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

Abstract

This wearable device comprises at least one sensor and at least one processor. The processor is configured to, on the basis of data obtained by using the sensor, identify a first model from among a plurality of models for identifying a fall; and on the basis of identifying a fall of a user of the wearable device by means of the first model, identify information about movement of the user during a first time interval, which is a time interval corresponding to the first model. The processor is configured to, on the basis of the information about the movement of the user during the first time interval, provide a notification indicating that the fall of the user has been identified, and on the basis of a user input for the notification, change the time interval corresponding to the first model from the first time interval to a second time interval.

Description

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

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

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

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

[0004] According to one embodiment, a wearable device may include at least one sensor, a memory, and at least one processor operatively coupled to the at least one sensor and the memory. The at least one processor may be configured to identify a first model among a plurality of models for identifying a fall based on data acquired using the at least one sensor. The at least one processor may be configured to identify a value for movement of the user during a first time interval corresponding to the first model, based on identifying a fall of the user of the wearable device through the first model. The at least one processor may be configured to provide a notification indicating that the fall of the user is identified based on identifying that the value for movement of the user during the first time interval is within a specified range. The at least one processor may be configured to change the time interval corresponding to the first model from the first time interval to a second time interval based on a user input for the notification.

[0005] According to one embodiment, a method of a wearable device may include an operation of identifying a first model among a plurality of models for identifying a fall based on data acquired using at least one sensor of the wearable device. The method may include an operation of identifying a value for a movement of the user during a first time interval corresponding to the first model, based on identifying a fall of the user of the wearable device through the first model. The method may include an operation of providing a notification indicating that the fall of the user is identified based on identifying that the value for the movement of the user during the first time interval is within a specified range. The method may include an operation of changing the time interval corresponding to the first model from the first time interval to a second time interval based on a user input for the notification.

[0006] According to one embodiment, a non-transitory computer readable storage medium may store one or more programs. The one or more programs may include instructions that, when executed by at least one processor of a wearable device having at least one sensor, cause the wearable device to identify a first model among a plurality of models for identifying a fall based on data acquired using the at least one sensor. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to identify a value for a movement of the user during a first time interval corresponding to the first model, based on identifying a fall of the user of the wearable device through the first model. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to provide a notification indicating that a fall of the user has been identified based on identifying that the value for the user's movement during the first time interval is within a specified range. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to change the time interval corresponding to the first model from the first time interval to a second time interval based on a user input for the notification.

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

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

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

[0010] FIG. 4 is a simplified block diagram of a wearable device according to one embodiment.

[0011] FIG. 5 illustrates a flowchart of the operation of a wearable device according to one embodiment.

[0012] FIG. 6 illustrates an example of the operation of a wearable device over time, according to one embodiment.

[0013] FIG. 7 illustrates an example of operation of a wearable device and an external electronic device according to one embodiment.

[0014] FIG. 8 illustrates an example of multiple models stored in an external electronic device, according to one embodiment.

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

[0016] FIG. 10 illustrates an example in which data for identifying a fall is used in a wearable device and an external electronic device, according to one embodiment.

[0017] FIG. 11 illustrates an example of operation of a wearable device according to one embodiment.

[0018] 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.

[0019] FIG. 1 is a block diagram of an electronic device within a network environment according to one embodiment.

[0020] 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)).

[0021] 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.

[0022] 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.

[0023] 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).

[0024] 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).

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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).

[0035] 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.

[0036] 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).

[0037] 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.

[0038] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). According to one embodiment, the antenna module (197) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). According to one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (198) or the second network (199), may be selected from the plurality of antennas, for example, by the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device through the selected at least one antenna. According to some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0039] According to various embodiments, the antenna module (197) may form a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high-frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high-frequency band.

[0040] 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)).

[0041] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service by itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0042] FIGS. 2A and 2B illustrate perspective views of an electronic device according to an exemplary embodiment.

[0043] Referring to FIGS. 2A and 2B , an electronic device (200) according to one embodiment (e.g., the electronic device (101) of FIG. 1 ) may include a housing (210) including a first side (or front side) (210A), a second side (or back side) (210B), and a side surface (210C) surrounding a space between the first side (210A) and the second side (210B), and a fastening member (250, 260) connected to at least a portion of the housing (210) and configured to releasably fasten the electronic device (200) to a body part (e.g., a wrist or an ankle) of a user. In another embodiment (not shown), the housing may also refer to a structure forming a portion of the first side (210A), the second side (210B), and the side surface (210C) of FIGS. 2A and 2B . In one embodiment, the first side (210A) may be formed by a front plate (201) that is at least partially substantially transparent (e.g., a glass plate or a polymer plate comprising various coating layers). The second side (210B) may be formed by a substantially opaque back plate (207). The back plate (207) may be formed of, for example, coated or colored glass, ceramic, polymer, metal (e.g., aluminum, stainless steel (STS), or magnesium), or a combination of at least two of the foregoing materials. The side surface (210C) may be formed by a side bezel structure (or “side member”) (206) that is coupled to the front plate (201) and the back plate (207) and comprises a metal and / or a polymer. In some embodiments, the back plate (207) and the side bezel structure (206) may be formed integrally and comprise the same material (e.g., a metal material such as aluminum). The above-mentioned fastening member (250, 260) may be formed of various materials and shapes. The integral and multiple unit links may be formed to be mutually movable by a combination of at least two of the above-mentioned materials, such as woven fabric, leather, rubber, urethane, metal, ceramic, or a combination of the above-mentioned materials.

[0044] According to one embodiment, the electronic device (200) may include at least one of a display (220, see FIG. 3), an audio module (205, 208), a sensor module (211), a key input device (202, 203, 204), and a connector hole (209). In some embodiments, the electronic device (200) may omit at least one of the components (e.g., the key input device (202, 203, 204), the connector hole (209), or the sensor module (211)) or may additionally include other components.

[0045] The display (220) may be visually exposed, for example, through a significant portion of the front plate (201). The shape of the display (220) may correspond to the shape of the front plate (201), and may have various shapes such as a circle, an oval, or a polygon. The display (220) may be combined with or disposed adjacent to a touch detection circuit, a pressure sensor capable of measuring the intensity (pressure) of a touch, and / or a fingerprint sensor.

[0046] The audio module (205, 208) may include a microphone hole (205) and a speaker hole (208). The microphone hole (205) may have a microphone positioned therein for acquiring external sounds, and in some embodiments, multiple microphones may be positioned therein to detect the direction of sounds. The speaker hole (208) may be used as an external speaker and a receiver for calls. In some embodiments, the speaker hole (208) and the microphone hole (205) may be implemented as a single hole, or a speaker may be included without the speaker hole (208) (e.g., a piezo speaker).

[0047] The sensor module (211) can generate an electric signal or data value corresponding to an internal operating state of the electronic device (200) or an external environmental state. The sensor module (211) can include, for example, a biometric sensor module (211) (e.g., an HRM sensor) disposed on the second surface (210B) of the housing (210). The electronic device (200) can further include at least one of a sensor module not shown, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0048] The sensor module (211) may include electrode areas (213, 214) forming a portion of the surface of the electronic device (200) and a biosignal detection circuit (not shown) electrically connected to the electrode areas (213, 214). For example, the electrode areas (213, 214) may include a first electrode area (213) and a second electrode area (214) arranged on a second surface (210B) of the housing (210). The sensor module (211) may be configured such that the electrode areas (213, 214) obtain an electrical signal from a portion of the user's body, and the biosignal detection circuit detects the user's bioinformation based on the electrical signal.

[0049] The key input devices (202, 203, 204) may include a wheel key (202) disposed on a first side (210A) of the housing (210) and rotatable in at least one direction, and / or a side key button (203, 204) disposed on a side surface (210C) of the housing (210). The wheel key may have a shape corresponding to the shape of the front plate (201). In other embodiments, the electronic device (200) may not include some or all of the above-mentioned key input devices (202, 203, 204), and the key input devices (202, 203, 204) that are not included may be implemented in another form, such as a soft key, on the display (220). The connector hole (209) may include another connector hole (not shown) that may accommodate a connector (e.g., a USB connector) for transmitting and receiving power and / or data with an external electronic device, and may accommodate a connector for transmitting and receiving audio signals with the external electronic device. The electronic device (200) may further include, for example, a connector cover (not shown) that covers at least a portion of the connector hole (209) and blocks the inflow of external foreign substances into the connector hole.

[0050] The fastening member (250, 260) can be detachably fastened to at least a portion of the housing (210) using a locking member (251, 261). The fastening member (250, 260) can include one or more of a fixing member (252), a fixing member fastening hole (253), a band guide member (254), and a band fastening ring (255).

[0051] The fixing member (252) can be configured to fix the housing (210) and the fastening members (250, 260) to a part of the user's body (e.g., a wrist or an ankle). The fastening member fastening hole (253) can fix the housing (210) and the fastening members (250, 260) to a part of the user's body in response to the fastening member (252). The band guide member (254) is configured to limit the range of motion of the fastening member (252) when the fastening member (252) is fastened to the fastening member fastening hole (253), thereby allowing the fastening members (250, 260) to be fastened in close contact with a part of the user's body. The band fixing ring (255) can limit the range of motion of the fastening members (250, 260) when the fastening member (252) and the fastening member fastening hole (253) are fastened.

[0052] FIG. 3 illustrates an exploded perspective view of an exemplary electronic device according to one embodiment.

