WO2024090826A1

WO2024090826A1 - Electronic device and method for performing authentication using gesture of user

Info

Publication number: WO2024090826A1
Application number: PCT/KR2023/015063
Authority: WO
Inventors: 정원석; 안진엽; 유병욱; 조원준; 우준영
Original assignee: 삼성전자 주식회사
Priority date: 2022-10-27
Filing date: 2023-09-27
Publication date: 2024-05-02

Abstract

An electronic device may comprise a sensor for sensing movement of the electronic device, a memory, and a processor. The processor may: output a signal for requesting a preconfigured authentication gesture, and sense signals corresponding to a first gesture and a second gesture of a user by using the sensor; identify whether a degree, at which the signals corresponding to the first gesture and the second gesture match signals corresponding to preconfigured multiple gestures, exceeds a designated level; on the basis that the signals corresponding to the first gesture and the second gesture match the multiple gestures at a level exceeding the designated level, determine a user characteristic on the basis of at least one of signal waveforms of the first gesture and the second gesture, a signal waveform between the first gesture and the second gesture, and a signal waveform of a connection operation of the first gesture and the second gesture; and perform authentication of the user on the basis of whether the determined user characteristic matches a user characteristic pre-stored in the memory.

Description

Electronic device and method for performing authentication using a user's gestures

This document relates to electronic devices, for example, to an electronic device and method for performing authentication using a user's gestures.

Gesture authentication technology is a technology that authenticates a user based on a gesture image acquired by an electronic device. Gesture authentication technology can verify the identity of the person being authenticated in a non-contact manner. Recently, gesture authentication technology has been widely used in various application fields such as security systems, mobile authentication, and multimedia data retrieval due to its convenience and efficiency.

Among user interface technologies, gesture recognition can largely measure gesture recognition using images and other sensors other than image sensors, such as IMU (Inertial Measurement Unit) and PPG (Photoplethysmogram), including acceleration and angular velocity sensors. It can be classified as gesture recognition using existing sensors. Gesture recognition technology using sensors other than image sensors is increasingly applied to wearable devices compared to gesture recognition technology using image sensors.

The electronic device may acquire biometric signals and/or movement signals corresponding to gestures performed by the user, and extract features from signal patterns corresponding to the biometric signals and/or movement signals. However, the user's movement signal obtained based on performing a gesture may be difficult to distinguish from other movement signals, to the extent that the user can be authenticated only with the movement signal itself.

Biometric authentication technology using fingerprints or electrocardiograms can be used in wearable devices, but additional biometric sensor development is required to implement the biometric authentication function, and operating the biometric authentication function may place a load on the system, making it difficult to use. there is.

Biological signal recognition in wearable devices must take into account not only the user's stationary state but also the active state, so it can be difficult to accurately recognize the state.

Electronic devices may include sensors, memory, and processors that detect movement of the electronic device. The processor outputs a signal requesting a preset authentication gesture, detects signals corresponding to the user's first and second gestures using a sensor, and signals corresponding to the first and second gestures are connected to a plurality of preset Check whether the degree of matching with the signal corresponding to the gestures exceeds a specified level, and based on the signal corresponding to the first gesture and the second gesture matching the plurality of gestures by exceeding the specified level, Determining user characteristics based on at least one of a signal waveform of a first gesture and a second gesture, a signal waveform between a first gesture and a second gesture, or a signal waveform of a connecting operation of the first gesture and a second gesture, and determining user characteristics User authentication can be performed based on whether the user's characteristics match pre-stored user characteristics in this memory.

An authentication method of an electronic device includes outputting a signal requesting a preset authentication gesture, detecting signals corresponding to the user's first and second gestures using a sensor, and detecting signals corresponding to the first and second gestures of the user. An operation of checking whether the degree to which a signal matches a signal corresponding to a plurality of preset gestures exceeds a specified level, based on whether the signal corresponding to the first gesture and the second gesture matches the plurality of gestures , an operation of determining user characteristics based on at least one of the signal waveforms of the first gesture and the second gesture, the signal waveform between the first gesture and the second gesture, or the signal waveform of the connecting operation of the first gesture and the second gesture, and It may include performing user authentication based on whether the determined user characteristics match user characteristics pre-stored in memory.

An electronic device according to an embodiment can recognize user gestures and perform accurate user authentication while maintaining security.

The electronic device according to one embodiment can perform user authentication without running the biometric authentication function, thereby preventing system overload due to running the biometric authentication function.

