Wearable device and method for identifying tapping pattern

Abstract

According to an embodiment, a method for a wearable device comprises an operation of identifying, during a designated time duration, data regarding tapping by using one or more sensors of the wearable device. The method comprises an operation of identifying a first tapping pattern of the wearable device on the basis of the data. The method comprises an operation of, on the basis of identifying a function corresponding to the first tapping pattern, transmitting a first signal regarding the function to an external electronic device connected to the wearable device.

Description

Wearable device and method for identifying tapping patterns

[0001] The descriptions below relate to a wearable device and method for identifying tapping patterns.

[0002] Wearable devices can be worn on a part of the user's body. Wearable devices can be provided in various forms. For example, a wearable device may include a ring-shaped device that is intended to be worn on a part of the user's body. By including sensors that provide user-related information, wearable devices can provide users with diverse user experiences.

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

[0004] According to one embodiment, a wearable device may include a ring-shaped housing having a first side facing a part of a user's body and a second side opposite to the first side, a communication circuit disposed between the first side and the second side, one or more sensors disposed between the first side and the second side, and at least one processor disposed between the first side and the second side and operatively coupled to the communication circuit and the one or more sensors. The at least one processor may be configured to identify data regarding tapping using the one or more sensors during a specified time period. The at least one processor may be configured to identify a first tapping pattern of the wearable device based on the data. The at least one processor may be configured to transmit a first signal regarding the function to an external electronic device connected to the wearable device based on identifying a function corresponding to the first tapping pattern.

[0005] According to one embodiment, a method of a wearable device may include an operation of identifying data using one or more sensors of the wearable device during a specified time period. The method may include an operation of identifying a first tapping pattern of the wearable device based on the data. The method may include an operation of transmitting a first signal regarding the function to an external electronic device connected to the wearable device based on identifying a function corresponding to the first tapping pattern.

[0006] FIG. 1 is a block diagram of an electronic device within a network environment according to one embodiment.

[0007] FIG. 2 illustrates an example of a wearable device and external electronic devices connected to the wearable device, according to one embodiment.

[0008] FIG. 3A illustrates an example of a simplified block diagram of a wearable device according to one embodiment.

[0009] FIG. 3b is an example of a partial cross-sectional view of a wearable device according to one embodiment.

[0010] Figure 4 illustrates a flowchart regarding the operation of a wearable device according to one embodiment.

[0011] FIGS. 5A to 5D illustrate examples of data identified through an acceleration sensor according to one embodiment.

[0012] FIGS. 6A to 6D illustrate examples of multiple tapping patterns according to one embodiment.

[0013] FIG. 7 illustrates an example of operation of a wearable device for identifying an impact pattern according to one embodiment.

[0014] Figure 8 illustrates a flowchart regarding the operation of a wearable device according to one embodiment.

[0015] Figure 9 illustrates a flowchart regarding the operation of a wearable device according to one embodiment.

[0016] FIG. 10A illustrates a flowchart of the operation of a wearable device according to one embodiment.

[0017] FIG. 10b illustrates an example of operation of a wearable device according to one embodiment.

[0018] FIG. 11 illustrates a flowchart regarding the operation of a wearable device according to one embodiment.

[0019] FIG. 12 illustrates a flowchart regarding the operation of a wearable device according to one embodiment.

[0020] FIG. 13 illustrates an example of an operation for learning a model in a wearable device according to one embodiment.

[0021] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings so that those skilled in the art can easily implement the present disclosure. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein. In connection with the description of the drawings, the same or similar reference numerals may be used for identical or similar components. Furthermore, in the drawings and related descriptions, descriptions of well-known functions and configurations may be omitted for clarity and conciseness.

[0022] FIG. 1 is a block diagram of an electronic device within a network environment according to one embodiment.

[0023] Referring to FIG. 1, in a network environment (100), an electronic device (101) may communicate with an electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with at least one of an electronic device (104) or a server (108) via a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include a processor (120), a memory (130), an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a connection terminal (178), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)).

[0024] The processor (120) may control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) by executing, for example, software (e.g., a program (140)), and may perform various data processing or calculations. According to one embodiment, as at least a part of the data processing or calculation, the processor (120) may store a command or data received from another component (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the command or data stored in the volatile memory (132), and store the resulting data in a non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or a secondary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or together therewith. For example, if the electronic device (101) includes a main processor (121) and a secondary processor (123), the secondary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a specified function. The secondary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0025] The auxiliary processor (123) may control at least a part of functions or states associated with at least one component (e.g., a display module (160), a sensor module (176), or a communication module (190)) of the electronic device (101), for example, on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). In one embodiment, the auxiliary processor (123) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, on the electronic device (101) itself where the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (108)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model can include multiple artificial neural network layers.The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0026] The memory (130) can store various data used by at least one component (e.g., processor (120) or sensor module (176)) of the electronic device (101). The data can include, for example, software (e.g., program (140)) and input data or output data for commands related thereto. The memory (130) can include volatile memory (132) or non-volatile memory (134).

[0027] The program (140) may be stored as software in the memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0028] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0029] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0030] The display module (160) can visually provide information to an external party (e.g., a user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0031] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), output sound through the sound output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0032] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0033] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). In one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0034] The connection terminal (178) may include a connector through which the electronic device (101) may be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0035] A haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. According to one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0036] The camera module (180) can capture still images and videos. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0037] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

[0038] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0039] The communication module (190) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (194) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (104) via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0040] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) may support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC realization.

[0041] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). According to one embodiment, the antenna module (197) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). According to one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (198) or the second network (199), may be selected from the plurality of antennas, for example, by the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device via the at least one selected antenna. According to some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0042] In one embodiment, the antenna module (197) may form a mmWave antenna module. In one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high-frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high-frequency band.

[0043] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0044] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service by itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that have received the request may execute at least a part of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101).

[0045] The electronic device (101) may process the result as is or additionally and provide it as at least part of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service 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 utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199). The electronic device (101) may be applied to intelligent services (e.g., smart homes, smart cities, smart cars, or healthcare) based on 5G communication technology and IoT-related technology.

[0046] According to one embodiment, a wearable device (e.g., electronic device (101)) can operate while being worn by a user. While being worn by a user, the wearable device can identify (or detect) the user's physical activity. For example, while being worn on a part of the user's body (e.g., a finger), the wearable device can identify the movement of a part of the user's body. By moving a part of the user's body, the user can perform a function corresponding to the movement.

[0047] For example, a wearable device may identify a tapping pattern (or movement pattern) of the wearable device using one or more sensors (e.g., an acceleration sensor) included in the wearable device. For example, the tapping pattern of the wearable device may include a pattern (or movement) that is generated by repeatedly applying an impact of a specified magnitude or greater to the wearable device within a specified time period. The wearable device may identify a function corresponding to the tapping pattern of the wearable device. The wearable device may perform the identified function or transmit a signal regarding the identified function to an external electronic device connected to the wearable device to perform the identified function.

[0048] The operation of a wearable device according to the above-described embodiment may be described below. The wearable device described below may correspond to the electronic device (101) of FIG. 1. For convenience of explanation, the following specification will describe the structure and operation of a wearable device formed in a ring shape. However, the present invention is not limited thereto. For example, the wearable device may be implemented in various forms that can be worn by a user, such as a smart watch, a smart band, a smart ring, wireless earphones, or smart glasses.

[0049] FIG. 2 illustrates an example of a wearable device and external electronic devices connected to the wearable device, according to one embodiment.

[0050] Referring to FIG. 2, the wearable device (200) may include a housing (210) that includes a first side (211) facing a part of the user's body (e.g., a finger) and a second side (212) opposite the first side (211). For example, the wearable device (200) may include a ring-shaped housing (210). As an example, the wearable device (200) may be configured in a ring shape.

[0051] According to one embodiment, the wearable device (200) may be referred to as a wearable device that can be worn by a user. The wearable device (200) may be worn on a part of the user's body (e.g., a finger). For example, the wearable device (200) may be worn on a part of the user's body. For example, the wearable device (200) may be fastened to a part of the user's body. For example, the wearable device (200) may be detachable from a part of the user's body. For example, the wearable device (200) may have a shape corresponding to a part of the user's body in order to be worn on a part of the user's body.

[0052] For example, the wearable device (200) may be worn by the user and thus come into contact with a part of the user's body. For example, the wearable device (200) may be configured to acquire information about the user through a part of the user's body by being worn by the user. For example, the wearable device (200) may provide information about the user through the wearable device (200) and / or an external electronic device (291, 292) connected to the wearable device (200). However, the present invention is not limited thereto.

[0053] According to one embodiment, at least a portion of the first surface (211) may come into contact with a part of the user's body when the wearable device (200) is worn by the user. For example, the first surface (211) may surround a part of the user's body on which the wearable device (200) is worn. For example, the first surface (211) may cover a part of the user's body on which the wearable device (200) is worn. For example, the first surface (211) may be configured to pressurize a part of the user's body when the wearable device (200) is worn by the user, thereby fastening the wearable device (200) to the part of the body. For example, the first surface (211) may be deformable by a part of the user's body. For example, the wearable device (200) can provide information about the user through the first side (211) based on haptic technology.

[0054] For example, the second surface (212) may form an outer appearance of the wearable device (200) together with the first surface (211). For example, the second surface (212) may form a ring-shaped housing (210) together with the first surface (211). For example, the second surface (212) may be a surface spaced apart from a part of the user's body when the wearable device (200) is worn by the user. For example, the first surface (211) may be referred to as an inner circumference surface of the housing (210). The second surface (212), which is opposite to the first surface (211), may be referred to as an outer circumference surface of the housing (210).

[0055] For example, the second surface (212) may be exposed to the outside when the wearable device (200) is worn by the user. The second surface (212) may be composed of at least one of titanium, stainless steel, and ceramic. The second surface (212) may be composed of a material for protection against external impact and / or scratches. Depending on the embodiment, the second surface (212) may be coated with an additional material for protection of the color and / or appearance of the wearable device (200).

[0056] For example, the first side (211) may be composed of the same and / or similar material as the second side (212). In some embodiments, at least a portion of the first side (211) may be composed of at least one of a molding material for acquiring data, transparent plastic, and / or glass. In some embodiments, at least a portion of the first side (211) may be composed of a metal for identifying a biosignal.

[0057] According to one embodiment, the wearable device (200) may further include a hole (270) formed by the first surface (211) for passing a part of the user's body through the wearable device (200) when the wearable device (200) is worn by the user. For example, the hole (270) may be penetrated by a part of the user's body when the wearable device (200) is worn by the user. The wearable device (200) may be configured to be fastened to a part of the user's body when the user wears the wearable device (200) by including a hole (270) configured to pass a part of the user's body through the hole.

[0058] According to one embodiment, the wearable device (200) may further include one or more components between the first side (211) and the second side (212). For example, the wearable device (200) may include a communication circuit, one or more sensors, and / or a processor between the first side (211) and the second side (212). The arrangement of the one or more components will be described later in FIG. 3B.

