WO2022215852A1

WO2022215852A1 - Electronic device and method of correcting fall detection in electronic device

Info

Publication number: WO2022215852A1
Application number: PCT/KR2022/002223
Authority: WO
Inventors: 김현성; 박찬웅; 강정관; 신승혁
Original assignee: 삼성전자 주식회사
Priority date: 2021-04-08
Filing date: 2022-02-15
Publication date: 2022-10-13
Also published as: KR20220139601A

Abstract

Various embodiments of the present disclosure disclose a method and a device for correcting a fall determination criterion in an electronic device. An electronic device according to various embodiments comprises: a sensor module; a memory; and a processor operatively connected to the sensor module and the memory, wherein the processor may be configured to: obtain sensing data from the sensor module; determine whether a fall situation is triggered, on the basis of the sensed data; if it is determined that the fall situation is triggered, determine a first state of a user; if the first state of the user corresponds to a specified condition, disregard the triggering of the fall situation; correct first threshold information related to the first state of the user; and apply the corrected first threshold information to reference information for determining a fall to monitor the triggering of the fall situation. Various embodiments are possible.

Description

Electronic Devices and Methods of Compensation for Fall Detection in Electronic Devices

Various embodiments of the present disclosure disclose a method and apparatus for calibrating a fall determination criterion in an electronic device.

With the development of digital technology, PDA (personal digital assistant), smart phone (smart phone), tablet PC (personal computer), artificial intelligence speaker (AI (artificial intelligent) speaker), wearable device (wearable device), digital camera ( Various types of electronic devices, such as digital cameras), and/or Internet of things (IoT) devices, are widely used. In order to support and increase functions of the electronic device, a hardware part and/or a software part of the electronic device are continuously being developed.

As an example, the electronic device may include a sensor that detects (or measures) various information (or data), and may provide various related information (or functions) based on the sensed data obtained from the sensor. For example, the electronic device may measure a bio-signal using a sensor, and may provide various information related to a user's health based on the measured bio-signal.

As another example, the electronic device may determine the user's fall situation using a sensor (eg, an inertial sensor). For example, the electronic device may detect an impact amount detected when the user falls by using an inertial sensor, and may determine that the impact amount is greater than or equal to a predetermined threshold value as a fall. According to an embodiment of the present disclosure, when determining that the fall is a fall, the electronic device may notify the user of the accident situation to a designated emergency contact.

However, in the conventional electronic device, when the amount of impact when the user falls is equal to or greater than a predetermined threshold, it is determined that the user falls. For this reason, even in a situation in which an amount of impact similar to that of a fall occurs in the user's daily life, false fall recognition may occur frequently. For example, in a situation in which the user has a high-activity movement (eg, activity exercise such as basketball or soccer), a relatively strong amount of impact may occur frequently. Even in this situation, the electronic device may Fall notifications may occur frequently, and users may have inconvenience due to unnecessary notifications.

In addition, users who are usually active and have motor nerves can respond quickly in case of a fall to protect against shock, whereas users who are not usually active and have poor motor nerves may respond slowly or may not be able to cope with a fall. have. In other words, in the case of a user with motor nerves, the impact on the body can be minimized by reaching out and touching the ground during a fall. You may not be able to reach out, and the impact on your body may be greater. This can cause greater bodily harm. For example, older people are less active and often have poor motor skills, so they may not be able to cope with falls. In this case, the user's body may absorb all the shock from the fall, the actual electronic device may not properly recognize the amount of impact caused by the fall, and as a result, the electronic device may not detect the user's actual fall situation. .

Various embodiments are disclosed with respect to a method and an apparatus capable of correcting a fall determination criterion of an electronic device.

Various embodiments disclose a method and apparatus capable of reducing a user's fall misperception by recognizing a user's activity state in an electronic device, and predicting a user's usual activity tendency to improve the fall detection .

In various embodiments, the electronic device determines a user's activity status for a short-term using a sensor (eg, an acceleration sensor and a barometric pressure sensor), and determines the user's activity status for a short-term period. Disclosed are a method and an apparatus capable of correcting a user awareness check threshold based on an activity state.

According to various embodiments, the electronic device predicts a user's activity propensity for a long-term period using a sensor (eg, an acceleration sensor), and predicts the user's activity propensity for the long-term period. Disclosed is a method and an apparatus capable of correcting a fall shock threshold suitable for a user based on the present invention.

Various embodiments provide a method and apparatus capable of correcting a fall detection threshold of an electronic device based on a fall detection threshold set for a user from an external device with which the electronic device is interlocked.

In various embodiments, the electronic device acquires correction information related to the fall determination criterion correction based on the user's activity state in the short-term period, the user's activity tendency in the long-term period, and/or the control of the external device, and based on the correction information Disclosed is a method and an apparatus capable of correcting the fall determination criteria.

An electronic device according to an embodiment of the present disclosure includes a sensor module, a memory, and a processor operatively connected to the sensor module and the memory, wherein the processor acquires sensing data from the sensor module, and the sensing data determines whether a fall situation trigger is based on It may be configured to correct the first threshold information related to the user's first state, and monitor the fall situation trigger by applying the corrected first threshold information to reference information for determining a fall.

An operation method of an electronic device according to an embodiment of the present disclosure includes an operation of acquiring sensed data from a sensor module, an operation of determining whether a fall situation is triggered based on the sensing data, and an operation of determining whether the fall situation triggers when the user's The operation of determining the state, the operation of ignoring the fall situation trigger if the user's state corresponds to a specified condition, correcting threshold information related to the user's state, and using the corrected threshold information as a criterion for determining a fall It may include the operation of monitoring the fall situation trigger by applying the information.

In various embodiments of the present disclosure to solve the above problems, a computer-readable recording medium recording a program for executing the method in a processor may be included.

Further scope of applicability of the present disclosure will become apparent from the following detailed description. However, it should be understood that the detailed description and specific embodiments, such as preferred embodiments of the present disclosure, are given by way of example only, since various changes and modifications within the spirit and scope of the present disclosure may be clearly understood by those skilled in the art.

According to an electronic device and an operating method thereof according to an embodiment of the present disclosure, the electronic device reduces the user's fall erroneous recognition based on the user's activity state, and the degree (or sensitivity ( sensitivity)) can be improved.

According to an embodiment of the present disclosure, the user's activity state is determined in a short-term, and the user's awareness check threshold is corrected based on this to correct unnecessary alarms (eg, false alarms) according to the false fall recognition. ) frequency can be reduced, thereby improving the usability of the electronic device.

According to an embodiment of the present disclosure, the fall detection sensitivity is adjusted by predicting the user's activity tendency in a long-term and correcting the most suitable fall detection threshold value for the user based on this. Improvements can be made to better detect falls even for undetected users.

According to an embodiment of the present disclosure, an electronic device supports a guardian to forcibly correct a fall detection threshold for a user vulnerable to a fall, so that the guardian can better monitor and care for a user vulnerable to a fall. can

In addition, various effects directly or indirectly identified through this document may be provided.

In connection with the description of the drawings, the same or similar reference numerals may be used for the same or similar components.

1 is a block diagram of an electronic device in a network environment according to various embodiments of the present disclosure;

2 is a diagram illustrating a perspective view of a front surface of an electronic device according to an exemplary embodiment.

3 is a diagram illustrating a perspective view of a rear surface of an electronic device according to an exemplary embodiment.

4 is a diagram schematically illustrating an example of a block configuration of an electronic device according to an embodiment of the present disclosure.

5 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present disclosure.

6 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present disclosure.

7 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present disclosure.

8 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present disclosure.

9 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present disclosure.

10 is a diagram illustrating an example of a user interface related to a fall situation notification provided by an electronic device according to an embodiment of the present disclosure.

11 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present disclosure.

12 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present disclosure.

13 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present disclosure.

14 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present disclosure.

15 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present disclosure.

16 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present disclosure.

17 is a diagram illustrating an example of detecting a code conversion point in an electronic device according to an embodiment of the present disclosure.

18 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present disclosure.

19 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present disclosure.

20 is a diagram illustrating an example of a user interface provided by an electronic device according to an embodiment of the present disclosure.

21 is a diagram illustrating an example of providing a fall detection service in an electronic device according to an embodiment of the present disclosure.

1 is a block diagram of an electronic device 101 in a network environment 100 according to various embodiments of the present disclosure.

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

The processor 120, for example, executes software (eg, a program 140) to execute at least one other component (eg, a hardware or software component) of the electronic device 101 connected to the processor 120. It can control and perform various data processing or operations. According to one embodiment, as at least part of data processing or operation, the processor 120 converts commands or data received from other components (eg, the sensor module 176 or the communication module 190 ) to the volatile memory 132 . may be stored in , process commands or data stored in the volatile memory 132 , and store the result data in the non-volatile memory 134 . According to an embodiment, the processor 120 is a main processor 121 (eg, a central processing unit (CPU) or an application processor (AP)) or an auxiliary processor capable of operating independently or together with it ( 123) (eg, graphic processing unit (GPU), neural processing unit (NPU), image signal processor (ISP), sensor hub processor, or communication processor (CP, communication processor)) may be included. For example, when the electronic device 101 includes the main processor 121 and the sub-processor 123 , the sub-processor 123 uses less power than the main processor 121 or is set to be specialized for a specified function. can The auxiliary processor 123 may be implemented separately from or as a part of the main processor 121 .

The auxiliary processor 123 is, for example, on behalf of the main processor 121 or the main processor 121 while the main processor 121 is in an inactive (eg, sleep) state. At least one of the components of the electronic device 101 (eg, the display module 160 , the sensor module 176 , or At least some of functions or states related to the communication module 190 may be controlled. According to an embodiment, the coprocessor 123 (eg, an image signal processor or a communication processor) may be implemented as part of another functionally related component (eg, the camera module 180 or the communication module 190). have. According to an embodiment, the auxiliary processor 123 (eg, a neural network processing unit) may include a hardware structure specialized for processing an artificial intelligence model. Artificial intelligence models can be created through machine learning. Such learning may be performed, for example, in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (eg, the server 108). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but in the above example not limited The artificial intelligence model may include a plurality of artificial neural network layers. Artificial neural networks include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the above example. The artificial intelligence model may include, in addition to, or alternatively, a software structure in addition to the hardware structure.

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

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 an application 146 . have.

The input module 150 may receive a command or data to be used by a component (eg, the processor 120 ) of the electronic device 101 from the outside (eg, a user) of the electronic device 101 . The input module 150 may include, for example, a microphone, a mouse, a keyboard, a key (eg, a button), or a digital pen (eg, a stylus pen).

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

The display module 160 may visually provide information to the outside (eg, a user) of the electronic device 101 . The display module 160 may include, for example, a control circuit for controlling a display, a hologram device, or a projector and a corresponding device. According to an embodiment, the display module 160 may include a touch sensor configured to sense a touch or a pressure sensor configured to measure the intensity of a force generated by the touch.

The audio module 170 may convert a sound into an electric signal or, conversely, convert an electric signal into a sound. According to an embodiment, the audio module 170 acquires a sound through the input module 150 or an external electronic device (eg, a sound output module 155 ) directly or wirelessly connected to the electronic device 101 . The electronic device 102) (eg, a speaker or headphones) may output a sound.

The sensor module 176 detects an operating state (eg, power or temperature) of the electronic device 101 or an external environmental state (eg, a user state), and generates an electrical signal or data value corresponding to the sensed state. can do. According to an embodiment, the sensor module 176 may include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, It may include a temperature sensor, a humidity sensor, or an illuminance sensor.

The interface 177 may support one or more specified protocols that may be used by the electronic device 101 to directly or wirelessly connect with an external electronic device (eg, 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.

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

The haptic module 179 may convert an electrical signal into a mechanical stimulus (eg, vibration or movement) or an electrical stimulus that the user can perceive through tactile or kinesthetic sense. According to an embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 may capture still images and moving images. According to an embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 may manage power supplied 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).

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

The communication module 190 is a direct (eg, wired) communication channel or a wireless communication channel between the electronic device 101 and an external electronic device (eg, the electronic device 102, the electronic device 104, or the server 108). It can support establishment and communication performance through the established communication channel. The communication module 190 may include one or more communication processors that operate independently of the processor 120 (eg, an application processor) and support direct (eg, wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (eg, 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 (eg, : It may include a local area network (LAN) communication module, or a power line communication module). A corresponding communication module among these communication modules is a first network 198 (eg, a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (eg, legacy It may communicate with the external electronic device 104 through a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (eg, a telecommunication network such as a LAN or a wide area network (WAN)). These various types of communication modules may be integrated into one component (eg, a single chip) or may be implemented as a plurality of components (eg, multiple chips) separate from each other. The wireless communication module 192 uses subscriber information (eg, International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199 . The electronic device 101 may be identified or authenticated.

The wireless communication module 192 may support a 5G network after a 4G network and a next-generation communication technology, for example, a new radio access technology (NR). NR access technology includes high-speed transmission of high-capacity data (eMBB, enhanced mobile broadband), minimization of terminal power and massive machine type communications (mMTC), or high reliability and low latency (URLLC, ultra-reliable and low-latency). communications) can be supported. The wireless communication module 192 may support a high frequency band (eg, mmWave band) to achieve a high data rate, for example. The wireless communication module 192 uses various techniques for securing performance in a high-frequency band, for example, beamforming, massive multiple-input and multiple-output (MIMO), all-dimensional multiplexing. It may support technologies such as input/output (FD-MIMO, full dimensional MIMO), an array antenna, analog beam-forming, or a large scale antenna. The wireless communication module 192 may support various requirements defined in the electronic device 101 , an external electronic device (eg, the electronic device 104 ), or a network system (eg, the second network 199 ). According to an embodiment, the wireless communication module 192 may include a peak data rate (eg, 20 Gbps or more) for realizing eMBB, loss coverage (eg, 164 dB or less) for realizing mMTC, or U-plane latency for realizing URLLC ( Example: Downlink (DL) and uplink (UL) each 0.5 ms or less, or round trip 1 ms or less) can be supported.

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

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, the mmWave antenna module comprises a printed circuit board, an RFIC disposed on or adjacent to a first side (eg, bottom side) of the printed circuit board and capable of supporting a designated high frequency band (eg, mmWave band); and a plurality of antennas (eg, an array antenna) disposed on or adjacent to a second side (eg, top or side) of the printed circuit board and capable of transmitting or receiving signals of the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (eg, a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and a signal ( e.g. commands or data) can be exchanged with each other.

