Electronic device and method for performing fall detection

Abstract

According to an example, an electronic device includes at least one sensor, a memory, and a processor. The processor is configured to acquire first information related to a first walking mode of a user of the electronic device and second information related to a walking environment of the user. The processor is configured to identify a first value indicating a difference between the first walking mode and a reference walking mode based on the first information and third information related to the reference walking mode, the third information being included in the memory. The processor is configured to identify a second value indicative of a difference between the first walking mode and the second walking mode. The processor is configured to identify a threshold associated with fall detection based on the first value, the second value, and the second information. The processor is configured to perform a function for fall detection.

Description

Technical Field

[0001] The following description relates to electronic devices and methods for performing fall detection. Background Art

[0002] With the development of technology, various services are provided by electronic devices. The electronic device can identify the movement of a user carrying the electronic device by using at least one sensor. The electronic device can identify various activity states of the user based on the movement of the user. For example, the electronic device can identify information about the user's pace. The electronic device can provide the user with various services related to the user's pace.

[0003] The above information may be provided as related art for the purpose of assisting understanding of the present disclosure. No assertion or determination is made as to whether any of the above may be applicable as prior art related to the present disclosure. Summary of the invention

[0004] Technical Solution

[0005] According to an example, an electronic device may include at least one sensor, a memory, and a processor. The processor may be configured to obtain first information related to a first walking pattern of a user of the electronic device and second information related to a walking environment of the user using the at least one sensor. The processor may be configured to identify a first value indicating a difference between the first walking pattern and the reference walking pattern based on the first information and third information related to the reference walking pattern stored in the memory. The processor may be configured to identify a second value indicating a difference between the first walking pattern and the second walking pattern based on the first information and fourth information related to the second walking pattern of the user, wherein the fourth information is stored in the memory and identified based on the walking history of the user. The processor may be configured to identify a threshold value related to fall detection based on the first value, the second value, and the second information. The processor may be configured to perform a function for fall detection based on a comparison between the threshold value and a value obtained by at least one sensor.

[0006] According to an example, a method of an electronic device may include: obtaining, using at least one sensor of the electronic device, first information related to a first walking pattern of a user of the electronic device and second information related to a walking environment of the user; identifying a first value indicating a difference between the first walking pattern and a reference walking pattern based on the first information and third information related to a reference walking pattern stored in a memory; identifying a second value indicating a difference between the first walking pattern and the second walking pattern based on the first information and fourth information related to a second walking pattern of the user, wherein the fourth information is stored in the memory and identified based on a walking history of the user; identifying a threshold value related to fall detection based on the first value, the second value, and the second information; and performing a function for fall detection based on a comparison between the threshold value and a value obtained by at least one sensor. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0008] Figure 2 is a simplified block diagram of an electronic device according to an example.

[0009] Figure 3 A simplified block diagram of a processor included in an electronic device is shown according to an example.

[0010] Figure 4 is a flow chart illustrating the operation of an electronic device according to an example.

[0011] Figure 5 is a flow chart illustrating the operation of an electronic device according to an example.

[0012] Figures 6a to 6c An example for identifying a trend of a user's walking environment according to an example is shown.

[0013] Figures 7a to 7c An example for identifying a trend of a user's walking environment according to an example is shown.

[0014] Figure 8 An example for identifying a trend of a user's walking environment according to an example is shown.

[0015] Fig. 9 An example of an operation of an electronic device according to an example for learning data is shown.

[0016] Fig.10 is a flow chart illustrating the operation of an electronic device according to an example.

[0017] Fig.11 An example of operation of an electronic device according to an example is shown.

[0018] Fig.12 An example of operation of an electronic device according to an example is shown.

[0019] Fig.13 is a flow chart illustrating the operation of an electronic device according to an example.

[0020] Fig.14 An example of a specified matrix according to an example is shown.

[0021] Fig.15 An example of a matrix indicating a population according to an example is shown.

[0022] Fig.16 An example of a matrix indicating information about fitness according to an example is shown.

[0023] Fig.17 An example of a matrix indicating at least a portion of a population according to an example is shown.

[0024] Fig.18 An example of a learning result of a walking pattern according to an example is shown.

[0025] Fig.19 Examples of operating modes of a processor according to examples are shown.

[0026] Fig.20a and Fig.20b An example in which the value of the walking mode according to the example is displayed as three-dimensional coordinates is shown.

[0027] Fig.21a and 21b An example of a screen for warning of a fall hazard according to an example is shown. DETAILED DESCRIPTION

[0028] Figure 1 is a block diagram of an electronic device in a network environment according to various embodiments.

[0029] Figure 1 1 is a block diagram of an electronic device 101 in a network environment 100 according to various embodiments. Figure 1, the electronic device 101 in the network environment 100 may communicate with the electronic device 102 via the first network 198 (e.g., a short-range wireless communication network), or communicate with at least one of the electronic device 104 or the server 108 via the second network 199 (e.g., a long-range wireless communication network). According to an embodiment, the electronic device 101 may communicate with the electronic device 104 via the server 108. According to an embodiment, the electronic device 101 may include a processor 120, a memory 130, an input module 150, a sound output module 155, a display module 160, an audio module 170, a sensor module 176, an interface 177, a connection terminal 178, a haptic module 179, a camera module 180, a power management module 188, a battery 189, a communication module 190, a user identification module (SIM) 196, or an antenna module 197. In some embodiments of the present disclosure, at least one of the above components (e.g., the connection terminal 178) may be omitted from the electronic device 101, or one or more other components may be added to the electronic device 101. In some embodiments of the present disclosure, some of the above-mentioned components (eg, sensor module 176, camera module 180, or antenna module 197) may be implemented as a single integrated component (eg, display module 160).

[0030] The processor 120 may run, for example, software (e.g., program 140) to control at least one other component (e.g., hardware component or software component) of the electronic device 101 connected to the processor 120, and may perform various data processing or calculations. According to one embodiment, as at least part of the data processing or calculation, the processor 120 may store a command or data received from another component (e.g., sensor module 176 or communication module 190) in the volatile memory 132, process the command or data stored in the volatile memory 132, and store the resultant data in the non-volatile memory 134. According to an embodiment, the processor 120 may include a main processor 121 (e.g., a central processing unit (CPU) or an application processor (AP)) or an auxiliary processor 123 (e.g., a graphics processing unit (GPU), a neural processing unit (NPU), an image signal processor (ISP), a sensor hub processor, or a communication processor (CP)) that is independent of or combined with the main processor 121 in operation. For example, when the electronic device 101 includes a main processor 121 and an auxiliary processor 123, the auxiliary processor 123 may be adapted to consume less power than the main processor 121, or adapted to be dedicated to a specific function. The auxiliary processor 123 may be implemented as a separate part from the main processor 121, or as part of the main processor 121.

[0031] When the main processor 121 is in an inactive (e.g., sleep) state, the auxiliary processor 123 (rather than the main processor 121) may control at least some of the functions or states related to at least one component among the components of the electronic device 101 (e.g., display module 160, sensor module 176, or communication module 190), or when the main processor 121 is in an active state (e.g., running an application), the auxiliary processor 123 may control at least some of the functions or states related to at least one component among the components of the electronic device 101 (e.g., display module 160, sensor module 176, or communication module 190) together with the main processor 121. According to an embodiment, the auxiliary processor 123 (e.g., an image signal processor or a communication processor) may be implemented as part of another component (e.g., camera module 180 or communication module 190) that is functionally related to the auxiliary processor 123. According to an embodiment, the auxiliary processor 123 (e.g., a neural processing unit) may include a hardware structure dedicated to artificial intelligence model processing. The artificial intelligence model may be generated by machine learning. For example, such learning may be performed by the electronic device 101 where the artificial intelligence is executed or via a separate server (e.g., server 108). The learning algorithm may include, but is not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, for example. The artificial intelligence model may include multiple artificial neural network layers. The artificial neural network may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or a deep Q network or a combination of two or more thereof, but is not limited thereto. Additionally or alternatively, the artificial intelligence model may include a software structure in addition to a hardware structure.

[0032] The memory 130 may store various data used by at least one component of the electronic device 101 (e.g., the processor 120 or the sensor module 176). The various data may include, for example, software (e.g., the program 140) and input data or output data for commands related thereto. The memory 130 may include a volatile memory 132 or a non-volatile memory 134.

[0033] The program 140 may be stored as software in the memory 130 , and may include, for example, an operating system (OS) 142 , middleware 144 , or applications 146 .

[0034] The input module 150 may receive commands or data to be used by other components (e.g., the processor 120) of the electronic device 101 from outside the electronic device 101 (e.g., a user). The input module 150 may include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus).

[0035] The sound output module 155 can output sound signals to the outside of the electronic device 101. The sound output module 155 may include, for example, a speaker or a receiver. The speaker can be used for general purposes such as playing multimedia or playing records. The receiver can be used to receive incoming calls. Depending on the embodiment, the receiver can be implemented as a separate part from the speaker, or as part of the speaker.

[0036] The display module 160 may visually provide information to the outside (e.g., user) of the electronic device 101. The display device 160 may include, for example, a display, a holographic device, or a projector, and a control circuit for controlling a corresponding one of the display, the holographic device, and the projector. According to an embodiment, the display module 160 may include a touch sensor adapted to detect a touch or a pressure sensor adapted to measure the strength of a force caused by a touch.

[0037] The audio module 170 may convert sound into an electrical signal, or vice versa. According to an embodiment, the audio module 170 may obtain sound via the input module 150, or output sound via the sound output module 155 or a speaker or earphone of an external electronic device (e.g., electronic device 102) directly (e.g., wired) or wirelessly connected to the electronic device 101.

[0038] The sensor module 176 may detect an operating state (e.g., power or temperature) of the electronic device 101 or an environmental state (e.g., a user's state) outside the electronic device 101, and then generate an electrical signal or data value corresponding to the detected state. According to an embodiment, the sensor module 176 may include, for example, a gesture sensor, a gyro sensor, an atmospheric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an infrared (IR) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illumination sensor.

[0039] The interface 177 may support one or more specific protocols that will be used to connect the electronic device 101 directly (e.g., wired) or wirelessly to an external electronic device (e.g., the electronic device 102). According to an embodiment, the interface 177 may include, for example, a high-definition multimedia interface (HDMI), a universal serial bus (USB) interface, a secure digital (SD) card interface, or an audio interface.

[0040] The connection end 178 may include a connector, wherein the electronic device 101 can be physically connected to an external electronic device (e.g., the electronic device 102) via the connector. According to an embodiment, the connection end 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0041] The haptic module 179 may convert the electrical signal into mechanical stimulation (eg, vibration or motion) or electrical stimulation that can be recognized by the user via his sense of touch or kinesthetic sense. According to an embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulator.

[0042] The camera module 180 may capture still images or moving images. According to an embodiment, the camera module 180 may include one or more lenses, an image sensor, an image signal processor, or a flash.

[0043] The power management module 188 may manage power supply to the electronic device 101. According to an embodiment, the power management module 188 may be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

[0044] The battery 189 may power at least one component of the electronic device 101. According to an embodiment, the battery 189 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0045] The communication module 190 may support establishing a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device 101 and an external electronic device (e.g., electronic device 102, electronic device 104, or server 108), and perform communication via the established communication channel. The communication module 190 may include one or more communication processors capable of operating independently from the processor 120 (e.g., an application processor (AP)) and supporting direct (e.g., wired) communication or wireless communication. According to an embodiment, the communication module 190 may include a wireless communication module 192 (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (e.g., a local area network (LAN) communication module or a power line communication (PLC) module). A corresponding one of these communication modules may communicate via a first network 198 (e.g., a short-range communication network such as Bluetooth TM , Wireless Fidelity (Wi-Fi) Direct, or Infrared Data Association (IrDA)) or a second network 199 (for example, a long-distance communication network such as a traditional cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (for example, a LAN or a wide area network (WAN)))) to communicate with an external electronic device. These various types of communication modules can be implemented as a single component (for example, a single chip), or these various types of communication modules can be implemented as multiple components separated from each other (for example, multiple chips). The wireless communication module 192 can use the user information (for example, the International Mobile Subscriber Identity (IMSI)) stored in the user identification module 196 to identify and authenticate the electronic device 101 in the communication network (such as the first network 198 or the second network 199).

[0046] The wireless communication module 192 can support 5G networks after 4G networks and next-generation communication technologies (e.g., new radio (NR) access technology). NR access technology can support enhanced mobile broadband (eMBB), massive machine type communication (mMTC), or ultra-reliable low-latency communication (URLLC). The wireless communication module 192 can support high-frequency bands (e.g., millimeter wave bands) to cope with, for example, high data transmission rates. The wireless communication module 192 can support various technologies for ensuring performance on high-frequency bands, such as, for example, beamforming, massive multiple-input multiple-output (massive MIMO), full-dimensional MIMO (FD-MIMO), array antennas, analog beamforming, or massive antennas. The wireless communication module 192 can support various requirements specified in the electronic device 101, an external electronic device (e.g., an electronic device 104), or a network system (e.g., a second network 199). According to an embodiment, the wireless communication module 192 may support a peak data rate for implementing eMBB (e.g., 20 Gbps or greater), loss coverage for implementing mMTC (e.g., 164 dB or less), or U-plane delay for implementing URLLC (e.g., 0.5 ms or less for each of the downlink (DL) and uplink (UL), or 1 ms or less round trip).