[0059] According to one embodiment, the wearable device (200) may be connected to an external electronic device (291) and / or an external electronic device (292). For example, the user of the external electronic device (291) may be the same as the user of the wearable device (200). For example, the external electronic device (291) may be a device that is connected to the wearable device (200) in close proximity and used by the same user. For example, the user of the external electronic device (292) may not be the same as the user of the wearable device (200). For example, the external electronic device (292) may be a server configured for an emergency rescue request service. However, the present invention is not limited thereto.

[0060] For example, the wearable device (200) may be connected to an external electronic device (291). For example, the wearable device (200) may be connected to the external electronic device (291) using various radio access technologies (RATs) (e.g., Bluetooth communication, wireless local area network (WLAN)). For example, the wearable device (200) may control the external electronic device (291) or be controlled by the external electronic device (291). For example, the wearable device (200) may receive a request for information about a user from the external electronic device (291). The wearable device (200) may transmit information about the user to the external electronic device (291) based on the request received from the external electronic device (291).

[0061] For example, the wearable device (200) may be connected to an external electronic device (292). For example, the wearable device (200) may be connected to the external electronic device (292) using various radio access technologies (RATs) (e.g., satellite communication, cellular communication). For example, the wearable device (200) may be directly connected to the external electronic device (292). For example, the wearable device (200) may transmit information about the user to the external electronic device (292). The external electronic device (292) may store information about the user or provide a service (e.g., emergency rescue service) for the user. According to an embodiment, the wearable device (200) may also be connected to the external electronic device (292) through the external electronic device (291).

[0062] FIG. 3A illustrates an example of a simplified block diagram of a wearable device according to one embodiment.

[0063] Referring to FIG. 3a, the wearable device (200) may correspond to the electronic device (101) of FIG. 1.

[0064] According to one embodiment, the wearable device (200) may include a processor (310), a communication circuit (320), a sensor (330), and / or a memory (340). Depending on the embodiment, the wearable device (200) may include at least one of the processor (310), the communication circuit (320), the sensor (330), and / or the memory (340). For example, at least some of the processor (310), the communication circuit (320), the sensor (330), and / or the memory (340) may be omitted depending on the embodiment.

[0065] According to one embodiment, the processor (310) may correspond to the processor (120) of FIG. 1. The processor (310) may be operatively or operably coupled with or connected to the communication circuit (320), the sensor (330), and the memory (340). The processor (310) being operatively or operably coupled with the communication circuit (320), the sensor (330), and the memory (340) may mean that the processor (310) can control the communication circuit (320), the sensor (330), and the memory (340). For example, the communication circuit (320), the sensor (330), and the memory (340) may be controlled by the processor (310).

[0066] Although illustrated based on different blocks, the embodiment is not limited thereto, and some of the hardware of FIG. 3A (e.g., at least a portion of the processor (310), the communication circuit (320), and the memory (340)) may be included in a single integrated circuit, such as a system on a chip (SoC).

[0067] According to one embodiment, the processor (310) may be configured with at least one processor. For example, the processor (310) may be configured with a main processor that performs high-performance processing and a secondary processor that performs low-power processing. At least some of the sensors (330) may be connected to the secondary processor. At least some of the sensors connected to the secondary processor may acquire data about the user for 24 hours. According to one embodiment, one of the main processor and the secondary processor may be activated depending on the state and / or operation of the wearable device (200). For example, the secondary processor may be activated when the battery of the wearable device (200) is low. For example, the main processor may be activated when accurate data about the user is required.

[0068] According to one embodiment, the processor (310) may include a hardware component for processing data based on one or more instructions. The hardware component for processing data may include, for example, an arithmetic and logic unit (ALU), a field programmable gate array (FPGA), and / or a central processing unit (CPU).

[0069] For example, the processor (310) may include an application processor, a supplementary processor (e.g., a sensor hub, a microcontroller unit (MCU)), a central processor unit (CPU), a neural processing unit (NPU), a graphic processing unit (GPU), and / or a processor for IoT (e.g., a processor integrated with a communication module).

[0070] According to one embodiment, the processor (310) can determine the operating time of the sensor (330). The processor (310) can control the operation of the sensor (330). The processor (310) can process information obtained from the sensor (330). For example, the processor (310) can identify a tapping pattern of the wearable device (200) using the sensor (330). The processor (310) can identify the tapping pattern of the wearable device (200) based on data obtained using the sensor (330). An embodiment for identifying the tapping pattern of the wearable device (200) based on data obtained using the sensor (330) will be described below.

[0071] According to one embodiment, the wearable device (200) may include a communication circuit (320). The communication circuit (320) may correspond to at least a portion of the communication module (190) of FIG. 1. For example, the communication circuit (320) may be used for various radio access technologies (RATs). For example, the communication circuit (320) may be used to perform Bluetooth communication, wireless local area network (WLAN) communication, Zigbee communication, near field communication (NFC), ultra wide band (UWB) communication, or ANT+ communication. For example, the communication circuit (320) may be used to perform cellular communication. For example, the processor (310) may establish a connection with an external electronic device (e.g., the external electronic device (291) or the external electronic device (292) of FIG. 2) through the communication circuit (320). For example, the processor (310) may identify (or measure) the location of the wearable device (200) based on a wireless signal (e.g., a global positioning system (GPS) signal) received or transmitted by the communication circuit (320). According to an embodiment, the communication circuit (320) may be configured to be integrated with the processor (310).

[0072] For example, the processor (310) may transmit data to an external electronic device (e.g., an external electronic device (291)) connected to the wearable device (200) using the communication circuit (320). The processor (310) may transmit information about a tapping pattern to the external electronic device based on a request for information about a tapping pattern (or data about a tapping) from the external electronic device using the communication circuit (320). According to an embodiment, when a user is in an emergency situation, the processor (310) may transmit (e.g., broadcast) a signal (e.g., a BLE (Bluetooth low energy) signal) for a rescue request to a nearby electronic device using the communication circuit (320).

[0073] According to one embodiment, the wearable device (200) may include a sensor (330). The sensor (330) may be used to obtain various information. For example, the sensor (330) may be used to obtain data about the user's body and / or data about tapping. As an example, the sensor (330) may be used to obtain body temperature data (or body temperature information), heart rate data (or heart rate information), and / or motion data (or motion information) of the user. For example, the sensor (330) may be composed of at least one sensor. The sensor (330) may include at least one sensor. For example, the sensor (330) may correspond to the sensor module (176) of FIG. 1.

[0074] For example, the sensor (330) may include an acceleration sensor (331). The acceleration sensor (331) may be used to identify changes in acceleration of the wearable device (200). For example, the acceleration sensor (331) may identify (or measure, detect) acceleration of the wearable device (200) in three directions: the x-axis, the y-axis, and the z-axis. For example, the acceleration sensor (331) may be configured to identify acceleration that is about four times or more the acceleration of gravity in any axis. For example, the acceleration sensor (331) may have a resolution of a specified size (e.g., 16 bits).

[0075] For example, the sensor (330) may include a gyro sensor (332). The gyro sensor (332) may identify (or measure, detect) the angular velocity of the wearable device (200) in three directions: the x-axis, the y-axis, and the z-axis. According to an embodiment, the wearable device (200) may include an inertial sensor including an acceleration sensor (331) and a gyro sensor (332).

[0076] For example, the wearable device (200) may use at least one of an acceleration sensor (331) and / or a gyro sensor (332) to identify at least one of data regarding a tap, data regarding a user's gesture, data regarding an amount of impact, data regarding an orientation of the wearable device (200), and / or activity information of the user (e.g., sitting, moving, sports activity).

[0077] For example, the sensor (330) may include a photoplethysmography (PPG) sensor (333). The PPG sensor (333) may be used to measure pulse (or a change in blood volume within a blood vessel) by identifying a change in light sensitivity according to a change in blood vessel volume. The PPG sensor (333) may include one or more photodiodes (PDs) and one or more light emitting diodes (LEDs). For example, the PPG sensor (333) may be used to identify a change in blood flow within a blood vessel during a heartbeat. The PPG sensor (333) may identify a change in blood flow within a blood vessel during a heartbeat when the light sensor is in contact with the skin over a peripheral blood vessel. The processor (310) may identify blood flow and a change in blood flow based on the PPG signal and waveform.

[0078] For example, the PPG sensor (331) may include a transmissive PPG sensor and / or a reflective PPG sensor.

[0079] For example, the PPG sensor (331) can output light toward the user's skin via one of an LED (e.g., green, red, or IR (infrared)), a laser, and a VCSEL (vertical cavity surface emitting laser). The PPG sensor (331) can identify light reflected and / or transmitted from the user's skin via at least one of a PD and / or a CMOS (complementary metal oxide semiconductor) camera. The PPG sensor (331) can store the identified value via an ADC (analog to digital converter) in a memory (340) (or buffer) based on the reflected and / or transmitted light.

[0080] For example, a transmissive PPG sensor can identify light passing through a blood vessel through a PD positioned opposite the LED. The transmissive PPG sensor can identify the user's blood flow based on the intensity of the light passing through the blood vessel. For example, a reflective PPG sensor can output light toward the user's skin through an LED. The reflective PPG sensor can identify light that is reflected by the blood vessel and at least partially received through a PD positioned on substantially the same surface as the LED. The reflective PPG sensor can identify the user's blood flow based on the intensity of the light reflected by the blood vessel. For example, multiple light sources can be used, such as LEDs. For example, green light, which is complementary to blood, can be used as the LED.

[0081] For example, the sensor (330) may include a temperature sensor (334). The temperature sensor (334) may be used to identify (or measure) skin temperature on a part of the user's body (e.g., wrist or forehead). For example, the temperature sensor (334) may include a contact temperature sensor and a non-contact temperature sensor.

[0082] Although not shown, the sensor (330) may further include various sensors for obtaining (or identifying, measuring, detecting) various data about the user and / or the wearable device (200).

[0083] According to one embodiment, the wearable device (200) may include a memory (340). The memory (340) may be used to store information or data. For example, the memory (340) may be used to store data obtained from a user. For example, the memory (340) may correspond to the memory (130) of FIG. 1. For example, the memory (340) may be a volatile memory unit or units. For example, the memory (340) may be a non-volatile memory unit or units. As another example, the memory (340) may be another form of computer-readable media, such as a magnetic or optical disk. For example, the memory (340) may store data obtained based on operations performed by the processor (310) (e.g., algorithm execution operations). For example, the memory (340) may store data obtained by the sensor (330) (e.g., data regarding tapping). According to an embodiment, the memory (340) may be configured in an integrated form with the processor (310).

[0084] For example, the processor (310) may store information about a tapping pattern identified based on data acquired from a sensor (330) (e.g., an acceleration sensor (331)) in the memory (340). As an example, the processor (310) may store information about a tapping pattern based on a specified time interval. For example, the processor (310) may store one or more user-defined tapping patterns set by a user and / or one or more preset tapping patterns that are stored by default in the wearable device (200) in the memory (340). For example, the processor (310) may store one tapping pattern and one function in association. As an example, the processor (310) may store a function corresponding to a tapping pattern in the memory (340) in the form of a table.