According to an embodiment, the command or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199 . Each of the external

electronic devices

102 or 104 may be the same as or different from the electronic device 101 . According to an embodiment, all or part of the operations performed by the electronic device 101 may be executed by one or more external

electronic devices

102 , 104 , or 108 . For example, when the electronic device 101 needs to perform a function or service automatically or in response to a request from a user or other device, the electronic device 101 may perform the function or service itself instead of executing the function or service itself. Alternatively or additionally, one or more external electronic devices may be requested to perform at least a part of the function or the service. One or more external electronic devices that have received the request may execute at least a part of the requested function or service, or an additional function or service related to the request, and transmit a result of the execution to the electronic device 101 . The electronic device 101 may process the result as it is or additionally and provide it as at least a part of a response to the request. For this, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an Internet of things (IoT) device. The server 108 may be an intelligent server using machine learning and/or neural networks. According to an embodiment, the external electronic device 104 or the server 108 may be included in the second network 199 . The electronic device 101 may be applied to an intelligent service (eg, smart home, smart city, smart car, or health care) based on 5G communication technology and IoT-related technology.

The electronic device according to various embodiments disclosed in this document may have various types of devices. The electronic device may include, for example, a portable communication device (eg, a smart phone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a home appliance device. The electronic device according to the embodiment of the present document is not limited to the above-described devices.

The various embodiments of this document and terms used therein are not intended to limit the technical features described in this document to specific embodiments, but it should be understood to include various modifications, equivalents, or substitutions of the embodiments. In connection with the description of the drawings, like reference numerals may be used for similar or related components. The singular form of the noun corresponding to the item may include one or more of the item, unless the relevant context clearly dictates otherwise. As used herein, "A or B", "at least one of A and B", "at least one of A or B", "A, B or C", "at least one of A, B and C", and "A , B, or C," each of which may include any one of the items listed together in the corresponding one of the phrases, or all possible combinations thereof. Terms such as “first”, “second”, or “first” or “second” may simply be used to distinguish the component from other such components, and refer to those components in other aspects (e.g., importance or order) is not limited. It is said that one (eg, first) component is “coupled” or “connected” to another (eg, second) component, with or without the terms “functionally” or “communicatively”. When referenced, it means that one component can be connected to the other component directly (eg by wire), wirelessly, or through a third component.

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

Various embodiments of the present document include one or more instructions stored in a storage medium (eg, internal memory 136 or external memory 138) readable by a machine (eg, electronic device 101). may be implemented as software (eg, the program 140) including For example, the processor (eg, the processor 120 ) of the device (eg, the electronic device 101 ) may call at least one of the one or more instructions stored from the storage medium and execute it. This makes it possible for the device to be operated to perform at least one function according to the called at least one command. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' only means that the storage medium is a tangible device and does not contain a signal (eg, electromagnetic wave), and this term is used in cases where data is semi-permanently stored in the storage medium and It does not distinguish between temporary storage cases.

According to one embodiment, the method according to various embodiments disclosed in this document may be provided in a computer program product (computer program product). Computer program products may be traded between sellers and buyers as commodities. The computer program product is distributed in the form of a machine-readable storage medium (eg compact disc read only memory (CD-ROM)), or via an application store (eg Play Store ^TM ) or on two user devices ( It can be distributed (eg downloaded or uploaded) directly, online between smartphones (eg: smartphones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created in a machine-readable storage medium such as a memory of a server of a manufacturer, a server of an application store, or a relay server.

According to various embodiments, each component (eg, a module or a program) of the above-described components may include a singular or a plurality of entities, and some of the plurality of entities may be separately disposed in other components. have. According to various embodiments, one or more components or operations among the above-described corresponding components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (eg, a module or a program) may be integrated into one component. In this case, the integrated component may perform one or more functions of each component of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. . According to various embodiments, operations performed by a module, program, or other component are executed sequentially, in parallel, repetitively, or heuristically, or one or more of the operations are executed in a different order. , may be omitted, or one or more other operations may be added.

Hereinafter, an example of the electronic device 101 including a sensor according to various embodiments will be described.

2 is a diagram illustrating a perspective view of a front surface of an electronic device according to an exemplary embodiment. 3 is a diagram illustrating a perspective view of a rear surface of an electronic device according to an exemplary embodiment.

2 and 3 , an electronic device 101 according to an exemplary embodiment has a first side (or front side) 210A, a second side (or back side) 210B, and a first side 210A. and a side surface 210C surrounding a space between the second surface 210B, and a housing 210 connected to at least a portion of the housing 210 and configured to attach the electronic device 101 to a user's body part (eg, a wrist). and/or may include

fastening members

250 and 260 configured to be detachably attached to the ankle). In another embodiment (not shown), the housing 210 may refer to a structure that forms part of the first side 210A, the second side 210B, and the side surface 210C of FIGS. 2 and 3 . may be

According to an embodiment, the first surface 210A may be formed by the front plate 201 (eg, a glass plate including various coating layers or a polymer plate) at least a portion of which is substantially transparent.

According to an embodiment, the second surface 210B may be formed by a substantially opaque back plate 207 . The back plate 207 may be made of, for example, coated or tinted glass, ceramic, polymer, metal (eg, aluminum, stainless steel (STS), or magnesium), or a combination of at least two of the foregoing. can be formed by

According to one embodiment, the side surface 210C is coupled to the front plate 201 and the rear plate 207 by a side bezel structure (or “side member”) 206 comprising a metal and/or a polymer. can be formed. In some embodiments, the back plate 207 and the side bezel structure 206 are integrally formed and may include the same material (eg, a metal material such as aluminum).

According to an embodiment, the binding

members

250 and 260 may be formed of various materials and shapes. For example, the binding

members

250 and 260 may be formed so that an integral and a plurality of unit links can flow with each other by a woven fabric, leather, rubber, urethane, metal, ceramic, or a combination of at least two of the above materials. .

According to an embodiment, the electronic device 101 includes the display 220 (eg, the display module 160 of FIG. 1 ), the

audio modules

205 and 208 , and the sensor module 211 (eg, the sensor of FIG. 1 ). module 176 ),

key input devices

202 , 203 , 204 , and a connector hole 209 . In some embodiments, the electronic device 101 omits at least one of the components (eg, the

key input device

202 , 203 , 204 , the connector hole 209 , or the audio module 205 , 208 ) or Other components may be additionally included.

According to one embodiment, the display 220 may be visually exposed through, for example, a substantial portion of the front plate 201 . The shape of the display 220 may be a shape corresponding to the shape of the front plate 201 , and may have various shapes such as a circle, an oval, or a polygon. The display 220 may be coupled to or disposed adjacent to a touch sensing circuit, a pressure sensor capable of measuring the intensity (pressure) of a touch, and/or a fingerprint sensor.

According to an embodiment, the

audio modules

205 and 208 may include a microphone hole 205 and a speaker hole 208 . In the microphone hole 205, a microphone for acquiring an external sound may be disposed therein, and in some embodiments, a plurality of microphones may be disposed to detect the direction of the sound. The speaker hole 208 may be used as an external speaker and/or a receiver for calls. In some embodiments, the speaker hole 208 and the microphone hole 205 may be implemented as a single hole, or a speaker may be included without the speaker hole 208 (eg, a piezo speaker).

According to an embodiment, the sensor module 211 may generate an electrical signal or data value corresponding to an internal operating state of the electronic device 101 or an external environmental state. The sensor module 211 may include, for example, a biometric sensor module (eg, a heart rate measurement (HRM) sensor, a photoplethysmography (PPG) sensor) disposed on the second surface 210B of the housing 210 . . The electronic device 101 may include a sensor module not shown, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a color sensor, an IR (infrared) sensor, and a biometric sensor (eg, a fingerprint). sensor and/or iris sensor), a temperature sensor, a humidity sensor, and an illuminance sensor.

According to an embodiment, the sensor module 211 may include an electrode region (not shown) forming a part of the surface of the electronic device 101 and a biosignal detection circuit (not shown) electrically connected to the electrode region. have. For example, the electrode region may include a first electrode region (not shown) and a second electrode region (not shown) disposed on the second surface 210B of the housing 210 . The sensor module 211 may be configured such that the electrode region acquires an electrical signal from a part of the user's body, and the biosignal detection circuit detects the user's biometric information based on the electric signal.

According to one embodiment, the

key input device

202 , 203 , 204 includes a wheel key 202 disposed on a first face 210A of the housing 210 and rotatable in at least one direction, and/or a housing ( It may include side

key buttons

203 and 204 disposed on the side 210C of 210 . The wheel key 202 may have a shape corresponding to the shape of the front plate 201 . In another embodiment, the electronic device 101 may not include some or all of the above-mentioned

key input devices

202, 203, 204 and the non-included

key input devices

202, 203, 204 display a display. It may be implemented in other forms, such as soft keys on 220 .

According to an embodiment, the connector hole 209 may accommodate a connector (eg, a USB connector) for transmitting/receiving power and/or data with an external electronic device, and a connector for transmitting/receiving an audio signal with an external electronic device. It may include another connector hole (not shown) that can accommodate the . The electronic device 101 may further include, for example, a connector cover (not shown) that covers at least a portion of the connector hole 209 and blocks the inflow of foreign substances into the connector hole 209 .

According to an embodiment, the binding

members

250 and 260 may be detachably attached to at least a partial region of the housing 210 using the locking

members

251 and 261 . The binding

members

250 and 260 may include one or more of the fixing member 252 , the fixing member fastening hole 253 , the band guide member 254 , and the band fixing ring 255 .

The fixing member 252 may be configured to fix the housing 210 and the binding

members

250 and 260 to a part of the user's body (eg, a wrist or an ankle). The fixing member fastening hole 253 may correspond to the fixing member 252 to fix the housing 210 and the

coupling members

250 and 260 to a part of the user's body. The band guide member 254 is configured to limit the range of motion of the fixing member 252 when the fixing member 252 is fastened with the fixing member fastening hole 253, so that the fixing

members

250 and 260 are attached to a part of the user's body. It can be made to adhere and bind. The band fixing ring 255 may limit the range of movement of the fixing

members

250 and 260 in a state in which the fixing member 252 and the fixing member coupling hole 253 are fastened.

According to an embodiment, the electronic device 101 may further include components not shown. For example, the electronic device 101 may include an antenna (eg, the antenna module 197 of FIG. 1 ), a support member (eg, a bracket), a battery (eg, the battery 189 of FIG. 1 ), a printed circuit board, and / or a sealing member. The support member may be formed of, for example, a metallic material and/or a non-metallic (eg, polymer) material. The support member may have a display 220 coupled to one surface and a printed circuit board coupled to the other surface. The printed circuit board may be equipped with a processor (eg, processor 120 in FIG. 1 ), memory (eg, memory 130 in FIG. 1 ), and/or an interface (eg, interface 177 in FIG. 1 ). have.

According to an embodiment, the processor (eg, the processor 120 of FIG. 1 ) may include, for example, a micro controller unit (MCU), a central processing unit (CPU), a graphic processing unit (GPU), and a sensor. It may include one or more of a sensor processor, an application processor (AP), and a communication processor (CP).

According to an embodiment, the memory (eg, the memory 130 of FIG. 1 ) may include, for example, a volatile memory or a non-volatile memory.

According to an embodiment, the interface (eg, interface 177 in FIG. 1 ) may be, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, and/or an audio interface. may include. The interface may, for example, electrically or physically connect the electronic device 101 to an external electronic device, and may include a USB connector, an SD card/MMC connector, or an audio connector.

According to one embodiment, a battery (eg, battery 189 in FIG. 1 ) is a device for supplying power to at least one component of electronic device 101 , for example a non-rechargeable primary battery; rechargeable secondary cells, or fuel cells. At least a portion of the battery may be disposed substantially coplanar with the printed circuit board, for example. The battery may be integrally disposed inside the electronic device 101 or may be detachably disposed with the electronic device 101 .

According to one embodiment, the antenna (eg, the antenna module 197 of FIG. 1 ) may include, for example, a near field communication (NFC) antenna, a wireless charging antenna, and/or a magnetic secure transmission (MST) antenna. can The antenna may, for example, perform short-range communication with an external electronic device or wirelessly transmit/receive power required for charging, and may transmit a short-range communication signal or a magnetic-based signal including payment data. In other embodiments, the antenna structure may be formed by some or a combination of the side bezel structure 206 and/or support members.

According to an embodiment, the sealing member may be configured to block moisture and foreign substances from flowing into a space surrounded by the side bezel structure 206 and the rear plate 207 from the outside.

Referring to FIG. 4 , the electronic device 101 according to an embodiment includes a communication module 190 , a display module 160 , and a sensor module 410 (eg, the sensor module 176 of FIG. 1 and/or FIG. 2 ). and the sensor module 211 of FIG. 3 ), the memory 130 , and/or the processor 120 .

According to an embodiment, the communication module 190 is a legacy network (eg, a 3G network and/or a 4G network), a 5G network, an out of band (OOB), and/or a next-generation communication technology (eg, a new radio (NR) technology). ) can be supported. According to an embodiment, the communication module 190 may correspond to the wireless communication module 192 as illustrated in FIG. 1 . According to an embodiment, the electronic device 101 communicates with an external device (eg, the server 108 and/or other

electronic devices

102 and 104 of FIG. 1 ) through a network using the communication module 190 . can be done According to an embodiment, the communication module 190 may transmit data generated in the electronic device 101 to an external device and may receive data transmitted from the external device.

According to an embodiment, the display module 160 may correspond to the display module 160 as described in the description with reference to FIG. 1 . According to an embodiment, the display module 160 may visually provide various information to the outside (eg, a user) of the electronic device 101 . According to an embodiment, the display module 160 includes a touch sensing circuit (or a touch sensor) (not shown), a pressure sensor capable of measuring the intensity of a touch, and/or a touch panel (eg, a magnetic field type stylus) detecting a stylus pen. : digitizer).

According to one embodiment, the display module 160 is a signal (eg, voltage, light quantity, resistance, electromagnetic signal and / Alternatively, a touch input and/or a hovering input (or a proximity input) may be sensed by measuring a change in the amount of electric charge. According to an embodiment, the display module 160 may include a liquid crystal display (LCD), an organic light emitted diode (OLED), and an active matrix organic light emitted diode (AMOLED). According to some embodiments, the display module 160 may be configured as a flexible display.

According to an embodiment, the display module 160 may visually provide various information (eg, a user interface) related to setting of a fall determination criterion and various result information processed for detection of a fall under the control of the processor 120 . have.

According to an embodiment, the sensor module 410 may correspond to the sensor module 176 as described in the description with reference to FIG. 1 and/or the sensor module 211 as described in the description with reference to FIGS. 2 and 3 . can According to the present disclosure, the sensor module 410 may include an acceleration sensor 420 and a barometric pressure sensor 430 . According to some embodiments, the sensor module 410 may include an inertial sensor that can replace the acceleration sensor 420 . According to an embodiment of the present disclosure, the electronic device 101 may detect a user's fall based on sensing data using the acceleration sensor 420 and the barometric pressure sensor 430 of the sensor module 410 .

According to an embodiment, the memory 130 may correspond to the memory 130 as described in the description with reference to FIG. 1 . According to an embodiment, the memory 130 may store various data used by the electronic device 101 . The data may include, for example, input data or output data for an application (eg, the program 140 of FIG. 1 ) and a command related thereto.

According to an embodiment, the memory 130 may include an application related to operating a function (or operation) that may be performed by the processor 120 to perform fall detection by correcting a criterion for determining a fall. can For example, the fall determination criterion correction may be performed by a fall notification application.