[0047] The antenna module 197 may transmit or receive a signal or power to or from the outside of the electronic device 101 (e.g., an external electronic device). According to an embodiment, the antenna module 197 may include an antenna including a radiating element including a conductive material or a conductive pattern formed in or on a substrate (e.g., a printed circuit board (PCB)). According to an embodiment, the antenna module 197 may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication scheme used in a communication network (such as the first network 198 or the second network 199) may be selected from the plurality of antennas by, for example, the communication module 190 (e.g., the wireless communication module 192). A signal or power may then be transmitted or received between the communication module 190 and the external electronic device via the selected at least one antenna. According to an embodiment, another component (e.g., a radio frequency integrated circuit (RFIC)) other than the radiating element may be additionally formed as part of the antenna module 197.

[0048] According to various embodiments, the antenna module 197 may form a millimeter wave antenna module. According to an embodiment, the millimeter wave antenna module may include a printed circuit board, a radio frequency integrated circuit (RFIC), and a plurality of antennas (e.g., array antennas), wherein the RFIC is disposed on a first surface (e.g., bottom surface) of the printed circuit board, or adjacent to the first surface and capable of supporting a specified high frequency band (e.g., millimeter wave band), and the plurality of antennas are disposed on a second surface (e.g., top surface or side surface) of the printed circuit board, or adjacent to the second surface and capable of transmitting or receiving signals of the specified high frequency band.

[0049] At least some of the above components may be connected to each other via an inter-peripheral communication scheme (e.g., a bus, a general purpose input output (GPIO), a serial peripheral interface (SPI), or a mobile industry processor interface (MIPI)) and communicatively transmit signals (e.g., commands or data) therebetween.

[0050] According to an example, a command or data may be sent or received between the electronic device 101 and the external electronic device 104 via the server 108 connected to the second network 199. Each of the electronic device 102 or the electronic device 104 may be a device of the same type as the electronic device 101, or a device of a different type from the electronic device 101. According to an example, all or some operations to be run on the electronic device 101 may be run on one or more of the external electronic device 102, the external electronic device 104, or the server 108. For example, if the electronic device 101 should automatically perform a function or service or should perform a function or service in response to a request from a user or another device, the electronic device 101 may request the one or more external electronic devices to perform at least a part of the function or service instead of running the function or service, or the electronic device 101 may request the one or more external electronic devices to perform at least a part of the function or service in addition to running the function or service. The one or more external electronic devices that receive the request may perform the requested at least a part of the function or service, or perform another function or another service related to the request, and transmit the result of the execution to the electronic device 101. The electronic device 101 may provide the result as at least a part of the reply to the request when the result is further processed or when the result is not further processed. To this end, cloud computing technology, distributed computing technology, mobile edge computing (MEC) technology or client-server computing technology, for example, may be used. The electronic device 101 may use, for example, distributed computing or mobile edge computing to provide ultra-low latency services. In another example, the external electronic device 104 may include an Internet of Things (IoT) device. The server 108 may be an intelligent server using machine learning and / or neural networks. According to the example, the external electronic device 104 or the server 108 may be included in the second network 199. The electronic device 101 may be applied to intelligent services (e.g., smart homes, smart cities, smart cars or health care) based on 5G communication technology or IoT-related technologies.

[0051] According to an example, an electronic device (e.g., Figure 1 The electronic device 101 of the embodiment of the present invention can provide a notification to the user based on identifying that the user has fallen. The electronic device can learn the walking pattern of the user before identifying that the fall has occurred. Based on the difference between the learned walking pattern and the walking pattern identified by the sensor, the electronic device can identify a threshold value related to fall detection of the user. The electronic device can perform a function for fall detection based on the identified threshold value.

[0052] In the following description, an example for identifying a threshold value related to fall detection of a user based on a difference between a learned walking pattern and a walking pattern recognized by a sensor and performing a function for fall detection based on the recognized threshold value may be described.

[0053] Figure 2 is a simplified block diagram of an electronic device according to an example.

[0054] refer to Figure 2 The electronic device 200 may be configured in various forms. The electronic device 200 may be implemented in various forms that can be carried by a user (e.g., a smart phone, a smart watch, a smart bracelet, a smart ring, a wireless headset, or smart glasses). For example, the electronic device 200 may correspond to Figure 1 Electronic device 101.

[0055] According to an example, the electronic device 200 may include a processor 210, a sensor 220, a memory 230, and / or a communication circuit 240. According to an example, the electronic device 200 may include at least one of the processor 210, the sensor 220, the memory 230, and the communication circuit 240. For example, according to an example, at least a portion of the processor 210, the sensor 220, the memory 230, and the communication circuit 240 may be omitted.

[0056] According to an example, the processor 210 may correspond to Figure 1 The processor 210 may be operably or practicably coupled or connected to the sensor 220, the memory 230, and the communication circuit 240. For example, the processor 210 being operably coupled to another component may mean that the processor 210 may control the other component. For example, the processor 210 may control the operation of the sensor 220. The processor 210 may activate or deactivate the sensor 220. The processor 210 may process information obtained from the sensor 220.

[0057] According to an example, the processor 210 may include at least one processor. The processor 210 may include at least one processor. According to an example, the processor 210 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).

[0058] According to an example, the electronic device 200 may include a sensor 220. The sensor 220 may include at least one sensor. For example, the sensor 430 may correspond to Figure 1 The sensor module 176 may include at least one of an acceleration sensor 221, a gyro sensor 222, and an air pressure sensor 223.

[0059] For example, the acceleration sensor 221 can identify (or measure or detect) the acceleration of the electronic device 200 in three directions of the x-axis, y-axis, and z-axis. For example, the gyroscope sensor 222 can identify (or measure or detect) the angular velocity of the electronic device 200 in three directions of the x-axis, y-axis, and z-axis. According to an example, the electronic device 200 may include an inertial sensor configured with the acceleration sensor 221 and the gyroscope sensor 222.

[0060] For example, the air pressure sensor 223 may identify (or measure or detect) the air pressure around the electronic device 200. The processor 210 may identify the height (or altitude) of the electronic device 200 from the ground based on data about the air pressure around the electronic device 200 identified using the air pressure sensor 223.

[0061] Although not shown, the sensor 220 may further include a sensor for obtaining (or identifying, measuring, and detecting) various data about the user.

[0062] For example, the sensor 220 may include a body temperature sensor. The processor 210 may measure the skin temperature of a part of the user's body through the body temperature sensor. The processor 210 may obtain the user's body temperature based on the skin temperature of the part of the user's body.

[0063] For example, the sensor 220 may include a heart rate variability (HRV) sensor. The processor 210 may measure heartbeat regularity or variability through the HRV sensor. The processor 210 may obtain information about heartbeat regularity or variability through the HRV sensor.

[0064] According to an example, the electronic device 200 may include a memory 230. The memory 230 may be used to store information or data. For example, the memory 230 may be used to store data obtained from a user. For example, the memory 230 may correspond to Figure 1 Memory 230 of the embodiment of the present invention. For example, memory 230 may be one or more volatile memory units. For example, memory 230 may be one or more non-volatile memory units. For example, memory 230 may be another form of computer-readable medium, such as a magnetic disk or an optical disk. For example, memory 230 may store data obtained based on operations performed by processor 210 (e.g., algorithm execution operations). For example, memory 230 may store data obtained by sensor 220 (e.g., data related to the user's walking).

[0065] According to an example, the electronic device 200 may include a communication circuit 240. For example, the communication circuit 240 may correspond to Figure 1 The communication module 190.

[0066] For example, the communication circuit 240 may be used for various radio access technologies (RATs). For example, the communication circuit 240 may be used to perform Bluetooth communication, wireless local area network (WLAN) communication, or ultra-wideband (UWB) communication. For example, the communication circuit 240 may be used to perform cellular communication.

[0067] According to an example, in addition to the components shown, the electronic device 200 may also include various components. For example, the electronic device 200 may include a display. The display may be used to display various screens. For example, the display may be used to output content, data, or signals through the screen. For example, the display may correspond to Figure 1 The display module 160 of the electronic device 200 may further include a global positioning system (GPS) circuit. The GPS circuit may be used to identify the location of the electronic device 200.

[0068] Figure 3 A simplified block diagram of a processor included in an electronic device is shown according to an example.

[0069] refer to Figure 3 , the processor 210 may include at least one of a fall detection circuit 310, a fall risk identification circuit 320, and a fall notification circuit 330. The processor 210 may monitor whether a fall occurs using the fall detection circuit 310. The processor 210 may predict the user's fall risk by monitoring the user's walking and analyzing and learning the user's walking pattern until the fall risk is predicted by using the fall risk identification circuit 320.

[0070] For example, the fall detection circuit 310 may include at least one of a fall motion analysis circuit 311, a fall environment analysis circuit 312, a fall sensitivity adjustment circuit 313, and a fall identification circuit 314. The processor 210 may identify the movement of the user when detecting a fall by using the fall motion analysis circuit 311 of the fall detection circuit 310. The processor 210 may identify the environment around the electronic device 200 (or the user) when detecting a fall by using the fall environment analysis circuit 312 of the fall detection circuit 310. In the case where the possibility of the user falling is high, the processor 210 may change the sensitivity of the fall identification by using the fall sensitivity adjustment circuit 314 of the fall detection circuit 310. The processor 210 may identify that the user has fallen based on information about the movement of the user by using the fall identification circuit 314 of the fall detection circuit 310.

[0071] For example, the fall risk identification circuit 320 may include at least one of a walking environment identification circuit 321, a walking pattern learning circuit 322, a walking pattern analysis circuit 323, and a fall risk prediction circuit 324. The processor 210 may identify the environment around the electronic device 200 (or the user) when the user is walking by using the walking environment identification circuit 321 of the fall risk identification circuit 320. The processor 210 may continuously learn the walking pattern of the user when the user is walking by using the walking pattern learning circuit 322 of the fall risk identification circuit 320. The processor 210 may obtain and store information about the walking pattern of the user by using the walking pattern analysis circuit 323 of the fall risk identification circuit 320. The processor 210 may identify the probability of the user falling based on the walking environment and the walking pattern by using the fall risk prediction circuit 324 of the fall risk identification circuit 320.

[0072] For example, the fall notification circuit 330 may include at least one of a fall occurrence notification circuit 331 and a fall risk notification circuit 332. By using the fall occurrence notification circuit 331 of the fall notification circuit 330, when a user falls, the processor 210 may provide a notification indicating that the user has fallen to the user or another user, and may send a rescue signal through an emergency contact. In the case where the probability of a user falling is high, the processor 210 may provide a notification indicating that the probability of falling is high to the user by using the fall risk notification circuit 332 of the fall notification circuit 330.

[0073] Figure 4 is a flow chart illustrating the operation of an electronic device according to an example.

[0074] refer to Figure 4 In operation 410, the processor 210 may obtain first information related to a first walking pattern (e.g., gait, movement pattern, etc.—this feature should not be understood as limited to walking, but may also be a running pattern or other movement pattern) and second information related to a walking environment (e.g., the surrounding environment of the electronic device 200, a geographic area near the electronic device 200, walking conditions, etc.). For example, the processor 210 may obtain first information related to a first walking pattern of a user of the electronic device 200 and second information related to the walking environment of the user by using the sensor 220 (or at least one sensor) and / or the communication circuit 240.

[0075] According to an example, the processor 210 may obtain first information related to a first walking pattern of a user of the electronic device 200 by using the sensor 220. For example, the processor 210 may obtain the first information related to the first walking pattern based on first data obtained during a specified time interval (e.g., a first time period) by using the acceleration sensor 221. The processor 210 may obtain a trend of acceleration corresponding to each of the three axis directions during the specified time interval. The processor 210 may identify the user's first walking pattern based on a difference in a sliding window sum, wherein the difference in the sliding window sum is identified based on the trend of the obtained acceleration. This will be referred to later. Figures 6a to 8 The operation of the processor 210 to identify the first walking pattern of the user is described.

[0076] According to an example, the processor 210 may obtain second information related to a walking environment of a user of the electronic device 200. For example, the processor 210 may obtain the second information related to the walking environment of the user based on second data obtained by using at least one of the acceleration sensor 221, the gyroscope sensor 222, the air pressure sensor 223, and the communication circuit 240. For example, the second information may include information about the slope of a moving path of the electronic device 200 (i.e., information about the slope of a path on which the electronic device 200 is moving or traveling), information about a moving state of the electronic device 200, and information about weather during a specified time interval.

[0077] For example, the processor 210 can obtain information about the movement state of the electronic device 200 by setting at least a portion of the second data obtained by using at least one of the acceleration sensor 221, the gyroscope sensor 222, and the air pressure sensor 223 as input data of a specified model (e.g., an artificial intelligence and / or machine learning model) indicated by a plurality of parameters. For example, a plurality of parameters of the specified model can be set based on learning of the third data identified according to the user's walking history. This will be referred to later. Fig. 9 An example is described in which the processor 210 obtains information on the movement state of the electronic device 200 by using a prescribed model.

[0078] For example, the processor 210 may obtain information about the weather during a specified time interval from an external electronic device (e.g., a server) by using the communication circuit 240. For example, the external electronic device may know the weather near the electronic device 200. For example, the processor 210 may obtain information about the slope of a moving path from an external electronic device (e.g., a server) by using the communication circuit 240. For example, the external electronic device may store data about the topology of an environment or area where the electronic device 200 is located, so that information about the slope of the path on which the electronic device 200 is moving (e.g., information about the slope of the ground in the direction of movement of the electronic device 200) may be obtained.

