of the user's hand sensed by the second wearable electronic device received from the second wearable electronic device.

[0230] According to one embodiment, a method of operation of a wearable electronic device (501) that can be worn on the head may include an action of confirming a gesture corresponding to a first movement of the user's hand based on images captured through a camera (510) included in the wearable electronic device using an artificial intelligence model (720). According to one embodiment, a method of operation of the wearable electronic device may include an action of displaying a notification indicating that the identification mode of the wearable electronic device obtained using a generative AI model (1310), based on confirming that the identification reliability of the gesture is lower than a first threshold, is switched to a first identification mode using a first wearable electronic device (502) worn on the hand, and a guide information for identifying the gesture. According to one embodiment, a method of operation of the wearable electronic device may include an action of receiving first feature information regarding a second movement of the user's hand sensed by the first wearable electronic device from the first wearable electronic device. According to one embodiment, the method of operating the wearable electronic device may include the operation of confirming a gesture corresponding to the second movement of the user's hand based on the first feature information and images captured through the camera using the artificial intelligence model.

[0231] According to one embodiment, the operation of displaying the notification and the guide information may include the operation of obtaining the notification and the guide information based on providing the generative AI model with tracking information for the hand, information about an identification mode for identifying the hand, information about the type of gesture, and the identification reliability.

[0232] According to one embodiment, the operation of displaying the notification and the guide information may include the operation of generating a prompt including tracking information for the hand, information about an identification mode for identifying the hand, information about the type of gesture, and the identification reliability. According to one embodiment, the operation of displaying the notification and the guide information may include the operation of obtaining the notification and the guide information based on providing the prompt to the generative AI model.

[0233] According to one embodiment, the operation of displaying the notification and the guide information may include requesting characteristic information regarding the movement of the hand from the first wearable electronic device through the communication circuit, while displaying the notification and the guide information through the display of the electronic device. According to one embodiment, the notification may further include information regarding the reason for switching the identification mode.

[0234] According to one embodiment, the method of operation of the wearable electronic device may further include the operation of switching the identification mode from a second identification mode, which is set to identify a user's gesture with low power using images acquired through the camera, to the first identification mode based on confirming that the identification reliability is lower than the first threshold value.

[0235] According to one embodiment, the method of operation of the wearable electronic device may further include the operation of switching the identification mode to a third identification mode configured to precisely identify the user's gesture, based on confirming that the identification reliability of the gesture corresponding to the second movement is lower than the second threshold when the hand is identified within the field of view (FOV) of the camera.

[0236] According to one embodiment, the method of operation of the wearable electronic device may further include an operation of checking the probability that the hand moves out of the field of view (FOV) of the camera by the first movement. According to one embodiment, the method of operation of the wearable electronic device may further include an operation of changing the identification mode to the first identification mode while requesting feature information about the hand from the first wearable electronic device worn on the hand through the communication circuit, based on confirming that the probability is higher than a specified value.

[0237] According to one embodiment, the operation of checking the probability may include checking the probability that the hand moves out of the field of view of the camera by the first movement, based on at least one of the position of the hand by the first movement, the speed of movement of the hand, or the direction of movement of the hand.

[0238] According to one embodiment, the operation of displaying the notification and the guide information may include the operation of displaying the notification and the guide information when it is confirmed that the hand moves out of the camera's field of view due to the first movement.

[0239] According to one embodiment, in a computer-readable non-transient storage medium (130, 530) storing instructions, the instructions, when executed individually or collectively by at least one processor (520), cause a wearable electronic device (501) to use an artificial intelligence model (720) to identify a gesture corresponding to a first movement of a user's hand based on images captured through a camera (510) included in the wearable electronic device, and based on confirming that the identification reliability of the gesture is lower than a first threshold, to display a notification indicating that the identification mode of the wearable electronic device is switched to a first identification mode using a first wearable electronic device worn on the hand, and guide information for identifying the gesture, and receive first feature information regarding a second movement of the user's hand sensed by the first wearable electronic device from the first wearable electronic device, and use the artificial intelligence model to use the first feature information and the camera Based on the captured images, it may cause the user to confirm a gesture corresponding to the second movement of the hand.