According to an embodiment, the fall notification application may be stored as software (eg, the program 140 of FIG. 1 ) on the memory 130 , and may be executable by the processor 120 . According to an embodiment, the fall determination criterion correction function by the fall notification application recognizes the user's activity state to reduce the user's false perception of a fall, and predicts the user's usual activity tendency to improve the fall detection It may be a function that supports

According to an embodiment, the memory 130 may store data related to operating the fall determination criterion correction function. According to an embodiment, the data may include reference information 440 , correction information 445 , state information 450 , and/or sensing data 455 .

In one embodiment, the criterion information 440 may include designated information related to a fall determination criterion for determining a user's fall condition trigger.

In an embodiment, the correction information 445 is based on sensing data (eg, acceleration sensing data and/or air pressure sensing data) acquired from the acceleration sensor 420 and/or the barometric pressure sensor 430 for a specified period of time, It may include at least one correction value (or threshold information) for correcting the fall determination criterion.

According to an embodiment, the correction information 445 may include the user's activity state (eg, relatively short period of time (eg, a relatively short period of time (eg, within 5 minutes, 10 minutes, 30 minutes, ...)) for a short period of time. It may be calculated based on the user's exercise (eg, intense exercise such as basketball or soccer) for a short period of time. According to an embodiment, the correction information 445 includes a result of checking whether or not the user is conscious (eg, whether or not a user's feedback for a fall notification is collected, checking whether the user's movement exists and/or the amount of movement) and whether or not the user is aware of the result. A correction value related to correction of the check threshold value (eg, first threshold information) may be included.

According to an embodiment, the correction information 445 may include a user's activity tendency (eg, relative may be calculated based on the user's accumulated exercise for a long period (eg, the user's accumulated gait duration and/or the number of steps during a specified period). According to an embodiment, the correction information 445 may include a correction value (eg, second threshold information) related to correction of the fall impact threshold. According to an embodiment, the correction information 445 may include a correction value (eg, third threshold information) related to a fall detection correction level received from a specified external device interworking with the electronic device 101 .

In one embodiment, the state information 450 is a user's first state (eg, user of activity status) may be included. According to an embodiment, the state information 450 is a user's second state (eg: user's activity tendency).

In an embodiment, the sensing data 455 may include acceleration sensing data obtained from the acceleration sensor 420 and air pressure sensing data obtained from the air pressure sensor 430 .

According to an embodiment, the memory 130 may store at least one module for processing a fall determination correction function, which may be performed by the processor 120 . For example, the memory 130 may include at least a portion of the fall detection module 470 , the correction module 480 , and/or the state identification module 490 in the form of software (or the form of instructions).

According to an embodiment, the processor 120 may control a function (or operation) to perform fall detection by correcting a criterion (eg, reference information 440 ) for determining a fall. According to an embodiment, the processor 120 recognizes the user's activity state in the electronic device 101 to reduce the user's false perception of a fall, and predicts the user's usual activity tendency to improve the detection of the fall. You can control the related action (or processing).

According to an embodiment, the processor 120 may control an operation (or function) related to correcting the fall determination criterion of the electronic device 101 . According to an embodiment, the processor 120 recognizes the user's activity state in the electronic device 101 to reduce the user's false perception of a fall, and predicts the user's usual activity tendency to improve the detection of the fall. You can control related actions.

According to an embodiment, the processor 120 determines the user's activity status for a short-term by using the acceleration sensor 420 and the barometric pressure sensor 430 in the electronic device 101 . It is possible to determine and process an operation related to correcting the user awareness check threshold value based on the user's activity state in a short period of time. According to an embodiment, the processor 120 predicts a user's activity propensity for a long-term by using the acceleration sensor 420 in the electronic device 101, and in the long-term period may process an operation related to correcting a fall shock threshold suitable for the user based on the user's activity tendency.

According to an embodiment, the processor 120 may process an operation related to correcting a fall detection threshold of the electronic device based on a fall detection threshold set for a user from an external device interworking with the electronic device 101 . can

According to an embodiment, in the electronic device 101 , the processor 120 provides correction information related to the fall determination criterion correction based on the user's activity state in the short-term period, the user's activity tendency in the long-term period, and/or the control of the external device in the electronic device 101 . , and may process an operation related to correcting a fall determination criterion based on the correction information.

According to an embodiment, the processor 120 may include at least one module for processing a fall determination correction function. For example, the processor 120 may include a fall detection module 470 , a correction module 480 , and/or a status identification module 490 .

According to one embodiment, the fall detection module 470 is based on the sensing data obtained from the acceleration sensor 420 and/or the barometric pressure sensor 430 (eg, the acceleration sensing data and/or the barometric pressure sensing data). It can handle various actions related to detecting a fall situation trigger. Detecting a user's fall situation trigger according to an embodiment will be described with reference to the drawings to be described later.

According to an embodiment, the correction module 480 calculates various correction values related to the fall determination correction based on the acceleration sensing data and/or the air pressure sensing data, and is related to correcting the fall determination criterion based on the correction value. It can handle a variety of actions. Calculating various correction values according to an embodiment, and correcting a fall determination criterion based on the correction values will be described with reference to the drawings to be described later.

According to an embodiment, the state identification module 490 may process various operations related to identifying the user's state based on the acceleration sensing data and/or the air pressure sensing data. It will be described with reference to the drawings to be described later in connection with identifying the user's state according to an embodiment.

According to an embodiment, at least some of the fall detection module 470, the correction module 480 and/or the state identification module 490 are included in the processor 120 as a hardware module (eg, circuitry), or and/or may be implemented as software comprising one or more instructions executable by the processor 120 . For example, operations performed by the processor 120 may be stored in the memory 130 and executed by instructions that cause the processor 120 to operate when executed.

According to various embodiments, the processor 120 may control various operations related to a normal function of the electronic device 101 in addition to the above functions. For example, the processor 120 may control its operation and screen display when a specified application is executed. As another example, the processor 120 may receive input signals corresponding to various touch events or proximity event inputs supported by a touch-based or proximity-based input interface, and control function operation accordingly.

According to various embodiments, the electronic device 101 is not limited to the components illustrated in FIG. 4 , and at least one component may be omitted or added. According to an embodiment, the electronic device 101 may include a voice recognition module (not shown). For example, the voice recognition module (not shown) may represent an embedded ASR (eASR) module and/or an embedded NLU (eNLU).

Various embodiments described in the present disclosure may be implemented in a recording medium readable by a computer or a similar device using software, hardware, or a combination thereof. In various embodiments, the recording medium determines whether to trigger a fall situation based on an operation of acquiring sensing data from the sensor module 410 (eg, the acceleration sensor 420 and/or the barometric pressure sensor 430 ), and the sensing data operation, determining the user's state when it is determined as a fall situation trigger, ignoring the fall situation trigger if the user's state corresponds to a specified condition, and correcting threshold information related to the user's state, and It may include a computer-readable recording medium recording a program for executing an operation of monitoring the trigger of the fall situation by applying the corrected threshold information to reference information for determining a fall.

The electronic device 101 according to an embodiment of the present disclosure includes a sensor module 410 (eg, the acceleration sensor 420 and the barometric pressure sensor 430 of FIG. 4 ), a memory 130 , and the sensor module 410 . and a processor 120 operatively connected to the memory 130, wherein the processor 120 obtains sensing data from the sensor module 410, and determines whether a fall situation triggers based on the sensing data. and if it is determined as the fall situation trigger, the user's first state is determined, and if the user's first state corresponds to the specified condition, the fall situation trigger is ignored, and related to the user's first state It may be configured to correct the first threshold information, and monitor the fall situation trigger by applying the corrected first threshold information to reference information for determining a fall.

According to an embodiment of the present disclosure, the first state of the user includes a user's activity status, and the processor 120 uses the sensing data to perform a designated short-term ) may be set to determine the user's activity state, and correct a user awareness check threshold based on the user's activity state in the short-term period.

According to an embodiment of the present disclosure, the processor 120 determines a second state of the user based on the sensed data, corrects second threshold information related to the user's second state, and 2 by applying threshold information to the reference information for determining a fall may be configured to monitor the trigger for the fall.

According to an embodiment of the present disclosure, the second state of the user includes a user's activity propensity, and the processor 120 uses the sensing data to perform a specified long-term period. ) may be set to predict the user's activity propensity and correct the fall shock threshold based on the user's activity propensity in the long-term section.

According to an embodiment of the present disclosure, the processor 120 receives third threshold information related to a fall detection correction level for the user from a specified external device interworking with the electronic device 101 , and the third threshold It can be set to correct the fall shock threshold based on the information.

According to an embodiment of the present disclosure, the processor 120 may be configured to correct the fall shock threshold value based on the second threshold information and the third threshold information.

According to an embodiment of the present disclosure, the processor 120 determines a fall misrecognition occurrence situation based on the fall situation trigger, and when determining the fall misrecognition occurrence situation, at least one information related to the activity state of the user It can be set to update parameters.

According to an embodiment of the present disclosure, the processor 120 accumulates a fall trigger count based on the fall trigger detection, and the accumulated fall trigger count for a predetermined time exceeds a specified threshold value. In this case, it may be set to determine the fall situation trigger as a frequent fall misrecognition occurrence situation.

According to an embodiment of the present disclosure, the processor 120 determines whether the user is conscious or not, if the fall trigger count accumulated for a predetermined time is less than or equal to a specified threshold value, and determines that the user is unconscious, the fall situation It may be set to determine the trigger as the user's fall situation and provide an emergency notification.

According to an embodiment of the present disclosure, the processor 120 collects a user's feedback on the emergency notification, and based on the user's feedback collection, the user's feedback corresponds to a dismiss selection In the case of a user action to do so, a dismiss count may be accumulated, and when the dismiss count exceeds a specified threshold value for a certain period of time, the fall situation trigger may be set to be determined as a frequent fall misrecognition occurrence situation.

According to an embodiment of the present disclosure, when it is determined that the frequent fall misrecognition occurs, the processor 120 designates a session for a frequent fall misrecognition occurrence section, extracts a feature of the user's activity state, and extracts It may be set to update the activity state parameter of the user based on the characteristics of the user's activity state.

According to an embodiment of the present disclosure, the processor 120 extracts acceleration sensing data for a specified period based on the acceleration sensing data, and provides information on the user activity state based on the extracted acceleration sensing data for a predetermined period. It may be configured to extract a feature, measure a first similarity with respect to a user activity state, and update a user activity state history using the first similarity.

According to an embodiment of the present disclosure, the processor 120 extracts acceleration sensing data of a predetermined section before the fall shock based on the fall trigger detection, and based on the extracted acceleration sensing data for a predetermined section, the user extracting a feature of an activity state, measuring a second similarity with respect to a user activity state, and determining that the frequent fall misrecognition occurs when the second similarity exceeds a specified threshold value, and comparing the first similarity with the first similarity An average similarity with respect to the recent user activity state history is calculated based on the second similarity, and when the average similarity exceeds a specified threshold value, it may be set to correct a user awareness check threshold.

According to an embodiment of the present disclosure, the processor 120 determines the user's exercise situation through recognition of the user's walking situation by using the acceleration sensing data, and accumulates and updates the exercise time when the exercise is maintained for a predetermined time or more, It may be set to predict the user's activity tendency based on the user's accumulated exercise time for the time.

According to an embodiment of the present disclosure, the processor 120 calculates a walking speed (WF) of the user, and based on the result of calculating the walking speed, the calculated walking speed is a specified walking speed threshold. When it exceeds, it may be determined as the user's exercise situation, and set to predict the user's activity tendency based on the user's exercise situation.

According to an embodiment of the present disclosure, the processor 120 is configured to correct a fall shock threshold correction value corrected based on the user's activity tendency based on the fall situation trigger detection, and based on the sensing data, the fall situation trigger Determining whether the fall condition corresponds to the fall condition, correcting the user awareness check threshold value, checking the user consciousness based on the corrected user awareness check threshold value, and determining that there is no user consciousness , it may be set to determine the user's fall situation and provide an emergency notification.

Hereinafter, a method of operating the electronic device 101 according to various embodiments will be described in detail. Operations performed by the electronic device 101 described below are performed by a processor (eg, the processor 120 of FIG. 1 or FIG. 4 ) including at least one processing circuitry of the electronic device 101 . can be executed According to an embodiment, the operations performed by the electronic device 101 may be stored in the memory 130 and, when executed, may be executed by instructions that cause the processor 120 to operate.

According to an embodiment, in FIG. 5 , an operation example of correcting a fall determination criterion through a process for improving misrecognition when detecting a fall in the electronic device 101 and determining whether or not a final fall occurs based on the result of the correction may be shown. .

Referring to FIG. 5 , in operation 501 , the processor 120 of the electronic device 101 acquires (or collects) first sensing data (eg, acceleration sensing data) and second sensing data (eg, air pressure sensing data). can do. According to an embodiment, the processor 120 may obtain acceleration sensing data and barometric pressure sensing data from a sensor module (eg, the sensor module 176 of FIG. 1 and/or the sensor module 211 of FIGS. 2 and 3 ). can According to an embodiment, the acceleration sensing data may be obtained from the acceleration sensor 420 , and the air pressure sensing data may be obtained from the air pressure sensor 430 . According to an embodiment, the processor 120 may operate to detect a user's fall based on sensing data (eg, acceleration sensing data and air pressure sensing data) using the acceleration sensor 420 and the barometric pressure sensor 430 . .

According to an embodiment, the processor 120 may acquire acceleration sensing data and barometric pressure sensing data in real time, periodically, selectively and/or based on a specified user input sensing. According to an embodiment, the acceleration sensing data may mean sensing data obtained from the acceleration sensor 420 . Hereinafter, for convenience of explanation, it will be described that the acceleration sensing data is obtained from the acceleration sensor 420, but the acceleration sensing data may be obtained from a sensor other than the acceleration sensor 420 (eg, an inertial sensor). .

According to an embodiment, the processor 120 may store the acceleration sensing data and the atmospheric pressure sensing data in the memory 130 . According to an embodiment, the processor 120 is configured to determine a user's activity status for a first predetermined time (eg, a specified short time (eg, 5 minutes) according to a specified short-term. Acceleration sensing data and air pressure sensing data acquired during a time within 10 minutes, 30 minutes, ...) may be stored in the memory 130 . According to an embodiment, the processor 120 is configured to predict a user's activity propensity for a second predetermined time (eg, a specified long period of time (eg, a unit of time) according to a specified long-term period. Acceleration sensing data and barometric pressure sensing data acquired during (daily, weekly, and/or monthly)) may be stored in the memory 130 .

In one embodiment, the activity state of the user may indicate an activity amount or activity level for the activity and/or movement of the user. For example, the user's activity state may indicate a state of the user's activity for a first predetermined time, such as a state in which the user is engaged in an intense activity such as basketball or soccer, or a state in which the user is still with little movement. .