[0079] In operation 420, the processor 210 may identify a first value (or other indication) indicating a difference between the first walking pattern and the reference walking pattern. For example, the processor 210 may identify a first value indicating a difference between the first walking pattern and the reference walking pattern based on the first information and third information related to the reference walking pattern stored in the memory 230. For example, the processor 210 may identify (e.g., via comparison or calculation) a first similarity between the first walking pattern and the reference walking pattern based on the first information and the third information. For example, the first value may be a scalar value, a vector, an array of values, etc.

[0080] According to an example, the processor 210 may store the third information related to the reference walking pattern in the memory 230. For example, the reference walking pattern may mean a walking pattern of a general user (e.g., a generalized or average walking pattern associated with a plurality of users, a default walking pattern associated with a typical user, etc.). For example, the processor 210 may receive the third information related to the reference walking pattern from an external electronic device (e.g., a server) by using the communication circuit 240. The processor 210 may store the received third information in the memory 230. The third information may be obtained based on the walking patterns related to a plurality of users stored in the external electronic device (e.g., a server). For example, the third information related to the reference walking pattern may be a state stored in the memory 230 of the electronic device 200.

[0081] According to an example, the processor 210 may identify a first value indicating a difference between the first walking pattern and the reference walking pattern based on the first information and the third information. The processor 210 may identify one or more values ​​associated with the first walking pattern. The processor 210 may identify one or more values ​​associated with the reference walking pattern. For example, the reference walking pattern may correspond to one or more values ​​associated with the first walking pattern, such as by being associated with the same walking pattern features as those walking pattern features based on which the one or more values ​​associated with the first walking pattern are identified.

[0082] The processor 210 may identify a first value indicating a difference between the first walking pattern and the reference walking pattern based on a Euclidean distance between a value associated with the first walking pattern (e.g., (one or more) representative values) and a value associated with the reference walking pattern (e.g., (one or more) representative values). The processor 210 may identify a first similarity between the first walking pattern and the reference walking pattern based on a Euclidean distance between a value associated with the first walking pattern and a value associated with the reference walking pattern. For example, the first value and the first similarity indicating a difference between the first walking pattern and the reference walking pattern may be inversely proportional. The more the first value indicating a difference between the first walking pattern and the reference walking pattern increases, the smaller the first similarity may be. The smaller the first value indicating a difference between the first walking pattern and the reference walking pattern is, the larger the first similarity may be.

[0083] In operation 430, the processor 210 may identify a second value (or other indication) indicating a difference between the first walking pattern and the second walking pattern. For example, the processor 210 may identify a second value indicating a difference between the first walking pattern and the second walking pattern based on the first information and fourth information related to the second walking pattern of the user that is stored in the memory 230 and identified based on the walking history of the user. For example, the processor 210 may identify a second similarity between the first walking pattern and the second walking pattern based on the first information and the fourth information. For example, the second value may be a scalar value, a vector, an array of values, etc.

[0084] According to an example, the processor 210 may store the fourth information related to the second walking pattern in the memory 230. The processor 210 may identify the fourth information related to the second walking pattern based on the walking history of the user. For example, the processor 210 may perform learning of the walking pattern of the user based on the walking history of the user. The processor 210 may obtain the fourth information related to the second walking pattern based on performing learning of the walking pattern of the user. The processor 210 may store the obtained fourth information in the memory 230. For example, the fourth information related to the second walking pattern may be a state stored in the memory 230 of the electronic device 200.

[0085] For example, the processor 210 may identify a group including values ​​related to a walking pattern (e.g., gait or other movement pattern) according to a plurality of steps of the user based on the walking history of the user. For example, the processor 210 may identify a first step of the user by using the sensor 220. The processor 210 may identify a feature value (e.g., a value to be used later with respect to the walking pattern) according to the first step based on identifying that the number of the first steps is equal to or greater than a specified number. Fig.12The processor 210 may identify a feature value of a second step of the user that occurs before identifying the first step. The feature value of the second step may be stored in the memory 230. For example, the feature value of the second step may be a value learned based on the walking history of the user.

[0086] The processor 210 may identify a value associated with a walking pattern according to the first step based on the feature value according to the first step. The processor 210 may identify a value associated with a walking pattern according to the second step based on the feature value according to the second step. The processor 210 may configure the value associated with a walking pattern according to the first step and the value associated with a walking pattern according to the second step as a group including values ​​(or other information) associated with a walking pattern according to a plurality of steps.

[0087] For example, the processor 210 may identify at least a portion of the group based on information about fitness according to a plurality of steps, the information about fitness being obtained based on the identified group. The processor 210 may perform learning of the user's walking pattern based on at least a portion of the group. Figures 10 to 18 A specific example of an operation of performing learning of a user's walking pattern is described.

[0088] According to an example, the processor 210 may identify a second value indicating a difference between the first walking mode and the second walking mode based on the first information and the fourth information. The processor 210 may identify one or more values ​​associated with the first walking mode. The processor 210 may identify one or more values ​​associated with the second walking mode. For example, these may correspond to one or more values ​​associated with the first walking mode, such as by being associated with the same walking mode features as those walking mode features based on which the one or more values ​​associated with the first walking mode are identified. The processor 210 may identify a second value indicating a difference between the first walking mode and the second walking mode based on the Euclidean distance between the value associated with the first walking mode and the value associated with the second walking mode. The processor 210 may identify a second similarity between the first walking mode and the second walking mode based on the Euclidean distance between the value associated with the first walking mode and the value associated with the second walking mode. For example, the second value and the second similarity indicating a difference between the first walking mode and the second walking mode may be inversely proportional. The larger the second value indicating a difference between the first walking mode and the second walking mode, the smaller the second similarity may be. The smaller the second value indicating a difference between the first walking mode and the second walking mode, the larger the second similarity may be.

[0089] In operation 440, the processor 210 may identify a threshold value (e.g., a preset value, a predefined indication) related to fall detection. For example, the processor 210 may identify a threshold value related to fall detection based on the first value, the second value, and the second information (or based on at least one of the first value, the second value, and the second information, such as based on the second value and the second information).

[0090] In operation 450 , the processor 210 may perform a function for fall detection. For example, the processor 210 may perform a function for fall detection based on a comparison between the identified threshold and a value obtained by the sensor 220 .

[0091] For example, the threshold value associated with fall detection may include a reference value (or other indication) for determining whether the user has fallen. The value obtained by the sensor 220 may include a value related to the impact amount, a value related to the amount of air pressure change, and / or a value related to the time when the user's activity stops. For example, the processor 210 may identify that the user has fallen based on identifying that the value used to determine that the user has fallen is equal to or greater than the threshold. For example, the threshold value associated with fall detection may include a first threshold value related to the impact amount, a second threshold value related to the amount of air pressure change, and / or a third threshold value related to the activity stop maintenance time (or activity stop time, for example, the amount of time that an activity (such as exercise, such as walking) is stopped or interrupted).

[0092] For example, the processor 210 may identify (or determine, assume, detect, etc.) that the user has fallen based on identifying (or determining) that the value related to the impact amount obtained by the sensor 220 is equal to or greater than the first threshold. For example, the processor 210 may identify that the user has fallen based on identifying that the value related to the air pressure change amount obtained by the sensor 220 is equal to or greater than the second threshold. For example, the processor 210 may identify that the user has fallen based on identifying that the time when the user's activity stops is equal to or greater than the third threshold.

[0093] For example, the processor 210 can identify that the user has fallen based on identifying that the value related to the impact amount obtained by the sensor 220 is equal to or greater than the first threshold, the value related to the air pressure change amount obtained by the sensor 220 is equal to or greater than the second threshold, and the time when the user's activity stops is equal to or greater than the third threshold.

[0094] According to an example, the processor 210 may identify a probability value of a fall occurring based on a first value, a second value, and a second information. The processor 210 may identify a threshold as one of a plurality of candidate thresholds based on the identified probability value. For example, the processor 210 may identify the risk of falling as one of “low”, “normal”, “high”, and “very high” based on the probability value that the user has fallen. The processor 210 may identify one of a plurality of candidate thresholds based on the risk of falling. In a non-limiting example, a plurality of candidate thresholds based on the risk of falling may be set as shown in Table 1.

[0095]

Table 1

[0096]

[0097]

[0098] Referring to Table 1, a plurality of candidate thresholds can be set according to the risk of falling. For example, when the risk of falling is "low", the first threshold value related to the impact amount can be set to 20 [G] (gravity). When the risk of falling is "low", the second threshold value related to the amount of change in air pressure can be set to 0.4 [hPa] (hPascal). When the risk of falling is "low", the third threshold value related to the activity stop time can be set to 60 [s] (seconds). For example, when the risk of falling is "normal", the first threshold value related to the impact amount can be set to 20 [G]. When the risk of falling is "normal", the second threshold value related to the amount of change in air pressure can be set to 0.4 [hPa]. When the risk of falling is "normal", the third threshold value related to the activity stop time can be set to 60 [s] (seconds). For example, when the risk of falling is "high", the first threshold value related to the impact amount can be set to 15 [G]. When the risk of falling is "high", the second threshold value related to the air pressure change amount can be set to 0.32 [hPa]. When the risk of falling is "high", the third threshold value related to the activity stop time can be set to 40 [s] (seconds). For example, when the risk of falling is "very high", the first threshold value related to the impact amount can be set to 10 [G]. When the risk of falling is "very high", the second threshold value related to the air pressure change amount t can be set to 0.32 [hPa]. When the risk of falling is "very high", the third threshold value related to the activity stop time can be set to 40 [s] (seconds). According to the example, the processor 210 can identify the probability value that the user will fall, and can identify the risk of falling based on the identified probability value. The processor 210 can change the sensitivity of the fall detection algorithm by changing the threshold value related to fall detection according to the risk of falling. The processor 210 can quickly identify that a fall has occurred by changing the thresholds related to fall detection (e.g., the first threshold value, the second threshold value, and the third threshold value).

[0099] For example, the processor 210 may send a signal to an external electronic device worn by the user and connected to the electronic device 200 for causing the external electronic device 2100 to provide a notification for warning of a fall risk through the external electronic device based on the identified probability value and the second information.

[0100] Figure 5 is a flow chart illustrating the operation of an electronic device according to an example.

[0101] refer to Figure 5In operation 510, the processor 210 may identify data related to the user's walking (or other directional movement, such as running, etc.). For example, the processor 210 may identify data related to the acceleration of the electronic device 200 by using the acceleration sensor 221. For example, the processor 210 may identify data related to the angular velocity of the electronic device 200 by using the gyroscope sensor 222. For example, the processor 210 may identify data related to the air pressure (or the altitude of the electronic device 200) by using the air pressure sensor 223.

[0102] In operation 520, the processor 210 may recognize the user's walking (or other movement). For example, the processor 210 may recognize the user's walking based on data related to the user's walking.

[0103] According to an example, the processor 210 may identify the user's steps based on data related to the user's walking. The processor 210 may identify the user's walking based on data about 3-axis acceleration. The processor 210 may identify the user's walking based on identifying that the user's steps are equal to or greater than a specified number of times.

[0104] In operation 530, the processor 210 may identify the amount of air pressure change based on recognizing the user's walking. For example, the processor 210 may identify the amount of air pressure change based on recognizing the user's walking by using the data recognized by the air pressure sensor 223. For example, the data recognized by using the air pressure sensor 223 may be used to recognize the altitude of the electronic device 200. The processor 210 may recognize whether the altitude of the electronic device 200 has changed based on the data recognized by using the air pressure sensor 223.

[0105] In operation 540, the processor 210 may identify whether the amount of change in air pressure (i.e., the amount by which the air pressure has changed) is less than the first amount of change in air pressure. The processor 210 may identify whether the amount of change in air pressure during a specified time interval (e.g., time period) is less than the first amount of change in air pressure. For example, the processor 210 may identify whether the amount of change in air pressure at the end of the specified time interval is less than the first amount of change in air pressure. The processor 210 may identify that the air pressure decreases during the specified time interval based on identifying that the amount of change in air pressure during the specified time interval is less than the first amount of change in air pressure.

[0106] In operation 550, in the case where the air pressure variation is less than the first air pressure variation, the processor 210 may identify that the user is walking uphill. The processor 210 may identify that the user is walking uphill based on identifying that the air pressure variation is less than the first air pressure variation. In some examples, a negative value of the air pressure variation (e.g., indicating a decrease in air pressure between the beginning of a specified time interval and the end of a specified time interval) may be interpreted as indicating that the user is walking uphill. In other examples, in order to interpret it as the user walking uphill, the air pressure variation is required to be less than the first air pressure variation (which itself may be a negative value), thereby requiring the air pressure variation to have at least a specified magnitude, rather than simply being below zero. According to the above example, the processor 210 may filter out situations where the user is walking on a substantially flat ground with only slight undulations (which results in a slight decrease in air pressure within a specified time interval), rather than when the user is walking on a more general uphill path.

[0107] In operation 560, in the case where the air pressure change amount is equal to or greater than the first air pressure change amount, the processor 210 may identify whether the air pressure change amount is equal to or greater than the second air pressure change amount. For example, the processor 210 may identify whether the air pressure change amount is equal to or greater than the second air pressure change amount based on identifying that the air pressure change amount is equal to or greater than the first air pressure change amount. For example, the processor 210 may identify whether the air pressure change amount during the specified time interval is equal to or greater than the second air pressure change amount. For example, the processor 210 may identify whether the air pressure change amount at the end of the specified time interval is greater than the second air pressure change amount. The processor 210 may identify that the air pressure increases during the specified time interval based on identifying that the air pressure change amount during the specified time interval is equal to or greater than the second air pressure change amount.