According to one embodiment, the electronic device displays a table with authentication actions to the user so that the user does not have to memorize all of the authentication actions, thus improving user experience, while maintaining security by applying an offset to make it difficult for anyone but a designated user to pass security authentication. You can.

1 is a block diagram of an electronic device in a network environment, according to various embodiments.

Figure 2 is a block diagram of a wearable device according to one embodiment.

Figure 3 is a block diagram showing the configuration of an electronic device, according to one embodiment.

Figure 4a is a flowchart showing the segmentation process of a gesture signal performed by a user. Figure 4b shows the waveform of a gesture signal performed by the user.

Figure 5 shows a probability mass function for scoring authentication gestures.

Figure 6 is a flowchart showing a method of performing authentication using a user's gesture of an electronic device according to an embodiment.

1 is a block diagram of an electronic device 101 in a network environment 100, according to various embodiments. Referring to FIG. 1, in the network environment 100, the electronic device 101 communicates with the electronic device 102 through a first network 198 (e.g., a short-range wireless communication network) or a second network 199. It is possible to communicate with at least one of the electronic device 104 or the server 108 through (e.g., a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108. According to one embodiment, the electronic device 101 includes a processor 120, a memory 130, an input module 150, an audio 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 may include 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 (e.g., sensor module 176, camera module 180, or antenna module 197) are integrated into one component (e.g., display module 160). It can be.

The processor 120, for example, executes software (e.g., program 140) to operate at least one other component (e.g., hardware or software component) of the electronic device 101 connected to the processor 120. It can be controlled and various data processing or calculations can be performed. According to one embodiment, as at least part of data processing or computation, the processor 120 stores commands or data received from another component (e.g., sensor module 176 or communication module 190) in volatile memory 132. The commands or data stored in the volatile memory 132 can be processed, and the resulting data can be stored in the non-volatile memory 134. According to one embodiment, the processor 120 includes a main processor 121 (e.g., a central processing unit or an application processor) or an auxiliary processor 123 that can operate independently or together (e.g., a graphics processing unit, a neural network processing unit ( It may include a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device 101 includes a main processor 121 and a secondary processor 123, the secondary processor 123 may be set to use lower power than the main processor 121 or be specialized for a designated function. You can. The auxiliary processor 123 may be implemented separately from the main processor 121 or as part of it.

The auxiliary processor 123 may, for example, act on behalf of the main processor 121 while the main processor 121 is in an inactive (e.g., sleep) state, or while the main processor 121 is in an active (e.g., application execution) state. ), together with the main processor 121, at least one of the components of the electronic device 101 (e.g., the display module 160, the sensor module 176, or the communication module 190) At least some of the functions or states related to can be controlled. According to one embodiment, co-processor 123 (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module 180 or communication module 190). there is. According to one embodiment, the auxiliary processor 123 (eg, neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. Artificial intelligence models can be created through machine learning. For example, such learning may be performed in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (e.g., server 108). Learning algorithms may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but It is not limited. An artificial intelligence model may include multiple artificial neural network layers. Artificial neural networks include deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), restricted boltzmann machine (RBM), belief deep network (DBN), bidirectional recurrent deep neural network (BRDNN), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the examples described above. In addition to hardware structures, artificial intelligence models may additionally or alternatively include software structures.

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. Data may include, for example, input data or output data for software (e.g., program 140) and instructions related thereto. Memory 130 may include volatile memory 132 or non-volatile memory 134.

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

The input module 150 may receive commands or data to be used in a component of the electronic device 101 (e.g., the processor 120) from outside the electronic device 101 (e.g., a user). The input module 150 may include, for example, a microphone, mouse, keyboard, keys (eg, buttons), or digital pen (eg, stylus pen).

The sound output module 155 may output sound signals to the outside of the electronic device 101. The sound output module 155 may include, for example, a speaker or a receiver. Speakers 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 the speaker or as part of it.

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

The audio module 170 can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module 170 acquires sound through the input module 150, the sound output module 155, or an external electronic device (e.g., directly or wirelessly connected to the electronic device 101). Sound may be output through the electronic device 102 (e.g., speaker or headphone).

The sensor module 176 detects the operating state (e.g., power or temperature) of the electronic device 101 or the external environmental state (e.g., user state) and generates an electrical signal or data value corresponding to the detected state. can do. According to one embodiment, the sensor module 176 includes, for example, a gesture sensor, a gyro sensor, an air 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, humidity sensor, or light sensor.