In an embodiment, the activity tendency of the user may indicate a tendency according to the user's activity tendency. In an embodiment, the user's activity tendency may be divided into, for example, at least two phases (eg, active and inactive phase) based on the user's gait-based cumulative exercise time for the second predetermined time. .

According to one embodiment, in the following, the user's activity propensity is divided into four stages (eg, sedentary first stage, somewhat active second stage, active third stage, and / or a very active fourth step) is an example, but various embodiments are not limited thereto.

According to an embodiment, the processor 120 may reset or delete the stored acceleration sensing data and the atmospheric pressure sensing data according to the short-term section from the memory 130 when the first predetermined time has elapsed. According to an embodiment, when the second predetermined time elapses, the processor 120 may reset or delete the stored acceleration sensing data and the atmospheric pressure sensing data according to the long-term section from the memory 130 . According to some embodiments, the processor 120 uses the acceleration sensing data and the air pressure sensing data stored in the memory 130 to process the information necessary for the user (or requested by the user), and then the stored acceleration sensing data and the air pressure sensing Data can be deleted from the memory 130 .

In operation 503 , the processor 120 may detect (or detect) a user's fall (or fall of the electronic device 101 ) situation.

According to an embodiment, the processor 120 may extract a gravity direction feature from the acceleration sensing data. According to an embodiment, the acceleration sensing data obtained from the acceleration sensor 420 is a local frame value (or coordinate value) corresponding to a relative coordinate system, for example, coordinates of the x-axis, y-axis, and z-axis. can have a value. According to an embodiment, the processor 120 may convert the acceleration sensing data from a local frame to a navigation frame.

In an embodiment, the navigation frame corresponds to an absolute coordinate system, and may have, for example, coordinate values corresponding to east (or west), north (or south), and up (or down). According to an embodiment, the processor 120 may extract an acceleration component in a direction perpendicular to the ground from the acceleration sensing data converted into a navigation frame.

For example, the processor 120 may extract a value less than 0 as a gravity direction feature from an up value (or a u-axis value) of acceleration sensing data converted into a navigation frame. According to an embodiment, the processor 120 removes a value greater than 0 based on 0 from among vertical direction components (eg, a component in the direction of gravity + a component in a direction opposite to the direction of gravity) obtained through the up value, and removes a value greater than 0. A small value can be extracted as a gravitational direction feature. In an embodiment, a value greater than 0 may be a component in a direction opposite to the gravitational direction (eg, an up direction), and a value less than 0 may be a component in a direction of gravity (eg, a down direction). In one embodiment, the gravitational direction feature may include only a gravitational direction component with a value less than zero.

According to an embodiment, the processor 120 may calculate (or calculate or measure) the air pressure change rate or the maximum air pressure change amount from the air pressure sensing data. According to an embodiment, the processor 120 may calculate the air pressure change rate or the maximum air pressure change amount based on the impact time due to the user's fall. According to an embodiment, the processor 120 may calculate the acceleration magnitude using coordinate values of the x-axis, y-axis, and z-axis of the acceleration sensing data. For example, the processor 120

can be calculated as a 3-axis acceleration magnitude (eg, magnitude).

According to an embodiment, when the electronic device 101 falls, a high (or large) acceleration magnitude may be detected, and the processor 120 sets the highest point (eg, a maximum peak value) of the acceleration magnitude at the time of impact. can be judged as According to an embodiment, the processor 120 may calculate a rate of change in air pressure or a maximum amount of change in air pressure for a predetermined time (eg, a time before the time of the impact to a time after the time of the impact) based on the time of impact.

In an embodiment, the air pressure change rate may be expressed as a gradient, for example, the air pressure change rate may mean a gradient of the air pressure change for a predetermined time. In an embodiment, the maximum amount of change in atmospheric pressure may be expressed as a peak to peak, and for example, the maximum amount of change in atmospheric pressure may mean the amount of change from the lowest value of atmospheric pressure to the highest value of atmospheric pressure. According to an embodiment, the processor 120 may detect a fall situation (or a fall situation) when the air pressure change rate exceeds a specified speed threshold value and the maximum atmospheric pressure change amount exceeds the specified change threshold value.

According to an embodiment, when the air pressure change rate is less than or equal to the specified speed threshold value and the maximum atmospheric pressure change amount exceeds the specified change threshold value, the processor 120 may detect that there is no fall condition. According to another embodiment, the processor 120 falls when the electronic device 101 does not detect a movement in the direction of gravity, the air pressure change rate is less than or equal to a specified speed threshold value, and the maximum atmospheric pressure change amount exceeds the specified change threshold value. It can be perceived as not being the case.

In operation 505 , the processor 120 may perform a fall misrecognition process based on detecting a fall situation. According to an embodiment, when the detected fall condition is detected in operation 503 , the processor 120 may determine whether the detected fall condition corresponds to a fall of the actual user. For example, the processor 120 may determine whether the sensed fall situation substantially corresponds to a user's fall, or a situation other than the user's fall (eg, due to a fall of the electronic device 101 due to an active movement of the user). It can be judged whether or not it corresponds to the impact amount detection).

According to various embodiments, the processor 120 may determine whether the sensed fall situation is an actual user's fall by further considering whether the user's consciousness is detected based on the time of impact. According to various embodiments, the processor 120 may detect a specified interaction with the user (eg, a user input based on a user interface) with respect to the detected fall situation. In an embodiment, the user interface may include an interface for notifying a fall situation occurrence and confirming whether a fall accident has occurred.

According to various embodiments, the processor 120 may further consider whether the user is wearing the electronic device 101 based on the time of impact to determine whether the sensed fall situation is actually the user's fall. For example, based on whether the electronic device 101 is worn at the time of impact, the processor 120 determines whether the sensed fall situation is an actual fall of the user, or the electronic device 101 is released when the electronic device 101 is released. ) can be distinguished whether it is a falling situation. According to an embodiment, the processor 120 uses a sensor (eg, an IR sensor) included in a rear plate (eg, 207 of FIG. 3 ) of the electronic device 101 to allow the user to wear the electronic device 101 and It can be determined whether there is

In operation 507, the processor 120 may determine whether the user falls based on the result of the performance. According to an embodiment, the processor 120 may determine that the sensed fall situation is the actual fall of the user when the user is unconscious (eg, there is little movement of the user) based on the time of impact.

According to an embodiment, when there is no interaction specified by the user (eg, user input based on the user interface) through the user interface provided based on the time of impact, or an emergency request is selected, the processor 120 detects a fall situation. It can be determined that it is the actual user's fall. According to an embodiment, when the user is conscious (eg, there is a movement of the user), the processor 120 may determine that the sensed fall situation is a fall misrecognition (eg, a false alarm). According to an embodiment, the processor 120 determines that the detected fall situation is a fall when an interaction (eg, a user input based on a user interface) specified by the user through the user interface provided based on the impact point is a dismiss selection. It may be determined to be a misrecognition (eg, a false alarm). According to an embodiment, the processor 120 may correct a default fall determination criterion when determining the fall erroneous recognition.

According to various embodiments, the processor 120 measures a bio-signal using a sensor (eg, a bio-sensor) based on the time of impact, and further considers the measured bio-signal to determine whether the user is conscious. can

According to an embodiment, the processor 120 may correct the fall determination criterion based on at least the user's activity state in the short-term period, the user's active tendency in the long-term period, and/or the user's intentional setting. According to an embodiment, a description will be made with reference to the drawings described below in relation to correcting a fall determination criterion. According to various embodiments, the processor 120 may determine the actual user's fall accident based on the corrected reference information according to the fall determination criterion correction, and/or adjust the sensitivity of the fall situation detection. have.

In operation 509 , the processor 120 may provide a notification related to the fall accident when determining that it is the actual user's fall. According to an embodiment, the processor 120 may display a user interface related to a fall accident on a display (eg, the display module 160 of FIG. 1 and/or the display 220 of FIGS. 2 and 3 ). . In an embodiment, the user interface may include an interface for notifying the occurrence of a fall situation and confirming that a fall accident has occurred, or for informing a method for coping with the occurrence of a fall accident.

According to an exemplary embodiment, the processor 120 may notify a notification related to a user's fall accident to a designated external device based on a user input (eg, an SOS request) based on a user interface. According to an embodiment, the processor 120 sends a notification related to a fall accident to a speaker (eg, the sound output device 150 of FIG. 1 ) and/or vibration (eg, the haptic module ( 179)), and transmit it to a specified external device (eg, at least one other electronic device and/or server based on a specified contact).

According to an embodiment, in FIG. 6 , an example of an operation of correcting the fall detection based on the user's activity state and the user's activity tendency may be shown. According to various embodiments, a short-term period and a long-term period for the fall detection correction of the electronic device 101 are divided, and the user's context for the short-term period and the long-term period It is possible to perform fall detection correction based on

According to an embodiment, in the case of a short period, the electronic device 101 notifies the user's activity state or fall detection for a relatively short period (eg, within 5 minutes, 10 minutes, 30 minutes, ...) Fall detection parameters can be calibrated based on the user's feedback. According to an embodiment, in the case of a long-term section, the electronic device 101 controls the user's activity tendency (eg, the user's You can calibrate fall detection parameters based on your usual amount of exercise or activity level.

Referring to FIG. 6 , in operation 601 , the processor 120 of the electronic device 101 may acquire (or collect) acceleration sensing data and air pressure sensing data. According to an embodiment, the acceleration sensing data may be obtained from the acceleration sensor 420 , and the air pressure sensing data may be obtained from the air pressure sensor 430 . According to an embodiment, the processor 120 may operate to detect a user's fall based on sensing data (eg, acceleration sensing data and air pressure sensing data) using the acceleration sensor 420 and the barometric pressure sensor 430 . . According to an embodiment, the processor 120 may acquire acceleration sensing data and barometric pressure sensing data in real time, periodically, based on selective and/or specified state sensing.

In operation 611, the processor 120 may determine the user's activity state based on the specified short-term period. According to an embodiment, the processor 120 acquires an acceleration acquired during a first predetermined time (eg, within a specified short time (eg, 5 minutes, 10 minutes, 30 minutes, ...)) according to a specified short-term period. The sensing data and the atmospheric pressure sensing data may be stored in the memory 130 , and the user's activity state may be determined based on the corresponding short-term acceleration sensing data and the atmospheric pressure sensing data.

In operation 613 , the processor 120 may correct (eg, first correct) a user awareness check threshold value based on the user's activity state in the short-term period. According to an embodiment, when the user engages in vigorous exercise such as basketball or soccer, a frequent fall notification may occur, and in this case, the user may receive an unnecessary fall notification during exercise. According to an embodiment, the processor 120 determines whether the user is conscious or not, even if a fall is triggered while the user's activity is maintained in a short period of time, in the operation of determining whether the user is conscious, it is not determined as a fall accident situation by the user's movement The detection duration may be corrected to be substantially longer than the default. In this way, the processor 101 may reduce the frequency of occurrence of a false alarm according to the false fall recognition.

In operation 641 , the processor 120 may detect a user's fall situation by applying a correction value according to the first correction. According to an embodiment, the processor 120 may correct the fall determination criterion with the correction value according to the first correction, and detect the user's fall condition based on the corrected fall determination criterion.

In operation 621 , the processor 120 may predict the activity tendency of the user based on the specified long-term section. According to an embodiment, the processor 120 acquires acceleration sensing data acquired during a second predetermined time period (eg, a specified long period of time (eg, hourly unit, daily unit, weekly unit, and/or monthly unit)) according to a specified long-term section. and the atmospheric pressure sensing data may be stored in the memory 130 , and the user's activity tendency may be predicted based on the acceleration sensing data and the atmospheric pressure sensing data of a corresponding long-term section.

In operation 623, the processor 120 generates a fall shock threshold correction value (eg, an initial fall shock threshold correction value) or updates (eg, updates a preset fall shock threshold correction value) based on the user's activity tendency in the long-term section ) can do. According to an embodiment, in the case of a user who is usually somewhat active and motor nerves, the user can quickly extend his hand to protect the body from shock in the event of a fall, whereas in the case of a user who is not usually active and has somewhat lower motor nerves In the event of a fall, you may not be able to protect your body from the impact by reaching out. For example, in the case of an elderly user, the amount of activity may be low, motor nerves may not be good, and in the event of a fall, there may be many cases where the user cannot reach out and touch the ground and receive a direct shock from the body. In this case, the user's body may absorb all the shock of the user's fall, and the fall shock may not be properly transmitted to the electronic device 101 and thus may not be detected as a fall situation.

According to an embodiment, the processor 120 predicts the user's activity tendency based on the acceleration sensing data and the atmospheric pressure sensing data for a second predetermined time according to the long-term section, and when the user is somewhat inactive, the fall shock threshold A fall impact threshold correction value (eg, a first fall impact threshold correction value) may be generated to substantially lower the value from the default. According to another embodiment, the processor 120 predicts the user's activity tendency based on the acceleration sensing data and the atmospheric pressure sensing data for a second predetermined time according to the long-term section, and defaults the fall shock threshold when the user is active. A fall impact threshold correction value (eg, a second fall impact threshold correction value) may be generated to substantially increase from .

In operation 625 , the processor 120 may correct a predefined fall shock threshold value (eg, a second correction) based on the fall shock threshold correction value. According to an embodiment, the processor 120 may correct the fall shock threshold value in a lowering direction based on the first fall shock threshold correction value to increase the fall detection sensitivity for users who are not active in nature. have. According to an embodiment, the processor 120 may lower the fall detection sensitivity for the active user by correcting the fall shock threshold value in a direction to increase the fall shock threshold value based on the second fall shock threshold correction value.

According to various embodiments, the fall shock threshold correction operation 625 may be updated based on the fall shock threshold correction value by the user's guardian. According to an embodiment, in operation 631, the processor 120 obtains (or receives) a fall shock threshold correction value set by the external device from an external device designated as the user's guardian (eg, the guardian's electronic device). and a fall impact threshold may be corrected (eg, a third correction) based on the received fall impact threshold correction value. For example, a fall shock threshold correction value is generated and transmitted to the user's electronic device 101 so that a guardian can substantially lower the fall shock threshold value from the default for a user vulnerable to a fall, and the electronic device receiving it ( 101), by automatically correcting the fall shock threshold, it is possible to better monitor and care for the user who is vulnerable to falls by the guardian for the fall accident situation.

According to various embodiments, the fall impact threshold correction operation of operation 625 may be performed sequentially, in reverse sequence, in parallel, and/or heuristically by the second correction operation and the third correction operation. For example, the processor 120 may configure a fall shock threshold correction value related to the second correction (eg, a first fall shock threshold correction value) and a fall shock threshold correction value related to the third correction (eg, a second fall shock threshold correction). value), a fall impact threshold may be corrected (eg, fourth correction).

In operation 641 , the processor 120 may detect a user's fall situation by applying a correction value according to the second correction, the third correction, or the fourth correction. According to an embodiment, the processor 120 corrects the fall determination criterion with a correction value according to the second correction, the third correction, or the fourth correction, and detects the user's fall situation based on the corrected fall criterion. can do.