[0240] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs.

[0241] The electronic device according to the various embodiments disclosed in this document may be of various forms. The electronic device may include, for example, a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a consumer electronics device. The electronic device according to the embodiments of this document is not limited to the devices described above.

[0242] The various embodiments of this document and the terms used therein are not intended to limit the technical features described in this document to specific embodiments, and should be understood to include various modifications, equivalents, or substitutions of said embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of said items unless the relevant context clearly indicates otherwise. In this document, each of phrases such as "A or B," "at least one of A and B," "at least one of A or B," "A, B or C," "at least one of A, B and C," and "at least one of A, B, or C" may include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. Terms such as “first,” “second,” or “first” or “second” may be used simply to distinguish a component from another component and do not limit the components in any other aspect (e.g., importance or order). Where any (e.g., first) component is referred to as “coupled” or “connected” to another (e.g., second) component, with or without the terms “functionally” or “communicationally,” it means that said component may be connected to said other component directly (e.g., via a wire), wirelessly, or through a third component.

[0243] The term “module” as used in the various embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit, for example. A module may be a component formed integrally, or a minimum unit of said component or a part thereof that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0244] Various embodiments of this document may be implemented as software (e.g., program (140)) comprising one or more instructions stored in a storage medium (e.g., internal memory (136) or external memory (138)) readable by a machine (e.g., electronic device (101, 501)). For example, a processor (e.g., processor (120, 520)) of the machine (e.g., electronic device (101, 501)) may call at least one of the one or more instructions stored from the storage medium and execute it. This enables the machine to be operated 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 that can be executed by an interpreter. The storage medium readable by the machine may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' is a device in which the storage medium is tangible, and It merely means that it does not contain a signal (e.g., electromagnetic waves), and this term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily on a storage medium.

[0245] According to one embodiment, the method according to the various embodiments disclosed herein may be provided by being included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application store (e.g., Play Store™) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0246] According to various embodiments, each component (e.g., module or program) of the components described above may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to various embodiments, one or more of the components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Generally or additionally, multiple components (e.g., module or program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as those performed by the corresponding component among the multiple components prior to integration. According to various embodiments, operations performed by the module, program, or other components 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

1. In a wearable electronic device (501) that can be worn on the head, Camera (510); Communication circuit (570); Display (560); At least one processor (520); and The device includes a memory (530) for storing instructions, and when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device, Using an artificial intelligence model (720), a gesture corresponding to the first movement of the user's hand is identified based on images captured through the camera, and Based on confirming that the identification reliability of the above gesture is lower than a first threshold, a notification indicating that the identification mode of the wearable electronic device obtained using a generative AI model (1310) is switched to a first identification mode using a first wearable electronic device worn on the hand, and guide information for identifying the gesture are displayed through the display. Receiving first feature information regarding a second movement of the user's hand sensed by the first wearable electronic device from the first wearable electronic device, and A wearable electronic device that causes a gesture corresponding to the second movement of the user's hand to be identified based on the first feature information and images captured through the camera using the artificial intelligence model.

2. In paragraph 1, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device, A wearable electronic device that causes the acquisition of the notification and the guide information based on providing the generative AI model with tracking information about the hand, information about an identification mode for identifying the hand, information about the type of gesture, and the identification reliability.

3. In any one of claims 1 to 2, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device, Generate a prompt including tracking information for the hand, information on an identification mode for identifying the hand, information on the type of gesture, and identification reliability, and A wearable electronic device that causes the acquisition of the notification and guide information based on providing the prompt to the generative AI model.

4. In any one of claims 1 to 3, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device, Requesting feature information regarding the movement of the hand to the first wearable electronic device (502) through the communication circuit, and causing the notification and guide information to be displayed through the display, The above notification is a wearable electronic device that further includes information regarding the reason for switching the identification mode.

5. In any one of claims 1 to 4, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device, A wearable electronic device that causes the identification mode to switch from a second identification mode, which is configured to identify a user's gesture at low power using images acquired through the camera, to the first identification mode based on confirming that the identification reliability is lower than the first threshold.