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

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

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

The power management module 188 can manage power supplied to the electronic device 101. According to one embodiment, the power management module 188 may be implemented as at least a part of, for example, 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, the battery 189 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

Communication module 190 is configured to provide a direct (e.g., wired) communication channel or wireless communication channel between electronic device 101 and an external electronic device (e.g., electronic device 102, electronic device 104, or server 108). It can support establishment and communication through established communication channels. Communication module 190 operates independently of processor 120 (e.g., an application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (e.g., : LAN (local area network) communication module, or power line communication module) may be included. Among these communication modules, the corresponding communication module is a first network 198 (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (e.g., legacy It may communicate with an external electronic device 104 through a telecommunication network such as a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN). These various types of communication modules may be integrated into one component (e.g., a single chip) or may be implemented as a plurality of separate components (e.g., multiple chips). The wireless communication module 192 uses subscriber information (e.g., 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 can be confirmed or authenticated.

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

The antenna module 197 may transmit or receive signals or power to or from the outside (eg, an external electronic device). According to one embodiment, the antenna module 197 may include an antenna including a radiator made of a conductor or a conductive pattern formed on a substrate (eg, PCB). According to one 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 to the plurality of antennas by, for example, the communication module 190. can be selected. Signals or power may be transmitted or received between the communication module 190 and an external electronic device through the at least one selected antenna. According to some embodiments, in addition to the radiator, other components (eg, radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module 197.

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, a mmWave antenna module includes a printed circuit board, an RFIC disposed on or adjacent to a first side (e.g., bottom side) of the printed circuit board and capable of supporting a designated high-frequency band (e.g., mmWave band); And a plurality of antennas (e.g., array antennas) disposed on or adjacent to the second side (e.g., top or side) of the printed circuit board and capable of transmitting or receiving signals in 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 (e.g., bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and signal ( (e.g. commands or data) can be exchanged with each other.

According to one embodiment, commands 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 of the same or different type as the electronic device 101. According to one embodiment, all or part of the operations performed in the electronic device 101 may be executed in one or more of the external

electronic devices

102, 104, or 108. For example, when the electronic device 101 must perform a certain function or service automatically or in response to a request from a user or another device, the electronic device 101 may perform the function or service instead of executing the function or service on its own. Alternatively, or additionally, one or more external electronic devices may be requested to perform at least part of the function or service. One or more external electronic devices that have received the request may execute at least part of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device 101. The electronic device 101 may process the result as is or additionally and provide it as at least part of a response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology can 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. Server 108 may be an intelligent server using machine learning and/or neural networks. According to one embodiment, the external electronic device 104 or server 108 may be included in the second network 199. The electronic device 101 may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

Electronic devices according to various embodiments disclosed in this document may be of various types. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to embodiments of this document are not limited to the above-described devices.

The various embodiments of this document and the terms used herein are not intended to limit the technical features described in this document to specific embodiments, but should be understood to include various changes, equivalents, or replacements of the embodiments. In connection with the description of the drawings, similar reference numbers may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the above items, unless the relevant context clearly indicates 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 Each of phrases such as “at least one of , B, or C” may include any one of the items listed together in the corresponding phrase, or any possible combination thereof. Terms such as "first", "second", or "first" or "second" may be used simply to distinguish one component from another, and to refer to those components in other respects (e.g., importance or order) is not limited. One (e.g., first) component is said to be “coupled” or “connected” to another (e.g., second) component, with or without the terms “functionally” or “communicatively.” Where mentioned, it means that any of the components can be connected to the other components directly (e.g. wired), 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 logic, logic block, component, or circuit, for example. It can be used as A module may be an integrated part or a minimum unit of the parts or a part thereof that performs one or more functions. For example, according to one embodiment, the module may be implemented in the form of an application-specific integrated circuit (ASIC).

Various embodiments of the present document are one or more instructions stored in a storage medium (e.g., built-in memory 136 or external memory 138) that can be read by a machine (e.g., electronic device 101). It may be implemented as software (e.g., program 140) including these. For example, a processor (e.g., processor 120) of a device (e.g., electronic device 101) may call at least one command among one or more commands stored from a storage medium and execute it. This allows the device to be operated to perform at least one function according to the at least one instruction called. The one or more instructions may include code generated by a compiler or code that can be executed by an interpreter. A storage medium that can be read by a device 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 signals (e.g. electromagnetic waves). This term refers to cases where data is stored semi-permanently in the storage medium. There is no distinction between temporary storage cases.