According to an embodiment, in FIG. 7 , an example of an operation of detecting a fall situation based on a user state (eg, an activity state of the user) may be illustrated.

Referring to FIG. 7 , in operation 701 , the processor 120 of the electronic device 101 may acquire acceleration sensing data and air pressure sensing data. According to an exemplary embodiment, the processor 120 may obtain acceleration sensing data from the acceleration sensor 420 , and may obtain air pressure sensing data from the air pressure sensor 430 . According to an embodiment, the processor 120 may acquire acceleration sensing data and barometric pressure sensing data in real time, periodically, selectively, and/or based on a specified user input sensing.

In operation 703 , the processor 120 may detect a trigger of a fall situation based on the acquired sensing data (eg, acceleration sensing data and/or air pressure sensing data). According to an embodiment, the processor 120 may calculate the acceleration magnitude using coordinate values of the x-axis, y-axis, and z-axis of the acceleration sensing data. For example, the processor 120

can be calculated as the magnitude of the acceleration.

According to an embodiment, the processor 120 may determine the highest point of the acceleration magnitude as the impact point. According to an embodiment, the processor 120 may calculate an air pressure change rate and a maximum air pressure change amount from the air pressure sensing data based on the time of impact. According to an embodiment, the processor 120 may calculate the magnitude of the acceleration, the rate of change in air pressure, and the maximum amount of change in atmospheric pressure for a predetermined time before and after the time of the impact or at the time of the impact. In an embodiment, the maximum change in atmospheric pressure may be expressed as a peak to peak.

According to an embodiment, the processor 120 may identify (or determine) a trigger of a fall situation based on the acceleration magnitude, the air pressure change rate, and/or the maximum air pressure change amount. For example, the processor 120 detects a movement in the direction of gravity of the electronic device 101 (eg, when an acceleration magnitude exceeds a specified shock threshold value) based on the time of impact, and determines the speed threshold at which the air pressure change rate is specified. When the value exceeds the value and the maximum change in barometric pressure exceeds a specified change threshold, it may be determined that triggering of a fall situation is applicable.

In an embodiment, the specified impact threshold value, the specified speed threshold value, and/or the specified change threshold value may be preset in the electronic device 101 as a fall determination criterion. According to an exemplary embodiment, when the air pressure change rate is less than or equal to the specified speed threshold value and the maximum atmospheric pressure change amount exceeds the specified change threshold value, the processor 120 may determine that it does not correspond to a trigger of a fall situation. According to another embodiment, if the processor 120 does not detect a movement in the direction of gravity of the electronic device 101 , the air pressure change rate is less than or equal to the specified speed threshold value, and the maximum atmospheric pressure change amount exceeds the specified change threshold value, It may be determined that it does not correspond to the trigger of the fall situation.

According to another embodiment, when the subject that determines whether the user falls is, for example, set to an external electronic device, the processor 120 transmits the acquired sensing data (eg, acceleration sensing data and/or air pressure sensing data). It may be transmitted to an external electronic device and detect whether the user has fallen by the external electronic device. According to an embodiment, the external electronic device may receive sensing data from the electronic device 101 and determine whether the user has fallen based on the received sensing data and updated correction information.

In operation 705 , when determining the trigger of the fall situation, the processor 120 may determine the user's state. According to an embodiment, the processor 120 may determine whether the user is conscious. According to an embodiment, the processor 120 may determine whether the user is conscious based on the acceleration sensing data of the acceleration sensor 420 . According to an embodiment, when the user is unconscious after a fall, it can be seen that there is virtually no movement of the user.

According to an embodiment, the processor 120 may determine that the user is not conscious if the amount of change of the acceleration sensing data for a predetermined time is less than or equal to a predetermined level. According to an embodiment, the acceleration sensing data may include raw acceleration data for three axes of an x-axis, a y-axis, and a z-axis, and the acceleration magnitude is normalized to the three acceleration axes. It can contain values. According to an embodiment, when determining whether the user is conscious, the processor 120 may determine by analyzing the acceleration sensing data of the acceleration sensor 420, that is, raw data, and/or all of the information of the three acceleration axes. It may be determined by analyzing the included acceleration magnitude.

According to some embodiments, the processor 120 may determine the user's state based on a specified interaction. According to an embodiment, the processor 120 generates a notification related to a fall situation to the user and, based on an interaction corresponding to receiving a user input corresponding thereto, determines the user's state (eg, whether the user is conscious). can judge

In operation 707 , the processor 120 may determine whether the user's state corresponds to a specified condition. According to an embodiment, the processor 120 checks whether the user is conscious, and determines that the user is conscious when determining that the user is conscious, and does not correspond to the specified condition when determining that the user is not conscious. it can be decided that As another example, the processor 120 may determine that the user is conscious when there is a first designated interaction with respect to a notification related to a fall situation from the user. For another example, the processor 120 may determine that the user is unconscious when there is a second designated interaction or no interaction itself with respect to a notification related to a fall situation from the user.

In operation 707 , when the user's state does not correspond to the specified condition (eg, 'No' in operation 707 ), in operation 709 , the processor 120 may detect a fall situation. According to an embodiment, when the user's state does not correspond to the specified condition, the processor 120 may determine that the user is unconscious, and determine that the trigger of the fall situation is substantially the user's fall situation.

According to an embodiment, the processor 120 may determine whether the trigger of the falling situation is actually the user's falling situation by further considering whether the user is wearing the electronic device 101 . For example, based on whether the electronic device 101 is worn at the time of impact, the processor 120 determines whether the sensed fall situation is an actual fall of the user, or the electronic device 101 is released when the electronic device 101 is released. ) can be distinguished whether it is a falling situation. According to an embodiment, the processor 120 uses a sensor (eg, an IR sensor) included in a rear plate (eg, 207 of FIG. 3 ) of the electronic device 101 to allow the user to wear the electronic device 101 and It can be determined whether there is

In operation 711 , the processor 120 may provide a designated notification related to the fall accident when it is determined that the actual user has fallen. According to an embodiment, the processor 120 may notify a notification related to a user's fall accident to a designated external device. According to an embodiment, the processor 120 sends a notification related to a fall accident to a speaker (eg, the sound output device 150 of FIG. 1 ) and/or vibration (eg, the haptic module ( 179)), and transmit it to a specified external device (eg, at least one other electronic device and/or server based on a specified contact).

In operation 707, when the user's state corresponds to the specified condition (eg, 'Yes' in operation 707), in operation 713, the processor 120 ignores the trigger of the fall situation and corrects the first threshold information. have. According to an embodiment, the processor 120 may determine the user's activity state based on the short-term period. According to an embodiment, the processor 120 is configured within a predetermined time (eg, within a specified short time (eg, 5 minutes, 10 minutes, 30 minutes, ...) before and/or after the point of impact according to a specified short-term interval. time), the user's activity state may be determined based on the acceleration sensing data and the atmospheric pressure sensing data.

According to an embodiment, the processor 120 may correct (eg, first correct) a user awareness check threshold value based on the user's activity state in a short period of time. According to an embodiment, when the user engages in vigorous exercise such as basketball or soccer, a frequent fall notification may occur, and in this case, the user may receive an unnecessary fall notification during exercise.

According to an embodiment, the processor 120 determines whether the user is conscious even if a fall is triggered while the user's activity is maintained in a short period of time (for example, in operation 705), The detection duration may be corrected to be substantially longer than a default so that it is not determined as a trigger. In this way, the processor 101 may reduce the frequency of occurrence of a false alarm according to the false fall recognition.

In operation 715 , the processor 120 may correct the fall determination criterion based on the corrected first threshold information. According to an embodiment, the processor 120 may update a corresponding parameter of the fall determination criterion based on a correction value (eg, a user awareness check threshold value) corrected according to the first correction.

In operation 717 , the processor 120 may monitor a trigger of a fall situation based on the corrected fall determination criterion. According to an embodiment, the processor 120 may detect a user's fall situation based on a corrected fall determination criterion to which a correction value according to the first correction is applied.

According to an embodiment, in FIG. 8 , an example of an operation of determining the user's activity state during a short period may be shown.

Referring to FIG. 8 , in operation 801 , the processor 120 of the electronic device 101 may acquire acceleration sensing data and air pressure sensing data. According to an exemplary embodiment, the processor 120 may obtain acceleration sensing data from the acceleration sensor 420 , and may obtain air pressure sensing data from the air pressure sensor 430 .

In operation 803 , the processor 120 may determine a situation in which frequent fall misrecognition may occur. According to an embodiment, when a false alarm occurs during any activity of the user, the processor 120 performs a user's first specified interaction (eg, dismiss) for a given interface (eg, a notification related to a fall situation). (dismiss) input) is performed more than a predetermined number of times, or when the number of triggers of a fall situation for a predetermined time is greater than or equal to a predetermined number of times, it may be determined that a frequent fall misrecognition is possible. An operation of determining a situation in which a frequent misrecognition of a fall may occur according to an embodiment will be described with reference to the drawings to be described later.

In operation 805 , the processor 120 may update the user's activity state parameter based on determining a situation in which frequent misrecognition of a fall may occur. According to an embodiment, the processor 120 may extract a feature of the user's activity state based on the acceleration sensing data of the acceleration sensor 420 and update the user's activity state parameter for the corresponding situation. In an embodiment, the activity state parameter of the user is, for example, a ratio for each activity level for the entire activity session of the user and/or a parameter indicating an average activity level within the session. can be

9 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present disclosure. 10 is a diagram illustrating an example of a user interface related to a fall situation notification provided by an electronic device according to an embodiment of the present disclosure.

According to an embodiment, in FIG. 9 , an example of an operation for determining a situation in which a frequent fall misrecognition may occur may be illustrated. According to an embodiment, in FIG. 10 , an example of a user interface capable of interacting with the user in response to a fall situation notification may be shown.

Referring to FIG. 9 , in operation 901 , the processor 120 of the electronic device 101 may acquire acceleration sensing data and air pressure sensing data. According to an exemplary embodiment, the processor 120 may obtain acceleration sensing data from the acceleration sensor 420 , and may obtain air pressure sensing data from the air pressure sensor 430 .

In operation 903 , the processor 120 may detect a trigger of a fall situation based on the acquired sensing data (eg, acceleration sensing data and/or air pressure sensing data). According to one embodiment, the processor 120 is based on the magnitude of acceleration (eg, 3-axis acceleration magnitude), the rate of change of air pressure and/or the maximum amount of change in atmospheric pressure for a predetermined time before and after the time of impact, or at the time of impact. Thus, it is possible to identify (or determine) the trigger of the fall situation. For example, the processor 120 determines that the magnitude of the acceleration exceeds a specified threshold value based on the time of impact, the rate of change in air pressure for a certain time exceeds the specified rate threshold, and the maximum change in air pressure at that time is the specified change threshold. If the value is exceeded, it may be determined that it corresponds to triggering of a fall situation.

In operation 905 , the processor 120 may accumulate a fall trigger count based on the fall trigger detection.

In operation 907 , the processor 120 may determine whether a fall trigger count accumulated for a predetermined time exceeds a specified threshold value (eg, a specified predetermined number of times). According to an embodiment, the processor 120 may delete counts other than a predetermined time period to determine the accumulated fall trigger count.

For example, it may be assumed that the current time is 9:30 am, and the accumulated fall trigger count for a specified predetermined time (eg, about 10 minutes) is determined. In this case, the processor 120 only needs to check the fall trigger count from 9:20 am to 9:30 am, and when it is 9:31 am, the fall from 9:21 am to 9:31 am You can check the trigger count. At this time, the processor 120 excludes the fall trigger counts between 9:20 am and 9:21 am, and adds up the fall trigger counts between 9:30 am and 9:31 am to count the cumulative fall trigger count. can be calculated.

In operation 907, the processor 120 determines that the fall trigger count accumulated for a predetermined time exceeds a specified threshold (eg, a specified predetermined number of times) (eg, 'Yes' in operation 907), in operation 923, triggers a fall can be judged as a situation in which frequent fall misrecognition occurs.

In operation 907 , the processor 120 determines that the fall trigger count accumulated for a predetermined time does not exceed a specified threshold value (eg, a specified predetermined number of times) (eg, 'No' in operation 907), for example, a fall trigger If the count is less than or equal to the specified threshold, in operation 909 , it may be determined whether the user is conscious. According to an embodiment, the processor 120 may determine whether the user is conscious based on the acceleration sensing data of the acceleration sensor 420 .

According to an embodiment, when the user is unconscious after a fall, it can be seen that there is virtually no movement of the user. According to an embodiment, the processor 120 may determine that the user is not conscious if the amount of change of the acceleration sensing data for a predetermined time is less than or equal to a predetermined level. According to an embodiment, the acceleration sensing data may include raw acceleration data for three axes of an x-axis, a y-axis, and a z-axis, and the acceleration magnitude is normalized to the three acceleration axes. It can contain values.

According to an embodiment, when determining whether the user is conscious, the processor 120 may determine by analyzing the acceleration sensing data of the acceleration sensor 420, that is, raw data, and/or all of the information of the three acceleration axes. It may be determined by analyzing the included acceleration magnitude.

In operation 911 , when determining that the user is unconscious in operation 909 , the processor 120 may determine that the user has fallen and generate an emergency notification (eg, SOS notification). According to an embodiment, the processor 120 may provide a user interface related to an emergency notification to a display (eg, the display module 160 of FIG. 1 and/or the display 220 of FIGS. 2 and 3 ). . An example of this is shown in FIG. 10 .

In operation 913 , the processor 120 may collect user feedback on the emergency notification. According to an embodiment, the processor 120 may collect feedback based on a user interaction input based on the user interface as illustrated in FIG. 10 .

For example, referring to FIG. 10 , the user interface may be composed of at least one of text, an image, and a video. According to an embodiment, the processor 120 may provide a fall notification through a speaker (eg, the sound output device 150 of FIG. 1 ) and/or vibration (eg, the haptic module 179 of FIG. 1 ). . According to an embodiment, the user interface includes an emergency notification object 1010 (eg, emergency notification content (eg, Fall detected. Do you need help?)), an emergency request object 1020 (eg, SOS transmission object), and / or an emergency notification cancellation object 1030 (eg, a dismiss object) may be included. According to an embodiment, the user performs a specified action based on the emergency request object 1020 or the emergency notification cancellation object 1030 within a predetermined time for a given user interface, or for a predetermined time when unconscious It may not perform any action.

In operation 915 , the processor 120 determines, based on the user's feedback collection, whether the user's feedback is a user action corresponding to the dismiss selection based on the emergency notification cancellation object 1030 (eg, user action == dismiss); It may be determined whether it is a user action (eg, user action == SOS transmission) corresponding to the emergency notification request based on the emergency request object 1020 or whether there is no user action for a predetermined time.