[0053] Referring to FIG. 3, an electronic device (300) (e.g., the electronic device (101) of FIG. 1 or the electronic device (200) of FIGS. 2A to 2B) may include a side bezel structure (310), a wheel key (320) (e.g., the wheel key (202) of FIG. 2A), a front plate (201), a display (220), a first antenna (350), a second antenna (355), a support member (360) (e.g., a bracket), a battery (370), a printed circuit board (380), a sealing member (390), a rear plate (393) (e.g., the rear plate (207) of FIG. 2B), and fastening members (395, 397) (e.g., the fastening members (250, 260) of FIG. 2A). At least one of the components of the electronic device (300) may be identical or similar to at least one of the components of the electronic device (200) of FIG. 1 or FIGS. 2A to 2B, and any overlapping descriptions will be omitted below. The support member (360) may be disposed inside the electronic device (300) and connected to the side bezel structure (310), or may be formed integrally with the side bezel structure (310). The support member (360) may be formed of, for example, a metal material and / or a non-metallic (e.g., a polymer) material. The support member (360) may have a display (220) coupled to one surface and a printed circuit board (380) coupled to the other surface. A processor, a memory, and / or an interface may be mounted on the printed circuit board (380). The processor may include, for example, one or more of a central processing unit, a GPU (graphics processing unit), an application processor, a sensor processor, or a communication processor.

[0054] The memory may include, for example, volatile memory or non-volatile memory. The interface may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, and / or an audio interface. The interface may electrically or physically connect the electronic device (300) to an external electronic device, for example, and may include a USB connector, an SD card / MMC connector, or an audio connector.

[0055] The battery (370) is a device for supplying power to at least one component of the electronic device (300), and may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell. At least a portion of the battery (370) may be disposed substantially on the same plane as, for example, the printed circuit board (380). The battery (370) may be disposed integrally within the electronic device (200), or may be disposed detachably from the electronic device (200).

[0056] The first antenna (350) may be positioned between the display (220) and the support member (360). The first antenna (350) may include, for example, a near field communication (NFC) antenna, a wireless charging antenna, and / or a magnetic secure transmission (MST) antenna. The first antenna (350) may, for example, perform short-range communication with an external device, wirelessly transmit and receive power required for charging, and transmit a magnetic-based signal including a short-range communication signal or payment data. In another embodiment, the antenna structure may be formed by a portion or a combination of the side bezel structure (310) and / or the support member (360).

[0057] The second antenna (355) may be positioned between the printed circuit board (380) and the back plate (393). The second antenna (355) may include, for example, a near field communication (NFC) antenna, a wireless charging antenna, and / or a magnetic secure transmission (MST) antenna. The second antenna (355) may, for example, perform short-range communication with an external device, wirelessly transmit and receive power required for charging, and transmit a magnetic-based signal including a short-range communication signal or payment data. In another embodiment, the antenna structure may be formed by a portion or a combination of the side bezel structure (310) and / or the back plate (393).

[0058] A sealing member (390) may be positioned between the side bezel structure (310) and the rear plate (393). The sealing member (390) may be configured to block moisture and foreign substances from entering the space surrounded by the side bezel structure (310) and the rear plate (393) from the outside.

[0059] According to one embodiment, a wearable device (e.g., electronic device 101 of FIG. 1, electronic device 200 illustrated in FIGS. 2A and 2B, or electronic device 300 illustrated in FIG. 3) may be worn by a user and may operate. For example, the wearable device may be worn on a part of the user's body (e.g., wrist, finger, or face). According to one embodiment, the wearable device may be used to identify whether a fall has occurred to the user of the wearable device. For example, the wearable device may identify whether a fall has occurred to the user using one of a plurality of models. However, the fall of the user identified through one of the plurality of models may not be accurate. Based on user input, the wearable device may obtain information about the user's feedback and change parameters for identifying a fall.

[0060] The operation of the wearable device according to the above-described embodiment may be described below. The wearable device described below may correspond to the electronic device (101) of FIG. 1, the electronic device (200) of FIGS. 2A and 2B, and / or the electronic device (300) of FIG. 3.

[0061] FIG. 4 is a simplified block diagram of a wearable device according to one embodiment.

[0062] Referring to FIG. 4, the wearable device (400) may be implemented in various forms. For example, the wearable device (400) may be implemented in various forms that can be worn by a user, such as a smart watch, a smart band, a smart ring, wireless earphones, or smart glasses. For example, the wearable device (400) may correspond to the electronic device (101) of FIG. 1, the electronic device (200) of FIGS. 2A and 2B, and / or the electronic device (300) of FIG. 3.

[0063] According to an embodiment, the wearable device (400) may be connected to and operate with an electronic device connected to the wearable device (400). For example, at least some of the operations of the wearable device (400) described below may be performed by an electronic device connected to the wearable device (400).

[0064] According to one embodiment, the wearable device (400) may include a processor (410), a communication circuit (420), a sensor (430), a memory (440), a display (450), and / or a global positioning system (GPS) circuit (460). Depending on the embodiment, the wearable device (400) may include at least one of the processor (410), the communication circuit (420), the sensor (430), the memory (440), the display (450), and the GPS circuit (460). For example, at least some of the processor (410), the communication circuit (420), the sensor (430), the memory (440), the display (450), and the GPS circuit (460) may be omitted depending on the embodiment.

[0065] According to one embodiment, the processor (410) may correspond to the processor (120) of FIG. 1. The processor (410) may be operatively or operably coupled with or connected to a communication circuit (420), a sensor (430), a memory (440), a display (450), and a GPS circuit (460). For example, operatively coupling the processor (410) with another component may mean that the processor (410) can control the other component. The processor (410) may control the communication circuit (420), the sensor (430), the memory (440), the display (450), and the GPS circuit (460). For example, the processor (410) may determine an operating point of the sensor (430). The processor (410) may control the operation of the sensor (430). The processor (410) can activate or deactivate the sensor (430). The processor (410) can process information obtained from the sensor (430).

[0066] According to one embodiment, the processor (410) may be composed of at least one processor. The processor (410) may include at least one processor. According to one embodiment, the processor (410) 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).

[0067] According to one embodiment, the wearable device (400) may include a communication circuit (420). For example, the communication circuit (420) may correspond to at least a portion of the communication module (190) of FIG. 1.

[0068] For example, the communication circuit (420) can be used for various radio access technologies (RATs). For example, the communication circuit (420) can be used to perform Bluetooth communication, wireless local area network (WLAN) communication, or ultra wideband (UWB) communication. For example, the communication circuit (420) can be used to perform cellular communication.

[0069] For example, the processor (410) may establish a connection with an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)) via the communication circuit (420). For example, the processor (410) may establish a connection with a server via the communication circuit (420).

[0070] According to one embodiment, the wearable device (400) may include a sensor (430). The sensor (430) may be used to acquire various external information. For example, the sensor (430) may be used to acquire data regarding the user's body. For example, the sensor (430) may be used to acquire data regarding the user's condition, data regarding the user's movement, and / or data regarding the user's heart rate. For example, the sensor (430) may correspond to the sensor module (176) of FIG. 1.

[0071] According to one embodiment, the sensor (430) may be composed of at least one sensor. For example, the sensor (430) may include at least one of an acceleration sensor (431), a gyro sensor (432), an HR sensor (433), and a barometric pressure sensor (434).

[0072] For example, the acceleration sensor (431) can identify (or measure, detect) the acceleration of the wearable device (400) in three directions of the x-axis, the y-axis, and the z-axis. For example, the gyro sensor (432) can identify (or measure, detect) the angular velocity of the wearable device (400) in three directions of the x-axis, the y-axis, and the z-axis. According to an embodiment, the wearable device (400) may include an inertial sensor composed of an acceleration sensor (431) and a gyro sensor (432).

[0073] For example, it may include a heart rate (HR) sensor (433) (or heart rate variability (HRV) sensor). The processor (410) may measure the regularity or variability of the heartbeat through the HR sensor (433). The processor (410) may obtain information about the regularity or variability of the heartbeat through the HR sensor (433).

[0074] For example, the barometric pressure sensor (434) can identify (or measure, detect) the barometric pressure around the wearable device (400). The processor (410) can identify the height (or altitude) at which the wearable device (400) is located above the ground based on data regarding the barometric pressure around the wearable device (400) identified using the barometric pressure sensor (434).

[0075] Although not shown, the sensor (430) may further include a sensor for obtaining (or identifying, measuring, detecting) various data about the user.

[0076] For example, the sensor (430) may include a body temperature sensor. The processor (410) may measure the skin temperature of a part of the user's body through the body temperature sensor. The processor (410) may obtain the user's body temperature based on the skin temperature of the part of the user's body.

[0077] For example, the sensor (430) may include a photoplethysmography (PPG) sensor. The PPG sensor may be used to measure pulse (or changes in blood volume within a blood vessel) by identifying changes in light sensitivity due to changes in blood vessel volume. For example, the PPG sensor may be used to identify information about changes in a user's heart rate, information about a user's stress level based on HRV, information about a user's sleep stage, information about a user's breathing rate, and information about a user's blood pressure.

[0078] For example, the sensor (430) may include a blood sugar sensor. The processor (410) may identify the user's blood sugar level by identifying (or measuring) the current generated by an electrochemical reaction with blood sugar in the blood.

[0079] According to one embodiment, the wearable device (400) may include a memory (440). The memory (440) may be used to store information or data. For example, the memory (440) may be used to store data obtained from a user. For example, the memory (440) may correspond to the memory (130) of FIG. 1. For example, the memory (440) may be a volatile memory unit or units. For example, the memory (440) may be a non-volatile memory unit or units. For example, the memory (440) may be another form of computer-readable media, such as a magnetic or optical disk. For example, the memory (440) may store data obtained based on operations performed by the processor (410) (e.g., algorithm execution operations). For example, the memory (440) may store data obtained from a sensor (430) (e.g., data regarding a user's heart rate).

[0080] For example, the display (450) can be used to display various screens. The display (450) can be controlled by a processor (410) including a circuit such as a GPU (graphics processing unit) to output visualized information to a user. The display (450) can be used to output content, data, or signals through the screen. For example, the display (450) can correspond to the display module (160) of FIG. 1.

[0081] For example, the GPS circuit (460) may be used to receive GPS signals. Depending on the usage area or bandwidth, GPS may include at least one of GLONASS (global navigation satellite system), Beidou Navigation Satellite System (hereinafter "beidou"), QZSS (quasi-zenith satellite system), IRNSS (Indian reginal satellite system), or Galileo (the European global satellite-based navigation system). The wearable device (400) may obtain data on the location of the wearable device (400) using the GPS circuit (460).