According to one embodiment, methods according to various embodiments disclosed in this document may be included and provided in a computer program product. Computer program products are commodities and can be traded between sellers and buyers. The computer program product may be distributed in the form of a machine-readable storage medium (e.g. compact disc read only memory (CD-ROM)) or through an application store (e.g. Play StoreTM) or on two user devices (e.g. It can be distributed (e.g. downloaded or uploaded) directly between smart phones) or online. In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily created in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

According to various embodiments, each component (e.g., module or program) of the above-described components may include a single or plural entity, and some of the plurality of entities may be separately placed in other components. there is. According to various embodiments, one or more of the components or operations described above may be omitted, or one or more other components or operations may be added. Alternatively or additionally, multiple components (eg, modules or programs) may be integrated into a single 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 of the plurality of components prior to the integration. . According to various embodiments, operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, or omitted. Alternatively, one or more other operations may be added.

Figure 2 is a block diagram of a wearable device according to one embodiment.

Referring to FIG. 2, a wearable device 201 according to an embodiment includes a touch sensor 210, an inertial sensor 215, a biometric sensor 220, a communication module 225, a processor 230, and an interface 235. , it may include an output module 240, a memory 245, a battery 250, and a charging module 255. In the wearable device 201, at least one of the components (eg, output module 240) may be omitted, or one or more other components (eg, proximity sensor) may be added.

The touch sensor 210 may include a touch circuit configured to detect a touch (or a touch signal). The touch sensor 210 may be a capacitive touch sensor or a resistive touch sensor. The touch sensor 210 can detect a single touch, multi-touch, surface touch, or palm touch.

The inertial sensor 215 may be a sensor that measures the acceleration or impact strength of a moving object. The inertial sensor 215 may be an acceleration sensor or a gyroscope.

The biometric sensor 220 can acquire biometric information by contacting a part of the user's body. For example, the biometric sensor 220 may include a photoplethysmogram (PPG) configured to calculate a blood pressure value. Alternatively, the biometric sensor 220 may be electrocardiogram (ECG), galvanic skin response (GSR), electroencephalogram (EEG), bioimpedence assessment (BIA), or ballistocardiogram (BCG). ) may include an electrode capable of measuring at least one of the following. The biometric sensor 220 is composed of a light emitting unit and a light receiving unit to obtain biometric information, and can obtain biometric information by outputting a signal through the light emitting unit and acquiring a signal reflected by the signal output through the light receiving unit. . Alternatively, the biometric sensor 220 may output a current through an electrode and obtain biometric information based on a current received from the user's body using the output current.

The communication module 225 may establish a wireless communication channel with an electronic device (e.g., the electronic device 101 of FIG. 1) and support communication through the established communication channel. The communication module 225 includes Bluetooth, low-power Bluetooth, Wi-Fi, ultra wide band (UWB), adaptive network topology (ANT+), long term evolution (LTE), 5th generation mobile electronic communication (5G), and NB-IoT. It may be connected to the electronic device 101 through (narrowband internet of things), or may be connected to an access point or network. The communication module 225 may transmit sensing information (or sensing signals) (e.g., posture information, touch information, biometric information, proximity information) to the electronic device 101. The communication module 225 may be the same or similar to the wireless communication module 192 of FIG. 1 .

The processor 230 may control the operation of the wearable device 201. The processor 230 controls other components included in the wearable device 201 (e.g., touch sensor 210, inertial sensor 215, communication module 225, etc.) and performs various data processing or calculations. You can. For example, the processor 230 may determine the wearing state and rotation state of the wearable device 201 based on sensing information obtained from the inertial sensor 215. The processor 230 may periodically control the sensing information to be transmitted to the electronic device 101 through the communication module 225. The processor 230 may include at least one of a main processor (eg, micro controller unit), a sensor hub, a Bayer processor, or a neural processor. The processor 230 may be the same or similar to the processor 120 of FIG. 1 .

The interface 235 may be physically connected to the wearable device case 205. For example, when the interface 235 is physically connected to the wearable device case 205, it can be charged with the battery 250 through the charging module 255. The charging module 255 can manage the power supplied to the wearable device 201. The charging module 255 can charge the battery 250 with power received through the interface 235. The charging module 255 may be the same or similar to the power management module 188 of FIG. 1 . The battery 250 may supply power to at least one component of the wearable device 201. Battery 250 may be the same or similar to battery 189 of FIG. 1 .

The output module 240 may provide information to the user. For example, the output module 240 may include a display (e.g., display module 160 of FIG. 1), an LED, a speaker (e.g., sound output device 155 of FIG. 1), and a haptic module (e.g., haptic module of FIG. 1). It may include at least one of the modules 179). The output module 240 may display notification information, light or flash an LED, output an audio signal, or output vibration under the control of the processor 230.