In operation 915 , the processor 120 performs an operation when the user action is not a dismiss selection (eg, 'No' in operation 915 ), for example, when the user action is an urgent notification request or there is no user action At 917, an SOS may be dispatched based on the designated emergency contact. According to an embodiment, when the user is unconscious and does not take any action for a certain period of time or selects the emergency request object 1020 , the processor 120 sends an emergency rescue to an external device corresponding to at least one predetermined emergency contact. contact can be sent.

According to an embodiment, when detecting the selection of the emergency request object 1020 , the processor 120 may notify the user of a notification related to a fall accident to a designated outside. According to an embodiment, the processor 120 sends a notification related to a fall accident to a speaker (eg, the sound output device 150 of FIG. 1 ) and/or vibration (eg, the haptic module ( 179)) can also be provided.

In operation 915 , when the user action is a dismiss selection (eg, 'Yes' in operation 915 ), in operation 919 , the processor 120 may accumulate a dismiss count.

In operation 921 , the processor 120 may determine whether the dismiss count exceeds a specified threshold value (eg, a specified predetermined number of times) for a predetermined time. According to an embodiment, the processor 120 may delete counts other than a predetermined time to determine the accumulated dismiss count. For example, it may be assumed that the current time is 9:30 AM, and the accumulated dismiss count for a specified predetermined time (eg, about 10 minutes) is determined. In this case, the processor 120 only needs to check the dismiss count from 9:20 am to 9:30 am, and when it is 9:31 am, the dismiss count from 9:21 am to 9:31 am You can check the miss count. At this time, the processor 120 excludes the dismiss count between 9:20 am and 9:21 am, and adds the dismiss count between 9:30 am and 9:31 am to count the cumulative dismiss count. can be calculated.

In operation 921, the processor 120 returns to operation 901 if the dismiss count accumulated for a predetermined time does not exceed a specified threshold (eg, a specified predetermined number of times) (eg, 'No' in operation 921), Operations 901 or less may be performed. For example, the processor 120 may return to operation 901 to acquire acceleration sensing data and air pressure sensing data in real time or periodically to determine a situation in which frequent fall misrecognition may occur.

In operation 921 , when the dismiss count accumulated for a predetermined time exceeds a specified threshold value (eg, a specified predetermined number of times) (eg, 'Yes' in operation 921 ), in operation 923 , the processor 120 triggers a fall situation can be judged as a situation in which frequent fall misrecognition occurs. For example, when the user does not actually fall and the emergency notification of the electronic device 101 in operation 911 is determined to be an unnecessary notification, the user may dismiss the corresponding notification. According to an embodiment, when the number of times the user dismises the emergency notification for a predetermined time exceeds a predetermined number, the processor 120 may determine that frequent fall misrecognition occurs.

According to an embodiment, in FIG. 11 , an example of an operation of updating a user's activity state parameter may be shown.

Referring to FIG. 11 , in operation 1101 , the processor 120 of the electronic device 101 may determine that the detected fall condition trigger is a condition in which frequent fall misrecognition is possible. According to an embodiment, the processor 120 may determine a fall misrecognition occurrence situation in operation 923 based on the operation described in the description with reference to FIG. 9 .

In operation 1103 , the processor 120 may designate a session for a frequent fall misrecognition occurrence section. According to an embodiment, as illustrated in FIG. 9 , the processor 120 may designate a predetermined time designated for determining the dismiss count as a session for a frequent fall misrecognition occurrence section. In an embodiment, the session may indicate a section in which resources required for determining the user's activity state are designated (or collected) as one group.

In operation 1105 , the processor 120 may extract a feature of the user's activity state. According to an embodiment, the processor 120 may extract a feature of the user's activity state based on the acceleration sensing data of the acceleration sensor 420 . In an embodiment, the characteristic of the user's activity state may indicate information necessary to determine the user's activity state. For example, the characteristics of the user's activity state may be defined by the following <Equation 1>.

In <Equation 1>, "S" may represent a characteristic of the user's activity state, "F ⁽¹⁾ " may indicate a first activity amount (or a first characteristic value) for a short-term section, " F ⁽²⁾ " may represent a second activity amount (or a second characteristic value) for the long-term interval. For example, the characteristics of the user's activity state include the first activity amount (eg, first feature value) and the second activity amount (eg, second characteristic value) for the short-term section of the user in the corresponding session and long-term section. It can be expressed as information based on

In operation 1107 , the processor 120 may update the activity state parameter of the user based on the extracted characteristics of the user's activity state. Updating of a user's activity state parameter according to an embodiment will be described with reference to the drawings to be described later.

According to an embodiment, in FIG. 12 , an example of an operation of extracting a feature of a user's activity state may be shown.

Referring to FIG. 12 , in operation 1201 , the processor 120 of the electronic device 101 may perform segmentation on a predetermined session. For example, in order to extract activity information for a short-term section of the user for the corresponding session, the processor 120 may perform segmentation by dividing the corresponding session into a specified number of small units.

In operation 1203, the processor 120 may calculate a sum (diff_sum) of difference values of triaxial acceleration magnitudes for each segment subdivided according to the subdivision. According to an embodiment, the processor 120 calculates a 3-axis acceleration magnitude (eg, an acceleration normalization value (magnitude)) for each segment as in the example of Equation 2 below, and the following <Equation 2> 3>, the sum (diff_sum) of the difference values of the three-axis acceleration magnitudes for each segment can be calculated.

In <Equation 2>, "a _NORM " may represent a 3-axis acceleration magnitude, for example, an acceleration normalization normalized value (magnitude), "x" is acceleration x-axis data, and "y" is acceleration y-axis data. , "z" may represent acceleration z-axis data, respectively.

In operation 1205 , the processor 120 may level the activity amount (or feature value) for each segment. According to an embodiment, if it is defined, it can be expressed as an example of <Equation 4> below.

In <Equation 4>, "L _i " may indicate leveling of the amount of activity for the corresponding segment, "α _j " may indicate a representative value of the corresponding segment, and "β" may indicate the interval threshold value of the corresponding segment. can indicate

In operation 1207 , the processor 120 may calculate a ratio for each activity level for the entire session. For example, the processor 120 may calculate the first feature value based on the ratio for each activity level. According to an embodiment, the processor 120 may calculate a ratio (eg, a first feature value) for each activity level with respect to all segments of the entire session as shown in Equation 5 below. For example, the first feature value (eg, F ⁽¹⁾ ) may mean an activity amount (eg, first activity amount) of a user in a short-term section for the corresponding session.

In <Equation 5>, "F ⁽¹⁾ " may represent the first activity amount (or first characteristic value) for the short-term interval, "C _i " may represent the count of the j-th activity level, "N" may represent the total count of "α _j ".

In operation 1209 , the processor 120 may calculate an average activity level within a corresponding session. For example, the processor 120 may calculate the second characteristic value based on the average activity level within the corresponding session. According to an embodiment, the processor 120 may calculate the second feature value based on the average of the first feature values, as in the example of Equation 6 below, for example, the second feature value (eg, F ⁽²⁾ ) may mean an activity amount (eg, a second activity amount) of the user for a long-term section for the corresponding session.

In operation 1211 , the processor 120 may normalize the first feature value calculated in operation 1207 and the second feature value calculated in operation 1209 .

In operation 1213 , the processor 120 may extract a feature of the user's activity state based on a result of performing normalization on the first feature value and the second feature value. According to an embodiment, the processor 120 performs min-max normalization on the first feature value and the second feature value, as in the example of Equation 7 below, and then performs the min-max normalization on the user's activity. It can be extracted as a feature of the state.

According to an embodiment, in FIG. 13 , an example of an operation of correcting a user awareness check threshold value may be illustrated.

Referring to FIG. 13 , in operation 1301 , the processor 120 of the electronic device 101 may acquire acceleration sensing data and air pressure sensing data. According to an exemplary embodiment, the processor 120 may obtain acceleration sensing data from the acceleration sensor 420 , and may obtain air pressure sensing data from the air pressure sensor 430 .

In operation 1303, the processor 120 may extract acceleration sensing data for a specified predetermined period based on the acceleration sensing data.

In operation 1305, the processor 120 may extract a feature of the user activity state based on the extracted acceleration sensing data for a predetermined period. According to an embodiment, the processor 120 may extract a feature of the user activity state by a method corresponding to that described in the description with reference to FIG. 11 .

In operation 1307, the processor 120 may analyze a user activity state similarity (eg, a first similarity). According to an embodiment, the processor 120 may measure the first degree of similarity with respect to the user activity state by using a similar coefficient. According to an embodiment, the processor 120 may measure the first similarity by using at least one of various similarity analysis methods. According to an embodiment, the processor 120 may measure the similarity based on at least one similarity analysis method among various similarity analysis methods such as cosine similarity and/or Pearson correlation coefficient. . In an embodiment, Equation 8 below may represent an example of a Pearson correlation coefficient. For example, the Pearson correlation coefficient is a coefficient representing the linear correlation of a variable (x, y), and it is possible to calculate a value between +1 and -1 of a variable (or correlation coefficient) according to the Cauchy-Schwarz inequality. can For example, “1” may indicate a positive linear correlation, “0” may indicate a non-linear correlation (eg, no correlation), and “-1” may indicate a total negative linear correlation.

In operation 1309, the processor 120 may update the user activity state history by using the user activity state similarity (eg, the first similarity). According to an embodiment, the processor 120 may periodically update the user activity state history based on operations 1301 to 1309 .

In operation 1321 , the processor 120 may detect a trigger of a fall situation using sensing data (eg, acceleration sensing data and/or air pressure sensing data). According to one embodiment, the processor 120 is based on the magnitude of acceleration (eg, 3-axis acceleration magnitude), the rate of change of air pressure and/or the maximum amount of change in atmospheric pressure for a predetermined time before and after the time of impact, or at the time of impact. Thus, it is possible to identify (or determine) the trigger of the fall situation. According to one embodiment, the processor 120, as described in the description with reference to the drawings above, the 3-axis acceleration magnitude (magnitude) exceeds a specified threshold value, and the rate of change of air pressure for a predetermined time exceeds a predetermined threshold value And, when the amount of change in atmospheric pressure at the time exceeds a certain threshold, a fall situation may be triggered.

In operation 1323 , the processor 120 may extract acceleration sensing data of a predetermined section before the fall shock based on the fall trigger detection.

In operation 1325 , the processor 120 may extract a feature of the user activity state based on the extracted acceleration sensing data for a predetermined period. According to an embodiment, the processor 120 may extract a feature of the user activity state by a method corresponding to that described in the description with reference to FIG. 11 .

In operation 1327, the processor 120 may analyze a user activity state similarity (eg, a second similarity). According to an embodiment, the processor 120 may measure the second similarity with respect to the user's activity state immediately before the fall shock by using the similarity coefficient. According to an embodiment, the processor 120 may measure the second similarity by using at least one of various similarity analysis methods as in the operation of measuring the first similarity.

In operation 1329 , the processor 120 may determine whether the second similarity exceeds a specified threshold value.

In operation 1329 , when the second similarity does not exceed a specified threshold value (eg, 'No' in operation 1329 ), the processor 120 returns to operation 1301 to perform operations 1301 or less. For example, the processor 120 may return to operation 1301 to obtain acceleration sensing data and barometric pressure sensing data in real time or periodically to correct a user awareness check threshold value.

In operation 1329 , when the second similarity exceeds a specified threshold value (eg, 'Yes' in operation 1329 ), the processor 120 may proceed to operation 1331 . According to an embodiment, when the second similarity exceeds a specified threshold value, the processor 120 determines that the user activity state is similar to the activity state at the time of determining that, for example, frequent fall misrecognition is possible. can

In operation 1331 , the processor 120 may calculate an average similarity with respect to recent user activity state histories. According to an embodiment, an average similarity with respect to a recent user activity state history may be calculated based on the first and second similarities.

In operation 1333 , the processor 120 may determine whether the average similarity exceeds a specified threshold value.

In operation 1333 , when the average similarity does not exceed a predetermined threshold (eg, 'No' in operation 1333 ), the processor 120 returns to operation 1301 to perform operations 1301 or less. For example, the processor 120 may return to operation 1301 to obtain acceleration sensing data and barometric pressure sensing data in real time or periodically to correct a user awareness check threshold value.

In operation 1333 , when the average similarity exceeds a predetermined threshold (eg, 'Yes' in operation 1333 ), in operation 1335 , the processor 120 may correct the user awareness check threshold value. According to an embodiment, the user consciousness check threshold value may indicate a duration by which the user can check whether the user moves after a fall.

According to an embodiment, the processor 120 may correct the duration by replacing it with a specific large value, and/or correct the duration based on the duration of the corresponding activity state based on the user's activity state history. may be For example, the processor 120 may set the duration to be longer as the duration of the corresponding activity state is longer based on the user's activity state history.

According to an embodiment, in FIG. 14 , an example of an operation of detecting a fall situation based on a user state (eg, a user's activity tendency) may be illustrated.

Referring to FIG. 14 , in operation 1401 , in operation 1401 , in the electronic device 101 , the processor 120 may acquire acceleration sensing data and air pressure sensing data. According to an exemplary embodiment, the processor 120 may obtain acceleration sensing data from the acceleration sensor 420 , and may obtain air pressure sensing data from the air pressure sensor 430 . According to an embodiment, the processor 120 may acquire acceleration sensing data and barometric pressure sensing data in real time, periodically, selectively, and/or based on a specified user input sensing.

In operation 1403, the processor 120 may determine the user's state based on the acquired sensing data (eg, acceleration sensing data and/or air pressure sensing data). According to an embodiment, the processor 120 may determine the user's activity tendency during a specified long-term section. According to an embodiment, the processor 120 acquires acceleration sensing data and barometric pressure acquired during a predetermined period (eg, a specified long period of time (eg, hourly unit, daily unit, weekly unit, and/or monthly unit)) according to a specified long-term section. The sensing data may be stored in the memory 130 , and the user's activity tendency may be predicted based on the acceleration sensing data and the atmospheric pressure sensing data of the corresponding long-term section.

In operation 1405 , the processor 120 may generate a correction value corresponding to the user's state. According to an embodiment, the processor 120 generates a fall shock threshold correction value (eg, an initial fall shock threshold correction value) or updates (eg, a preset fall shock threshold correction value) based on the user's activity propensity for a long-term section value update).

In operation 1407 , the processor 120 may correct the second threshold information. According to an embodiment, the processor 120 sets the default fall shock threshold of the electronic device 101 based on the fall shock threshold correction value corrected based on the user's predicted activity tendency for a predetermined period according to the specified long-term section. A value may be corrected (eg, a second correction).

In operation 1409 , the processor 120 may correct the fall determination criterion based on the corrected second threshold information. According to an embodiment, the processor 120 may update a corresponding parameter of the fall determination criterion based on a correction value (eg, a fall shock threshold value) corrected according to the second correction.

In operation 1411 , the processor 120 may monitor a trigger of a fall situation based on the corrected fall determination criterion. According to an embodiment, the processor 120 may detect a user's fall situation based on a corrected fall determination criterion to which a correction value according to the second correction is applied.