[0085] Although not illustrated in FIG. 3a, the wearable device (200) may further include various components. For example, the wearable device (200) may further include at least one of an antenna, a power management integrated circuit (PMIC), a battery, and a flexible printed circuit board (FPCB).

[0086] FIG. 3b is an example of a partial cross-sectional view of a wearable device according to one embodiment.

[0087] Referring to FIG. 3B, the wearable device (200) (e.g., the electronic device (101) of FIG. 1) may be formed in a ring shape. For example, the housing (210) of the wearable device (200) may be formed in a ring shape that can be worn on a user's finger. In FIGS. 2 and 3B, a wearable device (200) having a smooth ring shape is illustrated as an example, but the present invention is not limited thereto. For example, the wearable device (200) may be implemented as a housing including a plurality of flat surfaces. For example, a wearable device (200) having a ring shape that does not have a smooth surface may also be understood as an embodiment of the present disclosure.

[0088] According to one embodiment, the ring-shaped housing (210) may include a first side that comes into contact with the user's body when worn by the user (e.g., the first side (211) of FIG. 2), a second side that is exposed to the outside (e.g., the second side (212) of FIG. 2), and a side surface between the first side (211) and the second side (212). For example, the first side (211) and the second side (212) may include a space for including (or arranging) at least one component (e.g., a processor (310), a communication circuit (320), a sensor (330), and a memory (340)).

[0089] According to one embodiment, a PCB (351) may be placed between the first side (211) and the second side (212) of the wearable device (200). For example, a processor (310), a communication circuit (320), an acceleration sensor (331), a gyro sensor (332), a PPG sensor (333), a temperature sensor (334), a memory (340), and / or a PMIC (354) may be placed on the PCB (351). For example, the PCB (351) may be composed of a rigid region and a flexible region. As an example, the rigid region may be referred to as a rigid flexible printed circuit board (RFPCB). As an example, the flexible region may be referred to as a flexible printed circuit board (FPCB).

[0090] For example, the PPG sensor (333) may include one or more light-emitting circuits (333-1), one or more light-receiving circuits (333-2), and a control circuit (333-3). As an example, the one or more light-emitting circuits (333-1) and the one or more light-receiving circuits (333-2) may be arranged toward the first side (211). As an example, the control circuit (333-3) may be arranged toward the second side (212).

[0091] For example, the PMIC (354) may be used to manage power of the wearable device (200). The PMIC (354) may be used to provide (or distribute) power to components requiring power in the wearable device (200). The PMIC (354) may support a wired charging method (e.g., terminal, pogo pin) or a wireless charging method (e.g., wireless power consortium (WPC), NFC) for charging the wearable device (200) through the charging interface (353).

[0092] According to one embodiment, a battery (352) may be placed between the first side (211) and the second side (212) of the wearable device (200). The battery (352) may be composed of at least one battery (or battery pack). For example, the battery (352) may be configured such that at least one battery is connected in series and / or in parallel. For example, the battery (352) may be composed of a flexible battery pack. For example, the battery (352) may be charged and / or discharged as a secondary battery. For example, the material constituting the battery (352) may be composed of various materials. For example, the material constituting the battery (352) may include at least one of lithium ion and mercury.

[0093] According to one embodiment, an antenna (355) may be positioned between the first side (211) and the second side (212) of the wearable device (200). For example, the antenna (355) may be composed of a single antenna and / or multiple segmented antennas. According to an embodiment, the antenna (355) may be composed of a part of the housing (210) of the wearable device (200). For example, the antenna (355) may be electrically connected to the communication circuit (320) via the PCB (351).

[0094] Although not illustrated, the wearable device (200) may include various other components in addition to the illustrated components. For example, the wearable device (200) may include a display. The display may be positioned on the outer surface of the housing (210).

[0095] FIG. 4 illustrates a flowchart of the operation of a wearable device according to an embodiment. The wearable device (200) can perform the operations illustrated in FIG. 4. Hereinafter, the operations illustrated in FIG. 4 will be described as being performed by a processor (e.g., the processor (310) of FIG. 3A). In the following embodiments, the operations may be performed sequentially, but are not necessarily performed sequentially. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0096] Referring to FIG. 4, in operation 410, the processor (310) can identify data regarding tapping. For example, the wearable device (200) can identify data regarding tapping using the sensor (330) during a specified time period.

[0097] According to one embodiment, the processor (310) may identify data regarding tapping during a specified time interval. For example, the specified time interval may be one of a plurality of time intervals set based on a sliding window scheme. The processor (310) may identify the plurality of time intervals based on the sliding window scheme. For example, at least some of the plurality of time intervals may overlap each other. As an example, a first time interval may be set to be 10 [ms] (milli-seconds) from a specified time point. A second time interval may be set to be 10 [ms] from a time point that is 1 [ms] past the specified time point.

[0098] According to one embodiment, the processor (310) may receive a request from an external electronic device to identify a tapping pattern of the wearable device (200). In response to the request to identify the tapping pattern, the processor (310) may use the sensor (330) to identify data regarding the tapping during a specified time period from the time at which the request is received to identify the tapping pattern of the wearable device (200).

[0099] According to one embodiment, the data regarding the tapping may include data regarding the orientation of the wearable device (200) and data regarding changes in acceleration of the wearable device (200). For example, the processor (310) may identify the data regarding the orientation of the wearable device (200) and the data regarding changes in acceleration of the wearable device (200) using at least one of the acceleration sensor (331) and / or the gyro sensor (332).

[0100] For example, a specific example of a processor (310) identifying data regarding tapping using an acceleration sensor (331) will be described later in FIGS. 5A to 5D.

[0101] According to one embodiment, the processor (310) can identify information about the user's body using the sensor (330). Based on the information about the user's body, the processor (310) can identify that the user's state is a designated state. While the user's state is a designated state, the processor (310) can identify data about tapping.

[0102] For example, the processor (310) may identify that the user's body temperature is above a specified temperature (e.g., approximately 39 degrees Celsius). The processor (310) may identify that the user's condition is at risk. Based on the identification that the user's condition is at risk, the processor (310) may identify data regarding tapping to perform an emergency rescue request.

[0103] For example, the processor (310) may use the sensor (330) to identify that a user is in a vehicle. Based on identifying that a user is in a vehicle, the processor (310) may identify data regarding a tap to perform one of a plurality of functions that can be performed in the vehicle.

[0104] In operation 420, the processor (310) may identify a first tapping pattern of the wearable device (200). For example, the processor (310) may identify the first tapping pattern of the wearable device (200) based on the identified data.

[0105] According to one embodiment, the processor (310) may identify a first tapping pattern among a plurality of tapping patterns based on the identified data. The plurality of tapping patterns may include a tapping pattern in which a user taps the upper surface of an external object using a part of the user's body while wearing the wearable device (200). The plurality of tapping patterns may include a tapping pattern in which a user taps the side surface of an external object using a part of the user's body while wearing the wearable device (200). As an example, a tapping pattern in which a user taps the upper surface of an external object using a part of the user's body may be referred to as knocking. As an example, a tapping pattern in which a user taps the side surface of an external object using a part of the user's body may be referred to as bumping. Specific examples of the plurality of tapping patterns will be described later with reference to FIGS. 6A to 6D. According to an embodiment, the processor (310) may identify whether the user of the wearable device (200) is tapping the top surface of an external object or the side surface of an external object using a part of the body, based on the orientation of the wearable device (200).

[0106] According to one embodiment, the processor (310) may store information regarding the first tapping pattern in a memory (e.g., the memory (340) of FIG. 3A). For example, the processor (310) may store information regarding the first tapping pattern in the memory (340) based on a sliding window technique. The processor (310) may store information regarding the amount (or intensity) of the impact and information regarding the identified time point in the memory (340).

[0107] In operation 430, the processor (310) may transmit a first signal regarding the identified function to an external electronic device connected to the wearable device (200) based on identifying a function corresponding to the first tapping pattern.

[0108] According to one embodiment, the processor (310) may identify a function corresponding to the first tapping pattern. For example, each of the plurality of tapping patterns may correspond to a different function. For example, one of the plurality of tapping patterns may correspond to controlling (e.g., turning on and off) lighting connected to the wearable device (200). One of the plurality of tapping patterns may correspond to executing an application of an external electronic device (e.g., the external electronic device (291) of FIG. 2).

[0109] According to one embodiment, the plurality of tapping patterns may be identified based on information about the user's body and information about the environment of the wearable device (200). For example, the processor (310) may allow each of the plurality of tapping patterns to correspond to a different function depending on the situation. For example, when the user's body temperature is higher than a specified temperature, the processor (310) may allow each of the plurality of tapping patterns to correspond to a function for requesting emergency rescue. For example, when the user is driving, the processor (310) may allow each of the plurality of tapping patterns to correspond to a function for controlling the vehicle.

[0110] For example, the processor (310) may identify a plurality of tapping patterns based on information about the user's body and information about the environment of the wearable device (200). The processor (310) may identify a first tapping pattern among the plurality of tapping patterns. The processor (310) may identify functions corresponding to the first tapping patterns.

[0111] According to one embodiment, the processor (310) may transmit a first signal regarding the identified function to an external electronic device (e.g., external electronic device (291) or external electronic device (292)) connected to the wearable device (200).

[0112] For example, the first signal may be configured to cause the external electronic device to perform a function identified based on the first tapping pattern. The processor (310) may transmit the first signal to the external electronic device, which causes the external electronic device to perform the function identified based on the first tapping pattern. As an example, the function identified based on the first tapping pattern may be making a call to an emergency contact. The processor (310) may transmit the first signal to cause the external electronic device to transmit a second signal for making the outgoing call to a device corresponding to the emergency contact.

[0113] For example, the first signal may be transmitted to perform a function identified based on the first tapping pattern. The processor (310) may transmit the first signal to an external electronic device to perform the identified function. As an example, the function identified based on the first tapping pattern may be a function for notifying the user of an emergency state. The processor (310) may transmit the first signal to an external electronic device (e.g., a server) configured for an emergency rescue request service to indicate that the user is in an emergency state.

[0114] According to one embodiment, before identifying the first tapping pattern, the processor (310) may identify the second tapping pattern and a function corresponding to the second tapping pattern. The processor (310) may store the function corresponding to the second tapping pattern together with the second tapping pattern. Based on identifying that the first tapping pattern corresponds to the second tapping pattern, the processor (310) may identify the function corresponding to the second tapping pattern as the function corresponding to the first tapping pattern.

[0115] FIGS. 5A to 5D illustrate examples of data identified through an acceleration sensor according to one embodiment.

[0116] Referring to FIG. 5A, a processor (e.g., processor (310) of FIG. 3A) can identify a change in acceleration of a wearable device (200) through an acceleration sensor (e.g., acceleration sensor (331) of FIG. 3A). For example, the processor (310) can identify a change in acceleration through the acceleration sensor (331) based on a specified sampling rate (e.g., 100 Hz).