[0082] Figure 5 illustrates a flowchart of the operation of a wearable device according to one embodiment. In the following embodiments, the operations may be performed sequentially, but are not necessarily sequential. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0083] In operation 510, the processor (410) of the wearable device (400) may identify a first model (e.g., a first artificial intelligence model) among a plurality of models (e.g., a plurality of artificial intelligence models) for identifying (or detecting) a fall. For example, the processor (410) may identify the first model among the plurality of models for identifying a fall based on data acquired using the sensor (430).

[0084] According to one embodiment, the processor (410) can obtain multiple models. For example, the processor (410) can obtain multiple models from an external electronic device (e.g., a server). For example, the processor (410) can transmit user information to the external electronic device and obtain information about the multiple models from the external electronic device. For example, the processor (410) can obtain information about the multiple models by receiving one of the multiple sets from the external electronic device. For example, a specific operation of the processor (410) obtaining information about the multiple models from the external electronic device will be described below with reference to FIG. 7.

[0085] According to one embodiment, the processor (410) may obtain data using a sensor (430). For example, the processor (410) may obtain data on the amount of impact identified in the wearable device (400) using a sensor (430) (e.g., an acceleration sensor (431) or a gyro sensor (432)). For example, the processor (410) may obtain data on changes in air pressure using a sensor (430) (e.g., an air pressure sensor (434)).

[0086] The processor (410) can identify the type of the user's fall based on data acquired using the sensor (430). The type of the user's fall can be used to identify one of a plurality of models. For example, each of the plurality of models can be used to identify whether the user has fallen based on a fall type. For example, the fall types can include a hard fall (e.g., tripping or slipping), a soft fall, a fall from a height, a fall after a collision, a bicycle fall, a fall on a slope, and a bed fall. As an example, one of the plurality of models can be used to identify whether the fall occurred while riding a bicycle. The processor (410) can identify the type of the user's fall as a bicycle fall based on data regarding the acceleration of the wearable device (400) acquired using the acceleration sensor (431).

[0087] For example, the processor (410) can initially identify a fall situation by identifying the fall type. In the identified fall situation, the processor (410) can use a model corresponding to the fall situation to identify whether the user has fallen. A specific example of the user's fall type will be described later in FIG. 9.

[0088] According to one embodiment, the processor (410) may identify a first model among a plurality of models for identifying a fall. For example, the processor (410) may identify a plurality of models corresponding to a plurality of fall types. The processor (410) may identify the first model among the plurality of models by identifying the model corresponding to the user's fall type. For example, the processor (410) may identify one model among the plurality of models corresponding to a bicycle fall. The identified model may be configured to identify a bicycle fall.

[0089] In operation 520, the processor (410) may identify a user's fall through the first model. For example, based on identifying the first model, the processor (410) may set at least one of data acquired through the sensor (430), user activity information, and information regarding whether the wearable device (400) is worn as an input value of the first model. Based on the output value of the first model, the processor (410) may identify whether the user has fallen.

[0090] For example, the processor (410) can identify a user's fall based on the output value of the first model. The processor (410) can identify a user's fall through the first model. The processor (410) can identify a user's fall based on the first model.

[0091] In operation 530, the processor (410) may identify whether a value for the user's movement during a first time interval falls within a specified range. For example, the processor (410) may identify a value for the user's movement during a first time interval, which is a time interval corresponding to the first model, based on identifying a fall of the user through the first model.

[0092] For example, the processor (410) may identify a fall of the user through the first model and then identify a value for the user's movement during a first time interval corresponding to the first model to identify whether the user is conscious.

[0093] For example, multiple time intervals may correspond to multiple models. Each of the multiple models may correspond to a single time interval. The processor (410) may set different time intervals for identifying whether the user is conscious depending on the type of fall. For example, if a fall from a high place is identified, the processor (410) may set the time interval for identifying whether the user is conscious to be shorter than a reference time. For example, if a soft fall is identified, the processor (410) may set the time interval for identifying whether the user is conscious to be longer than a reference time.

[0094] For example, the processor (410) may identify (or determine, set) a time interval for identifying whether the user is conscious, depending on the severity of the fall type. For example, the processor (410) may identify (or determine, set) a time interval for identifying whether the user is conscious, depending on the likelihood of error in fall identification based on the fall type. For example, a threshold value for identifying a mild fall may be lower than a threshold value for identifying a severe fall (hard fall). The likelihood of error in fall identification for a mild fall may be higher. Therefore, after a mild fall is identified, the time interval for identifying whether the user is conscious may be set longer than a reference time.

[0095] In one embodiment, the processor (410) may identify information about the user's movements. For example, the processor (410) may identify information about the user's movements during a first time interval. For example, the information about the user's movements may include values ​​related to the user's movements. For example, the information about the user's movements may include information (or data) about the user's movement patterns.

[0096] The processor (410) may identify a value for the user's movement during the first time interval. For example, the processor (410) may monitor a value for the user's movement during the first time interval. For example, the processor (410) may identify a value for the user's movement during the first time interval based on at least one of an acceleration sensor (431), a gyro sensor (432), and / or a barometric pressure sensor (434). For example, after a fall of the user is identified, the processor (410) may identify a value for the user's movement during the first time interval to determine whether the user is conscious.

[0097] In operation 540, the processor (410) can identify whether a value for the user's movement during the first time interval is within a specified range. The processor (410) can monitor a value for the user's movement during the first time interval and identify whether the values ​​monitored during the first time interval are within a specified range.

[0098] In operation 550, if the value for the user's movement during the first time interval is outside the specified range, the processor (410) can identify that the user did not fall. Based on identifying that the value for the user's movement during the first time interval is outside the specified range, the processor (410) can identify that the user did not fall. If the processor (410) identifies that the user fell through the first model, based on identifying that the value for the user's movement during the first time interval is outside the specified range, the processor (410) can identify an error (or malfunction) in the first model.

[0099] In operation 560, if the value for the user's movement during the first time interval is within a specified range, the processor (410) may provide a notification indicating that a fall of the user has been identified. For example, the processor (410) may provide a notification indicating that a fall of the user has been identified based on identifying that the value for the user's movement during the first time interval is within a specified range.

[0100] For example, the processor (410) may identify that the user is unconscious based on identifying that the value for the user's movement during the first time interval is within a specified range. The processor (410) may provide at least one of a sound, vibration, and / or a message from the wearable device (400). The processor (410) may perform an emergency call based on the elapsed time of a specified period of time after the notification is provided. For example, the processor (410) may perform an emergency call based on the elapsed time of a specified period of time after the notification is provided, even if no user input is identified.

[0101] According to one embodiment, the processor (410) may display at least one screen based on the above notification. For example, the processor (410) may display a screen for an emergency call through the display (450) of the wearable device (400). Based on identifying an input for an object for an emergency call displayed on the screen, the processor (410) may perform a call connection through a designated phone number (e.g., 911 or a stored phone number).

[0102] In one embodiment, the processor (410) may provide a notification indicating that a user's fall has been identified based on information about the user's movements during the first time interval. For example, the processor (410) may provide a notification indicating that a user's fall has been identified based on a value for the user's movements included in the information about the user's movements during the first time interval.

[0103] In operation 570, the processor (410) may change the time interval corresponding to the first model from the first time interval to the second time interval based on the user input for the notification.

[0104] In one embodiment, the processor (410) may, based on identifying an input for an object to stop a notification displayed on the screen, display another screen (450) including a plurality of objects for requesting feedback on the notification. For example, the other screen may include objects for identifying whether the user's fall identified through the first model is accurate.

[0105] According to one embodiment, the processor (410) can identify whether the fall of the user identified through the first model is accurate based on identifying an input for one of a plurality of objects included in at least one screen for requesting feedback on a notification.

[0106] For example, the processor (410) may identify that a fall of a user identified through a first model is incorrect based on an input for a first object among a plurality of objects (e.g., object (664) of FIG. 6). Based on identifying that a fall of a user identified through the first model is incorrect, the processor (410) may change a time interval corresponding to the first model from the first time interval to the second time interval, which is longer than the first time interval.

[0107] For example, the processor (410) may identify whether a fall of a user identified through the first model is correct based on an input for a second object (e.g., object (662) or object (664) of FIG. 6) among a plurality of objects. Based on identifying whether a fall of a user identified through the first model is correct, the processor (410) may change a time interval corresponding to the first model from the first time interval to a second time interval that is shorter than the first time interval.

[0108] According to one embodiment, the time interval corresponding to the first model may be set in various ways. For example, the time interval corresponding to the first model may be set according to at least one of the product model of the wearable device (400), the country in which the wearable device (400) is located, and regulations. For example, the time interval corresponding to the first model (e.g., the first time interval) that is initially set may be changed according to at least one of the product model of the wearable device (400), the country in which the wearable device (400) is located, and regulations.

[0109] A specific example for changing the time interval corresponding to the first model described above from the first time interval to the second time interval will be described later in Fig. 11.

[0110] FIG. 6 illustrates an example of the operation of a wearable device over time, according to one embodiment.

[0111] Referring to FIG. 6, the processor (410) can identify a user's fall through the first model at time point (610). For example, the processor (410) can identify a first model among multiple models based on data acquired through the sensor (430) prior to time point (610). The processor (410) can identify a user's fall through the first model. For example, the operation of identifying a user's fall through the first model at time point (610) can be related to operation 520 of FIG. 5.

[0112] The processor (410) can identify a time interval corresponding to the first model. The processor (410) can identify the time interval corresponding to the first model as a first time interval (620). The processor (410) can identify a value for the user's movement within the first time interval (620). The processor (410) can identify whether the value for the user's movement within the first time interval (620) is within a specified range.