According to an embodiment, in FIG. 15 , an example of an operation of predicting an activity tendency of a user during a long-term section may be shown.

Referring to FIG. 15 , in operation 1501 , the processor 120 of the electronic device 101 may acquire acceleration sensing data from the acceleration sensor 420 .

In operation 1503, the processor 120 may recognize the user's gait situation by using the acquired acceleration sensing data. According to an embodiment, the processor 120 may determine the user's exercise situation through recognition of the user's gait based on the acceleration sensing data. For example, the processor 120 may recognize a situation in which the user's walking situation continues from the acceleration sensing data, and may determine that the user is exercising.

In operation 1505, the processor 120 may cumulatively update the exercise time when the exercise is maintained for a predetermined time or longer. According to an embodiment, the processor 120 may identify whether the user maintains the exercise for a predetermined time based on the acceleration sensing data for a predetermined time, and cumulatively update the exercise time for the predetermined time.

In operation 1507, the processor 120 may predict the user's activity tendency according to the accumulated exercise time for a predetermined time. According to an embodiment, the processor 120 may predict the activity tendency of the user according to the user's accumulated exercise time for a predetermined period (eg, daily, weekly, monthly, or more).

According to an embodiment, the processor 120 may predict the activity tendency of the user based on the activity tendency divided into at least four designated steps. In one embodiment, according to the user's accumulated exercise time, the first stage (eg sedentary), the second stage (eg somewhat active), the third stage (eg active), and the fourth stage (eg very active) Activity tendency can be divided into four stages as shown.

According to an embodiment, the processor 120 may determine the activity tendency of use as a first stage (eg, sedentary) when the user's accumulated exercise time during the specified predetermined period is very small. According to another embodiment, the processor 120 may determine the user's activity tendency as a fourth step (eg, very active) when the user's accumulated exercise time during a specified predetermined period is very large. According to an embodiment, the four steps of classifying for predicting the user's activity tendency are exemplary and not limited thereto. For example, more activity propensity steps may be divided to increase the resolution of the user's activity propensity.

16 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present disclosure. 17 is a diagram illustrating an example of detecting a code conversion point in an electronic device according to an embodiment of the present disclosure.

According to an embodiment, in FIG. 16 , an example of an operation of determining an activity tendency (or exercise tendency) through recognition of a user's gait based on acceleration sensing data may be shown.

Referring to FIG. 16 , in operation 1601 , the processor 120 of the electronic device 101 may obtain acceleration sensing data from the acceleration sensor 420 .

In operation 1603, the processor 120 may calculate an acceleration magnitude (eg, magnitude) for the 3-axis acceleration based on the acquired acceleration sensing data. According to an embodiment, the processor 120 may calculate the acceleration magnitude using coordinate values of the x-axis, y-axis, and z-axis of the acceleration sensing data. For example, the processor 120 is exemplified in <Equation 2>,

can be calculated as the magnitude of the acceleration.

In operation 1605, the processor 120 may extract a sliding window summing (SWS) difference signal. According to an embodiment, the processor 120 calculates the SWS for each sliding window as shown in the example of Equation 9 below, and the calculated SWS difference value for each sliding window as shown in the example of Equation 10 below ( e.g. SWS differential signal) can be calculated.

In operation 1607 , the processor 120 may detect a zero-crossing point (ZC). According to an embodiment, the processor 120 may detect a sign conversion point for the SWS differential signal. In an embodiment, the sign conversion point may indicate a point at which the sign of the function value converts from positive to negative or from negative to positive. An example of this is shown in FIG. 17 .

According to an embodiment, referring to FIG. 17 , according to an embodiment, the graph 1710 of FIG. 17 may represent acceleration sensing data (or acceleration value) obtained from the acceleration sensor 420 . According to an embodiment, the x-axis of the graph 1710 is a time axis, and may mean, for example, the number of samples per unit of time (eg, second). According to an embodiment, the y-axis of the graph 1710 may mean an acceleration value (eg, m/s2). According to an embodiment, one column of the x-axis in the graphic 1710 may mean one sample, and the time unit of one sample may be 10 ms.

In operation 1609, the processor 120 may detect a one-step section of the user based on the sign conversion point. According to an embodiment, the processor 120 may determine the sign conversion point in the graph 1710 of FIG. 17 as the user's step generation time. According to an embodiment, the processor 120 may detect a section between a previously detected code conversion point and a currently detected code conversion point as a user's one-step section.

In operation 1611 , the processor 120 may calculate a walking frequency (WF). According to an embodiment, the processor 120 may calculate the walking speed based on the time interval between the user's previous step and the user's current step, as shown in Equation 11 below.

In operation 1613 , the processor 120 may determine whether the calculated walking speed exceeds a specified walking speed threshold based on the result of calculating the walking speed.

In operation 1613, when the calculated walking speed does not exceed the specified walking speed threshold (eg, 'No' in operation 1613), the processor 120 returns to operation 1601 to perform operations 1601 or less. . For example, the processor 120 may return to operation 1601 to obtain acceleration sensing data in real time or periodically to determine the user's exercise situation.

In operation 1613 , when the calculated walking speed exceeds a specified walking speed threshold (eg, 'Yes' in operation 1613 ), in operation 1615 , the processor 120 may determine the user's exercise situation.

According to various embodiments, the electronic device 101 may update the fall shock threshold correction value based on the user activity tendency. According to an embodiment, the user activity tendency is, as exemplified in <Table 1> below, a first stage (eg, sedentary), a second stage (eg, somewhat active), a third stage (eg, active), and It can be divided into four stages like the fourth stage (eg, very active). In an embodiment, the four steps for predicting user activity propensity are exemplary and not limited thereto. According to an embodiment, the electronic device 101 may set a fall shock threshold correction value for each user activity tendency according to the first to fourth steps.

사용자 활동 성향User activity propensity	낙상 충격 임계 보정 값Fall Impact Threshold Correction Value
제1 단계 (예: sedentary)Step 1 (e.g. sedentary)	약 -10GAbout -10G
제2 단계 (예: somewhat active)Step 2 (e.g. somewhat active)	약 -5GAbout -5G
제3 단계 (예: active)Step 3 (e.g. active)	약 0about 0
제4 단계 (예: very active)Stage 4 (e.g. very active)	약 0about 0

As exemplified in <Table 1>, <Table 1> sets about -10G as a fall shock threshold correction value for the first stage (eg, sedentary) of user activity tendency, according to an embodiment, and user activity About -5G is set as the fall shock threshold correction value for the second stage (eg, somewhat active) of the propensity, and for the third stage (eg, active) and the fourth stage (eg, very active) of the user activity tendency An example in which about 0 is set as a fall impact threshold correction value may be shown, respectively. In an embodiment, “G” may represent an inertial force (G-force).

According to an embodiment, if the user is usually somewhat active and has motor nerves, he/she may quickly extend his/her hand in the event of a fall to protect against the impact of the body. However, in the case of a user who is not usually active and whose motor nerves are somewhat weak, they may not be able to protect themselves from the impact of the body by extending their hand during a fall, which may lead to a greater accident during a fall. For example, in the case of an elderly user, there may be many cases where the amount of activity is low and the motor nerves are not good. Therefore, in the case of an elderly user, in the case of a fall, the elderly user cannot reach out and touch the ground and receive a large shock directly from the body. can occur For example, in the case of an elderly user, the body may absorb all the shock from a fall, and the fall shock may not be properly transmitted to the actual electronic device 101 . In this case, the electronic device 101 may not detect a fall situation.

Accordingly, when the user's activity tendency is somewhat low, it may be necessary to correct the fall shock threshold to be relatively low compared to the default. A look-up table can be designed.

According to an embodiment, in FIG. 18 , an example of an operation of updating a fall shock threshold correction value by a user's guardian may be shown.

Referring to FIG. 18 , in operation 1801 , the processor 120 of the electronic device 101 may interwork with a designated external device (eg, another electronic device). According to an embodiment, the user's electronic device 101 (eg, the first electronic device) may be set to be linked in advance with the electronic device 101 (eg, the second electronic device) of the designated guardian. According to an embodiment, the user's electronic device 101 (eg, the first electronic device) is connected to a designated network (eg, a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (eg, a LAN or WAN)). may communicate with the guardian's electronic device 101 (eg, a second electronic device) through a telecommunication network such as .

In operation 1803, the processor 120 may receive information related to a fall detection correction level from an external device. According to an embodiment, the guardian of the user activates the fall detection correction function using the electronic device 101 of the guardian, You can set the fall detection compensation level. According to an embodiment, the guardian's electronic device 101 (eg, an external device) may generate information related to the set fall detection correction level and transmit it to the designated user's electronic device 101, and the user's electronic device ( 101) may receive information related to a fall detection correction level from an external device.

In operation 1805, the processor 120 may identify a fall detection correction level set by the guardian's electronic device 101 (eg, an external device) based on the information received from the external device.

In operation 1807 , the processor 120 may update the fall shock threshold correction value of the electronic device 101 based on the identified fall detection correction level. According to an embodiment, the processor 120 may set the fall shock threshold correction value set in the electronic device 101 to correspond to the identified fall detection correction level. An example thereof is exemplified in <Table 2> below.

낙상 감지 보정 수준Fall detection compensation level	낙상 충격 임계 보정 값Fall Impact Threshold Correction Value
제1 레벨1st level	약 0.9about 0.9
제2 레벨2nd level	약 0.7about 0.7
제3 레벨3rd level	약 0.5about 0.5

As exemplified in <Table 2>, <Table 2> may represent an example of correcting the fall impact threshold. According to an embodiment, the processor 120 may correct the fall shock threshold to be slightly lowered as the level of the fall detection correction level is lower, and lower the fall shock threshold value more as the level of the fall detection correction level is higher. can be corrected.

According to an embodiment, the processor 120 determines a fall shock threshold correction value (eg, a second correction value) and a fall shock threshold correction value (eg, a third correction value) by a guardian based on the user's activity propensity for a long period of time It is possible to correct the fall shock threshold by reflecting in the correction equation as in the example of <Equation 12> below. For example, the processor 120 may configure the electronic device ( 101) can also be corrected for the fall impact threshold.

In <Equation 12>, "I" may indicate a default fall shock threshold value, "θ ₂ " may indicate a second correction value, and "θ ₃ " may indicate a third correction value.

According to an embodiment, a case where the default fall shock threshold is 20G, the user's activity tendency is predicted to be in the third stage (eg, somewhat active), and in this case, the guardian sets the fall detection correction level to the first level Assuming, as exemplified in Equation 13 below, the fall impact threshold may be corrected as "13.5G".

According to an embodiment, in FIG. 19 , an example of an operation of detecting a user's fall situation by applying a correction value may be shown.

Referring to FIG. 19 , in operation 1901 , the processor 120 of the electronic device 101 may acquire acceleration sensing data and air pressure sensing data. According to an exemplary embodiment, the processor 120 may obtain acceleration sensing data from the acceleration sensor 420 , and may obtain air pressure sensing data from the air pressure sensor 430 .

In operation 1903, the processor 120 may measure the amount of 3-axis acceleration impulse. According to an embodiment, the processor 120 calculates an acceleration magnitude for a 3-axis acceleration by using the coordinate values of the x-axis, y-axis, and z-axis of the acceleration sensing data, and based on the magnitude of the acceleration, The amount of impact can be measured. For example, the processor 120

can be calculated as a 3-axis acceleration magnitude (eg, magnitude). According to an embodiment, when the electronic device 101 falls, a high (or large) acceleration magnitude may be detected, and the processor 120 sets the highest point (eg, a maximum peak value) of the acceleration magnitude at the time of impact. can be judged as

In operation 1905, the processor 120 may measure the air pressure change rate and the maximum air pressure change amount for a predetermined time. According to an embodiment, the processor 120 may measure the air pressure change rate and the maximum air pressure change amount from the air pressure sensing data for a predetermined time. In an embodiment, the air pressure change rate may mean a slope of the air pressure change for a predetermined time. In an embodiment, the maximum change in atmospheric pressure may be expressed as a peak-to-peak. According to an embodiment, the processor 120 may calculate the air pressure change rate and the maximum air pressure change amount based on the impact timing. In an embodiment, the processor 120 may calculate a rate of change in air pressure and a maximum amount of change in air pressure during a predetermined time before and after the time of impact or at the time of impact.

In operation 1907 , the processor 120 may apply a fall impact threshold. According to an embodiment, the processor 120 applies the corrected fall shock threshold correction value based on the user's activity propensity during the long-term section, as described in the description with reference to the above-described operation of correcting the fall shock threshold value. can do. According to an embodiment, when the default fall shock threshold is 20G and the fall shock threshold correction value is 0.9, the processor 120 may calculate 20 x 0.9 to determine the fall shock threshold to be 18G. For example, the processor 120 may correct a threshold value for a fall shock according to the occurrence of a fall trigger.

In operation 1909 , the processor 120 may identify (or determine) whether a fall condition is met. According to an embodiment, the processor 120 is configured to determine a fall situation condition when the amount of fall impact exceeds the specified impact threshold, the rate of change of air pressure exceeds the threshold of change, and the maximum amount of change in air pressure exceeds the specified threshold of change. (eg, fall trigger detection). In an embodiment, the impact threshold value, the speed threshold value, and/or the change threshold value may be preset in the memory 130 of the electronic device 101 .

In operation 1909, if the fall condition does not correspond to the falling condition (eg, 'No' in operation 1909), the processor 120 may return to operation 1901 and perform operations 1901 or less. For example, the processor 120 may return to operation 1901 to acquire acceleration sensing data and barometric pressure sensing data in real time or periodically, and apply a correction value to detect a user's fall situation.

In operation 1909, when the fall condition corresponds to (eg, 'Yes' in operation 1909), in operation 1911, the processor 120 may apply a user awareness check threshold. According to an embodiment, the processor 120 is a user awareness check threshold corrected based on the user's activity state during a short period as described in the description with reference to the operation of correcting the user awareness check threshold value described above. value can be applied. In an embodiment, the user awareness check threshold value may indicate a duration for checking that the user does not move after a fall.

According to an embodiment, the processor 120 may adjust the duration based on the user awareness check threshold value. According to an embodiment, the processor 120 may correct the duration by replacing it with a specific large value, and/or correct the duration based on the duration of the corresponding activity state based on the user's activity state history. may be For example, the processor 120 may set the duration to be longer as the duration of the corresponding activity state is longer based on the user's activity state history.

In operation 1913, the processor 120 may check whether the user is conscious. According to an embodiment, the processor 120 may check whether the user is conscious based on the corrected user awareness check threshold value. According to an embodiment, when a fall condition is met, for example, when triggered by a fall condition, the processor 120 does not immediately generate a fall notification and checks whether the user is conscious and then determines whether the user is actually in the fall condition. can be judged whether or not

According to an embodiment, the processor 120 uses the coordinate values of the x-axis, y-axis, and z-axis of the acceleration sensing data for a predetermined time to maintain the magnitude of the acceleration for the 3-axis acceleration below a specified threshold value. In this case, it may be determined that there is no movement of the user, and it may be determined that the user is not conscious.