[0108] In operation 570, in the case where the air pressure variation is equal to or greater than the second air pressure variation, the processor 210 can identify that the user is walking downhill. The processor 210 can identify that the user is walking downhill based on identifying that the air pressure variation is equal to or greater than the second air pressure variation. In some examples, a positive value of the air pressure variation (e.g., indicating an increase in air pressure between the beginning of a specified time interval and the end of a specified time interval) can be interpreted as indicating that the user is walking downhill. In other examples, in order to interpret it as the user is walking downhill, the air pressure variation is required to be greater than the second air pressure variation, thereby requiring the air pressure variation to have at least a specified amplitude, rather than simply above zero. According to the above example, the processor 210 can filter out the situation where the user walks on a substantially flat ground with only slight undulations (which causes a slight increase in air pressure within a specified time interval), rather than when the user is walking on a more general downhill path.

[0109] In operation 580, in the case where the air pressure change amount is less than the second air pressure change amount, the processor 210 may recognize that the user is walking on flat ground. For example, the processor 210 may recognize that the user is walking on flat ground based on recognizing that the air pressure change amount is less than the second air pressure change amount. For example, the processor 210 may recognize that the user is walking on flat ground based on recognizing that the air pressure change amount during the specified time interval is equal to or greater than the first air pressure change amount and less than the second air pressure change amount. For example, the air pressure change amount between the beginning and the end of the specified time interval being equal to or greater than the first air pressure change amount and less than the second air pressure change amount may correspond to a situation where the user is walking on substantially flat ground with only slight undulations (if any).

[0110] Figures 6a to 6c An example of identifying a trend of a user's walking environment according to an example of the present disclosure is shown.

[0111] Figures 7a to 7c An example of identifying a trend of a user's walking environment according to an example of the present disclosure is shown.

[0112] Figure 8 An example of identifying a trend of a user's walking environment according to an example of the present disclosure is shown.

[0113] refer to Figures 6a to 6c , trends 610 to 630 indicate changes in the acceleration of the electronic device 200 over time. The processor 210 can identify the acceleration of the 3 axes of the electronic device 200 by using the acceleration sensor 221. The processor 210 can identify the trend 610 by monitoring the acceleration in the first direction (or x-axis direction) over time. The processor 210 can identify the trend 620 by monitoring the acceleration in the second direction (or y-axis direction) over time. The processor 210 can identify the trend 630 by monitoring the acceleration in the third direction (or z-axis direction) over time. The processor 210 can identify the magnitude of the acceleration in the 3-axis direction. The processor 210 can identify the trend indicating the magnitude of the acceleration in the 3-axis direction. For example, the processor 210 can identify the magnitude of the acceleration in the 3-axis direction by using Equation 1.

[0114] [Equation 1]

[0115]

[0116] Referring to Equation 1, a NORM (a NORM ) is the magnitude of acceleration in the three-axis directions. x is the magnitude of acceleration in the first direction. y is the magnitude of acceleration in the second direction. The processor 210 may identify a trend indicative of the magnitude of acceleration over time based on identifying the magnitude of acceleration in the three-axis directions at a particular moment.

[0117] According to an example, the processor 210 may identify the difference of the sliding window sum based on the trend of the acceleration in the 3-axis direction over time. The processor 210 may identify the trend 640 indicating the change of the difference of the sliding window sum over time. For example, the processor 210 may identify the sliding window sum by using Equation 2. The processor 210 may identify the difference of the sliding window sum by using Equation 3.

[0118] [Equation 2]

[0119]

[0120] Referring to Equation 2, SWS(k) represents a sliding window summation of the magnitude of acceleration in the 3-axis directions. The processor 210 may identify SWS(k) by identifying a sliding window summation of the magnitude of acceleration from time k-N+1 to time k.

[0121] [Equation 3]

[0122] SWS diff(k) =SWS(k-N+1)-SWS(k)

[0123] Referring to Equation 3, SWS diff (k) represents the difference in the sliding window summation of the magnitude of acceleration in the three-axis directions. The processor 210 can identify (or obtain) the difference between the sliding window summation at time k-N+1 and the sliding window summation at time k as the difference in the sliding window summation at time k.

[0124] According to an example, the processor 210 may identify the trend 640 by setting the k and N values ​​so that the trend of the difference of the sliding window sum is configured to have a sine waveform. The processor 210 may identify at least one zero crossing point based on the trend 640. The time 641, the time 642, and the time 643 may be examples of zero crossing points.

[0125] For example, the processor 210 may identify one step based on the zero crossings. The processor 210 may identify a gap between a first zero crossing (e.g., time 643) and a second zero crossing (e.g., time 642) identified before the first zero crossing as a time interval indicating the one step. The processor 210 may identify the number of zero crossings as the number of steps in the trend 640. The processor 210 may identify that the user is walking or moving through another form of step-based movement based on identifying that the number of zero crossings within a specified time interval is equal to or greater than a specified number.

[0126] According to an example, the processor 210 may identify an amount of air pressure change (e.g., air pressure difference) based on identifying that the user is walking. The processor 210 may identify a trend 650 of air pressure over time within a specified time interval. The processor 210 may identify an amount of air pressure change within a specified time interval based on the trend 650. The processor 210 may identify that the amount of air pressure change within a specified time interval is equal to or greater than a first amount of air pressure change and less than a second amount of air pressure change based on the trend 650. The processor 210 may identify that the user is walking on flat ground based on identifying that the amount of air pressure change within a specified time interval is equal to or greater than a first amount of air pressure change and less than a second amount of air pressure change. For example, the processor 210 may identify that the user is walking on flat ground based on identifying that the air pressure is maintained (or substantially maintained, such as maintained within a set range) within a specified time interval.

[0127] refer to Figures 7a to 7c , trends 710 to 730 indicate changes in the acceleration of the electronic device 200 over time. Trend 710 may correspond to Figure 6a Trend 610. Trend 720 may correspond to Figure 6a Trend 620. Trend 730 may correspond to Figure 6a By identifying the magnitude of acceleration in the 3-axis directions based on trends 710 to 730, processor 210 may identify trend 740 indicating that the difference of the sliding window sum changes over time. Trend 740 may correspond to Figure 6b Trend 640.

[0128] According to an example, the processor 210 may identify at least one zero crossing point based on the trend 740. The processor 210 may identify the number of zero crossing points as the number of steps in the trend 740. The processor 210 may identify that the user is walking based on identifying that the number of zero crossing points within a specified time interval is equal to or greater than a specified number.

[0129] According to an example, the processor 210 may identify the amount of change in air pressure based on identifying that the user is walking. The processor 210 may identify a trend 750 of air pressure over time within a specified time interval. The trend 750 may correspond to Figure 6c Processor 210 may identify that the amount of change in air pressure over a specified time interval is equal to or greater than a second amount of change in air pressure based on trend 750. Processor 210 may identify that the user is walking downhill based on identifying that the amount of change in air pressure over a specified time interval is equal to or greater than a second amount of change in air pressure. For example, processor 210 may identify that the user is walking downhill based on identifying that the air pressure is increasing over a specified time interval.

[0130] Although not shown, the processor 210 may identify that the amount of change in air pressure is less than the first amount of change in air pressure within a specified time interval. The processor 210 may identify that the user is walking uphill based on identifying that the amount of change in air pressure is less than the first amount of change in air pressure within a specified time interval. For example, the processor 210 may identify that the user is walking uphill based on identifying that the air pressure decreases within a specified time interval.

[0131] refer to Figure 8 By identifying the magnitude of acceleration in the 3-axis directions, processor 210 can identify a trend 810 indicating the change in the difference of the sliding window sum over time. Trend 810 can correspond to Figure 6b Trend 640 or Figure 7b Trend 740.

[0132] According to an example, the processor 210 may identify at least one zero crossing point based on the trend 810. The processor 210 may identify the time intervals between the zero crossing points. For example, the processor 210 may identify the time interval 811, the time interval 812, and the time interval 813. The processor 210 may identify a plurality of time intervals including the time interval 811, the time interval 812, and the time interval 813.

[0133] According to an example, the processor 210 may identify the walking frequency based on a plurality of time intervals. The processor 210 may identify the walking frequency based on a representative value (e.g., an average value or a median value) of the plurality of time intervals. For example, the processor 210 may identify the representative value of the plurality of time intervals as the time interval 811. The processor 210 may identify the walking frequency based on the time interval 811. For example, the processor 210 may identify the walking frequency by using Equation 4.

[0134] [Equation 4]

[0135]

[0136] Referring to Equation 4, f is a walking frequency. te is a representative value of a plurality of time intervals. The processor 210 may identify a user's walking speed by identifying the walking frequency by using the representative values ​​of the plurality of time intervals.

[0137] Fig. 9 An example of operation of an electronic device according to an example is shown.

[0138] refer to Fig. 9, in operation 910, the processor 210 may obtain the second data. For example, the processor 210 may obtain the second data by using at least one of the sensor 220 and / or the communication circuit 240. For example, the processor 210 may obtain the second data by using at least one of the acceleration sensor 221, the gyroscope sensor 222, and the air pressure sensor 223 and the communication circuit 240. For example, the processor 210 may obtain the second data and / or the third data based on the pre-processed data obtained by the sensor 220. For example, the second data and the third data may include at least one of the posture change of the hand movement, the frequency characteristic, and the size (e.g., amplitude, degree, etc.) and / or direction of the movement of the hand movement. For example, the hand movement may mean the state of the user holding (or grasping) the electronic device 200. The processor 210 may identify at least one of the posture change of the hand movement (e.g., the change of how the user holds the electronic device), the frequency characteristic, and the size and / or direction of the movement of the hand movement by identifying the movement of the user when the user holds the electronic device 200.

[0139] For example, the second data and the third data may include data related to the acceleration of the electronic device 200 obtained by the acceleration sensor 221, data related to the angular velocity of the electronic device 200 obtained by the gyroscope sensor 222, data related to the air pressure around the electronic device 200 obtained by the air pressure sensor 223, and at least one of information about the weather obtained from an external electronic device through the communication circuit 240.

[0140] The second data may refer to data obtained at a second time. The third data may refer to data obtained at a first time earlier than the second time.

[0141] In operations 920 and 930, the processor 210 may learn the second data and / or the third data. The processor 210 may set a plurality of parameters of the specified model 940 based on learning the second data and / or the third data. For example, the specified model 940 may include an artificial intelligence model. For example, the specified model 940 may be configured (or designed) based on machine learning (such as a decision tree (DT), a support vector machine (SVM), a k-nearest neighbor (KNN), and / or a multilayer perceptron (MLP), etc.).

[0142] For example, the third data can be obtained according to the walking history of the user. The processor 210 can obtain the third data based on the walking history of the user. The first moment of obtaining the third data can be different from the second moment of obtaining the second data. The first moment of obtaining the third data can be earlier than the second moment of obtaining the second data. For example, the processor 210 can set (or change or update) the specified model 940 by learning the third data obtained at the first moment. The processor 210 can set (or change or update) multiple parameters of the specified model 940 by using the third data obtained at the first moment. The processor 210 can set the specified model 940 by learning the second data obtained at the second moment. The processor 210 can set (or change or update) multiple parameters of the specified model 940 by using the second data obtained at the second moment.

[0143] According to an example, the processor 210 may use at least a portion or all of the second data to learn the specified model 940. The processor 210 may set at least a portion or all of the second data as input data of the specified model 940. For example, the processor 210 may learn the second data when the operation mode of the specified model 940 is in the learning mode (or training phase). For example, the processor 210 may set the second data as input data of the specified model 940 when the operation mode of the specified model 940 is in the recognition mode (or recognition phase).

[0144] In operation 950, the processor 210 may obtain information about the movement state of the electronic device 200. For example, the processor 210 may obtain information about the movement state of the electronic device 200 based on setting at least a part or all of the second data as input data of the specified model 940. For example, the information about the movement state of the electronic device 200 may include information about the posture of the user carrying the electronic device 200. Therefore, in various examples, the method may follow operations 910, 940, and 950, wherein the obtained second data is used as input data of the specified model 940 to obtain information about the movement state; while in other examples, the method may follow operations 910, 920, 930, and 940, wherein the obtained second data and / or third data are learned, the parameters of the specified model are set using the second data and / or third data, and the specified model 940 may be set based on the parameters (in addition, operation 950 may also be performed when the second data is also used as input data of the specified model 940).

[0145] For example, the processor 210 may recognize that the user is carrying the electronic device 200 by using the user's hand based on the input data setting at least a part or all of the second data to specify the model 940 .

[0146] For example, the processor 210 may recognize that the user carries the electronic device 200 by using a pocket based on the input data setting at least a part or all of the second data as the designated model 940 .

[0147] For example, the processor 210 may recognize that the user is swinging his arm while holding the electronic device 200 based on setting at least a part or all of the second data as input data of the designated model 940 .

[0148] Fig.10 is a flow chart illustrating the operation of an electronic device according to an example.

[0149] refer to Fig.10 In operation 1010, the processor 210 may identify (or detect, determine, obtain, etc.) a first step (e.g., a first plurality of steps) of a user. For example, the processor 210 may identify the first step of the user based on the magnitude of acceleration in the 3-axis direction by using the acceleration sensor 221. For example, the processor 210 may identify a trend indicating a change in the difference of the sliding window sum over time based on the magnitude of acceleration in the 3-axis direction (e.g., Figure 6b Trends 640 and Figure 7b The processor 210 may identify a first step of the user based on at least one zero crossing associated with the identified trend 740.