The memory 245 may map and store the angle corresponding to the touch detection range (or touch area) of the touch sensor 210. When a plurality of touch sensors 210 are mounted on the wearable device 201, an angle corresponding to the touch detection range of each touch sensor can be mapped. Additionally, the memory 245 can store sensing information. The sensing information may store sensing information obtained from the inertial sensor 215 or posture information calculated by the processor 230 based on the sensing information. The sensing information may include biometric information obtained from the biometric sensor 220 and touch information obtained from the touch sensor 210. In addition, the sensing information may include proximity information when the wearable device 201 includes a proximity sensor.

The wearable device case 205 may include a ring interface 260, a detection module 265, a case processor 270, a power interface 275, a case battery 280, and a case charging module 285.

The ring interface 260 may be physically connected to the interface 235 of the wearable device 201. The detection module 265 may detect whether the wearable device 201 is mounted (or accommodated) in the wearable device case 205. When the wearable device 201 is mounted on the receiving portion of the wearable device case 205, the detection module 265 may transmit the signal to the case processor 270. The detection module 265 may include at least one sensor that detects whether the wearable device 201 is located in the wearable device case 205. For example, the detection module 265 may be a circuit that periodically “pings” contacts (eg, the interface 235) that are in contact with (or connected to) the wearable device 201. Detection module 265 may be a magnetic sensor, optical sensor, switch, Hall effect sensor, magnetic flux sensor, capacitive sensor, photodetector, proximity detector, momentary switch, mechanical sensor, or electrical sensor. Case battery 280 may supply power to at least one component of wearable device case 205 . Case battery 280 may be the same or similar to battery 189 of FIG. 1 . The power interface 275 may be physically connected to an external power supply.

The case processor 270 may control the operation of the wearable device case 205. The case processor 270 may control other components (e.g., detection module 265, charging module 285) included in the wearable device case 205 and perform various data processing or calculations. For example, when the wearable device 201 is connected, the case processor 270 can control the wearable device 201 to be charged.

The case charging module 285 may manage power supplied to the wearable device 201 or the wearable device case 205. The case charging module 285 can supply power to the wearable device 201. The case charging module 285 can charge the battery 280 with power received through the power interface 275. Case charging module 285 may be the same or similar to power management module 188 of FIG. 1 .

Referring to FIG. 3, the electronic device 300 (e.g., the electronic device 101 of FIG. 1) includes a communication circuit 310 (e.g., the communication module 190 of FIG. 1) and a memory 320 (e.g., FIG. 1) and a processor 330 (e.g., the processor 120 of FIG. 1), and at least some of the depicted components may be omitted or replaced.

According to one embodiment, the communication circuit 310 (e.g., the communication module 190 of FIG. 1) establishes a communication channel with an external device (e.g., the electronic device 103 of FIG. 1) and exchanges various data with the external device. It can support sending and receiving.

According to one embodiment, the memory 320 (eg, memory 120 of FIG. 1) may store a mapping table in which gestures and vibrations including standardized movements are mapped. The memory 330 may store information about an offset related to performing a gesture set for security purposes.

According to one embodiment, the processor 330 (e.g., processor 120 in FIG. 1) controls the overall operation of the electronic device 300 and signal flow between internal components of the electronic device 300, and performs data processing. It can be done.

User authentication and identification can be accomplished by a PIN or digital pattern. In the case of a PIN or digital pattern, there is a possibility that it may be physically exposed to others. Additionally, because the display screen of a wearable device is relatively small compared to electronic devices such as smartphones or laptops, usability may be reduced. These shortcomings and constraints may bring inconvenience to users in future services using wearable devices.

For example, a wearable device can be used as a control device for augmented reality (AR) or virtual reality (VR) services. In this case, the wearable device may be implemented without a physical display device including a display screen. Alternatively, even if a wearable device includes a display screen, recognizing display output and performing input may be inconvenient in terms of usability.

The electronic device according to an embodiment of this document may provide a user identification function through gesture recognition and a function to enhance security so that it can be used for identity authentication. The electronic device according to one embodiment applies a process of registering and authenticating various gesture combinations rather than simply distinguishing and identifying user-specific characteristics of gestures, thereby reducing inconvenience in terms of usability in recognizing display output and performing input. It can be improved.