6. In any one of claims 1 to 5, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device, A wearable electronic device that causes the identification mode to be switched to a third identification mode configured to precisely identify the user's gesture, based on confirming that the identification reliability of the gesture corresponding to the second movement is lower than the second threshold when the hand is identified within the field of view (FOV) of the camera.

7. In any one of claims 1 to 6, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device, Check the probability that the hand moves out of the camera's field of view (FOV) due to the first movement above, and A wearable electronic device that causes the identification mode to be changed to the first identification mode while requesting feature information about the hand from the first wearable electronic device worn on the hand through the communication circuit, based on confirming that the above probability is higher than a specified value.

8. In any one of claims 1 to 7, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device, A wearable electronic device that causes the probability of the hand moving out of the field of view of the camera by the first movement to be determined based on at least one of the position of the hand by the first movement, the speed of movement of the hand, or the direction of movement of the hand.

9. In any one of claims 1 through 8, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device, A wearable electronic device that causes the notification and guide information to be displayed when it is confirmed that the hand moves out of the camera's field of view by the first movement.

10. In any one of claims 1 to 9, when the instructions are executed individually or collectively by the at least one processor, the wearable electronic device, Based on confirming that the identification reliability of the above gesture is lower than the first threshold, feature information regarding the movement of the hand is requested to the second wearable electronic device (503) worn on the hand through the communication circuit, and A wearable electronic device that causes a gesture corresponding to the second movement to be identified by further considering second feature information regarding the second movement of the user's hand sensed by the second wearable electronic device received from the second wearable electronic device.

11. A method of operation (501) of a wearable electronic device that can be worn on the head, An action of confirming a gesture corresponding to a first movement of the user's hand based on images captured through a camera (510) included in the wearable electronic device using an artificial intelligence model (720); An action of displaying a notification indicating that the identification mode of the wearable electronic device obtained using a generative AI model (1310), which is switched to a first identification mode using a first wearable electronic device worn on the hand, and guide information for identifying the gesture, based on confirming that the identification reliability of the gesture is lower than a first threshold; The operation of receiving first feature information regarding a second movement of the user's hand sensed by the first wearable electronic device from the first wearable electronic device; and A method of operation of a wearable electronic device comprising confirming a gesture corresponding to the second movement of the user's hand based on the first feature information and images captured through the camera using the artificial intelligence model.

12. In Paragraph 11, the operation of displaying the above notification and the above guide information is, A method of operation of a wearable electronic device comprising the operation of obtaining the notification and the guide information based on providing the generative AI model with tracking information for the hand, information for an identification mode for identifying the hand, information for a type of gesture, and the identification reliability.

13. In any one of paragraphs 11 to 12, the operation of displaying the notification and the guide information is, An operation to generate a prompt including tracking information for the hand, information on an identification mode for identifying the hand, information on the type of gesture, and identification reliability; and A method of operation of a wearable electronic device comprising the operation of obtaining the notification and the guide information based on providing the prompt to the generative AI model.

14. In any one of paragraphs 11 to 13, the operation of displaying the notification and the guide information is, The operation includes requesting characteristic information regarding the movement of the hand from the first wearable electronic device (502) through the communication circuit, and displaying the notification and guide information through the display of the electronic device. The above notification is a method of operation of a wearable electronic device that further includes information regarding the reason for switching the identification mode.

15. In a computer-readable non-transient storage medium (130, 530) for storing instructions, When the above instructions are executed individually or collectively by at least one processor (520), the wearable electronic device (501) causes, Using an artificial intelligence model (720), a gesture corresponding to a first movement of the user's hand is identified based on images captured through a camera (510) included in the wearable electronic device, and Based on confirming that the identification reliability of the above gesture is lower than a first threshold, a notification indicating that the identification mode of the wearable electronic device obtained using a generative AI model (1310) is switched to a first identification mode using a first wearable electronic device worn on the hand, and guide information for identifying the gesture are displayed. Receiving first feature information regarding a second movement of the user's hand sensed by the first wearable electronic device from the first wearable electronic device, and A storage medium that causes a gesture corresponding to the second movement of the user's hand to be identified based on the first feature information and images captured through the camera using the artificial intelligence model.

Images