[0150] According to an example, the processor 210 may identify the first step by excluding the steps identified as outliers from the steps of the user. For example, the processor 210 may identify the first step by excluding outliers from the steps of the user based on the z-score.

[0151] In operation 1020, the processor 210 may identify (or determine, detect, calculate, etc.) whether the number of first steps is equal to or greater than a specified number of times. For example, the processor 210 may identify whether the number of first steps is equal to or greater than a specified number of times (e.g., 12 times) in order to identify a valid walking interval. The processor 210 may identify whether the user's steps occur continuously equal to or greater than a specified number of times based on a trend of changes over time in the difference of the sum of the indicating sliding windows identified according to the magnitude of the acceleration in the 3-axis directions. According to an example, the processor 210 may identify the number of first steps as at least one valid walking interval according to the specified number of times.

[0152] According to an example, the processor 210 may perform (e.g., repeat) operation 1010 based on recognizing (in operation 1020) that the number of first steps is not equal to or greater than the specified number of steps. For example, the processor 210 may recognize the first step of the user again based on recognizing that the number of first steps (recognized in the previous execution of operation 1010) is not equal to or greater than the specified number of times.

[0153] In operation 1030, the processor 210 may identify (or obtain, determine, detect, etc.) a feature value according to the first step. For example, the processor 210 may identify the feature value according to the first step based on identifying that the number of first steps is equal to or greater than a specified number of times.

[0154] According to an example, the feature value may include a value for indicating a walking pattern or walking stability. For example, the feature value may include a value of the magnitude of acceleration in the 3-axis direction. For example, the feature value may include at least one of a minimum value, a maximum value, an average value, a standard deviation, a root mean square (RMS) value, a peak-to-peak (P2P) value, a peak count value, a signal amplitude area (SMA) value, a mean amplitude deviation (MAD) value, and a coefficient of variance (COV) value.

[0155] At operation 1040, the processor 210 may identify (or obtain, detect, determine, etc.) a group. For example, the processor 210 may identify the group based on the feature value according to the first step. According to an example, the group may be configured with values ​​related to the walking pattern according to a plurality of steps of the user. For example, the processor 210 may identify the feature value according to the first step and identify the second step before identifying the first step (e.g., before identifying the first step in operation 1010), such as by performing an earlier Fig.10 The processor 210 may identify a value related to the walking pattern according to the first step based on the feature value according to the first step. The processor 210 may identify a value related to the walking pattern according to the second step based on the feature value according to the second step. The processor 210 may configure the value related to the walking pattern according to the first step and the value related to the walking pattern according to the second step as a group including values ​​related to the walking pattern according to multiple steps (for example, the group includes a set of values / feature values). The processor 210 may perform learning related to the walking pattern of the user based on the group. For example, the processor 210 may perform feature scaling by normalizing the group. The processor 210 may perform learning related to the walking pattern of the user based on the feature scaled data.

[0156] Hereinafter, a specific example of the operation of the processor 210 according to operations 1010 to 1040 will be described later.

[0157] Fig.11 An example of operation of an electronic device according to an example is shown.

[0158] Fig.12 An example of operation of an electronic device according to an example is shown.

[0159] refer to Fig.11, the processor 210 may identify a trend indicating acceleration in 3-axis directions by using the acceleration sensor 221. For example, the processor 210 may identify a trend 1110 indicating acceleration in a first direction (or x-axis direction). The processor 210 may identify a trend 1120 indicating acceleration in a second direction (or y-axis direction). The processor 210 may identify a trend 1130 indicating acceleration in a third direction (or z-axis direction). The processor 210 may identify a trend 1140 indicating a change in the difference of the sliding window sum based on the trends 1110 to 1130.

[0160] According to an example, the processor 210 may identify at least one zero crossing point based on the trend 1140. The processor 210 may identify the user's pace based on identifying the at least one zero crossing point. The processor 210 may identify a valid walking interval based on the user's pace.

[0161] According to an example, the processor 210 may identify valid walking intervals for each specified number of times (e.g., 10 times). For example, the processor 210 may identify valid walking intervals 1151, valid walking intervals 1152, and valid walking intervals 1153. The processor 210 may identify valid walking intervals for each specified number of times based on recognizing that the number of steps of the user is equal to or greater than the specified number of times.

[0162] According to an example, the processor 210 may identify a first step other than the step 1141 identified as an abnormal value among the steps configuring the effective walking interval 1151. The processor 210 may identify a feature value based on the first step. The processor 210 may identify feature values ​​of the steps configuring the effective walking interval 1152 and the effective walking interval 1153.

[0163] refer to Fig.12 , the processor 210 may identify 10 eigenvalues ​​per step. The processor 210 may configure the 10 eigenvalues ​​of the first step according to the 10 steps configured as a matrix 1210. The matrix 1210 may be configured as 10×10 (10×10—i.e., 10 rows and 10 columns). The eigenvalues ​​configuring the matrix 1210 are exemplary and may vary.

[0164] According to an example, the processor 210 may identify a feature value based on the trend 1140. The processor 210 may identify the feature value by using a difference in a sliding window sum configured at one pace. For example, the processor 210 may classify a trend indicating a change in a difference in a sliding window sum configured at one pace as a discrete value (e.g., n) according to a unit time interval. The processor 210 may identify the feature value using the classified value. Below are multiple examples of feature values, where it will be understood that any one or more of these types of feature values ​​may be implemented in the examples disclosed herein.

[0165] Fig.12 The values ​​shown in are exemplary values ​​and may not be described according to the examples described below.

[0166] For example, the first column may represent a minimum value (min). The processor 210 may identify the minimum value of the difference of the sliding window sum configured with one step as the value of the first column.

[0167] For example, the second column may represent a maximum value (max). The processor 210 may identify the maximum value of the difference of the sliding window sum configured with one step as the value of the second column.

[0168] For example, the third column may represent a mean value (MANE). The processor 210 may identify the mean value of the difference of the sliding window summation configured with one step as the value of the third column.

[0169] For example, the fourth column may represent a standard deviation. The processor 210 may identify the standard deviation of the difference of the sliding window sum configured with one step as the value of the fourth column.

[0170] For example, the fifth column may represent a root mean square (RMS) value. The processor 210 may identify the RMS value of the difference summed over the sliding window configured with one step as the value of the fifth column.

[0171] For example, the sixth column may represent a peak-to-peak (P2P) value. The processor 210 may identify the difference between peak values ​​indicating a trend of the difference of the sliding window summation configured for one step as the value of the sixth column.

[0172] For example, the seventh column may represent a peak count value. The processor 210 may identify the number of peaks indicating a trend of differences in the sliding window sum configured for one step as the value of the seventh column.

[0173] For example, the eighth column may represent a signal magnitude area (SMA) value. The processor 210 may identify the SMA value indicating a trend of the difference of the sliding window summation configured for one step as the value of the eighth column.

[0174] For example, the ninth column may represent a mean amplitude deviation (MAD) value. The processor 210 may identify a MAD value indicating a trend of differences in the sliding window summation configured for one step as a value of the ninth column.

[0175] For example, the tenth column may represent a coefficient of variance (COV) value. The processor 210 may identify the coefficient of variance (COV) value of the difference of the sliding window summation configured for one step as the value of the tenth column.

[0176] Fig.13 is a flow chart illustrating the operation of an electronic device according to an example.

[0177] refer to Fig.13 In operation 1310, the processor 210 may identify a value associated with the walking pattern according to the first step. For example, the processor 210 may identify a value associated with the walking pattern according to the first step based on a feature value according to the first step. For example, the processor 210 may identify a first matrix configured with a value associated with the walking pattern according to the first step by multiplying a matrix configured based on the feature value according to the first step by a specified matrix.

[0178] In operation 1320, the processor 210 may identify a value associated with the walking pattern according to the second step. For example, the processor 210 may identify the value associated with the walking pattern according to the second step based on the feature value according to the second step. For example, the processor 210 may identify a second matrix configured with the value associated with the walking pattern according to the second step by multiplying a matrix configured based on the feature value according to the second step by a specified matrix. For example, the value associated with the walking pattern according to the second step may be a value learned according to the walking history of the user.

[0179] According to an example, as the feature value of the first step and the feature value of the second step increase, the amount of calculation increases and the learning effect of the specified model may decrease. Therefore, the processor 210 can reduce the dimension of the walking pattern by identifying various feature values ​​as feature values ​​according to the walking pattern.

[0180] In operation 1330, the processor 210 may configure a group. For example, the processor 210 may configure a value associated with a walking pattern according to a first step and a value associated with a walking pattern according to a second step as a group including values ​​associated with a walking pattern according to a plurality of steps.

[0181] For example, the processor 210 may identify the walking pattern-related value according to the first step and the walking pattern-related value according to the second step as the walking pattern-related values ​​according to multiple steps. The processor 210 may represent the group including the walking pattern-related values ​​according to multiple steps as a third matrix.

[0182] In operation 1340, the processor 210 may identify at least a portion of the group based on information about fitness according to a plurality of steps. For example, the processor 210 may identify at least a portion of the group based on information about fitness according to a plurality of steps, wherein the information about fitness is obtained based on the group.

[0183] According to an example, the processor 210 can obtain information about fitness based on multiple steps based on a group. For example, the processor 210 can identify a representative value for each column in a group represented as a third matrix. For example, the processor 210 can identify the average value of each column in a group represented as a third matrix as a representative value. The processor 210 can obtain information about fitness based on multiple steps based on identifying the average value of each column in a group represented as a third matrix as a representative value. The information about fitness based on multiple steps can be represented as a fourth matrix. For example, in view of being formed by the average value of each column, information about fitness can be considered to provide a general indication of the fitness level of the subject / user based on the recorded / determined / identified feature value, and / or can allow identification of high fitness and / or low fitness of the subject / user (e.g., occasions where walking is more intensive or less intensive than the average value).

[0184] According to an example, the processor 210 may identify at least a portion of the group based on the information about fitness according to the plurality of steps. The processor 210 may identify at least a portion of the group in the order of high fitness according to the plurality of steps. For example, high fitness may refer to a feature value that is higher than an average value (i.e., higher than the information about fitness).

[0185] In operation 1350, the processor 210 may perform learning of a walking pattern. For example, the processor 210 may perform learning of a walking pattern of a user based on at least a portion of a group.

[0186] According to an example, the processor 210 may update the walking pattern by repeating the learning of the walking pattern. The processor 210 may recognize that the walking pattern has been sufficiently learned based on recognizing that the amount of change in the walking pattern according to the learning of the walking pattern is within a reference range. The processor 210 may stop learning the walking pattern based on recognizing that the walking pattern has been sufficiently learned.

[0187] Will refer to it later Figures 14 to 18 A specific example of operations according to operation 1310 to operation 1350 is described.

[0188] Fig.14 An example of a specified matrix according to an example is shown.

[0189] Fig.15 An example of a matrix indicating a population according to an example is shown.

[0190] Fig.16 An example of a matrix indicating information about fitness according to an example is shown.

[0191] Fig.17 An example of a matrix indicating at least a portion of a population according to an example is shown.

[0192] Fig.18 An example of a learning result of a walking pattern according to an example is shown.

[0193] refer to Fig.14 , the processor 210 may identify the matrix 1410. For example, the matrix 1410 may be stored in the memory 230. For example, the matrix 1410 may be in a pre-modeled state or an initial state. According to an example, the processor 210 may receive the matrix 1410 from an external electronic device (e.g., a server) connected to the electronic device 200.

[0194] According to an example, the matrix 1410 can be used to identify values ​​associated with a walking pattern according to a first pace. The matrix 1410 can be used to identify values ​​associated with a walking pattern according to a second pace.

[0195] refer to Fig.15 , the processor 210 may perform the above operations by multiplying the matrix 1410 (eg, Fig.12 The processor 210 may identify the value associated with the walking pattern according to the first step by multiplying the matrix 1410 with a matrix indicating the eigenvalues ​​of the first step (such as the matrix 1210) (e.g., by matrix multiplication). The processor 210 may indicate the value associated with the walking pattern according to the first step as a matrix 1512 (or a portion of a larger matrix 1510). For example, the number of first steps may be 10 times. The matrix 1512 may be configured with 10×3 (i.e., 10 rows and 3 columns).

[0196] The processor 210 may identify the value associated with the walking pattern according to the second step by multiplying the matrix 1410 indicating the eigenvalue of the second step. For example, the processor 210 may identify the value associated with the walking pattern according to the second step by multiplying the matrix 1410 with the matrix indicating the eigenvalue of the second step (e.g., by matrix multiplication). The processor 210 may indicate the value associated with the walking pattern according to the second step as a matrix 1511 (or a portion of a larger matrix 1510). For example, the number of second steps may be 10 times. The matrix 1511 may be configured with 10×3. For example, the second step may occur before the first step is identified. For example, the second step may be a step that is pre-learned before the first step is identified.

[0197] According to an example, the processor 210 may configure a group. The processor 210 may configure the value associated with the walking pattern according to the first step and the value associated with the walking pattern according to the second step as a group including values ​​associated with the walking pattern according to a plurality of steps. For example, the processor 210 may identify the matrix 1510 by combining the matrix 1512 including the value associated with the walking pattern according to the first step and the matrix 1511 including the value associated with the walking pattern according to the second step.

[0198] According to an example, the matrix 1510 may be configured with values ​​related to the walking pattern according to a plurality of steps. The rows of the matrix 1510 may represent a plurality of steps. The plurality of steps may be 20 times. The matrix 1510 may be configured with 20×3 (ie, 20 rows and 3 columns).