[0113] The processor (410) may provide a notification indicating that a fall of the user is identified based on identifying that a value for the user's movement during the first time interval (620) is within a specified range. For example, the processor (410) may provide a notification indicating that a fall of the user is identified at a time point (630) after the first time interval (620) has elapsed from the time point (610) based on identifying that a value for the user's movement during the first time interval (620) is within a specified range.

[0114] The processor (410) can identify whether the value for the user's movement during the first time interval (620) is within a specified range to minimize malfunction even if the user's fall is identified through the first model at time point (610). Based on identifying that the value for the user's movement during the first time interval (620) is within the specified range, the processor (410) can provide a notification at time point (630).

[0115] According to one embodiment, the processor (410) may provide a notification by displaying a screen (651) through the display (450). For example, the screen (651) may be displayed to perform an emergency call connection. The screen (651) may include an object (661) and an object (662). As an example, the processor (410) may perform an emergency call connection based on identifying an input to the object (662). The processor (410) may perform an emergency call connection based on the elapse of a specified time after the screen (651) is displayed. As an example, the processor (410) may switch the screen displayed on the display (450) from the screen (651) to the screen (652) based on identifying an input to the object (661).

[0116] For example, the screen (652) may include an object (663) and an object (664). The object (663) may indicate positive feedback regarding a notification provided by the wearable device (400). For example, the object (663) may indicate that the fall of the user identified by the wearable device (400) is accurate. The object (664) may indicate negative feedback regarding a notification provided by the wearable device (400). For example, the object (664) may indicate that the fall of the user identified by the wearable device (400) is inaccurate.

[0117] The processor (410) can identify, based on the input for the object (663), that the user has fallen, but that there is nothing wrong with the user. The processor (410) can identify, based on the input for the object (664), that the user has fallen, but that the user has not actually fallen, through the first model.

[0118] In one embodiment, the processor (410) may identify a designated input (e.g., a swipe input, a button input, or an input using a designated movement) while one of the screens (651) and (652) is displayed. Based on the designated input, the processor (410) may identify that the user has not performed a response to the notification.

[0119] According to the above-described embodiment, the processor (410) can identify the user's feedback based on the user's input for the notification. For example, the processor (410) can identify the user's feedback based on the display of the screen (651) and the screen (652). The user's feedback can be identified based on the input for one of the multiple objects displayed within the screen (651) and the screen (652).

[0120] For example, a user's feedback may be set to one of four feedback types. The first feedback type may indicate that a fall has actually occurred. The second feedback type may indicate that a fall has actually occurred but that there is no abnormality. The third feedback type may indicate that a fall has not actually occurred. The fourth feedback type may indicate no response.

[0121] For example, the processor (410) may identify a first feedback indicating that a fall has actually occurred based on an input to an object (662) of the screen (651). The processor (410) may identify a second feedback indicating that a fall has actually occurred but that there is no abnormality based on an input to an object (663) of the screen (652). The processor (410) may identify a third feedback indicating that a fall has not actually occurred based on an input to an object (664) of the screen (652). The processor (410) may identify a fourth feedback indicating no response based on a designated input (e.g., a swipe input, a button input, or an input through a designated movement) identified while one of the screens (651) and (652) is displayed.

[0122] According to one embodiment, the processor (410) may change the time interval corresponding to the first model based on user feedback. For example, the processor (410) may change the time interval corresponding to the first model from the first time interval to the second time interval based on user feedback.

[0123] For example, the processor (410) may identify that a bicycle fall has occurred to the user and provide a notification regarding the bicycle fall. The processor (410) may identify user feedback (or third feedback) indicating that a bicycle fall has not occurred. Based on the user feedback, the processor (410) may increase a time interval corresponding to a model for identifying a bicycle fall. If a malfunction (or error) occurs in the model for identifying a bicycle fall, the processor (410) may increase the time interval corresponding to the model, thereby reducing the probability of a malfunctioning notification. For example, as the time interval corresponding to the model increases, the probability of the user's movement increases. Therefore, although a bicycle fall is identified through the first model, the probability of the user's movement occurring within the time interval corresponding to the model may increase. Accordingly, the probability of a notification being generated may be reduced.

[0124] As described above, by personalizing the time it takes to identify whether the user is conscious, it has the effect of reducing the probability of providing (or generating) a notification due to a malfunction in fall identification depending on the user.

[0125] FIG. 7 illustrates an example of operation of a wearable device and an external electronic device according to one embodiment.

[0126] Referring to FIG. 7, operations 701 to 707 illustrated in FIG. 7 may be related to operations for obtaining information about multiple models from a wearable device (400).

[0127] In operation 701, the processor (410) may obtain user information. The user information may include at least one of the user's body information, the user's activity information, and / or the user's location information.

[0128] For example, a user's physical information may include at least one of the user's age, weight, height, and / or gender. For example, a user's activity information may indicate the user's activity level. The user's activity information may indicate one of the following: less active, slightly active, active, and very active. For example, a user's location information may indicate one of the following: general terrain and mountainous terrain.

[0129] In operation 702, the processor (410) of the wearable device (400) may transmit user information to an external electronic device (700). The external electronic device (700) may receive the user information from the wearable device (400). For example, the processor (410) may transmit a signal including at least one of the user's body information, the user's activity information, or the user's location information to the external electronic device. Although not illustrated, the processor (410) may transmit information about the user's fall type to the external electronic device (700).

[0130] For example, the external electronic device (700) may refer to a server that stores information about multiple models. For example, the external electronic device (700) may refer to a device for controlling the wearable device (400) while connected to the wearable device (400).

[0131] In operation 703, the external electronic device (700) may transmit information regarding a plurality of models to the wearable device (400). The processor (410) may receive information regarding the plurality of models from the external electronic device (700). For example, the external electronic device (700) may identify one of a plurality of sets for identifying a plurality of fall types. The external electronic device (700) may identify one of the plurality of sets based on user information received from the wearable device (400). The identified set may include a plurality of models. The external electronic device (700) may transmit information regarding the plurality of models to the wearable device (400) based on the identified set. The processor (410) may store information regarding the plurality of models in the memory (440). The processor (410) may store the plurality of models in the memory (440). A specific example of multiple sets stored in an external electronic device (700) will be described in FIG. 8.

[0132] For example, based on the user's age, activity level, and / or location, one of a plurality of sets may be identified. The external electronic device (700) may identify a plurality of models included in the identified set. The external electronic device (700) may transmit information about the plurality of models to the wearable device (400). The processor (410) may receive a plurality of models suitable for the user of the wearable device (400) from the external electronic device (700).

[0133] Although not shown, according to one embodiment, the processor (410) may transmit changed user information to an external electronic device (700) based on changes in user information. The processor (410) may receive information about a plurality of models identified based on the changed user information. The processor (410) may store information about the newly acquired plurality of models in the memory (440).

[0134] In operation 704, the processor (410) may identify the user's fall through the first model. For example, the processor (410) may identify the user's fall type. The processor (410) may identify the first model corresponding to the user's fall type among a plurality of models. The processor (410) may identify the user's fall through the first model. Operation 704 may correspond to operations 510 and 520.

[0135] At step 705, the processor (410) may provide a notification. For example, the processor (410) may identify a value for the user's movement during a first time interval, which is a time interval corresponding to the first model. Based on identifying that the value for the user's movement during the first time interval falls within a specified range, the processor (410) may provide a notification indicating that a fall has been identified for the user. Based on user input for the notification, the processor (410) may identify user feedback. Step 705 may correspond to steps 530, 540, 560, and 570 of FIG. 5 .

[0136] In operation 706, the processor (410) may transmit information about the user's feedback to an external electronic device (700). The external electronic device (700) may receive information about the user's feedback from the wearable device (400).

[0137] For example, the user's feedback may be set to one of four feedback types. The first feedback type may indicate that a fall has actually occurred. The second feedback type may indicate that a fall has actually occurred but that there is no abnormality. The third feedback type may indicate that a fall has not actually occurred. The fourth feedback type may indicate no response. A detailed description of the user's feedback types is omitted as it is described in Figure 6.

[0138] Although not shown, the processor (410) may transmit information acquired for providing notification (e.g., data acquired through a sensor (430), metadata) together with information regarding the user's feedback to an external electronic device (700).

[0139] In operation 707, the external electronic device (700) may update multiple models. For example, the processor (410) may update the multiple models based on information regarding the user's feedback. For example, the processor (410) may update the multiple models based on information regarding the user's feedback and information acquired for providing notifications. According to an embodiment, the external electronic device (700) may update the multiple models based on information acquired from other devices (or other wearable devices) as well as the wearable device (400).

[0140] According to one embodiment, information (or data) received from an external electronic device (700) may be used to learn, generate, and / or update multiple models based on regular time intervals. Table 1 shows examples of information (or data) received from an external electronic device (700).

[0141] Data or information Description User information Age, activity level, location, fall type User feedback Fall actually occurred, Fall actually occurred but no abnormality, Fall did not actually occur, no response Sensor data Acceleration sensor data, gyro sensor data, HR sensor data, timestamp Metadata Impact amount, user activity before and after impact (e.g., walking, running, cycling, or sleeping), wear status of wearable device

[0142] Referring to Table 1, information (or data) received from an external electronic device (700) can be classified as in Table 1 and stored in the memory of the external electronic device (700).

[0143] According to one embodiment, the external electronic device (700) can learn, generate, and / or update multiple models based on regular time intervals.

[0144] For example, the external electronic device (700) can update multiple models based on a regular time interval. The external electronic device (700) can update multiple models based on a specified cycle.

[0145] For example, the external electronic device (700) may update multiple models based on identifying that the number of times information (or data) has been received is greater than a specified number. The external electronic device (700) may update multiple models based on the received information.

[0146] For example, the external electronic device (700) can identify (or evaluate) whether the first model is suitable for identifying the user's fall based on information about the user's feedback. The external electronic device (700) can identify the type of fall based on information about the user's feedback. The external electronic device (700) can identify (or evaluate) whether the model (i.e., the first model) according to the corresponding fall type can accurately identify the user's fall.