According to one embodiment, the processor 330 may generate and output a request signal so that the user can perform a gesture for authentication based on the need for user authentication. The request signal may be a signal requesting gesture input for user authentication. For example, the request signal may be output in the form of vibration, sound, or image. While a request signal in the form of a sound or image can be physically exposed to others, a request signal in the form of vibration can be transmitted only to the user wearing the electronic device 300, which can be relatively advantageous in security. Hereinafter, the notification signal will be explained assuming the situation of vibration.

According to one embodiment, the memory 320 may non-transitory and/or temporarily store mapping data including a setting gesture corresponding to a request signal (eg, vibration). Mapping data may be data in which identification information (e.g., number) of the request signal, characteristics of the request signal (e.g., number of vibrations, length of vibration), and gestures are mapped. Mapping data may be implemented, for example, as shown in Table 1 below.

번호number	진동 타입Vibration type	제스처gesture
1One	짧은 진동 1번1 short vibration	G4,G1,G2G4,G1,G2
22	긴 진동 1번1 long vibration	G3,G2,G1G3,G2,G1
33	짧은 진동 2번2 short vibrations	G5,G1,G2G5,G1,G2
44	긴 진동 2번2 long vibrations	G6,G5,G1G6,G5,G1
55	짧은 진동 3번3 short vibrations	G1,G5,G6G1,G5,G6
66	짧은 진동 4번4 short vibrations	G1,G6,G5G1,G6,G5

According to one embodiment, while the user performs a gesture corresponding to a vibration type, the processor 330 may detect a signal corresponding to the gesture through a plurality of sensors. The processor 330 may determine whether the gestures performed by the user and the gesture corresponding to the vibration match based on the signal pattern. The processor 330 may use a method to check whether the size of the signal corresponding to the gesture matches the size of the pre-stored signal. Alternatively, the processor 330 may use a method of checking whether the waveform of the signal corresponding to the gesture matches the waveform of the pre-stored signal. For example, in a situation where the offset is set to 1, the processor 330 may provide an output corresponding to long vibration number 2 using a haptic module (e.g., haptic module 179 in FIG. 1). The gesture corresponding to long vibration number 2 is number 4 (G6, G5, G1), and if an offset is applied, the gesture that the user must perform can be number 5 (G1, G5, G6). The processor 330 may detect the user's gesture and determine whether it matches a combination of preset gestures (eg, G1, G5, G6). The processor 330 compares the size of gestures performed by the user with the size of gestures corresponding to vibration, or performs user authentication based on the waveforms of gestures performed by the user matching the waveforms of gestures corresponding to vibration. can do. According to one embodiment, the processor 330 is a preprocessor that preprocesses the signals of the sensor to remove unnecessary signal components including interference and noise, detects the movement of the signals, distinguishes the start and end of the signal, and cuts the signal. It may include a splitter, a feature extractor that calculates feature values from the divided signals, and a classifier that distinguishes gestures based on feature values. The preprocessor, splitter, feature extractor, and/or classifier may be implemented in software or as a separate circuit. The artificial intelligence learning model (not shown) can perform learning using a learning algorithm using a training set consisting of samples of gestures to be used as input and output the result. The processor 330 may receive results output from an artificial intelligence learning model and perform the function of a classifier.

According to one embodiment, the processor 330 includes a feature selector that selects features effective for user identification and assigns weights, and an identification device that calculates the probability of matching a set user based on the selected features and weights for the input signal. May include a score calculator. The feature selector and/or identification score calculator may be implemented in software or as a separate circuit. The processor 330 may select features effective for user identification at the development stage based on the training set. The processor 330 may select features effective for user identification using gesture samples directly performed by the user. Features effective for user identification may mean, for example, features in which the magnitude of an action is different for each user or features in which the time required to perform an action is different. The processor 330 may create a probability mass function (PMF) based on the characteristics of the input gestures. The processor 330 may use a probability mass function to determine the probability of how well the features of pre-stored user gestures match the features of the input gestures.

According to one embodiment, the processor 330 may request the user to register a gesture to be used for authentication during initial setup for user identification and authentication. The processor 330 may guide the registration of gestures by combining gestures. Gestures may generally include standardized gestures that anyone can easily perform. Stereotypical gestures can be preset, for example, open-clench-open (OCO), pinch with thumb and index finger, slice, knockdown. Alternatively, it may include at least one of a circle drawing motion with the index finger. This is just an example, and the type of gesture may not be limited to this.