[0199] refer to Fig.16 , the processor 210 may indicate each row in the matrix 1510 as a 3-dimensional coordinate system. The first column of the matrix 1510 may represent the coordinate value of the x-axis. The second column of the matrix 1510 may represent the coordinate value of the y-axis. The third column of the matrix 1510 may represent the coordinate value of the z-axis. For example, the first row of the matrix 1510 may correspond to the first coordinate. The second row of the matrix 1510 may correspond to the second coordinate, etc. The processor 210 may identify the first to twentieth coordinates based on the matrix 1510. The processor 210 may identify the average coordinate of the first to twentieth coordinates. The processor 210 may identify the Euclidean distance between each of the first to twentieth coordinates and the average coordinate. The processor 210 may identify the information about fitness according to multiple steps by identifying the Euclidean distance between each coordinate from the first to twentieth coordinates and the average coordinate. The processor 210 may indicate the information about fitness according to multiple steps as the matrix 1610. For example, each row of the matrix 1610 may represent the fitness of each step. For example, the first row of matrix 1610 may represent the fitness of the first step in the second step.For example, the fitness may be used to identify values ​​of the matrix that well represent the user's gait pattern.

[0200] refer to Fig.17 , the processor 210 may identify at least a portion of the group based on information about fitness according to a plurality of steps. For example, the processor 210 may identify at least a portion of the group by identifying the first 10 steps with high fitness. The processor 210 may indicate values ​​associated with the walking pattern according to the first 10 steps with (or showing) high fitness as a matrix 1710.

[0201] refer to Fig.18 , the processor 210 may execute according to Figures 14 to 17 operations to perform learning of the user's walking pattern.

[0202] According to an example, the processor 210 may identify 10 first coordinates 1810 corresponding to the matrix 1710. The processor 210 may identify the second coordinate 1820 by performing learning of the walking pattern using the values ​​related to the walking pattern included in the matrix 1710 and the newly obtained values ​​related to the walking pattern. The processor 210 may identify the Euclidean distance between the representative values ​​of the first coordinate 1810 and the second coordinate 1820. The processor 210 may identify the Euclidean distance between the representative values ​​of the first coordinate 1810 and the second coordinate 1820 as a change amount according to the learned walking pattern. The processor 210 may identify whether the change amount according to the learned walking pattern is within a reference range.

[0203] According to an example, whenever a valid walking interval is identified, the processor 210 may perform learning of the walking pattern. The processor 210 may learn the walking pattern of the user in an evolutionary manner. For example, the processor 210 may repeat learning of the walking pattern based on identifying that the amount of change according to the learned walking pattern is outside the reference range.

[0204] For example, the processor 210 may identify the third coordinate 1830 according to the learning of the walking pattern. The processor 210 may identify the Euclidean distance between the representative value of the second coordinate 1820 and the representative value of the third coordinate 1830 as the amount of change according to the learned walking pattern. The processor 210 may identify that the amount of change according to the learned walking pattern is outside the reference range. The processor 210 may identify the fourth coordinate according to the learning of the walking pattern. The processor 210 may identify the Euclidean distance between the representative value of the third coordinate 1830 and the representative value of the fourth coordinate 1840 as the amount of change according to the learned walking pattern. The processor 210 may identify that the amount of change according to the learned walking pattern (related to the third coordinate 1830 and the fourth coordinate 1840) is within the reference range. The processor 210 may identify that the walking pattern has been sufficiently learned based on identifying that the amount of change according to the learned walking pattern is within the reference range. The processor 210 may stop learning the walking pattern based on identifying that the walking pattern has been sufficiently learned.

[0205] Fig.19 Examples of operating modes of a processor according to examples are shown.

[0206] refer to Fig.19 , the processor 210 may operate in one of the first mode 1910, the second mode 1920, the third mode 1930, and the fourth mode 1940. For example, the processor 210 may learn a walking pattern based on the operation mode.

[0207] According to an example, the processor 210 may initialize the fourth information about the user's walking history and the second walking pattern stored in the memory 230 based on the first mode 1910. For example, the second walking pattern may be identified based on the user's walking history. The second walking pattern may represent a learned walking pattern. For example, in the first mode 1910, the processor 210 may operate in a state where the walking pattern is not learned. For example, the first mode 1910 may be referred to as an idle mode (or idle state).

[0208] According to an example, the processor 210 may obtain data about the walking pattern of the user by using the sensor 220 based on the second mode 1920. For example, the processor 210 may obtain data about the walking pattern of the user in the second mode 1920 in order to learn the walking pattern. For example, in the second mode 1920, the processor 210 may identify a valid walking interval. The processor 210 may obtain data about the walking pattern by identifying a feature value related to the steps in the valid walking interval.

[0209] According to an example, the processor 210 may learn data about the walking pattern of the user based on the third mode 1930. For example, in the third mode 1930, the processor 210 may learn data about the walking pattern obtained when operating in the second mode 1920. In the case where the data about the walking pattern is sufficiently obtained for learning, the processor 210 may learn data about the obtained walking pattern in the third mode 1930. For example, the processor 210 may update the walking pattern by learning the normal walking pattern of the user. The processor 210 may identify the learned walking pattern as the second walking pattern.

[0210] According to an example, the processor 210 may stop the operation of learning data about the user's walking pattern based on the fourth pattern 1940. For example, the processor 210 may stop the operation of learning data about the user's walking pattern based on recognizing that the amount of change according to the learned walking pattern is within a reference range.

[0211] According to an example, the processor 210 may set the operation mode of the processor 210 to the first mode 1910 based on recognizing that the user of the electronic device 200 is changed. The processor 210 may initialize fourth information about a second walking pattern, wherein the second walking pattern is a walking pattern learned in the first mode 1910. For example, the processor 210 may recognize that the user is changed based on recognizing that at least one of the gender and the name of the user is changed in the first mode.

[0212] According to an example, the processor 210 may change the operation mode of the processor 210 from the first mode 1910 to the second mode 1920 in response to recognizing a valid walking interval while operating in the first mode 1910 .

[0213] According to an example, the processor 210 may change the operation mode of the processor 210 from the second mode 1920 to the third mode 1930 in response to recognizing that sufficient data on walking patterns have been obtained for learning while operating in the second mode 1920 .

[0214] According to an example, the processor 210 may change the operation mode of the processor 210 from the third mode 1930 to the second mode 1920 in response to completing the learning of the walking pattern when operating in the third mode 1930. According to an example, the processor 210 may change the operation mode of the processor 210 from the third mode 1930 to the second mode 1920 based on the learning result of the walking pattern when operating in the third mode 1930. For example, the processor 210 may change the operation mode of the processor 210 from the third mode 1930 to the second mode 1920 based on recognizing that the amount of change in the walking pattern is within a reference range. For example, the processor 210 may change the operation mode of the processor 210 from the third mode 1930 to the second mode 1920 based on recognizing that the number of times the walking pattern has been learned (or evolved) is equal to or greater than a reference number of times. For example, the processor 210 may change the operation mode of the processor 210 from the third mode 1930 to the second mode 1920 based on recognizing that the time for which the walking pattern is learned is equal to or greater than a reference time.

[0215] According to an example, the processor 210 may change the operation mode of the processor 210 from the fourth mode 1940 to the first mode 1910 based on recognizing that the body outline of the user has changed (e.g., the user has changed) when operating in the fourth mode 1940. According to an example, the processor 210 may change the operation mode of the processor 210 from the fourth mode 1940 to the second mode 1920 based on recognizing that the amount of change in the walking pattern is outside the reference range when operating in the fourth mode 1940.

[0216] Fig.20a and Fig.20b An example in which the value of the walking mode according to the example is displayed as three-dimensional coordinates is shown.

[0217] According to an example, since each user has a different walking pattern (or walking habit), the processor 210 may not determine that the probability of a fall is high even if the user's walking pattern is different from a reference walking pattern (e.g., a walking pattern of a general user). The processor 210 may change the probability of a fall according to the user's walking environment.

[0218] According to an example, the processor 210 can identify a probability value of a fall occurring (i.e., the user has fallen or falls) based on a first value indicating a difference between a first walking pattern of the user and a reference walking pattern, a second value indicating a difference between the first walking pattern of the user and a second walking pattern of the user (e.g., a learned walking pattern), and second information about the walking environment of the user.

[0219] For example, the processor 210 may identify a first similarity based on a first value indicating a difference between the first walking pattern and the reference walking pattern. The processor 210 may identify a second similarity based on a second value indicating a difference between the first walking pattern and the second walking pattern. The processor 210 may identify a probability value of a user falling based on the first similarity, the second similarity, and the second information about the walking environment of the user.

[0220] For example, the processor 210 may identify a probability value of a user falling down by using Equation 5.

[0221] [Equation 5]

[0222]

[0223] Referring to Equation 5, “S” is the probability value of a user falling. 1 Set "S'". 1 ” is the similarity between the second walking pattern and the reference walking pattern. 1 ” is equal to or greater than the threshold value (“thrd” of Equation 5), S′ may be set to 0. 1 If it is less than the threshold, S' can be set to S 2 . "S 2 ” is the first similarity between the first walking pattern and the reference walking pattern. 3 " is the second similarity between the first walking pattern and the second walking pattern. "E" is the value of the walking environment identified based on the second information. Alpha ("α"), Beta ("β"), and Gamma ("γ") are coefficients. For example, the processor 210 can identify the value E of the walking environment based on the second information. The processor 210 can identify the value E of the walking environment based on weather, slope, walking frequency (or walking speed) and / or the moving state of the electronic device 200. For example, the processor 210 can identify the value E of the walking environment based on a fuzzy set. For example, the fuzzy set can be configured as shown in Table 2.

[0224]

Table 2

[0225] gather element W(Weather) w1 (sunny), w2 (cloudy), w3 (raining), w4 (snowing) S(slope) s1 (flat ground), s2 (slope) F (walking frequency) <![CDATA[f 1 (1.0)、f 2 (1.1)、f 3 (1.2)、…f n (2.3)]]> P (moving state) p1 (hand fixed), p2 (hand swinging)

[0226] Referring to Table 2, the processor 210 may configure one or more fuzzy sets. Set W may include elements for weather. Set S may include elements for slope. Set F may include elements for walking frequency. Set P may include elements for movement status. The processor 210 may identify at least a portion of the elements in the set W, set S, set F, and set P based on fuzzy reasoning according to the fuzzy membership function. The processor 210 may identify the value E of the walking environment based on the identified elements. Fig.20a , the processor 210 may identify the first coordinate 2010 based on a value associated with a first walking mode, wherein the first walking mode is the current walking mode. For example, the current walking mode may be obtained in a normal walking state. The processor 210 may identify the second coordinate 2020 based on a value associated with a second walking mode, wherein the second walking mode is a reference walking mode. The processor 210 may identify the similarity based on the Euclidean distance between a representative value (e.g., an average value) of the first coordinate 2010 and a representative value (e.g., an average value) of the second coordinate 2020. For example, the shorter the Euclidean distance, the similarity may be identified as a value close to 1. The processor 210 may identify the similarity as a value close to 1 based on the Euclidean distance between the representative value of the first coordinate 2010 and the representative value of the second coordinate 2020.

[0227] refer to Fig.20b , the processor 210 may identify the third coordinate 2030 based on a value associated with a first walking mode, wherein the first walking mode is the current walking mode. For example, the current walking mode may be acquired in an abnormal walking state. For example, the current walking mode may be acquired when the user is injured or has a limp on one leg. The processor 210 may identify the second coordinate 2020 based on a value associated with a second walking mode, wherein the second walking mode is a reference walking mode. The processor 210 may identify the similarity based on the Euclidean distance between the representative value of the third coordinate 2030 and the representative value of the second coordinate 2020. For example, the processor 210 may identify the similarity as a value close to zero based on the Euclidean distance between the representative value (e.g., average value) of the third coordinate 2030 and the representative value (e.g., average value) of the second coordinate 2020.

[0228] Fig.21a and 21b An example of a screen for warning of a fall hazard according to an example is shown.

[0229] refer to Fig.21a and Figure 21b, the processor 210 may provide a notification for warning of a risk of falling in advance through an external electronic device 2100 connected to the electronic device 200. For example, the processor 210 may send a signal to the external electronic device 2100 to cause the external electronic device 2100 to provide a notification for warning of a risk of falling.

[0230] According to an example, the processor 210 may identify a probability value that a user has or may fall. The processor 210 may identify a threshold as one of a plurality of candidate thresholds based on the identified probability value. For example, the processor 210 may identify the risk of falling as one of "low", "normal", "high" and "very high" based on the probability value that a user has or may fall. The processor 210 may identify one of a plurality of candidate thresholds based on the risk of falling. For example, a plurality of candidate thresholds based on the risk of falling may be set as shown in Table 1 above.

[0231] According to an example, the processor 210 can provide a notification in advance to warn the user of the danger of falling based on the probability value of the user having or possibly falling meeting a specified condition. According to an example, the processor 210 can display a notification to warn the user of the danger of falling by using the display of the electronic device 200.

[0232] refer to Fig.21a , the processor 210 may display a screen 2110 by using the display of the external electronic device 2100. The screen 2110 may include text 2111 indicating the risk of falling and text 2112 indicating a warning related to the walking environment. For example, the text 2112 may be configured to indicate information about the walking environment and information about risk factors. For example, the text 2112 may be configured to indicate information about the walking environment and information about risk factors. The text 2112 may be displayed to notify about the risk of rainy roads. Although Fig.21a A wearable device is shown as the external electronic device 2100, but the external electronic device is not limited thereto and may take the form of other types of electronic devices.