[0147] In operation 708, the external electronic device (700) may transmit information about the updated plurality of models to the wearable device (400). The processor (410) of the wearable device (400) may receive information about the updated plurality of models from the external electronic device (700).

[0148] For example, the processor (410) can identify whether the user information of the wearable device (400) has changed. As an example, the processor (410) can store information about the updated multiple models (or the updated multiple models) in the memory (440) based on identifying that the user information has not changed. As an example, the processor (410) can discard information about the updated multiple models based on identifying that the user information has changed. The processor (410) can transmit the changed user information to the external electronic device (700) and request information about the multiple models corresponding to the changed user information.

[0149] For example, the processor (410) may identify whether the updated plurality of models correspond to current user information. For example, based on identifying that the updated plurality of models correspond to current user information, the processor (410) may store information about the updated plurality of models (or the updated plurality of models) in the memory (440). For example, based on identifying that the updated plurality of models correspond to current user information, the processor (410) may store information about the updated plurality of models (or the updated plurality of models) in the memory (440). For example, based on identifying that the updated plurality of models do not correspond to current user information, the processor (410) may discard information about the updated plurality of models. The processor (410) may transmit user information to the external electronic device (700) and request information about the plurality of models corresponding to the user information.

[0150] FIG. 8 illustrates an example of multiple sets stored in an external electronic device, according to one embodiment.

[0151] Referring to FIG. 8, an external electronic device (700) may be configured to provide information about a plurality of models to a wearable device (400). For example, the external electronic device (700) may include a memory (850), a processor (860), and a communication circuit (870). For example, the processor (860) may correspond to the processor (120) of FIG. 1. The processor (860) may be configured to control the operation of the external electronic device (700). The communication circuit (870) may be used for communication with other electronic devices (e.g., the wearable device (400)). The memory (850) may be used to store a plurality of sets for identifying a user's fall. Hereinafter, the plurality of sets stored in the memory (850) will be described.

[0152] According to one embodiment, an external electronic device (700) (e.g., a processor (860)) may store multiple sets for identifying a user's fall in the memory (850) of the external electronic device (700). The external electronic device (700) may configure multiple models into multiple sets to identify falls in various situations and to identify falls of various users.

[0153] Multiple sets may be configured based on user information. Each of the multiple sets may include multiple models. Each of the multiple models may be configured to identify falls for different fall types.

[0154] According to one embodiment, the external electronic device (700) may identify one of a plurality of sets based on user information received from the wearable device (400). The processor (410) may identify a plurality of models included in the identified set based on the identification of one of the plurality of sets. The external electronic device (700) may transmit (or provide) information about the identified plurality of models to the wearable device (400). An operation of the external electronic device (700) transmitting (or providing) information about the identified plurality of models to the wearable device (400) may correspond to operation 703 of FIG. 7.

[0155] For example, the older the user is or the lower their activity level, the higher the probability of a soft fall. Accordingly, if the user is older or has a low activity level, the external electronic device (700) may provide the wearable device (400) with a model that indicates a high probability of identifying a soft fall.

[0156] For example, if a user is located in mountainous terrain, there may be a higher probability of falling down a slope or from a high place. Accordingly, if the user is located in mountainous terrain, the external electronic device (700) may provide the wearable device (400) with a model indicating a high probability of falling down a slope or from a high place.

[0157] FIG. 8 illustrates an example in which an external electronic device (700) manages (or stores) 16 sets, but is not limited thereto. The number of multiple sets managed (or stored) in the external electronic device (700) may be set in various ways.

[0158] The external electronic device (700) can manage (or store) the first set (801) to the sixteenth set (816). Each of the first set (801) to the sixteenth set (816) can include a plurality of models. For example, the first set (801) can include a plurality of models. Each of the plurality of models included in the first set (801) can be used (or configured) to identify falls for different fall types. For example, the fall types can include a hard fall (e.g., tripping or slipping), a soft fall, a fall from a height, a fall after a collision, a bicycle fall, a fall on a slope, and a bed fall. Each of the plurality of sets may include a plurality of models, including a model for identifying serious falls, a model for identifying minor falls, a model for identifying falls from a height, a model for identifying falls after a collision, a model for identifying bicycle falls, a model for identifying falls from a ramp, and a model for identifying falls from a bed.

[0159] For example, multiple sets can be configured as in Table 2 according to user information.

[0160] User InformationSetAgeActivity LevelLocationUnder 55sLess ActiveGeneral TerrainSet 1 (801)Mountain TerrainSet 2 (802)Slightly ActiveGeneral TerrainSet 3 (803)Mountain TerrainSet 4 (804)ActiveGeneral TerrainSet 5 (805)Mountain TerrainSet 6 (806)Very ActiveGeneral TerrainSet 7 (807)Mountain TerrainSet 8 (808)Over 55sLess ActiveGeneral TerrainSet 9 (809)Mountain TerrainSet 10 (810)Slightly ActiveGeneral TerrainSet 11 (811)Mountain TerrainSet 12 (812)ActiveGeneral TerrainSet 13 (813)Mountain TerrainSet 14 (814)Very ActiveGeneral TerrainSet 15 (815)Mountain TerrainSet 16 (816)

[0161] Referring to Table 2, among the plurality of sets, a set to be provided to the wearable device (400) may be identified based on a specified age (e.g., 55 years old). Among the plurality of sets, a set to be provided to the wearable device (400) may be identified based on the user's activity level (e.g., less active, slightly active, active, or very active). Among the plurality of sets, a set to be provided to the wearable device (400) may be identified based on the user's location (e.g., general terrain or mountainous terrain).

[0162] According to one embodiment, multiple sets may be established based on user information. The external electronic device (700) may classify and store information (or data) received from the wearable device (400) (or other devices) based on the user information. The external electronic device (700) may learn, generate, and / or update one of the multiple sets based on the user information.

[0163] Figure 9 illustrates a flowchart of the operation of a wearable device according to one embodiment. In the following embodiments, the operations may be performed sequentially, but are not necessarily sequential. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0164] Referring to FIG. 9, operations 910 and 920 may be related to operation 510 of FIG. 5.

[0165] In operation 910, the processor (410) can identify the type of fall of the user. For example, the processor (410) can identify the type of fall of the user based on data acquired through the sensor (430). The processor (410) can identify the type of fall of the user based on data identified using at least one of an acceleration sensor (431), a gyro sensor (432), an HR sensor (433), and / or a barometric pressure sensor (434).

[0166] For example, fall types may include hard falls (e.g., tripping or slipping), soft falls, falls from heights, falls after impacts, bicycle falls, falls on slopes, and bed falls.

[0167] For example, the processor (410) can identify the type of fall based on the user's activity information (e.g., riding a bicycle, sleeping status) prior to the occurrence of the impact. For example, the processor (410) can identify the type of fall based on changes in sensor data (e.g., data acquired through an acceleration sensor (431) and a gyro sensor (432), and / or data acquired through a barometric pressure sensor (434)) prior to the occurrence of the impact and after the occurrence of the impact.

[0168] For example, criteria for identifying fall types can be set as shown in Table 3 below.

[0169] Fall type judgment method Hard fall Strong impact + increased air pressure at the time of impact + Trip / Slip motion Soft fall Weak impact + increased air pressure at the time of impact Fall from a high place Strong impact + air pressure difference before and after impact exceeds the specified air pressure Fall after impact Vertical impact occurs after horizontal impact + increased air pressure at the time of vertical impact Bicycle fall Identifies user activity before impact as riding a bicycle Fall on a slope Rolling motion occurs after impact + increased air pressure during rolling motion Fall on bed Identifies user activity before impact as sleeping

[0170] Referring to Table 3, the processor (410) can identify the fall type as a serious fall based on identifying an impact amount greater than a specified size, an increase in air pressure at the time of impact, and a trip / slip motion. The processor (410) can identify the fall type as a minor fall based on identifying an impact amount less than a specified size and an increase in air pressure at the time of impact. The processor (410) can identify the fall type as a fall from a height based on identifying an impact amount greater than a specified size and an air pressure difference before and after the impact exceeding a specified air pressure. The above-described specified size can be set differently depending on the fall type.

[0171] The processor (410) can identify the fall type as a fall after a collision based on identifying the occurrence of a vertical impact after a horizontal impact and an increase in air pressure at the time of the vertical impact. The processor (410) can identify the fall type as a bicycle fall based on identifying the user activity prior to the impact as riding a bicycle. The processor (410) can identify the fall type as a fall on an incline based on identifying the occurrence of a rolling motion after the impact and an increase in air pressure during the rolling motion. The processor (410) can identify the fall type as a bed fall based on identifying the user activity prior to the impact as being in a sleeping state.

[0172] At operation 920, the processor (410) may identify a first model for identifying a fall based on the user's fall type among a plurality of models. For example, the user's fall type may be used to identify one of the plurality of models. For example, each of the plurality of models may be used to identify whether the user has fallen based on a fall type.

[0173] Referring to operations 910 and 920, the processor (410) can primarily identify a fall situation by identifying a fall type. In the identified fall situation, the processor (410) can identify whether the user has fallen using a model corresponding to the fall situation. For example, the operation of identifying the fall type in operation 910 may be an operation of identifying the fall type that is most likely to occur when an impact occurs. The processor (410) can identify whether the user has fallen using a model according to the identified fall type.

[0174] FIG. 10 illustrates an example in which data for identifying a fall is used in a wearable device and an external electronic device, according to one embodiment.

[0175] Referring to FIG. 10, the processor (410) of the wearable device (400) can obtain data (1001) through the sensor (430). The processor (410) can obtain user activity information (1002). The processor (410) can obtain information (1003) on whether the wearable device (400) is being worn.

[0176] Data (1001), activity information (1002), and information (1003) regarding whether the wearable device (400) is worn or not obtained through the sensor (430) can be transmitted to an external electronic device (700). For example, data (1001) obtained through the sensor (430) can be obtained based on extracting features through data regarding acceleration and angular velocity at the time of impact occurrence and before and after impact occurrence, data regarding air pressure, and data regarding HR.