According to an embodiment, when the magnitude of the acceleration exceeds a specified threshold value, the processor 120 may determine that the user's movement is sensed, and may determine that the user is conscious. In an embodiment, the determination of whether the user is conscious may be in order to further consider this, since, if the user is conscious even if the user falls, assistance from the surroundings may not be necessary.

In operation 1915 , if it is determined that the user is unconscious, the processor 120 may determine that the user has fallen, and may provide an emergency notification (eg, SOS notification). According to an embodiment, the processor 120 displays a user interface for collecting user feedback related to the emergency notification (eg, the display module 160 of FIG. 1 ), and/or the display of FIGS. 2 and 3 ( 220)) can be provided.

According to an embodiment, in FIG. 20 , an example of providing an adjustment option for adjusting sensitivity to fall detection in the electronic device 101 may be illustrated. According to an embodiment, the sensitivity to the fall detection may be set in the electronic device 101 . According to an embodiment, the sensitivity to fall detection is set by another electronic device interworking with the electronic device 101, and the electronic device 101 receives information related to the sensitivity set by the other electronic device, and the electronic device (101) can also be set.

As shown in FIG. 20 , FIG. 20 may show an example of displaying a user interface (or execution screen) of a specified application (eg, a fall notification application) through the display module 160 . According to an embodiment, the user interface includes a first item 2010 related to emergency contact setting, a second item 2020 related to SOS emergency call setting, and a third item related to setting a user input method for sending an SOS request ( 2030), a fourth item 2040 related to a fall detection setting (eg, on/off), and/or a fifth item 2050 related to a user learning setting for fall detection. .

According to an embodiment, various fall parameters (eg, fall shock amount and/or falls checked when triggering a fall situation) are set as defaults in relation to the detection of a user's fall according to the fourth item 2040 in the electronic device 101 The duration of checking that there is no movement afterward) may not accurately detect the fall situation of all users. According to an embodiment, a fall misrecognition may occur frequently in various situations in which an impact similar to a fall shock occurs in daily life according to a user.

For example, in a situation in which the user performs vigorous exercise such as basketball or soccer, a rather strong impact may occur in many cases. In this case, the electronic device 101 may generate an erroneous fall recognition so that a fall notification may occur frequently, and the user may feel uncomfortable with an unnecessary notification.

For another example, a user who is usually somewhat active and motor nerves can reach out quickly in case of a fall to protect against the impact of the body, whereas a user who is not usually active and has slightly less motor nerves may fall in the event of a fall. You may not be able to protect your body from impact by reaching out. For example, in the case of an elderly user, the amount of activity is small and the motor nerves are often not good. Therefore, when a user falls, he cannot reach out and touch the ground and receive a large shock directly from the body. In this case, the body may absorb all the shock of the user's fall, and the fall shock may not be properly transmitted in the actual electronic device 101 and thus may not be detected as a fall situation.

In various embodiments, a high-quality service may be provided by securing a falling performance fitted for each user for each user. To this end, the electronic device 101 according to various embodiments provides, for example, a learning (eg, user learning on fall detection) menu (or option) such as the fifth item 2050, and the corresponding menu Provides the most suitable fall detection function for the user of the electronic device 101 by collecting and learning user information (eg, information related to an activity state and/or activity tendency) through the activation (or on) setting of the electronic device 101 The fall parameters can be automatically corrected and provided. Through this, the electronic device 101 may provide a personalized fall detection service for each user.

According to an embodiment, in FIG. 21 , an example of an operation of providing a family management function for detection of a fall accident and a related user interface may be shown.

According to an embodiment, in FIG. 21 , the first electronic device 2101 may represent an example of the user's electronic device 101 , and the second electronic device 2102 may be the electronic device 101 of a guardian designated by the user. ) can be given as an example. According to an embodiment, the user's first electronic device 2101 may be set in advance with the second electronic device 2102 of the designated guardian. According to an embodiment, the user's first electronic device 2101 is a designated network (eg, a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a telecommunication network such as a computer network (eg, LAN or WAN))) It is possible to communicate with the second electronic device 2102 of the guardian through .

According to an embodiment, if there is a member of the family who is more vulnerable to a fall accident, the electronic device 101 may provide a function of correcting the fall notification to be more likely to occur for the corresponding member. For example, the electronic device 101 minimizes the user's (eg, the corresponding member's) fall recognition in an actual fall situation in the member's electronic device (eg, the first electronic device 2101), and the guardian A function to better monitor and care for members may be provided as an option 2110 .

For example, a fall shock threshold correction value is generated by the guardian's electronic device (eg, the second electronic device 2102) so that the guardian can substantially lower the fall shock threshold value from the default for the user who is vulnerable to falls, and the user's By automatically correcting the fall shock threshold in the electronic device (eg, the second electronic device 2101) transmitted to the electronic device (eg, the first electronic device 2101) and receiving it, the guardian for the fall accident situation can better monitor and care for users who are vulnerable to falls.

Referring to FIG. 21 , for example, a first member (eg, a mother), a second member (eg, a wife), a third member (eg, a son), and a fourth member (eg, a mother) in the second electronic device 2102 of the guardian When each electronic device (eg, a watch-type wearable device) of a member (eg, daughter) is linked, each member's electronic device (eg, the first electronic device 2101 ) is connected through the second electronic device 2102 . ), a fall cold function optimized for the member can be provided by correcting for each member.

According to an embodiment, the option 2110 for the family management function may be provided through a designated application (eg, a fall notification application) in the second electronic device 2102 . In an embodiment, the family management function makes it better to generate a fall notification on the electronic device 101 (eg, the second electronic device 2102 ) for a member of the user's family who is more vulnerable to a fall accident, and It may indicate a function that can be set to minimize previously recognized cases The family management function is provided to the electronic device 101 by, for example, the electronic device 101 (eg, the second electronic device 2102 ). It may indicate a function capable of forcibly correcting a fall parameter related to fall detection of another electronic device (eg, the first electronic device 2101) that is linked thereto.

According to an embodiment, as illustrated in FIG. 21 , the second electronic device 2102 is a first electronic device 2101 that is designated by a user (eg, a guardian) to execute a family management function. Information related to the designated fall detection correction level may be transmitted to the first electronic device 2101 in a designated communication method. According to an embodiment, the first electronic device 2101 receives information related to the fall detection correction level from the second electronic device 2102 , and the fall shock of the first electronic device 2101 based on the fall detection correction level Threshold correction values can be updated.

According to an embodiment, in the first electronic device 2101 , the fall parameter may be forcibly corrected by the guardian in a form in which the intention of the actual user of the first electronic device 2101 is not reflected. Accordingly, a frequent fall notification may occur in the first electronic device 2101 even when the user of the first electronic device 2101 does not actually fall. Accordingly, in various embodiments, when the fall detection function is corrected by another user (eg, a designated guardian), when it is determined that the fall is determined by the corrected threshold value, the first electronic device 2101 also generates a notification. Alternatively, the first electronic device 2101 may provide a function for generating a notification only in the second electronic device 2102 of a designated guardian without actually generating the notification.

For example, the first electronic device 2101 provides a user interface 2120 for setting a notification method for a fall detection function when the correction for the fall detection function is set by the second electronic device 2101, and the user interface ( It may be provided to set a notification method for the fall detection function by the user of the first electronic device 2101 through 2120 . For example, in the first electronic device 2101, when the fall detection function correction is set in the interlocked second electronic device 2102, whether the first electronic device 2101 agrees to provide a fall notification for a fall detection case based on the correction value It is possible to provide a user interface 2120 that can designate approval or rejection for the . According to an embodiment, the user of the first electronic device 2101 approves ( or agree) or refuse.

According to an embodiment, when approval is determined by the user based on the user interface 2120 (eg, the approval object 2130 is selected), the first electronic device 2101 performs the The first electronic device 2101 and the second electronic device 2102 may generate a notification for all detected fall cases. According to an embodiment, when rejection is determined by the user (eg, the rejection object 22140 is selected) based on the user interface 2120 , the first electronic device 2101 performs the For a case in which the fall is determined by the set correction value, the first electronic device 2101 may not generate a notification and the second electronic device 2102 may generate a notification.

An operation method performed by the electronic device 101 according to an embodiment of the present disclosure includes an operation of acquiring sensing data from a sensor module 410 (eg, the acceleration sensor 420 and the barometric pressure sensor 430 of FIG. 4 ); Determining whether a fall situation trigger is based on the sensed data, if it is determined as the fall situation trigger, determining the user's state, if the user's state corresponds to a specified condition, ignore the fall situation trigger , correcting threshold information related to the user's condition, and monitoring the fall situation trigger by applying the corrected threshold information to reference information for determining a fall.

According to an embodiment of the present disclosure, the user's status includes a user's activity status, and the user's activity status for a specified short-term is determined using the sensing data. and correcting a user awareness check threshold value based on the user's activity state in the short-term section.

According to an embodiment of the present disclosure, an operation of predicting a user's activity propensity for a specified long-term period using the sensing data, and an operation of predicting a user's activity propensity in the long-term period It may include an operation of correcting the fall impact threshold based on the

According to an embodiment of the present disclosure, an operation of receiving threshold information related to a fall detection correction level for the user from a specified external device interworking with the electronic device 101, and a fall shock threshold based on the received threshold information It may include an operation for correcting the value.

Various embodiments of the present disclosure disclosed in the present specification and drawings are merely provided for specific examples to easily explain the technical content of the present disclosure and help the understanding of the present disclosure, and are not intended to limit the scope of the present disclosure. Therefore, the scope of the present disclosure should be construed as including all changes or modifications derived from the technical spirit of the present disclosure in addition to the embodiments disclosed herein as being included in the scope of the present disclosure.

Claims

In an electronic device,

sensor module;

Memory; and

a processor operatively coupled to the sensor module and the memory, the processor comprising:

Obtaining sensing data from the sensor module,

Determining whether to trigger a fall situation based on the sensing data,

If it is determined as the fall situation trigger, determining the first state of the user,

if the first state of the user corresponds to a specified condition, ignoring the fall situation trigger, correcting first threshold information related to the first state of the user, and

An electronic device configured to monitor the fall situation trigger by applying the corrected first threshold information to reference information for determining a fall.
The method of claim 1,

The first state of the user includes a user's activity status,

The processor is

Using the sensing data to determine the user's activity state for a specified short-term,

The electronic device is set to correct a user awareness check threshold value based on the user's activity state in the short-term period.
The method of claim 1, wherein the processor comprises:

Determining a second state of the user based on the sensing data,

correcting second threshold information related to a second state of the user;

An electronic device configured to monitor the fall trigger by applying the corrected second threshold information to the reference information for determining a fall.
4. The method of claim 3,

The second state of the user includes a user's activity propensity,

The processor is

Predicting the user's activity tendency for a specified long-term by using the sensing data,

An electronic device configured to correct a fall shock threshold based on the user's activity tendency in the long-term section.
The method of claim 3, wherein the processor comprises:

receiving third threshold information related to a fall detection correction level for the user from a specified external device interworking with the electronic device;

The electronic device is configured to correct a fall shock threshold value based on the second threshold information and the third threshold information.
The method of claim 2, wherein the processor comprises:

Accumulating a fall trigger count based on the fall trigger detection,

When the accumulated fall trigger count for a certain time exceeds a specified threshold, it is determined that the fall trigger is a frequent fall misrecognition occurrence;

When determining the fall misrecognition occurrence situation, at least one parameter related to the user's activity state is updated,

If the accumulated fall trigger count for a certain time is less than or equal to a specified threshold, it is determined whether the user is conscious,

When it is determined that the user is unconscious, the electronic device is configured to determine the fall condition trigger as the user's fall condition and provide an emergency notification.
The method of claim 6, wherein the processor,

Collecting user feedback on the emergency notification,

Based on the user's feedback collection, if the user's feedback is a user action corresponding to the dismiss selection, accumulating a dismiss count,

The electronic device configured to determine that the fall event trigger is a frequent fall misrecognition occurrence when the dismiss count exceeds a specified threshold for a predetermined time.
The method of claim 6, wherein the processor,

If it is determined that the frequent fall misrecognition occurs, a session for the frequent fall misrecognition occurrence section is designated,

Extract features of the user's activity state,

An electronic device configured to update a user's activity state parameter based on the extracted characteristics of the user's activity state.
The method of claim 2, wherein the processor comprises:

Extracts the acceleration sensing data for a specified period based on the acceleration sensing data,

Based on the extracted acceleration sensing data for a certain period, the characteristics of the user activity state are extracted,

Measure a first similarity to the user activity state,

An electronic device configured to update a user activity state history using the first similarity.
10. The method of claim 9, wherein the processor,

Based on the fall trigger detection, it extracts the acceleration sensing data of a certain section before the fall shock,

Based on the extracted acceleration sensing data for a certain period, the characteristics of the user activity state are extracted,

Measure a second similarity to the user activity state,

If the second degree of similarity exceeds a specified threshold, it is determined that the frequent fall misrecognition occurs;

calculating an average similarity for recent user activity state histories based on the first similarity and the second similarity;

an electronic device configured to correct a user awareness check threshold value when the average similarity exceeds a specified threshold value.
5. The method of claim 4, wherein the processor,

Using the acceleration sensing data to determine the user's movement situation through recognition of the user's gait situation,

When the exercise is maintained for more than a certain time, the exercise time is updated cumulatively,

An electronic device configured to predict a user's activity tendency based on the user's accumulated exercise time for a predetermined time.
The method of claim 11 , wherein the processor comprises:

Calculate the user's walking speed (WF, walking frequency),

Based on the result of calculating the walking speed, if the calculated walking speed exceeds the specified walking speed threshold, it is determined as the user's exercise situation,

An electronic device configured to predict a user's activity propensity based on the user's exercise situation.
The method of claim 1, wherein the processor comprises:

Based on the fall situation trigger detection, correct the calibrated fall shock threshold correction value based on the user's activity tendency,

Determining whether a fall condition trigger corresponds to a fall condition based on the sensed data,

If it corresponds to the fall condition, correct the user awareness check threshold,

Based on the calibrated user awareness check threshold, check the presence or absence of user awareness,

When it is determined that the user is unconscious, the electronic device is configured to determine that the user has fallen and provide an emergency notification.
A method of operating an electronic device, comprising:

acquiring sensing data from a sensor module;

determining whether to trigger a fall situation based on the sensed data;

determining the state of the user when it is determined that the fall situation triggers;

if the user's status corresponds to a specified condition, ignoring the fall situation trigger and correcting threshold information related to the user's status; and

and applying the corrected threshold information to reference information for determining a fall to monitor the trigger for the fall situation.
15. The method of claim 14,

an operation of determining the user's activity state for a specified short-term by using the sensing data;

Correcting the user awareness check threshold value based on the user's activity state in the short-term section,

An operation of predicting a user's activity propensity for a specified long-term using the sensing data, and

and correcting the fall shock threshold based on the user's activity tendency in the long-term section.