[0233] refer to Figure 21b , the processor 210 may display a screen 2120 by using the display of the external electronic device 2100. The screen 2120 may include a text 2121 indicating a fall risk and a text 2122 indicating a warning related to the walking environment. For example, the text 2122 may be configured to indicate information about the walking environment and information about risk factors. The text 2122 may be displayed to guide the user's behavior on a snowy day.

[0234] According to an embodiment, the electronic device 200 may include at least one sensor 220, a memory 230, and a processor 210. The processor 210 may be configured to obtain first information related to a first walking pattern of a user of the electronic device 200 and second information related to a walking environment of the user using the at least one sensor 220. The processor 210 may be configured to identify a first value indicating a difference between the first walking pattern and the reference walking pattern based on the first information and third information related to the reference walking pattern stored in the memory 230. The processor 210 may be configured to identify a second value indicating a difference between the first walking pattern and the second walking pattern based on the first information and fourth information related to the second walking pattern of the user, wherein the fourth information is stored in the memory 230 and identified based on the walking history of the user. The processor 210 may be configured to identify a threshold value related to fall detection based on the first value, the second value, and the second information. The processor 210 may be configured to perform a function for fall detection based on a comparison between the threshold value and a value obtained by the at least one sensor 220.

[0235] According to an embodiment, the at least one sensor 220 may include an acceleration sensor 221, a gyro sensor 222, and an air pressure sensor 223. The processor 210 may also be configured to obtain first information related to the first walking pattern based on first data obtained during a specified time interval using the acceleration sensor 220. The processor 210 may also be configured to obtain second information related to the walking environment of the user based on second data obtained using at least one of the acceleration sensor 221, the gyro sensor 222, the air pressure sensor 223, and the communication circuit 240.

[0236] According to an embodiment, the processor 210 may be further configured to obtain the trend of acceleration corresponding to each of the three axis directions during a specified time interval. The processor 210 may also be configured to identify the user's first walking pattern based on the difference of the sliding window sum, wherein the difference of the sliding window sum is identified based on the trend of acceleration.

[0237] According to an embodiment, the second information related to the user's walking environment may include at least one of information about the slope of the moving path of the electronic device 200, information about the moving state of the electronic device 200, and information about the weather during a specified time interval.

[0238] According to an embodiment, the processor 210 may also be configured to obtain information about the moving state of the electronic device 200 by setting at least one of the second data as input data of a specified model indicated by a plurality of parameters stored in the memory 230. The plurality of parameters of the specified model may be configured based on learning of the third data obtained according to the user's walking history.

[0239] According to an embodiment, the electronic device 200 may further include a communication circuit 240. The processor 210 may further be configured to receive third information related to the reference walking pattern from a server connected to the electronic device 200 using the communication circuit 240. The processor 210 may further be configured to store the third information in the memory 230. The third information may be obtained based on a plurality of walking patterns related to a plurality of users stored in the server.

[0240] According to an embodiment, the processor 210 may be further configured to identify the first value based on the Euclidean distance between the value associated with the first walking pattern and the value associated with the reference walking pattern. The processor 210 may be further configured to identify the second value based on the Euclidean distance between the value associated with the first walking pattern and the value associated with the second walking pattern.

[0241] According to an embodiment, the processor 210 may be further configured to perform learning of the user's walking pattern based on the user's walking history. The processor 210 may be further configured to obtain fourth information related to the second walking pattern based on performing learning of the user's walking pattern. The processor 210 may be further configured to store the fourth information in the memory 230.

[0242] According to an embodiment, the processor 210 may be further configured to identify a group including values ​​related to a walking pattern according to a plurality of steps of the user based on the walking history of the user. The processor 210 may be further configured to identify at least a portion of the group based on information about fitness according to the plurality of steps, wherein the information about fitness is obtained based on the group. The processor 210 may be further configured to perform learning of the walking pattern of the user based on at least a portion of the group.

[0243] According to an embodiment, the processor 210 may be further configured to identify a first step of the user using at least one sensor 220. The processor 210 may be further configured to identify a feature value according to the first step based on identifying that the number of first steps is equal to or greater than a specified number. The processor 210 may be further configured to organize a group based on the feature value according to the first step.

[0244] According to an embodiment, the processor 210 may be further configured to identify a value associated with a walking pattern according to a first step based on a feature value according to the first step. The processor 210 may be further configured to identify a value associated with a walking pattern according to a second step based on a feature value according to a second step of the user that occurred before the first step was identified and stored in the memory 230. The processor 210 may be further configured to organize the value associated with a walking pattern according to the first step and the value associated with a walking pattern according to the second step into a group including values ​​associated with a walking pattern according to a plurality of steps.

[0245] According to an embodiment, the processor 210 may be further configured to run one of the first to fourth modes. The processor 210 may be further configured to initialize the user's walking history and the fourth information related to the second walking pattern stored in the memory 230 based on the first mode. The processor 210 may be further configured to obtain data related to the user's walking pattern using at least one sensor 220 based on the second mode. The processor 210 may be further configured to perform learning of data related to the user's walking pattern based on the third mode. The processor 210 may be further configured to stop performing learning of data related to the user's walking pattern based on the fourth mode.

[0246] According to an embodiment, the processor 210 may be further configured to identify a probability value of the user falling based on the first value, the second value and the second information. The processor 210 may be further configured to identify one of a plurality of candidate thresholds as a threshold based on the probability value.

[0247] According to an embodiment, the electronic device 200 may further include a communication circuit 240. The processor 210 may also be configured to send a signal to an external electronic device 2100 connected to the electronic device 200 and worn by the user to cause the external electronic device 2100 to provide a notification warning of a fall risk through the external electronic device 2100 based on the probability value and the second information.

[0248] According to an embodiment, the method of the electronic device 200 may include obtaining first information related to a first walking pattern of a user of the electronic device 200 and second information related to a walking environment of the user using at least one sensor 220 of the electronic device 200. The method may include identifying a first value indicating a difference between the first walking pattern and the reference walking pattern based on the first information and third information related to the reference walking pattern stored in the memory 230. The method may include: identifying a second value indicating a difference between the first walking pattern and the second walking pattern based on the first information and fourth information related to the second walking pattern of the user, wherein the fourth information is stored in the memory 230 and is identified based on the walking history of the user. The method may include identifying a threshold value related to fall detection based on the first value, the second value, and the second information. The method may include performing a function for fall detection based on a comparison between the threshold value and a value obtained by at least one sensor 220.

[0249] According to an embodiment, the method may further include: obtaining first information related to the first walking pattern based on first data obtained during a specified time interval using the acceleration sensor 221 of the electronic device 200. The method may include: obtaining second information related to the walking environment of the user based on second data obtained using at least one of the acceleration sensor 221 of the electronic device 200, the gyroscope sensor 222, the air pressure sensor 223 of the electronic device 200, and the communication circuit 240 of the electronic device 200.

[0250] According to an embodiment, the method may include obtaining a trend of acceleration corresponding to each of the three axis directions during a specified time interval. The method may include identifying a first walking pattern of the user based on a difference in a sliding window sum, wherein the difference in the sliding window sum is identified based on the trend of acceleration.

[0251] According to an embodiment, the second information related to the user's walking environment may include at least one of information about the slope of the moving path of the electronic device 200, information about the moving state of the electronic device 200, and information about the weather during a specified time interval.

[0252] According to an embodiment, the method may include identifying a first value based on a Euclidean distance between a value associated with the first walking pattern and a value associated with a reference walking pattern. The method may include identifying a second value based on a Euclidean distance between a value associated with the first walking pattern and a value associated with the second walking pattern.

[0253] According to an embodiment, the method may include performing learning of the user's walking pattern based on the user's walking history. The method may include obtaining fourth information related to the second walking pattern based on performing learning of the user's walking pattern. The method may include storing the fourth information in the memory 230.

[0254] According to an embodiment, the processor 210 may identify a fall risk before a user has a fall accident. The processor 210 may provide a notification to the user for warning of the fall risk based on the fall risk. In the case of a high fall risk, the processor 210 may change the sensitivity of the fall detection algorithm. For example, the processor 210 may adaptively change (or adjust) a threshold value related to fall detection. The electronic device according to various embodiments disclosed in this document may be various types of devices.

[0255] It should be understood that the present disclosure also includes examples, embodiments, aspects, etc. according to the following numbered paragraphs (the numbers associated with the figures given in these paragraphs are for illustrative purposes and should not be considered limiting):

[0256] Paragraph 1. Provided is an electronic device 200, comprising at least one sensor 220, a memory 230, and a processor 210; wherein the processor 210 may be configured to obtain (or detect, identify, etc.) first information related to a first walking pattern of a user of the electronic device 200 using the at least one sensor 220, and obtain (or detect, identify, etc.) second information related to a walking environment of the user; wherein the processor 210 may be configured to identify a first value indicating a difference between the first walking pattern and a reference walking pattern based on the first information and third information related to a reference walking pattern stored in the memory 230; wherein the processor 210 may be configured to identify a second value indicating a difference between the first walking pattern and the second walking pattern based on the first information and fourth information related to a second walking pattern of the user stored in the memory 230; wherein the processor 210 may be configured to identify a threshold value related to fall detection based on the first value, the second value, and the second information; and wherein the processor 210 may be configured to perform a function for fall detection based on a comparison between the threshold value and a value obtained by the at least one sensor 220.

[0257] Paragraph 2. The electronic device 200 according to Paragraph 1, wherein the fourth information related to the second walking pattern of the user is identified based on a walking history of the user.

[0258] Paragraph 3. The electronic device 200 according to paragraph 2, wherein the electronic device 200 may include: a communication circuit 240; wherein the at least one sensor 220 may include an acceleration sensor 221, a gyroscope sensor 222, and an air pressure sensor 223; wherein the processor 210 may also be configured to: obtain first information related to the first walking pattern based on first data obtained using the acceleration sensor 220 during a specified time interval; and / or wherein the processor 210 may be configured to obtain second information related to the walking environment of the user based on second data obtained using at least one of the acceleration sensor 221, the gyroscope sensor 222, the air pressure sensor 223, and the communication circuit 240. Optionally, the processor 210 may be configured to: receive third information related to the reference walking pattern from a server connected to the electronic device 200 using the communication circuit 240; wherein the processor 210 may also be configured to store the third information in the memory 230, and wherein the third information may be obtained based on a plurality of walking patterns related to a plurality of users stored in the server.

[0259] Paragraph 4. An electronic device 200 according to paragraph 3, wherein the processor 210 can also be configured to: obtain the trend of acceleration corresponding to each of the three axis directions during a specified time interval; and wherein the processor 210 can also be configured to identify the user's first walking pattern based on the difference of the sliding window sum, wherein the difference of the sliding window sum is identified based on the trend of acceleration.

[0260] Paragraph 5. An electronic device 200 according to paragraph 3 or paragraph 4, wherein the second information related to the user's walking environment may include at least one of information about the slope of a path or route on which the electronic device 200 is moving, information about the moving state of the electronic device 200, and information about the weather during a specified time interval.

[0261] Paragraph 6. An electronic device 200 according to paragraph 5, wherein the processor 210 can also be configured to obtain information about the movement state of the electronic device 200 by setting at least one of the second data as input data of a specified model indicated by multiple parameters stored in the memory 230; and wherein the multiple parameters of the specified model can be configured based on learning of third data obtained according to the walking history of the user.

[0262] Paragraph 7. An electronic device 200 according to paragraph 1 or paragraph 2, wherein the electronic device 200 may include a communication circuit 240; wherein the processor 210 may be configured to receive third information related to a reference walking pattern from a server connected to the electronic device 200 using the communication circuit 240; wherein the processor 210 may also be configured to store the third information in the memory 230; and wherein the third information may be obtained based on multiple walking patterns related to multiple users stored in the server.

[0263] Paragraph 8. An electronic device 200 according to any of paragraphs 1 to 7, wherein the processor 210 can also be configured to: identify the first value based on the Euclidean distance between the value associated with the first walking pattern and the value associated with the reference walking pattern; and wherein the processor 210 can also be configured to identify the second value based on the Euclidean distance between the value associated with the first walking pattern and the value associated with the second walking pattern.

[0264] Paragraph 9. An electronic device 200 according to paragraph 2, wherein the processor 210 can also be configured to: perform learning of the user's walking pattern based on the user's walking history; wherein the processor 210 can also be configured to: obtain fourth information related to the second walking pattern based on performing learning of the user's walking pattern; and wherein the processor 210 can also be configured to store the fourth information in the memory 230.

[0265] Paragraph 10. An electronic device 200 according to paragraph 9, wherein the processor 210 can also be configured to identify a group including values ​​related to the walking pattern according to multiple steps of the user based on the user's walking history; wherein the processor 210 can also be configured to identify at least a part of the group based on information about fitness according to multiple steps, wherein the information about fitness is obtained based on the group; and wherein the processor 210 can also be configured to perform learning of the user's walking pattern based on at least a part of the group.

[0266] Paragraph 11. An electronic device 200 according to paragraph 10, wherein the processor 210 can also be configured to: use at least one sensor 220 to identify the user's first step; wherein the processor 210 can also be configured to: based on identifying that the number of first steps is equal to or greater than a specified number, identify a characteristic value according to the first step; and wherein the processor 210 can also be configured to organize a group based on the characteristic value according to the first step.