[0177] Data (1001), activity information (1002), and information (1003) regarding whether the wearable device (400) is worn obtained through the sensor (430) may be preprocessed and provided to an external electronic device (700) or set as input values ​​of a model of the wearable device (400).

[0178] In operation 1010, an external electronic device (700) may acquire (or receive) data (1001), activity information (1002), and information (1003) regarding whether the wearable device (400) is worn or not acquired through a sensor (430) of a wearable device (400). The data (1001), activity information (1002), and information (1003) regarding whether the wearable device (400) is worn or not acquired through the sensor (430) may be provided to the external electronic device (700). The external electronic device (700) may identify a fall type based on the data (1001), activity information (1002), and information (1003) regarding whether the wearable device (400) is worn or not acquired through the sensor (430). An external electronic device (700) can learn a model corresponding to a fall type based on data (1001) obtained through a sensor (430), activity information (1002), and information (1003) on whether a wearable device (400) is worn.

[0179] In the above-described embodiment, an example in which the external electronic device (700) trains a model corresponding to a fall type based on data (1001) obtained through the sensor (430), activity information (1002), and information (1003) regarding whether the wearable device (400) is worn has been described, but is not limited thereto. The external electronic device (700) may train a model corresponding to a fall type based on various pieces of information. For example, the external electronic device (700) may train a model corresponding to a fall type by additionally utilizing a time interval (e.g., the first time interval (620) of FIG. 6) for identifying whether a value for the user's movement is within a specified range.

[0180] In operation 1020, the external electronic device (700) may acquire (or generate) a learned model based on learning a model corresponding to a fall type. The external electronic device (700) may provide the learned model to the wearable device (400). For example, the external electronic device (700) may update the model based on learning a model corresponding to a fall type. The external electronic device (700) may provide the updated model to the wearable device (400).

[0181] In operation 1030, the processor (410) of the wearable device (400) may obtain a learned model (or an updated model) from an external electronic device (700). The processor (410) may identify a fall of the user through the model obtained from the external electronic device (700). For example, the processor (410) may set data (1001) obtained through the sensor (430), activity information (1002), and information (1003) regarding whether the wearable device (400) is worn as input values ​​of the model. The processor (410) may identify a fall of the user based on the output value of the model.

[0182] According to an embodiment, operation 1030 may be performed in the external electronic device (700). For example, the external electronic device (700) may set data (1001), activity information (1002), and information (1003) regarding whether the wearable device (400) is worn as input values ​​of the model to evaluate the model. The external electronic device (700) may identify a fall of the user based on the output value of the model. The external electronic device (700) may perform the model learning operation and the model evaluation operation until the model's performance provides performance above a specified standard. The external electronic device (700) may store the model for which learning has been completed in the memory of the external electronic device (700) (or a server distinct from the external electronic device (700). The external electronic device (700) may provide the model for which learning has been completed to the wearable device (400).

[0183] FIG. 11 illustrates an example of operation of a wearable device according to one embodiment.

[0184] Referring to FIG. 11, the processor (410) may identify a value for the user's movement during a first time interval corresponding to the first model based on identifying the user's fall through the first model. The processor (410) may provide a notification indicating that the user's fall has been identified based on identifying that the value for the user's movement during the first time interval is within a specified range.

[0185] The processor (410) may display at least one screen through the display (450) based on the notification. The processor (410) may change the time interval corresponding to the first model from the first time interval to the second time interval based on identifying an input for one of a plurality of objects included in the at least one screen for requesting feedback on the notification.

[0186] For example, the processor (410) may display screens (651) and (652) based on the notification. Screens (651) and (652) may correspond to screens (651) and (652) illustrated in FIG. 6.

[0187] For example, a screen (651) may be displayed to perform an emergency call connection. The screen (651) may include an object (661) and an object (662). As an example, the processor (410) may perform an emergency call connection based on identifying an input to the object (662). The processor (410) may perform an emergency call connection based on a specified time elapsed after the screen (651) is displayed. As an example, the processor (410) may switch a screen displayed on the display (450) from the screen (651) to the screen (652) based on identifying an input to the object (661).

[0188] For example, the screen (652) may include an object (663) and an object (664). The processor (410) may identify, based on an input regarding the object (663), that the user has fallen, but that there is nothing wrong with the user. The processor (410) may identify, based on an input regarding the object (664), that the user has fallen, but that the user has not actually fallen, through the first model.

[0189] In one embodiment, the processor (410) may display the screen (1100) based on the time interval corresponding to the first model changing from the first time interval to the second time interval. For example, the screen (1100) may be used to indicate that a model for identifying a fall has been personalized based on user feedback.

[0190] In one embodiment, if the screen (1100) is displayed every time a user's feedback is identified, it may cause inconvenience to the user. Accordingly, the processor (410) may display the screen (1100) based on a specified condition.

[0191] For example, the processor (410) may display the screen (1100) based on whether the time interval corresponding to the first model changes by more than a specified time. As an example, the processor (410) may display the screen (1100) based on whether the time interval corresponding to the first model changes by more than 5 seconds. As an example, the time interval corresponding to the first model may be set to 30 seconds. The processor (410) may identify feedbacks from multiple users. The processor (410) may change the time interval corresponding to the first model each time user feedback is identified. The processor (410) may identify the time interval corresponding to the first model as 25 seconds. The processor (410) may display the screen (1100) based on identifying whether the time interval corresponding to the first model changes by more than 5 seconds.

[0192] For example, the processor (410) may display the screen (1100) based on whether the time interval corresponding to the first model changes more than a specified number of times. As an example, the processor (410) may display the screen (1100) based on whether the time interval corresponding to the first model changes more than 5 times.

[0193] According to one embodiment, the processor (410) may identify the user's feedback based on the user's input for the notification. For example, the processor (410) may identify the user's feedback based on the display of the screen (651) and the screen (652). The user's feedback may be identified based on the input to one of the multiple objects displayed within the screen (651) and the screen (652).

[0194] For example, a user's feedback may be set to one of four feedback types. The first feedback type may indicate that a fall has actually occurred. The second feedback type may indicate that a fall has actually occurred but that there is no abnormality. The third feedback type may indicate that a fall has not actually occurred. The fourth feedback type may indicate no response.

[0195] For example, the processor (410) may identify a first feedback indicating that a fall has actually occurred based on an input to an object (662) of the screen (651). The processor (410) may identify a second feedback indicating that a fall has actually occurred but that there is no abnormality based on an input to an object (663) of the screen (652). The processor (410) may identify a third feedback indicating that a fall has not actually occurred based on an input to an object (664) of the screen (652). The processor (410) may identify a fourth feedback indicating no response based on a designated input (e.g., a swipe input, a button input, or an input through a designated movement) identified while one of the screens (651) and (652) is displayed.

[0196] According to one embodiment, the processor (410) may change the time interval corresponding to the first model based on user feedback. For example, the processor (410) may change the time interval corresponding to the first model from the first time interval to the second time interval based on user feedback.

[0197] For example, a time interval corresponding to a fall type (a model corresponding to a fall type) can be set. The time interval corresponding to a fall type (a model corresponding to a fall type) can be set as shown in Table 4.

[0198]

[0199] Referring to Table 4, time intervals can be set according to the fall type (model corresponding to the fall type). The processor (410) can store a plurality of time intervals corresponding to a plurality of fall types (or a plurality of models) in the memory (440). For example, the processor (410) can store a plurality of time intervals in the memory (440) in association with a plurality of fall types (or a plurality of models). The processor (410) can update a time interval corresponding to a first model among the plurality of times stored in the memory (440) based on changing the time interval corresponding to the fall type (or the first model). For example, the processor (410) can set D based on user feedback. collision The value of can be changed. The processor (410) is D collision can be updated.

[0200] According to one embodiment, the processor (410) may change the time interval corresponding to the fall type (or the first model) based on user feedback. For example, the processor (410) may change the time interval corresponding to the fall type (or the first model) based on the following mathematical expression 1.

[0201]

[0202]

[0203] According to the above mathematical expression 1, the time interval corresponding to the fall type (or the first model) can be changed by reflecting the reward value according to the user's feedback. Using mathematical expression 1, the processor (410) can change the time interval corresponding to the fall type (or the first model) by considering not only the user's feedback at the current point in time but also the user's feedback history from the past.

[0204] As shown in the mathematical expression 1 described above, the maximum and minimum values ​​of the time interval corresponding to the fall type (or the first model) can be set. For example, the maximum value of the time interval can be set to 60 seconds. The minimum value of the time interval can be set to 0 seconds.

[0205] For example, the reward value (R) based on the user feedback described above can be set as shown in Table 5 below.

[0206] User Feedback R1st Feedback -22nd Feedback 0.53rd Feedback 24th Feedback 0

[0207] Referring to Table 5, based on receiving the first feedback indicating that a fall actually occurred, the processor (410) may set the value of R to -2. Based on the value of R set to -2, the processor (410) may change the time interval corresponding to the fall type (or first model) from the first time interval to the second time interval using Equation 1. When the first feedback is received, the time interval corresponding to the fall type (or first model) may be reduced.

[0208] Based on receiving second feedback indicating that a fall has actually occurred but there is no abnormality, the processor (410) may set the value of R to 0.5. Based on the value of R set to 0.5, the processor (410) may change the time interval corresponding to the fall type (or the first model) from the first time interval to the second time interval using Equation 1. When the second feedback is received, the time interval corresponding to the fall type (or the first model) may be increased. Based on receiving the second feedback, the processor (410) may identify that the user does not wish to be notified of a fall. Accordingly, the processor (410) may increase the time interval corresponding to the fall type (or the first model) based on receiving the second feedback.

[0209] Based on receiving third feedback indicating that a fall did not actually occur, the processor (410) may set the value of R to 2. Based on the value of R set to 2, the processor (410) may change the time interval corresponding to the fall type (or first model) from the first time interval to the second time interval using Equation 1. When the third feedback is received, the time interval corresponding to the fall type (or first model) may be increased.