[0117] According to one embodiment, the processor (310) may sample acceleration values ​​based on a sampling rate (e.g., 100 Hz) specified through the acceleration sensor (331). Graphs (501), (502), and (503) represent changes in acceleration values ​​over time obtained through the acceleration sensor (331). For example, graph (501) represents changes in acceleration values ​​over time in the x-axis direction. Graph (502) represents changes in acceleration values ​​over time in the y-axis direction. Graph (503) represents changes in acceleration values ​​over time in the z-axis direction. For example, the x-axis direction, the y-axis direction, and the z-axis direction may each correspond to the x-axis direction, the y-axis direction, and the z-axis direction of FIG. 2.

[0118] The processor (310) can set the ENU (east, north, up) coordinate system based on the gravity direction (e.g., the -z axis direction of FIG. 2). The processor (310) can identify the magnitude of acceleration based on the ENU coordinate system.

[0119] For example, the processor (310) can identify the magnitude of acceleration by using the acceleration values ​​identified in each of the x-axis, y-axis, and z-axis. For example, the magnitude of acceleration can be set as in the following mathematical expression 1.

[0120]

[0121] The above mathematical formula 1 is merely an example to aid understanding and is not limited thereto, and can be modified, applied, or expanded in various ways.

[0122] Referring to mathematical equation 1, M is the magnitude of acceleration. x is the acceleration value in the x-axis direction. y is the acceleration value in the y-axis direction. z is the acceleration value in the z-axis direction. For example, the unit of the above-mentioned acceleration value (or the magnitude of acceleration) is [m / s 2 ] may be.

[0123] Referring to FIG. 5b, the processor (310) can identify a graph (504) representing a change in the magnitude (M) of acceleration over time based on the mathematical expression 1 described above.

[0124] Referring to FIG. 5c, the processor (310) can remove noise using a low pass filter (LPF). Using the LPF, the processor (310) can identify a graph (505) showing changes in the magnitude of acceleration over time, from which noise has been removed.

[0125] Referring to FIG. 5D, the processor (310) can identify values ​​whose magnitude of acceleration is greater than or equal to a threshold value. For example, the processor (310) can identify peak values ​​for the graph (505). The processor (310) can identify a graph (506) composed of peak values ​​for the graph (505). Based on the graph (506), the processor (310) can identify the sampled time and magnitude of acceleration as shown in Table 1.

[0126]

[0127] Referring to Table 1, the processor (310) can identify the time at which a peak occurred and the magnitude of the acceleration at that time. The processor (310) can store the time at which a peak occurred and the magnitude of the acceleration at that time in a memory (e.g., the memory (340) of FIG. 3A) as shown in Table 1.

[0128] The processor (310) can identify at least one point in time at which a peak occurs based on changes in the magnitude of acceleration over time. The processor (310) can identify that the user has tapped an external object with a part of the user's body at at least one point in time.

[0129] For example, when a wearable device (200) is worn by a user, there is a high probability that movements with large displacement and high frequency will be detected. Accordingly, according to the above-described embodiment, movements unrelated to tapping movements can be filtered out.

[0130] According to the above-described embodiment, the processor (310) can identify (e.g., monitor) the amount of impact identified in the wearable device (200). The processor (310) can identify the time interval between peaks of the amount of impact. The processor (310) can identify a tapping pattern (or an impact pattern constituting a tapping pattern) based on the number of taps within a specified time, the amount of impact, and / or the time interval between peaks of the amount of impact.

[0131] According to one embodiment, the processor (310) may identify the orientation of the wearable device (200) using the acceleration sensor (331). For example, the processor (310) may identify whether the user taps the upper surface of an external object using a part of the body (e.g., a finger) based on the orientation of the wearable device (200). For example, the processor (310) may identify whether the user taps the side of an external object using a part of the body based on the orientation of the wearable device (200).

[0132] According to one embodiment, the processor (310) may identify that the user taps an external object with a part of the user's body (e.g., a finger) based on an acceleration value in the z-axis direction among the acceleration values ​​identified in each of the x-axis, y-axis, and z-axis. For example, when the user taps an external object with a part of the user's body (e.g., a finger), the change in acceleration in the z-axis direction may be the largest. Accordingly, the processor (310) may identify that the user taps an external object with a part of the user's body (e.g., a finger) based on an acceleration value in the z-axis direction among the identified acceleration values.

[0133] According to an embodiment, the processor (310) may identify the orientation of the wearable device (200) using a gyro sensor (e.g., the gyro sensor (332) of FIG. 3A) (or a geomagnetic sensor). The processor (310) may identify information about a movement trajectory of the wearable device (200) based on the orientation of the wearable device (200) identified using the gyro sensor (332) (or a geomagnetic sensor) and a change in acceleration of the wearable device (200) identified using the acceleration sensor (331). Based on the information about the movement trajectory of the wearable device (200), the processor (310) may identify that the user taps an external object with a part of the user's body (e.g., a finger).

[0134] FIGS. 6A to 6D illustrate examples of multiple tapping patterns according to one embodiment.

[0135] Referring to FIGS. 6A to 6D , a processor (e.g., processor (310) of FIG. 2A ) can identify a tapping pattern based on the orientation of the wearable device (200) and a tapping pattern generated within a specified time interval. For example, the tapping pattern can be configured based on the orientation of the wearable device (200) and the tapping pattern. For example, the tapping pattern (or tapping pattern) can be identified based on tapping strength and tapping cycle.

[0136] For example, the processor (310) may identify the impact patterns as different tapping patterns even if the impact patterns generated within a specified time interval are substantially the same, if the orientations of the wearable device (200) are different. The processor (310) may identify the impact patterns as different tapping patterns even if the orientations of the wearable device (200) are substantially the same, and the impact patterns generated within a specified time interval are different.

[0137] For example, the processor (310) can identify whether impact patterns are identical and / or similar based on the number of taps within a given time period, the amount of impact, and / or the time interval between peak-to-peak impact amounts.

[0138] For example, the processor (310) may identify a tapping pattern based on the direction in which the impact is applied to the wearable device (200) based on the location and / or direction of gravity at which the impact is applied to the wearable device (200). In FIGS. 6A to 6D , an example in which the processor (310) identifies a movement based on the orientation of the wearable device (200) will be described.

[0139] The axes (e.g., the x-axis, the y-axis, and the z-axis) of FIGS. 6A to 6D (e.g., the x-axis, the y-axis, and the z-axis of FIG. 2) may represent axes with respect to the external environment based on the orientation of the wearable device (200). The axes identified in the wearable device (200) (e.g., the x-axis, the y-axis, and the z-axis of FIG. 2) may be maintained even when the orientation of the wearable device (200) changes.

[0140] Referring to FIG. 6A, a user of a wearable device (200) may tap an upper surface (e.g., a surface facing in the opposite direction of gravity) of an external object by moving a part of the user's body (e.g., a finger or a wrist) while the back of the user's hand is facing in the opposite direction of gravity. As the user taps the upper surface of the external object, an impact may be applied to the wearable device (200). For example, an impact may be applied to the wearable device (200) in the +z direction with respect to the wearable device (200). The processor (310) may identify that the state of the wearable device (200) identified based on the impact applied to the wearable device (200) in the +z direction and the orientation of the wearable device (200) is a first state (e.g., a normal state). The processor (310) can identify a first tapping pattern among a plurality of tapping patterns based on identifying that the state of the wearable device (200) is a first state (e.g., normal state).

[0141] Referring to FIG. 6B, a user of a wearable device (200) may tap an upper surface of an external object (e.g., a surface facing in the opposite direction of gravity) by moving a part of the user's body (e.g., a finger or a wrist) while the back of the user's hand is facing the direction of gravity. As the user taps the upper surface of the external object, an impact may be applied to the wearable device (200). For example, an impact may be applied to the wearable device (200) in the -z direction with respect to the wearable device (200). The processor (310) may identify that the state of the wearable device (200) is a second state (e.g., an upside-down state) based on the impact applied to the wearable device (200) in the -z direction and the orientation of the wearable device (200). The processor (310) can identify a second tapping pattern among the plurality of tapping patterns based on identifying that the state of the wearable device (200) is a second state (e.g., an upside-down state).

[0142] Referring to FIG. 6C, a user of a wearable device (200) may tap a side surface (e.g., a side facing the +z direction) of an external object by moving a part of the user's body (e.g., a finger) while the back of the user's hand is facing a direction perpendicular to the direction of gravity (e.g., the +z direction among the axes identified in the wearable device (200). As the user taps the side surface of the external object, an impact may be applied to the wearable device (200). For example, an impact may be applied to the wearable device (200) in the +z direction with respect to the wearable device (200). The processor (310) may identify that the state of the wearable device (200) is a third state (e.g., a rotated state from a normal state) based on the impact applied to the wearable device (200) in the +z direction and the orientation of the wearable device (200). The processor (310) can identify a third tapping pattern among a plurality of tapping patterns based on identifying that the state of the wearable device (200) is a third state (e.g., from a normal state to a rotated state).

[0143] Referring to FIG. 6D, a user of a wearable device (200) may tap a side surface (e.g., a side facing in a direction perpendicular to the direction of gravity) of an external object by moving a part of the user's body (e.g., a finger) while the back of the user's hand is facing in a direction perpendicular to the direction of gravity. As the user taps the side surface of the external object, an impact may be applied to the wearable device (200). For example, an impact may be applied to the wearable device (200) in the +z direction with respect to the wearable device (200). The processor (310) may identify that an impact has occurred to the wearable device (200) in the +z direction, and that the state of the wearable device (200) identified based on the orientation of the wearable device (200) is a fourth state (e.g., a rotated state from a normal state). The processor (310) can identify a fourth tapping pattern among a plurality of tapping patterns based on identifying that the state of the wearable device (200) is a fourth state (e.g., a rotated state from a normal state) based on an impact generated in the +z direction on the wearable device (200) and an orientation of the wearable device (200).

[0144] FIG. 7 illustrates an example of operation of a wearable device for identifying an impact pattern according to one embodiment.

[0145] Referring to FIG. 7, a processor (e.g., processor (310) of FIG. 3A) can identify a tapping pattern based on the orientation of the wearable device (200) and a tapping pattern generated within a specified time interval. In FIG. 7, an example for identifying a tapping pattern based on a tapping pattern generated within a specified time interval by the processor (310) will be described.

[0146] In one embodiment, the processor (310) can identify an impact pattern (721) within a time interval (711). For example, the impact pattern (721) can be composed of one or more tap inputs. For example, the processor (310) can identify one or more tap inputs of a specified pattern within the time interval (711). The processor (310) can identify the impact pattern (721) based on one or more tap inputs of the specified pattern.

[0147] According to one embodiment, a tapping pattern may be identified based on an impact pattern that is continuously repeated a specified number of times. For example, the processor (310) may identify one or more tapping inputs in a time interval (711), and then identify the one or more tapping inputs in a time interval (712). The processor (310) may identify an impact pattern (722) in the time interval (712). The processor (310) may identify an impact pattern (721) in the time interval (711), and subsequently identify an impact pattern (722) in the time interval (712). The processor (310) may identify that the impact pattern (722) corresponds to the impact pattern (721). The processor (310) may subsequently identify impact patterns (721, 722) having the same pattern. The processor (310) can identify the tapping pattern based on identifying that the impact pattern (722) corresponds to the impact pattern (721).