According to one embodiment, the processor 330 can increase the accuracy of gesture recognition by using standardized gestures and more easily analyze characteristics of each gesture during the user identification process. It may be difficult for the processor 330 to accurately recognize and identify the characteristics of gestures performed randomly by the user rather than standardized gestures. The processor 330 can consistently extract features for each gesture by setting a range of predictable patterns. However, the processor 330 may have limitations in user identification performance when using only one gesture. Additionally, the electronic device 300 may become vulnerable to security in situations where the corresponding gesture is exposed.

In order to improve this problem, the electronic device 300 according to this document can set a gesture to be used for user identification by combining a plurality of gestures. The processor 330 can set a gesture to be used for user identification as a combination of a plurality of gestures and obtain more information from signal patterns generated while the user performs a plurality of gestures. For example, the processor 330 can extract user characteristics from the signal waveform of a user gesture, a signal waveform between a plurality of gestures, or a signal waveform of a connection operation, and improve user identification performance. Additionally, the processor 330 can strengthen security by increasing the complexity of authentication by setting a gesture to be used for user identification using a combination of a plurality of gestures. For example, when there are 10 types of gestures that can be set, the processor 330 can generate a total of 1000 (eg, 10*10*10) combinations using 3 gestures. Alternatively, when there are five types of gestures that can be set, the processor 330 can generate a total of 125 (e.g., 5*5*5) combinations using three gestures. The processor 330 can enhance security by increasing the types and number of combinations of gestures. However, this method may have the difficulty of requiring the user to memorize all types and combinations of gestures.

According to one embodiment, the processor 330 displays mapping data that describes the type of gesture and the combination of gestures corresponding to vibration to the user, thereby solving the difficulty of the user having to memorize all the types and combinations of gestures. However, while the electronic device 300 displays a table listing the types of gestures and combinations of gestures corresponding to vibrations to the user, the table may also be displayed to unregistered persons, increasing security risks. To prevent this security risk, the processor 330 can use an offset preset by the user. Offset can mean a specific single digit number, for example 1, 2, or 3.

According to one embodiment, the processor 330 may output a request signal in the form of vibration using a haptic module (eg, the haptic module 179 of FIG. 1). The user can feel the vibration and perform a combination of gestures that correspond to the vibration. In this process, a preset offset can be used. For example, in a situation where the preset offset value is 2, the processor 330 may output a signal in the form of 1 short vibration. The processor 330 may receive a gesture combination corresponding to the vibration of a number indicating the vibration plus an offset. The processor 330 may complete user authentication based on the fact that the gesture combination performed by the user matches G5, G1, and G2 corresponding to number 3, rather than G4, G1, and G2 corresponding to number 1. The offset value is only an example and is not fixed, and may be set differently by the user. User convenience can be improved because the user only needs to recognize one number (offset) that he or she has set.

Figure 4a is a flowchart showing the segmentation process of a gesture signal performed by a user.

According to one embodiment, a processor (eg, processor 330 in FIG. 3) may determine the entire section (or activation section) in which gestures were performed by the user. The processor 330 may detect a signal corresponding to the gesture if the user performs the gesture within a specified time after the request signal (e.g., vibration) is generated. The processor 330 may segment a continuous signal to recognize at least one gesture and identify a combination of gestures. The processor 330 may detect an intermission between consecutive signals and divide the signal based on the intermission. A rest period may refer to a section in which the movement of a signal is below a specified level for more than (or more than) a certain period of time. A cutoff point may refer to a section where the signal movement is temporarily below a specified level rather than over a certain period of time.

According to one embodiment, the processor 330 may determine whether a continuous signal is divided into a set number of gestures based on the rest period and the dividing point. The set number of gestures may mean the number of combinations of authentication gestures. For example, if the combination of gestures that the user must perform is G1, G5, and G6, the set number of gestures may mean 3. The processor 330 may first determine whether the continuous signal is divided into three gestures. When the processor 330 is divided into the set number of gestures, it can extract feature points based on this and determine whether the gestures performed by the user match preset gestures.

According to one embodiment, if the number of gestures performed by the user is greater or less than the set number of gestures, the processor 330 may consider the probability of several division cases based on a combination of gestures performed by the user. For example, in FIG. 4B, the processor 330 may detect a plurality of user gestures corresponding to vibration. The processor 330 may distinguish the first gesture 410 from the remaining gestures based on the pause period (e.g., rest period 415). In the first case, the processor 330 may distinguish the second gesture 420 and the third gesture 430 based on the first section 425. In the second case, the processor 330 may distinguish the second gesture 420 and the third gesture 430 based on the second section 435. In the second case, the processor 330 may determine that the probability that the user intended a specific gesture is relatively low compared to the second case because the implementation time of the third gesture 430 is relatively short. Based on this probability calculation, the processor 330 may determine that the user's gestures are more likely to be in the first case than in the second case. The processor 330 may determine the first gesture 410, the second gesture 420, and the third gesture 430 based on the first case, and determine whether they match preset gestures. The processor 330 may determine the combination of gestures intended by the user by considering the probabilities of several division cases (eg, first case, second case). The gesture processor 330 may determine whether the combination of gestures intended by the user matches preset gestures.