[0210] Based on receiving the fourth feedback indicating no response, the processor (410) may set the value of R to 2. Based on the value of R set to 2, the processor (410) may change the time interval corresponding to the fall type (or model) from the first time interval to the second time interval using Equation 1. When the fourth feedback is received, the time interval corresponding to the fall type (or the first model) may be maintained.

[0211] Below, an example of changing the time interval corresponding to the fall type (or first model) can be explained through a specific example.

[0212] In the first example, the fall type may be identified as a hard fall. There may be no previous fall history. The processor (410) may identify the first feedback. In this case, the variables in Equation 1 may be set as shown in Table 6 below.

[0213]

[0214] When the variables of mathematical expression 1 are set as in Table 6, the changed time interval (D t+1 (F t )) can be obtained (or calculated) as in mathematical expression 2.

[0215]

[0216] Referring to Equation 2, the time interval corresponding to the fall type (or the first model) can be changed from 30 seconds to 28.8 seconds.

[0217] In the second example, the fall type may be identified as a hard fall. Based on previous fall history, there are three consecutive instances of first feedback being identified. The processor (410) can identify the first feedback. In this case, the variables in Equation 1 may be set as shown in Table 7 below.

[0218]

[0219] When the variables of mathematical expression 1 are set as in Table 7, the changed time interval (D t+1 (F t )) can be obtained (or calculated) as in mathematical expression 3.

[0220]

[0221] Referring to mathematical expression 3, the time interval corresponding to the fall type (or the first model) can be changed from 27.3963 seconds to 26.7310 seconds.

[0222] In the third example, the fall type may be identified as a hard fall. There may be no previous fall history. The processor (410) may identify the third feedback. In this case, the variables in Equation 1 may be set as shown in Table 8 below.

[0223]

[0224] When the variables of mathematical expression 1 are set as in Table 8, the changed time interval (D t+1 (F t )) can be obtained (or calculated) as in mathematical expression 4.

[0225]

[0226] Referring to mathematical expression 4, the time interval corresponding to the fall type (or first model) can be changed from 30 seconds to 31.2 seconds.

[0227] In the fourth example, the fall type may be identified as a hard fall. Based on previous fall history, there is a history of three consecutive instances of third feedback being identified. The processor (410) can identify the third feedback. In this case, the variables in Equation 1 may be set as shown in Table 9 below.

[0228]

[0229] When the variables of mathematical expression 1 are set as in Table 9, the changed time interval (D t+1 (F t )) can be obtained (or calculated) as in mathematical expression 5.

[0230]

[0231] Referring to mathematical expression 5, the time interval corresponding to the fall type (or first model) can be changed from 33.6 seconds to 34.8 seconds.

[0232] Referring to Examples 1 through 4 described above, Examples 1 and 2 illustrate changes in the time interval corresponding to the fall type (or first model) for cases in which the first feedback is identified. As the first feedback is repeatedly identified, the time interval may become increasingly smaller. Accordingly, the processor (410) can provide notifications more quickly as it identifies the user's fall.

[0233] Referring to Examples 1 through 4 described above, Examples 3 and 4 illustrate changes in the time interval corresponding to the fall type (or the first model) for cases in which feedback 33 is identified. Based on repeated identification of the third feedback, the time interval may gradually increase. Accordingly, even when the processor (410) identifies a user's fall through the first model, it may not provide a fall notification because it may identify the user's consciousness during the increased time interval.

[0234] In the examples described above, the absolute values ​​of the reward values ​​for the first and third feedbacks are set identically. However, when the third feedback is received, the time interval may change more significantly when the three previous falls are reflected. For example, the change in the time interval may be greater when the third feedback is identified three consecutive times than when the first feedback is identified three consecutive times. Accordingly, the accuracy of the notifications provided to the user may be improved.

[0235] According to one embodiment, a wearable device may include at least one sensor, at least one processor including a processing circuit, and a memory storing instructions and including one or more storage media. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify a first model among a plurality of models for identifying a fall based on data acquired using the at least one sensor. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify information about movement of the user of the wearable device during a first time interval corresponding to the first model, based on identifying a fall of the user of the wearable device through the first model. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to provide a notification indicating that the fall of the user has been identified based on the information about the movement of the user during the first time interval. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to change the time interval corresponding to the first model from the first time interval to a second time interval based on a user input for the notification.

[0236] In one embodiment, the information about the user's movement may include a value for the user's movement. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to provide the notification indicating that the user's fall has been identified based on identifying that the value for the user's movement during the first time interval is within a specified range.

[0237] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify a fall type of the user based on the data. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify a first model among the plurality of models for identifying a fall according to the fall type of the user.

[0238] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to set at least one of the data, activity information of the user, or information regarding whether the wearable device is worn as an input value of the first model based on identifying the first model. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify the fall of the user based on an output value of the first model.

[0239] In one embodiment, the wearable device may include a display. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to display at least one screen through the display based on the notification. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify a user input based on identifying an input to one of a plurality of objects included in the at least one screen for requesting feedback on the notification.

[0240] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify a reward value based on the input for one of the plurality of objects. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to change the time interval corresponding to the first model from the first time interval to the second time interval based on the reward value.

[0241] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify, based on an input to a first object of the plurality of objects, that the fall of the user identified through the first model is incorrect. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to change the time interval corresponding to the first model from the first time interval to the second time interval, the second time interval being longer than the first time interval, based on identifying that the fall of the user identified through the first model is incorrect.

[0242] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify, based on an input to a second object among the plurality of objects, that the fall of the user identified through the first model is correct. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to change the time interval corresponding to the first model from the first time interval to the second time interval, the second time interval being shorter than the first time interval, based on identifying, based on the fall of the user identified through the first model, that the fall is correct.

[0243] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to store a plurality of time intervals corresponding to the plurality of models in the memory. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to update, based on changing the time interval corresponding to the first model, among the plurality of time intervals stored in the memory, after the plurality of time intervals corresponding to the plurality of models have been stored in the memory.

[0244] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to provide another notification regarding a change in the time interval corresponding to the first model based on a number of times the time interval corresponding to the first model has changed exceeding a specified number of times.

[0245] According to one embodiment, the wearable device may include a communication circuit and a global positioning system (GPS) circuit. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to transmit a signal including at least one of the user's body information, the user's activity information, or the user's location information to an external electronic device connected to the wearable device. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to receive, in response to the signal, information about the plurality of models identified based on at least one of the body information, the activity information, or the location information.

[0246] According to one embodiment, a wearable device may include at least one sensor, a memory, and at least one processor operatively coupled to the at least one sensor and the memory. The at least one processor may be configured to identify a first model among a plurality of models for identifying a fall based on data acquired using the at least one sensor. The at least one processor may be configured to identify information about a movement of the user during a first time interval corresponding to the first model, based on identifying a fall of the user of the wearable device through the first model. The at least one processor may be configured to provide a notification indicating that the fall of the user is identified based on the information about the movement of the user during the first time interval. The at least one processor may be configured to change the time interval corresponding to the first model from the first time interval to a second time interval based on a user input for the notification.

[0247] In one embodiment, the information about the user's movement may include a value for the user's movement. The at least one processor may be configured to provide the notification indicating that the user's fall has been identified based on identifying that the value for the user's movement during the first time interval is within a specified range.

[0248] According to one embodiment, the at least one processor may be configured to identify a fall type of the user based on the data. The at least one processor may be configured to identify, among the plurality of models, the first model for identifying a fall according to the fall type of the user.

[0249] According to one embodiment, the at least one processor may be configured to set at least one of the data, activity information of the user, or information regarding whether the wearable device is worn as an input value of the first model based on identifying the first model. The at least one processor may be configured to identify the fall of the user based on an output value of the first model.

[0250] According to one embodiment, the wearable device may include a display. The at least one processor may be configured to display at least one screen through the display based on the notification. The at least one processor may be configured to identify the user input based on identifying an input to one of a plurality of objects included in the at least one screen for requesting feedback on the notification.

[0251] In one embodiment, the at least one processor may be configured to identify a reward value based on the input for one of the plurality of objects. The at least one processor may be configured to change the time interval corresponding to the first model from the first time interval to the second time interval based on the reward value.

[0252] In one embodiment, the at least one processor may be configured to identify, based on an input for a first object among the plurality of objects, that the fall of the user identified through the first model is incorrect. The at least one processor may be configured to change the time interval corresponding to the first model from the first time interval to the second time interval, which is longer than the first time interval, based on identifying that the fall of the user identified through the first model is incorrect.

[0253] In one embodiment, the at least one processor may be configured to identify, based on an input for a second object among the plurality of objects, whether the fall of the user identified through the first model is correct. The at least one processor may be configured to change the time interval corresponding to the first model from the first time interval to the second time interval, which is shorter than the first time interval, based on identifying, based on the identification, that the fall of the user identified through the first model is correct.

[0254] According to one embodiment, the at least one processor may be configured to store a plurality of time intervals corresponding to the plurality of models in the memory. The at least one processor may be configured to update the time interval corresponding to the first model among the plurality of time intervals stored in the memory based on changing the time interval corresponding to the first model after the plurality of time intervals corresponding to the plurality of models are stored in the memory.

[0255] In one embodiment, the at least one processor may be configured to provide another notification regarding a change in the time interval corresponding to the first model based on a number of times the time interval corresponding to the first model has changed exceeding a specified number of times.

[0256] According to one embodiment, the wearable device may include a communication circuit and a global positioning system (GPS) circuit. The at least one processor may be configured to transmit a signal including at least one of the user's body information, the user's activity information, or the user's location information to an external electronic device connected to the wearable device. The at least one processor may be configured to receive, in response to the signal, information about the plurality of models identified based on at least one of the body information, the activity information, or the location information.