[0148] In one embodiment, the processor (310) can identify repetitive impact patterns. For example, based on identifying the impact pattern (721), the processor (310) can identify a time interval (711) in which the impact pattern (721) is identified. After the impact pattern (721) is identified, the processor (310) can identify the impact pattern (722). The processor (310) can identify whether the impact pattern (722) corresponds to the impact pattern (721). For example, the processor (310) can identify a similarity between the impact pattern (721) and the impact pattern (722). Based on identifying that the similarity between the impact pattern (721) and the impact pattern (722) is greater than or equal to a specified value, the processor (310) can identify that substantially identical impact patterns (721, 722) are generated.

[0149] The above example illustrates an example in which the processor (310) identifies a tapping pattern when a substantially identical impact pattern is repeated twice, but is not limited thereto. In an embodiment, the processor (310) may identify a tapping pattern based on the impact pattern when a substantially identical impact pattern is repeated five times.

[0150] FIG. 8 illustrates a flowchart of the operation of a wearable device according to an embodiment. The wearable device (200) can perform the operations illustrated in FIG. 8. Hereinafter, the operations illustrated in FIG. 8 will be described as being performed by a processor (e.g., the processor (310) of FIG. 3A). In the following embodiments, the operations may be performed sequentially, but are not necessarily performed sequentially. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0151] Referring to FIG. 8, operations 810 to 840 may be related to operations for setting a function corresponding to a tapping pattern in a wearable device (200).

[0152] At operation 810, the processor (310) may identify a tapping pattern. For example, the processor (310) may identify a tapping pattern while the wearable device (200) is operating in a mode for learning a tapping pattern.

[0153] According to one embodiment, the processor (310) may request the user to set a tapping pattern corresponding to a designated function (or a first function). For example, the processor (310) may request the user to set a tapping pattern for executing (or performing) the designated function (or the first function). As an example, the processor (310) may provide a notification requesting the user to set a tapping pattern for executing the designated function. Based on the notification, the processor (310) may identify data regarding the tapping. Based on the data regarding the tapping, the processor (310) may identify a tapping pattern.

[0154] According to one embodiment, the processor (310) can identify a tapping pattern using a model (e.g., a neural network model) indicated by a plurality of parameters. For example, the processor (310) can set data related to tapping as input values ​​of the model. The processor (310) can identify a tapping pattern based on the output values ​​of the model.

[0155] For example, the processor (310) can identify a tapping pattern using the model. The model may be a model configured to identify (or classify) a tapping pattern. The processor (310) can train the model to identify the tapping pattern. The model configured to identify (or classify) a tapping pattern may have low power consumption. For example, the power consumption required for training and using the model may be below a specified value.

[0156] In operation 820, the processor (310) may identify whether the tapping pattern conflicts with at least one tapping pattern stored in a memory (e.g., the memory (340) of FIG. 3A). For example, at least one tapping pattern corresponding to at least one function may be stored in the memory (340) of the wearable device (200). The at least one tapping pattern may include one or more preset tapping patterns and / or one or more user-defined tapping patterns. As an example, the one or more preset tapping patterns may be tapping patterns that are stored by default in the wearable device (200). The one or more preset tapping patterns may be tapping patterns that are pre-stored when the wearable device (200) is shipped. As an example, the one or more user-defined tapping patterns may be tapping patterns set by a user.

[0157] The processor (310) can identify whether the identified tapping pattern conflicts with one or more user-specified tapping patterns. The processor (310) can identify whether the identified tapping pattern corresponds to a tapping pattern specified by a user. The processor (310) can identify whether the identified tapping pattern conflicts with one or more user-specified tapping patterns based on identifying that the identified tapping pattern corresponds to one of the one or more user-specified tapping patterns. In some embodiments, the number of one or more user-specified tapping patterns may be limited. For example, the number of one or more user-specified tapping patterns may be limited to reduce the complexity of a model for identifying tapping patterns.

[0158] The processor (310) may identify whether the identified tapping pattern conflicts with one or more preset tapping patterns, if the identified tapping pattern does not conflict with one or more user-defined tapping patterns. The processor (310) may identify whether the identified tapping pattern corresponds to one or more preset tapping patterns that are stored by default in the wearable device (200). The processor (310) may identify whether the identified tapping pattern conflicts with one or more preset tapping patterns based on identifying that the identified tapping pattern corresponds to one or more preset tapping patterns that are stored by default in the wearable device (200).

[0159] In the above-described example, it has been described that the processor (310) performs an operation of identifying whether the identified tapping pattern collides with one or more preset tapping patterns when the identified tapping pattern does not collide with one or more user-specified tapping patterns, but is not limited thereto. For example, the processor (310) may identify whether the identified tapping pattern collides with one or more user-specified tapping patterns when the identified tapping pattern does not collide with one or more preset tapping patterns.

[0160] In operation 830, if the identified tapping pattern conflicts with at least one tapping pattern stored in the memory (340), the processor (310) may request to change the tapping pattern. The processor (310) may identify that the identified tapping pattern conflicts with at least one tapping pattern stored in the memory (340) based on identifying that the identified tapping pattern corresponds to one or more user tapping patterns and / or one or more preset tapping patterns. The processor (310) may request to change the tapping pattern based on identifying that the identified tapping pattern conflicts with at least one tapping pattern stored in the memory (340).

[0161] For example, the processor (310) may request the user to re-enter the tap pattern based on identifying that the identified tap pattern conflicts with one or more user-specified tap patterns.

[0162] For example, the processor (310) may request the user to re-enter the tap pattern based on identifying that the identified tap pattern conflicts with one or more preset tap patterns.

[0163] In operation 840, if the identified tapping pattern does not conflict with at least one tapping pattern stored in the memory (340), the processor (310) may store the tapping pattern in the memory (340) in association with a designated function. For example, the processor (310) may execute (or perform) the designated function based on identifying that the identified tapping pattern, based on data about the tapping, corresponds to a tapping pattern stored in association with the designated function.

[0164] According to one embodiment, the wearable device (200) may not provide the processing capacity to perform operations 810 to 840 described above. The wearable device (200) may store a tapping pattern in conjunction with a designated function using an external electronic device connected to the wearable device (200).

[0165] For example, an external electronic device may request a user to set a tapping pattern to execute a specified function through the wearable device (200). The external electronic device may request the wearable device (200) to identify data regarding the tapping. The processor (310) may use a sensor (e.g., sensor (330) of FIG. 3A) to identify (or obtain) data regarding the tapping. The processor (310) may transmit the data regarding the tapping to the external electronic device. The external electronic device may set the data regarding the tapping as an input value of a model for identifying the tapping pattern. The external electronic device may identify the tapping pattern as an output value of the model. Based on identifying the tapping pattern, the external electronic device may perform operations corresponding to operations 810 to 840.

[0166] FIG. 9 illustrates a flowchart of the operation of a wearable device according to an embodiment. The wearable device (200) can perform the operations illustrated in FIG. 9. Hereinafter, the operations illustrated in FIG. 9 will be described as being performed by a processor (e.g., the processor (310) of FIG. 3A ). In the following embodiments, the operations may be performed sequentially, but are not necessarily performed sequentially. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0167] Referring to FIG. 9, in operation 910, the processor (310) may identify data regarding tapping. For example, the wearable device (200) may identify data regarding tapping using a sensor (e.g., sensor (330) of FIG. 3A) during a specified time period. Operation 910 may correspond to operation 410 of FIG. 4.

[0168] In operation 920, the processor (310) may identify a first tapping pattern of the wearable device (200). For example, the processor (310) may identify the first tapping pattern of the wearable device (200) based on the identified data. Operation 920 may correspond to operation 420 of FIG. 4.

[0169] At operation 930, the processor (310) may identify that the user of the wearable device (200) is in an emergency state. For example, the processor (310) may identify that the user of the wearable device (200) is in an emergency state based on identifying a first tapping pattern. For example, the processor (310) may identify that the user of the wearable device (200) is in an emergency state based on identifying a first tapping pattern that is continuously repeated a specified number of times. According to an embodiment, the processor (310) may identify whether the first tapping pattern is continuously identified based on identifying the first tapping pattern. The processor (310) may not perform operation 930 in response to identifying the first tapping pattern. The processor (310) may perform operation 930 based on whether the first tapping pattern is continuously identified.

[0170] According to one embodiment, the processor (310) can identify that the first tapping pattern is repeated. After identifying the first tapping pattern according to operation 920, the processor (310) can repeatedly identify the first tapping pattern. The processor (310) can identify that the first tapping pattern is repeated continuously. The processor (310) can identify that the first tapping pattern is repeated continuously a specified number of times (e.g., three times).

[0171] For example, due to a malfunction and / or user error, the processor (310) may identify the first tapping pattern. The processor (310) may identify whether the first tapping pattern is repeated consecutively a specified number of times to identify whether the user intentionally performed a tapping motion corresponding to the first tapping pattern.

[0172] For example, a user may perform a tapping motion corresponding to a first tapping pattern, 'tap-break-tap-tap-tap'. The processor (310) may identify the first tapping pattern based on tapping data according to the user's tapping motion. The processor (310) may identify that the first tapping pattern is repeated three times. Based on identifying that the first tapping pattern is repeated three times, the processor (310) may identify that the user of the wearable device (200) is in an emergency state.

[0173] At operation 940, the processor (310) may transmit a first signal to an external electronic device to indicate that the user is in an emergency state.

[0174] For example, the processor (310) may provide a notification to determine whether the user is to transmit a first signal to indicate that the user is in an emergency state based on the user's identification of the user being in an emergency state. The processor (310) may provide the notification using at least one component. As an example, the processor (310) may provide the notification to determine whether the first signal is to be transmitted using at least one of a sound including a siren and a beep, a vibration, and / or a blinking of a light-emitting circuit. According to an embodiment, the processor (310) may provide the notification to determine whether the first signal is to be transmitted through an external electronic device connected to the wearable device (200).

[0175] The processor (310) may identify another tapping pattern for requesting transmission of a first signal based on the provision of the notification. In response to identifying another tapping pattern for requesting transmission of a first signal, the processor (310) may transmit a first signal to an external electronic device to indicate that the user is in an emergency state.

[0176] For example, the processor (310) may identify a tapping pattern indicating whether to transmit a first signal based on the provision of a notification. The processor (310) may instruct the second tapping pattern to transmit the first signal. The third tapping pattern may instruct not to transmit the first signal. Based on the identification of the second tapping pattern, the processor (310) may transmit a first signal to an external electronic device to indicate that the user is in an emergency state.

[0177] In one embodiment, an external electronic device may be configured for an emergency rescue request (SOS) service. For example, the external electronic device may include a server for performing the emergency rescue request service. In one embodiment, the external electronic device may include a device corresponding to a designated contact (e.g., an emergency contact). For example, the processor (310) may use a communication circuit (e.g., the communication circuit (320) of FIG. 3A) to directly transmit a first signal to the external electronic device to indicate that the user is in an emergency state.