[0267] Paragraph 12. An electronic device 200 according to paragraph 11, wherein the processor 210 can also be configured to identify a value associated with a walking pattern according to a first step based on a characteristic value according to the first step; wherein the processor 210 can also be configured to identify a value associated with a walking pattern according to a second step based on a characteristic value stored in the memory 230 according to a second step of the user that occurred before the first step was identified; and wherein the processor 210 can also be configured to organize the value associated with a walking pattern according to the first step and the value associated with a walking pattern according to the second step into a group including values ​​associated with a walking pattern according to multiple steps.

[0268] Paragraph 13. An electronic device 200 according to any one of paragraphs 1 to 12, wherein the processor 210 can also be configured to run one of the first mode to the fourth mode; wherein the processor 210 can also be configured to: based on the first mode, initialize the user's walking history and the fourth information related to the second walking pattern stored in the memory 230; wherein the processor 210 can also be configured to: based on the second mode, use at least one sensor 220 to obtain data related to the user's walking pattern; wherein the processor 210 can also be configured to: based on the third mode, perform learning of the data related to the user's walking pattern; and wherein the processor 210 can also be configured to stop performing learning of the data related to the user's walking pattern based on the fourth mode.

[0269] Paragraph 14. An electronic device 200 according to any one of paragraphs 1 to 13, wherein the processor 210 can also be configured to: identify a probability value of a user falling based on the first value, the second value and the second information; and wherein the processor 210 can also be configured to identify one of a plurality of candidate thresholds as a threshold based on the probability value.

[0270] Paragraph 15. An electronic device 200 according to paragraph 14, wherein the processor 210 can also be configured to send a signal to an external electronic device 2100 connected to the electronic device 200 and worn by a user (via a communication circuit 240 included in the electronic device) based on the probability value and the second information, so that the external electronic device 2100 provides a notification warning of the risk of falling through the external electronic device 2100.

[0271] Paragraph 16. A method of an electronic device 200 may include: using at least one sensor 220 of the electronic device 200 to obtain first information related to a first walking pattern of a user of the electronic device 200, and obtaining second information related to the walking environment of the user; identifying a first value indicating the difference between the first walking pattern and a reference walking pattern based on the first information and third information related to the reference walking pattern stored in the memory 230; identifying a second value indicating the difference between the first walking pattern and the second walking pattern based on the first information and fourth information related to the second walking pattern of the user stored in the memory 230; identifying a threshold value related to fall detection based on the first value, the second value and the second information; and performing a function for fall detection based on a comparison between the threshold value and a value obtained by at least one sensor 220.

[0272] Paragraph 17. The method according to paragraph 16, wherein the fourth information related to the user's second walking pattern is identified based on the user's walking history.

[0273] Paragraph 18. A method according to paragraph 17, wherein the method may include: obtaining first information related to a first walking pattern based on first data obtained using an acceleration sensor 221 of the electronic device 200 during a specified time interval; and wherein the method may include: obtaining second information related to the user's walking environment based on second data obtained using at least one of an acceleration sensor 221 of the electronic device 200, a gyroscope sensor 222, an air pressure sensor 223 of the electronic device 200, and a communication circuit 240 of the electronic device 200.

[0274] Paragraph 19. A method according to paragraph 18, wherein the method may include: obtaining the trend of acceleration corresponding to each of the three axis directions during a specified time interval; and wherein the method may include identifying the user's first walking pattern based on the difference of the sliding window sum, wherein the difference of the sliding window sum is identified based on the trend of acceleration.

[0275] Paragraph 20. A method according to paragraph 18 or paragraph 19, wherein the second information related to the user's walking environment may include at least one of information about the slope of the moving path of the electronic device 200, information about the moving state of the electronic device 200, and information about the weather during a specified time interval.

[0276] Paragraph 21. A method according to any one of paragraphs 16 to 20, wherein the method may include identifying a first value based on the Euclidean distance between a value associated with a first walking pattern and a value associated with a reference walking pattern, and wherein the method may include identifying a second value based on the Euclidean distance between a value associated with the first walking pattern and a value associated with a second walking pattern.

[0277] It should be understood that in addition to including the examples, embodiments, aspects, etc. disclosed throughout the above, the present disclosure also contemplates and includes each and every combination of the examples, embodiments, aspects, etc. disclosed throughout the above. For example, every combination of two or more examples disclosed herein should be considered to be included in the present disclosure. In addition, the present disclosure should also be considered to include examples that include each and every combination of various individual features disclosed herein, regardless of whether the individual features belong to the same or different examples, embodiments, aspects, etc. For the purpose of giving examples, even if there is no corresponding text disclosure, the present disclosure should be considered to include examples in which the combination Figure 5 Examples of any one or more individual features disclosed in combination with any one or more individual features disclosed in conjunction with FIG. 7 .

[0278] In addition, it should be understood that for all methods described herein (e.g., including incorporating Figure 4 , Figure 5 , Fig. 9 , Fig.10 and Fig.13 The present disclosure also includes versions of each method that omit or reorder one or more of the operations described. For example, the present disclosure includes versions of each method that include only one of any operations shown in each method, and versions of each method that include any combination of two or more of the operations shown in each method.

[0279] The various examples and terms used herein are not intended to limit the technical features described herein to specific examples, and should be understood to include various modifications, equivalents or substitutes of the examples. With respect to the description of the drawings, similar reference numerals may be used for similar or related components. Unless clearly indicated differently in the relevant context, the singular form of the noun corresponding to the project may include one or more of the projects. In this document, each of the phrases such as "A or B", "at least one of A and B", "A and / or B", "at least one of A or B", "at least one of A, B and C", "at least one of A, B or C", "A, B and / or C" and "at least one of A, B or C" may include any one of the phrases together, or all possible combinations thereof. Terms such as "first", "second" or "second" or "second" may be used simply to distinguish a corresponding component from another corresponding component, and do not limit other aspects (e.g., importance or order). When some (e.g., first) components are referred to as being "coupled" or "connected" (with or without the term "functional" or "communicative") to another (e.g., second) component, this means that some components may be connected directly (e.g., wired), wirelessly, or through a third component.

[0280] For example, the term "module" used in various examples of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be a minimum unit or part of a component of an overall configuration or a component that performs one or more functions. For example, according to an example, a module may be implemented in the form of an application specific integrated circuit (ASIC).

[0281] Various examples of this document may be implemented as software (e.g., a program) including one or more instructions stored in a storage medium (or external memory) readable by a device (e.g., wearable device 100). For example, a processor (e.g., a processor) of a device (e.g., wearable device 100) may call and execute at least one of the one or more instructions stored from the storage medium. This enables the device to operate according to at least one command called to perform at least one function. The one or more instructions may include code generated by a compiler or code that can be executed by an interpreter. The device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, the term "non-transitory" only means that the storage medium is tangible and does not include a device that signals (e.g., electromagnetic waves), and the term distinguishes between the case of semi-permanent storage of data and the case of temporary storage of data.

[0282] According to an example, the methods according to various examples disclosed in this document may be provided by being 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 device-readable storage medium (e.g., a compact disc read-only memory (CD-ROM)), or directly or through an application store (e.g., Play Store TM ) is distributed (e.g., downloaded or uploaded) online or between two user devices (e.g., smart phones). In the case of online distribution, at least some of the computer program product may be temporarily stored or temporarily created on a device-readable storage medium (such as a manufacturer's server, a server in an application store, or a memory in a relay server).

[0283] According to various examples, each of the above-mentioned components (e.g., modules or programs) can include a single object or multiple objects, and some of the multiple objects can be separated and arranged in other components. According to various examples, one or more components or operations of the above-mentioned corresponding components can be omitted, or one or more other components or operations can be added. Alternatively or additionally, multiple components (e.g., modules or programs) can be integrated into one component. In this case, the integrated component can perform one or more functions of each of the components in the same or similar manner as the corresponding components in the multiple components perform functions before integration. According to various examples, the operations performed by modules, programs or other components can be performed sequentially, in parallel, repeatedly or heuristically, performed in different orders, omitted, or one or more other operations can be added.

Claims

1. An electronic device (200), comprising: at least one sensor (220); Memory (230); as well as A processor, wherein the processor (210) is configured to: Using the at least one sensor (220), obtaining first information related to a first walking pattern of a user of the electronic device (200), and obtaining second information related to a walking environment of the user; identifying a first value indicating a difference between the first walking pattern and the reference walking pattern based on the first information and third information associated with the reference walking pattern stored in the memory (230); identifying a second value indicating a difference between the first walking pattern and the second walking pattern based on the first information and fourth information related to a second walking pattern of the user stored in the memory (230); identifying a threshold associated with fall detection based on the first value, the second value, and the second information; and The function for fall detection is performed based on a comparison between the threshold value and a value obtained by the at least one sensor (220).

2. The electronic device (200) according to claim 1, wherein: The fourth information related to the second walking pattern of the user is identified based on a walking history of the user.

3. The electronic device (200) according to claim 2, further comprising: a communication circuit (240), Wherein, the at least one sensor (220) comprises: Acceleration sensor (221); a gyroscope sensor (222); and Air pressure sensor (223), Wherein, the processor (210) is further configured to: obtaining the first information related to the first walking pattern based on first data obtained using the acceleration sensor (221) during a specified time interval; and The second information related to the walking environment of the user is obtained based on second data obtained using at least one of the acceleration sensor (221), the gyro sensor (222), the air pressure sensor (223) and the communication circuit (240).

4. The electronic device (200) according to claim 3, wherein: The processor (210) is further configured to: obtaining a trend of the acceleration corresponding to each of the three axis directions during the specified time interval; as well as The first walking pattern of the user is identified based on a sliding window sum difference, wherein the sliding window sum difference is identified based on a trend of the acceleration.

5. The electronic device (200) according to any one of claims 3 to 4, wherein: The second information related to the walking environment of the user includes at least one of information about the slope of the path on which the electronic device (200) is moving, information about the moving state of the electronic device (200), and information about the weather during the specified time interval.

6. The electronic device (200) according to claim 5, wherein: The processor (210) is further configured to obtain information about the movement state of the electronic device (200) by setting at least one of the second data as input data of a specified model indicated by a plurality of parameters stored in the memory (230), The plurality of parameters of the designated model are configured based on learning of third data obtained from the walking history of the user.

7. The electronic device (200) according to any one of claims 1 to 2, further comprising: a communication circuit (240), Wherein, the processor (210) is further configured to: receiving the third information related to the reference walking pattern from a server connected to the electronic device (200) using the communication circuit (240), and storing the third information in the memory (230), The third information is obtained based on a plurality of walking patterns associated with a plurality of users stored in the server.

8. The electronic device (200) according to any one of claims 1 to 6, wherein: The processor (210) is further configured to: identifying the first value based on a Euclidean distance between a value associated with the first walking pattern and a value associated with the reference walking pattern, and The second value is identified based on a Euclidean distance between a value associated with the first walking pattern and a value associated with the second walking pattern.

9. The electronic device (200) according to claim 2, wherein: The processor (210) is further configured to: performing learning of the walking pattern of the user based on the walking history of the user, acquiring the fourth information related to the second walking pattern based on performing learning of the walking pattern of the user, and The fourth information is stored in the memory (230).

10. The electronic device (200) according to claim 9, wherein: The processor (210) is further configured to: identifying a group including values ​​related to walking patterns according to a plurality of steps of the user based on the walking history of the user, identifying at least a portion of the population based on information about fitness based on the plurality of steps, wherein the information about fitness is obtained based on the population, and Learning of the walking pattern of the user is performed based on the at least a portion of the group.

11. The electronic device (200) according to claim 10, wherein: The processor (210) is further configured to: identifying a first step of the user using the at least one sensor (220), based on identifying that the number of the first steps is equal to or greater than a specified number, identifying a feature value according to the first steps, and The group is organized based on feature values ​​according to the first step.

12. The electronic device (200) according to claim 11, wherein: The processor (210) is further configured to: identifying a value associated with a walking pattern according to the first step based on the feature value according to the first step, identifying a value associated with the walking pattern according to a second step of the user occurring before the first step is identified based on a feature value associated with the walking pattern stored in the memory (230), and A group including the values ​​associated with the walking pattern according to the plurality of paces is organized based on the value associated with the walking pattern according to the first pace and the value associated with the walking pattern according to the second pace.

13. The electronic device (200) according to any one of claims 1 to 12, wherein: The processor (210) is further configured to: identifying a probability value of the user falling based on the first value, the second value and the second information, and Based on the probability value, one of a plurality of candidate thresholds is identified as the threshold.

14. The electronic device (200) of claim 13, further comprising a communication circuit (240), in, The processor (210) is further configured to: Based on the probability value and the second information, a signal is sent to an external electronic device (2100) connected to the electronic device (200) and worn by the user, causing the external electronic device (2100) to provide a notification warning of the risk of falling through the external electronic device (2100).

15. A method of an electronic device (200), comprising: Using at least one sensor (220) of the electronic device (200), obtaining first information related to a first walking pattern of a user of the electronic device (200), and obtaining second information related to a walking environment of the user; identifying a first value indicating a difference between the first walking pattern and the reference walking pattern based on the first information and third information related to the reference walking pattern stored in a memory (230) of the electronic device (200); identifying a second value indicating a difference between the first walking pattern and the second walking pattern based on the first information and fourth information related to a second walking pattern of the user stored in the memory (230); identifying a threshold associated with fall detection based on the first value, the second value, and the second information; as well as The function for fall detection is performed based on a comparison between the threshold value and a value obtained by the at least one sensor (220).

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