[0257] According to one embodiment, a method of a wearable device may include an operation of identifying a first model among a plurality of models for identifying a fall based on data acquired using at least one sensor of the wearable device. The method may include an operation of identifying information about a movement of the user during a first time interval corresponding to the first model, based on identifying a fall of the user of the wearable device through the first model. The method may include an operation of providing a notification indicating that the fall of the user is identified based on the information about the movement of the user during the first time interval. The method may include an operation of changing the time interval corresponding to the first model from the first time interval to a second time interval based on a user input for the notification.

[0258] In one embodiment, the information about the user's movement may include a value for the user's movement. The method may include an operation of providing a notification indicating that the user's fall has been identified based on identifying that the value for the user's movement during the first time interval is within a specified range.

[0259] In one embodiment, the method may include an operation of identifying a fall type of the user based on the data. The method may include an operation of identifying a first model among the plurality of models for identifying a fall according to the fall type of the user.

[0260] According to one embodiment, the method may include an operation of setting at least one of the data, activity information of the user, or information regarding whether the wearable device is worn as an input value of the first model based on identifying the first model. The method may include an operation of identifying the fall of the user based on an output value of the first model.

[0261] In one embodiment, the method may include an action of displaying at least one screen through the display of the wearable device based on the notification. The method may include an action of identifying the user input based on identifying an input for one of a plurality of objects included in the at least one screen for requesting feedback on the notification.

[0262] In one embodiment, the method may include an operation of identifying a reward value based on the input for one of the plurality of objects. The method may include an operation of changing the time interval corresponding to the first model from the first time interval to the second time interval based on the reward value.

[0263] According to one embodiment, the method may include an operation of identifying, based on an input for a first object among the plurality of objects, that the fall of the user identified through the first model is incorrect. The method may include an operation of changing the time interval corresponding to the first model from the first time interval to the second time interval, which is longer than the first time interval, based on identifying that the fall of the user identified through the first model is incorrect.

[0264] According to one embodiment, the method may include an operation of identifying, based on an input for a second object among the plurality of objects, whether the fall of the user identified through the first model is correct. The method may include an operation of changing the time interval corresponding to the first model from the first time interval to the second time interval, which is shorter than the first time interval, based on identifying, based on the identification, that the fall of the user identified through the first model is correct.

[0265] According to one embodiment, the method may include an operation of storing a plurality of time intervals corresponding to the plurality of models in the memory. The method may include an operation of updating the time interval corresponding to the first model among the plurality of time intervals stored in the memory based on changing the time interval corresponding to the first model after the plurality of time intervals corresponding to the plurality of models are stored in the memory.

[0266] According to one embodiment, the method may include an operation of transmitting a signal including at least one of the user's body information, the user's activity information, or the user's location information to an external electronic device connected to the wearable device. The method may include an operation of receiving, in response to the signal, information about the plurality of models identified based on at least one of the body information, the activity information, or the location information.

[0267] According to one embodiment, a non-transitory computer readable storage medium may store one or more programs. The one or more programs may include instructions that, when executed by at least one processor of a wearable device having at least one sensor, cause the wearable device to identify a first model among a plurality of models for identifying a fall based on data acquired using the at least one sensor. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to identify a value for a movement of the user during a first time interval corresponding to the first model, based on identifying a fall of the user of the wearable device through the first model. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to provide a notification indicating that a fall of the user has been identified based on identifying that the value for the user's movement during the first time interval is within a specified range. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to change the time interval corresponding to the first model from the first time interval to a second time interval based on a user input for the notification.

[0268] According to one embodiment, a non-transitory computer readable storage medium storing one or more programs may include instructions that, when executed by at least one processor of a wearable device having at least one sensor, cause the wearable device to identify a first model among a plurality of models for identifying a fall based on data acquired using the at least one sensor. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to identify information about a movement of the user of the wearable device during a first time interval, the first time interval corresponding to the first model, based on identifying a fall of the user of the wearable device through the first model. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to provide a notification indicating that the fall of the user has been identified based on the information about the user's movement during the first time interval. The one or more programs may include instructions that, when executed by the at least one processor, cause the wearable device to change the time interval corresponding to the first model from the first time interval to a second time interval based on a user input for the notification.

[0269] According to the above-described embodiment, the number of false alarms regarding a user's fall can be reduced. The wearable device can increase usability by appropriately setting a time interval for identifying the user's consciousness after a fall impact. According to the above-described embodiment, the wearable device can update a model (or algorithm) for identifying a fall based on user feedback. As the model (or algorithm) for identifying a fall is updated, accurate fall detection notifications can be provided to the user. According to the above-described embodiment, by changing the time interval for identifying the user's consciousness based on user feedback, even if the fall is incorrectly identified by the fall detection model, notifications due to false fall detection can be minimized. According to the above-described embodiment, the time interval for identifying the user's consciousness can be changed based on the user's feedback history. Therefore, the performance of the fall detection model (or algorithm) can be maximized by personalizing the model (or algorithm) for detecting a fall.

[0270] Electronic devices according to embodiments disclosed herein may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to embodiments disclosed herein are not limited to the aforementioned devices.

[0271] 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.

[0272] 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).

[0273] 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.

[0274] 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.

[0275] 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 wearable devices, At least one sensor; At least one processor comprising a processing circuit; and A memory storing instructions and including one or more storage media, The above instructions, when individually or collectively executed by the at least one processor, Identifying a first model among a plurality of models for identifying a fall based on data acquired using at least one sensor, Based on identifying a fall of a user of the wearable device through the first model, information about the user's movement is identified during a first time interval corresponding to the first model, Providing a notification indicating that a fall of the user is identified based on the information about the user's movement during the first time interval; Causing the wearable device to change the time interval corresponding to the first model from the first time interval to the second time interval based on user input for the above notification. Wearable devices.

2. In the first paragraph, the information about the user's movement is, Contains values ​​for the user's movements, The above instructions, when individually or collectively executed by the at least one processor, Causing the wearable device to provide the notification indicating that the fall of the user is identified based on identifying that the value for the movement of the user during the first time interval is within a specified range; Wearable devices.

3. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor, Based on the above data, the fall type of the user is identified, Causing the wearable device to identify the first model among the plurality of models for identifying a fall according to the fall type of the user. Wearable devices.

4. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor, Based on identifying the first model, at least one of the data, the user's activity information, or the information on whether the wearable device is worn is set as an input value of the first model, Causing the wearable device to identify the fall of the user based on the output value of the first model; Wearable devices.

5. In the first paragraph, the wearable device, Including more displays, The above instructions, when individually or collectively executed by the at least one processor, Based on the above notification, at least one screen is displayed through the display, Causing the wearable device to identify the user input based on identifying an input to one of a plurality of objects included in at least one screen for requesting feedback on the notification; Wearable devices.

6. In the fifth paragraph, when the instructions are individually or collectively executed by the at least one processor, Identifying a reward value based on the input for one of the plurality of objects, Based on the above compensation value, causing the wearable device to change the time interval corresponding to the first model from the first time interval to the second time interval. Wearable devices.

7. In the sixth paragraph, when the instructions are individually or collectively executed by the at least one processor, Based on the input for the first object among the plurality of objects, identifying that the fall of the user identified through the first model is incorrect, Causing the wearable device to change the time interval corresponding to the first model from the first time interval to the second time interval, which is longer than the first time interval, based on identifying that the fall of the user identified through the first model is incorrect. Wearable devices.

8. In the sixth paragraph, when the instructions are individually or collectively executed by the at least one processor, Based on the input for the second object among the plurality of objects, it is identified that the fall of the user identified through the first model is correct, Causing the wearable device to change the time interval corresponding to the first model from the first time interval to the second time interval shorter than the first time interval, based on identifying that the fall of the user identified through the first model is accurate. Wearable devices.

9. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor, Store multiple time intervals corresponding to the multiple models in the memory, After the plurality of time intervals corresponding to the plurality of models are stored in the memory, the wearable device causes the time interval corresponding to the first model among the plurality of time intervals stored in the memory to be updated based on changing the time interval corresponding to the first model. Wearable devices.

10. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor, Causing the wearable device to provide another notification regarding a change in the time interval corresponding to the first model based on the number of times the time interval corresponding to the first model has changed exceeding a specified number of times; Wearable devices.

11. In the first paragraph, the wearable device, Communication circuit; and Further including a GPS (global positioning system) circuit, The above instructions, when individually or collectively executed by the at least one processor, Transmitting a signal including at least one of the user's body information, the user's activity information, or the user's location information to an external electronic device connected to the wearable device, In response to said signal, causing said wearable device to receive information about said plurality of models identified based on at least one of said body information, said activity information, or said location information. Wearable devices.

12. In a method performed by a wearable device, An operation of identifying a first model among a plurality of models for identifying a fall based on data acquired using at least one sensor of the wearable device; An operation of identifying information about the user's movement during a first time interval corresponding to the first model, based on identifying a fall of the user of the wearable device through the first model; An action of providing a notification indicating that a fall of the user is identified based on the information about the user's movement during the first time interval; and An operation for changing the time interval corresponding to the first model from the first time interval to the second time interval based on the user input for the above notification, method.

13. In the 12th paragraph, the information about the user's movement is, Contains values ​​for the user's movements, The above method, Providing a notification indicating that a fall of the user is identified based on identifying that the value for the movement of the user during the first time interval is within a specified range; method.

14. In the 12th paragraph, the method, An action for identifying the fall type of the user based on the above data; and An operation for identifying the first model among the plurality of models for identifying a fall according to the fall type of the user, method.

15. A non-transitory computer readable storage medium storing one or more programs, wherein the one or more programs, when executed by at least one processor of a wearable device having at least one sensor, Identifying a first model among a plurality of models for identifying a fall based on data acquired using at least one sensor, Based on identifying a fall of a user of the wearable device through the first model, information about the user's movement is identified during a first time interval corresponding to the first model, Providing a notification indicating that a fall of the user is identified based on the information about the user's movement during the first time interval; instructions for causing the wearable device to change the time interval corresponding to the first model from the first time interval to a second time interval based on user input for the notification; Computer readable storage medium.

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