[0178] According to one embodiment, the external electronic device may include a device configured to transmit a second signal for an outgoing call to a device corresponding to one of a plurality of emergency contacts. The processor (310) may transmit a first signal to cause the external electronic device to transmit a second signal for an outgoing call to a device corresponding to one of the plurality of emergency contacts. The processor (310) may transmit the first signal to the external electronic device. The external electronic device may receive the first signal. Based on the first signal, the external electronic device may transmit a second signal for an outgoing call to a device corresponding to one of the plurality of emergency contacts.

[0179] According to one embodiment, the processor (310) may transmit a first signal to an external electronic device based on a specified time period to indicate that the user is in an emergency state. For example, the first signal may not be properly transmitted to the external device. Therefore, the processor (310) may transmit the first signal to the external electronic device based on the specified time period.

[0180] According to an embodiment, the external electronic device may include a satellite. The processor (310) may transmit a first signal to the external electronic device via satellite communication. The processor (310) may transmit the first signal to the external electronic device via satellite communication based on a specified time period.

[0181] According to an embodiment, the processor (310) may transmit a first signal based on a designated time period using a wireless BLE (Bluetooth low energy) disaster network. For example, the first signal may be broadcast. The processor (310) may transmit the first signal to external electronic devices within a designated distance from the wearable device (200), thereby indicating that the user of the wearable device (200) is in an emergency state.

[0182] According to one embodiment, the processor (310) may identify the location of the wearable device (200) based on the user's identification of being in an emergency state. The wearable device (200) may identify the location of the wearable device (200) using a location identification circuit (not shown). The processor (310) may activate the location identification circuit based on the user's identification of being in an emergency state.

[0183] According to one embodiment, the processor (310) may execute (or perform) a specified function on an external electronic device based on the user's identification of an emergency state. For example, the processor (310) may transmit a first signal to the external electronic device to execute a location tracking function of the external electronic device based on the user's identification of an emergency state.

[0184] According to one embodiment, the processor (310) may transmit information about the user (e.g., blood pressure information, body temperature information, and pulse information) and / or information about the wearable device (200) (e.g., battery information, location information) through the first signal.

[0185] According to one embodiment, the processor (310) can identify a tap input to an external object. To identify the tap input to the external object, the processor (310) can identify that a distance between the wearable device (200) and the external object is within a specified distance. The processor (310) can identify a first tap pattern while the distance between the wearable device (200) and the external object is within the specified distance. Based on identifying the first tap pattern, the processor (310) can transmit a first signal to the external electronic device.

[0186] For example, the processor (310) may identify an input of tapping an external object according to a first tapping pattern as an input for a user's emergency rescue request. To identify a tapping input for an external object, the processor (310) may identify that a distance between the wearable device (200) and the external object is within a specified distance. The processor (310) may identify the first tapping pattern while the distance between the wearable device (200) and the external object is within the specified distance. Based on identifying the first tapping pattern, the processor (310) may transmit a first signal for an emergency rescue request to the external electronic device.

[0187] FIG. 10A illustrates a flowchart of the operation of a wearable device according to an embodiment. The wearable device (200) can perform the operations illustrated in FIG. 10A. Hereinafter, the operations illustrated in FIG. 10A will be described as being performed by a processor (e.g., the processor (310) of FIG. 3A). In the following embodiments, the operations may be performed sequentially, but are not necessarily performed sequentially. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0188] Referring to FIG. 10A, in operation 1010, the processor (310) may identify a second tapping pattern. The processor (310) may identify the second tapping pattern based on data regarding tapping of the wearable device (200).

[0189] In operation 1020, the processor (310) may identify content provided by an external electronic device while the second tapping pattern is identified. The processor (310) may identify content provided by the external electronic device while the user is wearing the wearable device (200). For example, the content provided by the external electronic device may include music, video, multimedia content, and / or applications.

[0190] In operation 1030, the processor (310) may store a second tapping pattern in a memory (e.g., memory (340) of FIG. 3A) in association with the content. For example, the processor (310) may store the second tapping pattern in the memory (340) in association with the content by training a model (e.g., neural network model) indicated by a plurality of parameters based on information about the content (e.g., beats per minute (BPM) information, waveform information) and the second tapping pattern.

[0191] For example, the processor (310) may identify that a second tapping pattern occurs while the content is being provided. The processor (310) may identify that the second tapping pattern occurs at different times (or dates) while the content is being provided. For example, the processor (310) may repeatedly train a model (e.g., a neural network model) indicated by a plurality of parameters based on information about the content (e.g., beats per minute (BPM) information, waveform information) and the second tapping pattern. As the model is repeatedly trained, the accuracy of the model may increase.

[0192] In operation 1040, the processor (310) may identify data regarding the tapping. For example, the wearable device (200) may identify data regarding the tapping using a sensor (e.g., sensor (330) of FIG. 3A) during a specified time period. Operation 1040 may correspond to operation 410 of FIG. 4.

[0193] In operation 1050, the processor (310) may identify a first tapping pattern of the wearable device (200). For example, the processor (310) may identify the first tapping pattern of the wearable device (200) based on the identified data. Operation 1050 may correspond to operation 420 of FIG. 4.

[0194] In operation 1060, the processor (310) can identify that the first tapping pattern corresponds to the second tapping pattern. For example, the processor (310) can identify that the first tapping pattern corresponds to the second tapping pattern by setting the first tapping pattern as an input value of a model (e.g., a neural network model) indicated by a plurality of parameters.

[0195] For example, the processor (310) can identify a first impact pattern constituting a first tapping pattern. The processor (310) can identify a second impact pattern constituting a second tapping pattern. Based on the BPM information of the first impact pattern and the BPM information of the second impact pattern, the processor (310) can identify that the first tapping pattern corresponds to the second tapping pattern.

[0196] In operation 1070, the processor (310) may transmit a first signal to the external electronic device to cause the external electronic device to provide at least one piece of content related to the content. For example, in response to identifying that the first tap pattern corresponds to the second tap pattern, the processor (310) may transmit the first signal to the external electronic device to cause the external electronic device to provide at least one piece of content related to the content.

[0197] For example, the processor (310) may, in response to identifying that the first tap pattern corresponds to the second tap pattern, provide at least one piece of content via an external electronic device. For example, the at least one piece of content may include the content. For example, the at least one piece of content may include candidate content for presentation to the user, identified based on the content.

[0198] For example, the processor (310) may identify a second tapping pattern while the second music is being played on an external electronic device. Based on the identification of a first tapping pattern corresponding to the second tapping pattern, the processor (310) may recommend at least one piece of music related to the second music to the user. For example, the at least one piece of music may include the second music. Based on the identification of a first tapping pattern corresponding to the second tapping pattern, the processor (310) may recommend the second music.

[0199] FIG. 10b illustrates an example of operation of a wearable device according to one embodiment.

[0200] Referring to FIG. 10b, the processor (310) can train a model (1080) indicated by a plurality of parameters. The processor (310) can identify a second tapping pattern. While the second tapping pattern is being identified, the processor (310) can identify content provided by an external electronic device.

[0201] The processor (310) can train the model (1080) based on information about the content (e.g., BPM information or waveform information) and the second tapping pattern. The processor (310) can repeatedly train the model (1080).

[0202] For example, the processor (310) may identify that music is provided through an external electronic device while the second tapping pattern is being identified. The processor (310) may identify information about the music provided by the external electronic device. The information about the music provided by the external electronic device may include BPM information, genre (e.g., classical, jazz, pop, ballad, rhythm and blues, hip-hop, country music) information, singer information, title information, and / or mood information. The processor (310) may train the model (1080) based on the information about the music and the second tapping pattern.

[0203] The processor (310) can identify a first tapping pattern after the model (1080) is trained. The processor (310) can set the first tapping pattern as an input value of the model (1080). Based on the output value of the model (1080), the processor (310) can identify at least one content. The processor (310) can transmit a first signal to the external electronic device to cause the external electronic device to provide at least one content.

[0204] For example, the processor (310) may identify at least one piece of music based on setting the first tapping pattern as an input value of the model (1080). The processor (310) may identify at least one piece of music related to the music provided while the second tapping pattern is identified. For example, a BPM value of the at least one piece of music may be the same as and / or similar to a BPM value of the music provided while the second tapping pattern is identified. For example, a genre of the at least one piece of music may be the same as and / or similar to a genre of the music provided while the second tapping pattern is identified. For example, a singer of the at least one piece of music may be the same as a singer of the music provided while the second tapping pattern is identified. For example, a mood of the at least one piece of music may be the same as and / or similar to a mood of the music provided while the second tapping pattern is identified.

[0205] FIG. 11 illustrates a flowchart of the operation of a wearable device according to an embodiment. The wearable device (200) can perform the operations illustrated in FIG. 11. Hereinafter, the operations illustrated in FIG. 11 will be described as being performed by a processor (e.g., processor (310) of FIG. 3A). In the following embodiments, the operations may be performed sequentially, but are not necessarily performed sequentially. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0206] Referring to FIG. 11, in operation 1110, the processor (310) may identify a second tapping pattern. The processor (310) may identify the second tapping pattern based on data regarding the tapping. Operation 1110 may correspond to operation 1010 of FIG. 10A.

[0207] In operation 1120, the processor (310) may identify content (e.g., music) provided from an external electronic device while the second tapping pattern is identified. Operation 1120 may correspond to operation 1020 of FIG. 10A.

[0208] In operation 1130, the processor (310) may train a model (e.g., model (1080) of FIG. 10B) indicated by a plurality of parameters based on information about the second tapping pattern and content. For example, information about the content may include a BPM value of the content. The processor (310) may train a model indicated by a plurality of parameters based on the BPM value for the second tapping pattern and the BPM value of the content.

[0209] For example, the processor (310) can identify the difference between the BPM value for the second tapping pattern and the BPM value of the content. The processor (310) can train the model based on the difference between the BPM value for the second tapping pattern and the BPM value of the content.

[0210] For example, the processor (310) may detect a difference between the BPM value of content (e.g., music) and the BPM value of the second tapping pattern due to the user's habit of following a rhythm and the user's movements that are not identifiable by the wearable device (200). Accordingly, the processor (310) may train the model to compensate for the difference.

[0211] In operation 1140, the processor (310) may identify data regarding the tapping. For example, the wearable device (200) may identify data regarding the tapping using a sensor (e.g., sensor (330) of FIG. 3A) during a specified time period. Operation 1140 may correspond to operation 410 of FIG. 4.

[0212] In operation 1150, the processor (310) may identify a first tapping pattern of the electronic device. For example, the processor (310) may identify a first tapping pattern of the wearable device (200) based on the identified data. Operation 1150 may correspond to operation 420 of FIG. 4 .

[0213] In operation 1160, the processor (310) can correct the first tapping pattern based on setting the first tapping pattern to an input value of a model indicated by a plurality of parameters.

[0214] According to one embodiment, the processor (310) can identify a correction value based on setting the first tapping pattern as an input value of the model. The processor (310) can correct the first tapping pattern based on the correction value.

[0215] For example, the processor (310) can identify a BPM value for the first tapping pattern. For example, the BPM value for the first tapping pattern can be identified based on the following mathematical equation.

[0216]

[0217] The above mathematical formula 2 is merely an example to help understanding and is not limited thereto, and can be modified, applied, or expanded in various ways.

[0218] Referring to Equation 2, BPM is the BPM value for the first tapping pattern. N is the number of taps identified during a specified time. T is the specified time (unit: seconds).

[0219] The processor (310) can set the BPM value for the first tapping pattern as an input value of the model. The processor (310) can identify a correction value based on the output value of the model. The processor (310) can correct the BPM value for the first tapping pattern based on the identified correction value. For example, the processor (310) can correct the BPM value for the first tapping pattern based on the following mathematical equation.

[0220]

[0221] The above mathematical formula 3 is merely an example to aid understanding and is not limited thereto, and can be modified, applied, or expanded in various ways.

[0222] Referring to Equation 3, BPM_A is the corrected BPM value for the first tapping pattern. BPM is the BPM value for the first tapping pattern. α(alpha) is the correction value.

[0223] The processor (310) can correct the first tapping pattern based on correcting the BPM value for the first tapping pattern.

[0224] In operation 1170, the processor (310) may identify at least one content based on the corrected first tapping pattern. For example, the processor (310) may identify at least one content based on a BPM value (e.g., BPM_A) for the corrected first tapping pattern. For example, at least one content (e.g., music) having a BPM value that is the same as and / or similar to the BPM value for the corrected first tapping pattern may be identified.

[0225] According to one embodiment, the processor (310) can identify history information regarding content provided from an external electronic device. Based on the history information, the processor (310) can identify a plurality of contents. Among the plurality of contents, the processor (310) can identify at least one content (e.g., music) having a BPM value that is identical to and / or similar to the BPM value of the corrected first tapping pattern.

[0226] According to one embodiment, the processor (310) can identify a plurality of contents stored in an external electronic device. The processor (310) can identify, among the plurality of contents, at least one content (e.g., music) having a BPM value that is the same as and / or similar to the BPM value of the corrected first tapping pattern.

[0227] According to one embodiment, the processor (310) can identify a plurality of contents set as favorites by the user. The processor (310) can identify, among the plurality of contents, at least one content (e.g., music) having a BPM value that is the same as and / or similar to the BPM value of the corrected first tapping pattern.

[0228] FIG. 12 illustrates a flowchart of the operation of a wearable device according to an embodiment. The wearable device (200) can perform the operations illustrated in FIG. 12. Hereinafter, the operations illustrated in FIG. 12 will be described as being performed by a processor (e.g., the processor (310) of FIG. 3A). In the following embodiments, the operations may be performed sequentially, but are not necessarily performed sequentially. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0229] Referring to FIG. 12, at operation 1210, the processor (310) may receive a request to identify a tapping pattern from an external electronic device.

[0230] In operation 1220, the processor (310) can identify data regarding tapping of the electronic device during a specified time period from the time the request is received.

[0231] According to operations 1210 and 1220, a user of a wearable device (200) may request an external electronic device to perform a specified function. The external electronic device may provide a notification to confirm whether the specified function will be performed. The user may respond to the notification by tapping the wearable device (200).

[0232] According to one embodiment, the processor (310) may receive a request from an external electronic device to identify a tapping pattern. The processor (310) may activate an acceleration sensor (e.g., an acceleration sensor (331) of FIG. 3A) from the time the request is received. The processor (310) may identify data regarding the tapping during a designated time period from the time the request is received. The processor (310) may identify a tapping pattern based on the data regarding the tapping.

[0233] The processor (310) can determine whether to perform a specified function based on a tapping pattern. For example, the processor (310) can transmit a signal to an external electronic device to instruct the device to perform the specified function based on a first tapping pattern. For example, the processor (310) can transmit a signal to the external electronic device to instruct the device not to perform the specified function based on a second tapping pattern.

[0234] For example, an external electronic device may receive a voice input from a user requesting to turn on another external electronic device (e.g., a TV). The external electronic device may provide a notification to the user to confirm whether to turn on the other external electronic device. Along with the notification, the external electronic device may transmit a request to the wearable device (200) to identify a tapping pattern. Based on the request, the processor (310) of the wearable device (200) may identify the tapping pattern. Based on the tapping pattern, the processor (310) may transmit to the external electronic device either a signal for instructing to perform a specified function or a signal for instructing not to perform the specified function. The external electronic device may determine whether to turn on the other external electronic device (e.g., a TV) based on the signal received from the wearable device (200).

[0235] For example, an external electronic device may be configured to perform a phone function. The external electronic device may provide a notification when a call is received. The external electronic device may transmit a request to the wearable device (200) to identify a tapping pattern along with the notification. The processor (310) of the wearable device (200) may identify the tapping pattern based on the request. Based on the tapping pattern, the processor (310) may transmit to the external electronic device either a signal to instruct the external electronic device to perform a specified function or a signal to instruct the external electronic device not to perform the specified function. The external electronic device may determine whether to answer the call based on the signal received from the wearable device (200).

[0236] As described in the above-described embodiment, the processor (310) can activate a sensor (330) (e.g., an acceleration sensor (331)) based on a request received from an external electronic device and identify a tapping pattern for a specified period of time. Since the tapping pattern is identified based on the request, the possibility of malfunction can be reduced.

[0237] FIG. 13 illustrates an example of an operation for learning a model in a wearable device according to one embodiment.

[0238] Referring to FIG. 13, a processor of a wearable device (200) (e.g., processor (310) of FIG. 3a) can train a model (1310) indicated by a plurality of parameters. The model (1310) may correspond to the model (1080) of FIG. 10b.

[0239] According to one embodiment, the processor (310) may train the model (1310) based on various information. For example, the processor (310) may train the model (1310) based on at least one of data regarding the user's tapping, information regarding the user's body, information regarding the environment of the wearable device (200) (e.g., temperature, humidity, brightness), and / or information regarding surrounding electronic devices.

[0240] For example, the processor (310) may identify a plurality of tapping patterns based on at least one of data regarding the user's tapping, information regarding the user's body, information regarding the environment of the wearable device (200), and / or information regarding surrounding electronic devices. As an example, the information regarding the user's body may include at least one of body temperature information and / or heart rate information.

[0241] For example, the processor (310) may learn the model (1310) based on the user's heart rate information and the user's baseline body temperature information. If the user is in an emergency situation, the user's heart rate and body temperature may increase due to the activation of the user's sympathetic nerves. The processor (310) may set information about the user's body (e.g., heart rate information and body temperature information) and data about the user's tapping as input values ​​of the model (1310). The processor (310) may identify a first tapping pattern as an output value of the model (1310). The processor (310) may identify a function related to an emergency situation corresponding to the first tapping pattern.

[0242] According to the above-described embodiment, the processor (310) can identify an accurate tapping pattern by training the model (1310) based on not only data about tapping but also additional information (e.g., information about the user's body, information about the environment of the wearable device (200), information about the surrounding electronic devices). For example, the processor (310) can set data about tapping and additional information (e.g., information about the user's body, information about the environment of the wearable device (200), information about the surrounding electronic devices) as input values ​​of the model (1310). The processor (310) can identify an accurate tapping pattern based on the output value of the model (1310).

[0243] For example, the processor (310) can identify various states of the user based on additional information (e.g., information about the user's body, information about the environment of the wearable device (200), information about surrounding electronic devices). The processor (310) can identify an accurate tapping pattern by considering the various states of the user.

[0244] For example, the processor (310) can identify that the user is driving based on additional information (e.g., information about the user's body, information about the environment of the wearable device (200), information about the surrounding electronic devices). For example, the processor (310) can identify that the user is listening to music based on additional information (e.g., information about the user's body, information about the environment of the wearable device (200), information about the surrounding electronic devices). For example, the processor (310) can identify that the user is exercising based on additional information (e.g., information about the user's body, information about the environment of the wearable device (200), information about the surrounding electronic devices).

[0245] According to an embodiment, the processor (310) may identify sets of tapping patterns that are set differently depending on a situation. The processor (310) may identify one of a plurality of situations based on at least one of information about the user's body, information about the environment of the wearable device (200), and / or information about surrounding electronic devices. The processor (310) may identify a set of tapping patterns corresponding to one of the plurality of situations. The processor (310) may identify a plurality of tapping patterns included in the set of tapping patterns. For example, the processor (310) may identify a first tapping pattern among the plurality of tapping patterns based on data about the orientation of the wearable device (200) and data about changes in acceleration of the wearable device (200).

[0246] For example, the processor (310) may set information about the user's body, information about the environment of the wearable device (200), and / or information about surrounding electronic devices as input values ​​of the model (1310). The processor (310) may identify a set of tapping patterns as an output value of the model (1310). The processor (310) may identify a plurality of tapping patterns included in the set of tapping patterns. The processor (310) may identify a first tapping pattern among the plurality of tapping patterns based on data about the user's tapping. The processor (310) may identify a function corresponding to the first tapping pattern.

[0247] The above-described model (1310) may be composed of a single model or a combination of various models. For example, when the model (1310) is composed of a single model, the model (1310) may output different types of output values ​​depending on the type of the input value. For example, when the model (1310) is composed of a combination of multiple models, the model (1310) may output an output value through one of the multiple models depending on the type of the input value. Accordingly, the processor (310) may obtain various types of output values ​​based on inputting various types of input values ​​to the model (1310). Examples of output values ​​depending on the input value will be described below.

[0248] For example, the processor (310) can set data regarding tapping of the wearable device (200) as input values ​​of the model (1310). The processor (310) can identify a tapping pattern based on the output values ​​of the model (1310).

[0249] For example, the processor (310) can set a tapping pattern as an input value of the model (1310). The processor (310) can identify at least one content based on the output value of the model (1310).

[0250] For example, the processor (310) may set the first tapping pattern as an input value of the model (1310). Based on the output value of the model (1310), the processor (310) may identify that the first tapping pattern corresponds to a second tapping pattern stored in a memory (e.g., memory (340) of FIG. 3 a).

[0251] For example, the processor (310) can set the first tapping pattern as an input value of the model (1310). The processor (310) can correct the first tapping pattern based on the output value of the model (1310).

[0252] For example, the processor (310) may set the BPM value for the first tapping pattern as an input value of the model (1310). The processor (310) may identify a correction value based on the output value of the model (1310). The processor (310) may correct the BPM value for the first tapping pattern based on the identified correction value.

[0253] According to one embodiment, a wearable device (e.g., wearable device (290)) may include a ring-shaped housing (e.g., housing (210)) including a first side facing a part of a user's body and a second side opposite the first side, communication circuitry (e.g., communication circuitry (320)) disposed between the first side and the second side, one or more sensors (e.g., sensor (330)) disposed between the first side and the second side, at least one processor (e.g., processor (310)) disposed between the first side and the second side and including processing circuitry, and a memory (e.g., memory (340)) including one or more storage media storing instructions. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify data regarding a tapping, using the one or more sensors, during a designated time period. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify a first tapping pattern of the wearable device based on the data. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to transmit a first signal relating to the function to an external electronic device connected to the wearable device based on identifying the function corresponding to the first tapping pattern.

[0254] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify that a user of the wearable device is in an emergency state based on identifying the first tapping pattern that is continuously repeated a specified number of times. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to transmit the first signal to the external electronic device to indicate that the user is in the emergency state.

[0255] In one embodiment, the external electronic device may be configured to transmit a second signal for an outgoing call to a device corresponding to one of the plurality of emergency contacts. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to transmit the first signal to cause the external electronic device to transmit the second signal for the outgoing call to a device corresponding to one of the plurality of emergency contacts.

[0256] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to provide a notification for identifying whether the user is in the emergency state based on identifying the user as being in the emergency state and to determine whether the user is to transmit the first signal to indicate that the user is in the emergency state. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to transmit the first signal to the external electronic device to indicate that the user is in the emergency state in response to identifying, based on the provision of the notification, another tapping pattern for requesting transmission of the first signal.

[0257] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to transmit the first signal to the external electronic device to indicate that the user is in the emergency state based on a specified time period.

[0258] In one embodiment, the external electronic device may be configured for emergency SOS service.

[0259] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify information about the user's body using the one or more sensors. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify, based on the information about the body, that the user's state is a designated state. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify, while the user's state is the designated state, the function corresponding to the first tapping pattern identified based on the data.

[0260] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify that a distance between the wearable device and an external object is within a specified distance. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to transmit the first signal to the external electronic device based on identifying the first tapping pattern while the distance between the wearable device and the external object is within the specified distance.

[0261] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to receive a request from the external electronic device to identify a tapping pattern. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device, in response to the request, to identify data regarding the tapping using the one or more sensors during the designated time period from the time the request is received.

[0262] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify a second tapping pattern. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify content provided by the external electronic device while the second tapping pattern is being identified. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to store the second tapping pattern in the memory in association with the content.

[0263] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify that the first tapping pattern identified based on the data corresponds to the second tapping pattern. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device, in response to identifying that the first tapping pattern corresponds to the second tapping pattern, to transmit the first signal to the external electronic device to cause the external electronic device to provide at least one content related to the content.

[0264] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may be configured to cause the wearable device to train a model indicated by a plurality of parameters based on the second tapping pattern and information about the content. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to correct the first tapping pattern based on setting the first tapping pattern as an input value of the model. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify the at least one content based on the corrected first tapping pattern.

[0265] According to one embodiment, the at least one content may include candidate content for providing to the user identified based on the content.

[0266] According to one embodiment, the data regarding the tapping may include data regarding the orientation of the wearable device and data regarding changes in acceleration of the wearable device. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify the first tapping pattern among a plurality of tapping patterns based on the data regarding the orientation of the wearable device and the data regarding changes in acceleration of the wearable device.

[0267] According to one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify the plurality of tapping patterns based on at least one of information about the user's body, information about the environment of the wearable device, and information about a surrounding electronic device.

[0268] According to one embodiment, the specified time interval may be one of a plurality of time intervals set based on a sliding window scheme.

[0269] According to one embodiment, the first tapping pattern may be identified based on the tapping intensity and tapping cycle identified in the wearable device.

[0270] According to one embodiment, a method of a wearable device (e.g., wearable device (200)) may include an operation of identifying data regarding tapping using one or more sensors (e.g., sensor (330)) of the wearable device during a specified time period. The method may include an operation of identifying a first tapping pattern of the wearable device based on the data. The method may include an operation of transmitting a first signal regarding the function to an external electronic device connected to the wearable device (290) based on identifying a function corresponding to the first tapping pattern.

[0271] In one embodiment, the method may include an operation of identifying that the user of the wearable device (290) is in an emergency state based on identifying the first tapping pattern that is continuously repeated a specified number of times. The method may include an operation of transmitting the first signal to the external electronic device to indicate that the user is in the emergency state.

[0272] In one embodiment, the external electronic device may be configured to transmit a second signal for an outgoing call to a device corresponding to one of the plurality of emergency contacts. The method may include transmitting the first signal to cause the external electronic device to transmit the second signal for the outgoing call to a device corresponding to one of the plurality of emergency contacts.

[0273] In one embodiment, the method may include providing a notification to identify whether the user is to transmit the first signal to indicate that the user is in the emergency state, based on the user's identification of the emergency state. The method may include, in response to identifying another tapping pattern for requesting transmission of the first signal, transmitting the first signal to the external electronic device to indicate that the user is in the emergency state, based on the provision of the notification.

[0274] According to one embodiment, a non-transitory computer readable storage medium may store one or more programs. The one or more programs may include instructions that, when executed by at least one processor (e.g., processor 310) of a wearable device (e.g., wearable device (290)) having one or more sensors (e.g., sensor (330)) and communication circuitry (e.g., communication circuitry (320)), cause the wearable device to identify data regarding tapping using the one or more sensors (330) during a designated time period. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to identify a first tapping pattern of the wearable device (200) based on the data. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to transmit a first signal relating to the function to an external electronic device connected to the wearable device based on identifying a function corresponding to the first tapping pattern.

[0275] Wearable devices according to embodiments disclosed herein may take various forms. Wearable devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Wearable devices according to embodiments disclosed herein are not limited to the aforementioned devices.

[0276] The embodiments of this document and the terminology used herein are not intended to limit the technical features described in this document to specific embodiments, but should be understood to include various modifications, equivalents, or substitutes of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise. In this document, each of the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can include any one of the items listed together in the corresponding phrase among the phrases, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another component (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0277] In one embodiment of this document, the term "module" used may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0278] One embodiment of the present document may be implemented as software (e.g., a program (140)) including one or more instructions stored in a storage medium (e.g., an internal memory (136) or an external memory (138)) readable by a machine (e.g., an electronic device (101)). For example, a processor (e.g., a processor (120)) of the machine (e.g., an electronic device (101)) may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the machine to operate to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0279] According to one embodiment, the method according to one embodiment disclosed in the present document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disc read only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) via an application store (e.g., Play Store™) or directly between two user devices (e.g., smart phones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0280] According to one embodiment, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and arranged in other components. According to one embodiment, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to one embodiment, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Claims

1. In a wearable device (200), A ring-shaped housing (210) including a first side facing a part of a user's body and a second side opposite to the first side; A communication circuit (320) disposed between the first surface and the second surface; One or more sensors (330) arranged between the first surface and the second surface; At least one processor (310) disposed between the first surface and the second surface and including a processing circuit; and A memory (340) comprising one or more storage media storing instructions, The above instructions, when individually or collectively executed by the at least one processor (310), cause the wearable device (200) to: During a specified time period, data regarding tapping is identified using one or more of the sensors (330), Based on the above data, the first tapping pattern of the wearable device (200) is identified, Based on identifying a function corresponding to the first tapping pattern, causing an external electronic device connected to the wearable device (200) to transmit a first signal regarding the function. Wearable device (200).

2. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor (310), the wearable device (200) Based on identifying the first tapping pattern that is repeated continuously a specified number of times, the user of the wearable device (200) is identified as being in an emergency state, Causing said user to transmit said first signal to said external electronic device to indicate that said user is in said emergency state; Wearable device (200).

3. In the second paragraph, the external electronic device, configured to transmit a second signal for an outgoing call to a device corresponding to one of the plurality of emergency contacts; The above instructions, when individually or collectively executed by the at least one processor (310), cause the wearable device (200) to: causing said external electronic device to transmit said first signal to cause said external electronic device to transmit said second signal for said outgoing call to a device corresponding to one of said plurality of emergency contacts; Wearable device (200).

4. In the second paragraph, when the instructions are individually or collectively executed by the at least one processor (310), the wearable device (200) Based on the user identifying that the user is in the emergency state, providing a notification to identify whether the user is to transmit the first signal to indicate that the user is in the emergency state; Based on the provision of said notification, in response to identifying another tapping pattern for requesting transmission of said first signal, causing said user to transmit said first signal to said external electronic device to indicate that said user is in said emergency state. Wearable device (200).

5. In the second paragraph, when the instructions are individually or collectively executed by the at least one processor (310), the wearable device (200) Based on a specified time period, causing said user to transmit said first signal to said external electronic device to indicate that said user is in said emergency state. Wearable device (200).

6. In the second paragraph, the external electronic device, Configured for emergency SOS service Wearable device (200).

7. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor (310), the wearable device (200) Using one or more of the above sensors (330), information about the user's body is identified, Based on the information about the above body, identify that the user's condition is a specified condition, causing said function to be identified corresponding to said first tapping pattern identified based on said data while said user's state is said specified state; Wearable device (200).

8. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor (310), the wearable device (200) Identifying that the distance between the wearable device (200) and an external object is within a specified distance, Causing the external electronic device to transmit the first signal based on identifying the first tapping pattern while the distance between the wearable device (200) and the external object is within the specified distance. Wearable device (200).

9. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor (310), the wearable device (200) Receive a request to identify a tapping pattern from said external electronic device, In response to said request, causing said data to be identified using said one or more sensors (330) during said specified time period from the time when said request is received. Wearable device (200).

10. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor (310), the wearable device (200) Identify the second tapping pattern, While the second tapping pattern is identified, the content provided from the external electronic device is identified, Causing the second tapping pattern to be stored in the memory (340) in connection with the above content. Wearable device (200).

11. In the 10th paragraph, when the instructions are individually or collectively executed by the at least one processor (310), the wearable device (200) Identifying that the first tapping pattern identified based on the above data corresponds to the second tapping pattern, In response to identifying that the first tapping pattern corresponds to the second tapping pattern, causing the external electronic device to transmit the first signal to cause the external electronic device to provide at least one content related to the content. Wearable device (200).

12. In the 11th paragraph, the instructions, when individually or collectively executed by the at least one processor (310), cause the wearable device (200) to: Based on the second tapping pattern and the information about the content, a model indicated by a plurality of parameters is trained, Based on setting the first tapping pattern as an input value of the model, the first tapping pattern is corrected, Based on the above-mentioned corrected first tapping pattern, causing the at least one content to be identified, Wearable device (200).

13. In paragraph 11, at least one content, Contains candidate content to be provided to the identified user based on the above content. Wearable device (200).

14. In the method of wearable device (200), An action of identifying data regarding tapping using one or more sensors (330) of the wearable device (200) during a specified time period; An operation of identifying a first tapping pattern of the wearable device (200) based on the above data; and An operation of transmitting a first signal regarding the function to an external electronic device connected to the wearable device (200) based on identifying a function corresponding to the first tapping pattern. method.

15. In a non-transitory computer readable storage medium storing one or more programs, the one or more programs, when executed by at least one processor (310) of a wearable device having one or more sensors (330) and a communication circuit (320), During a specified time period, data regarding tapping is identified using one or more of the sensors (330), Based on the above data, the first tapping pattern of the wearable device (200) is identified, Including instructions for causing the wearable device (200) to transmit a first signal regarding the function to an external electronic device connected to the wearable device (200) based on identifying a function corresponding to the first tapping pattern. A non-transitory computer-readable storage medium.

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