Figure 4b shows the waveform of a gesture signal performed by the user.

According to one embodiment, the processor 330 may distinguish signals based on pauses and break points and determine the number of gestures performed by the user. The rest period 415 may refer to a section in which the movement of the signal is below a specified level for more than (or more than) a certain period of time. The

stop sections

425 and 435 may refer to sections where the signal movement is temporarily below a specified level rather than for a certain period of time.

In FIG. 4B, processor 330 can determine the start and end of gesture signals performed by the user. Section 440 may refer to the entire section indicating the start and end of gesture signals performed by the user. The processor 330 may distinguish the first gesture 410 from the rest based on the rest period 415. The section 450 may refer to the section of the signal corresponding to the first gesture 410 and the remaining signals. The processor 330 may detect the first section 425 and the second section 435 in the waveform of gesture signals performed by the user. The processor 330 may distinguish the second gesture 420 and the third gesture 430 based on the first section 425, and may distinguish the second gesture 420 and the third gesture 430 based on the second section 435. Gestures 430 can also be distinguished. Section 460 may refer to a situation in which the second gesture 420 and the third gesture 430 are divided based on the first section 425. Section 470 may refer to a situation in which the second gesture 420 and the third gesture 430 are divided based on the second section 435.

According to one embodiment, the processor 330 uses the user's second gesture (e.g., the first section 425, the second section 435) based on a certain section (e.g., the first section 425, the second section 435) between the section 460 and the section 480 including the section 470. It may be decided whether to distinguish between the 420) and the third gesture 430. The processor 330 may extract features for

sections

460 and 470 and perform a classification operation to distinguish gestures based on the extracted features. The processor 330 may determine which case among section 460 and section 470 is closer to the gesture based on the feature values. For example, if the processor 330 distinguishes the second gesture 420 and the third gesture 430 based on the second dividing point 435, the time corresponding to the third gesture is relatively short, so that the gesture intended by the user It can be decided that it is not. In this case, the processor 330 may distinguish the second gesture 420 and the third gesture 430 based on the first division point 425 rather than the second division point 435. Section 490 may refer to a situation in which the processor 330 finally divides a plurality of gestures of the user based on the rest period 415 and the first dividing point 425.

According to one embodiment, an artificial intelligence learning model (not shown) may perform learning using a learning algorithm using a training set consisting of samples of gestures to be used as input and output a result. The processor 330 may perform the function of a classifier by receiving the results output from the artificial intelligence learning model. The training set may include gesture samples from various people. The artificial intelligence learning model (not shown) collects the user's gesture samples and performs learning based on them to perform authentication with higher accuracy for a specific user. The processor 330 can clearly identify people's gestures using an artificial intelligence learning model and training set. Processor 330 can clearly identify people's gestures and perform authentication of specific users.

Figure 5 shows a probability mass function for scoring authentication gestures.

According to one embodiment, the processor (e.g., processor 330 in FIG. 3) determines that samples of gestures pre-stored by user collection actions and gestures performed by the user are based on the characteristics of the user gesture signal during the user identification procedure. You can compare how likely they are and score them. Features used in the user identification procedure may refer to features used for gesture classification (e.g., each signal waveform, pause, and dividing point). For example, the processor 330 uses signal waveforms corresponding to the first gesture 410, the second gesture 420, and the third gesture 430 to classify the gestures used in the user identification procedure. It can be extracted. Alternatively, the processor 330 may use a rest period 415 between the first gesture 410 and the second gesture 420 or a first dividing point 425 and a second gesture between the second gesture 420 and the third gesture 430. 2 Using the dividing point 435, features for classifying gestures used in the user identification procedure can be extracted.

The processor 330 may generate a probability mass function (PMF) based on the characteristics of the input gestures. The processor 330 may use a probability mass function to determine the probability of how well the features of pre-stored user gestures match the features of the input gestures. The probability mass function may be stored on memory (e.g., memory 320 in FIG. 3).

The probability mass function can be derived after setting the user's authentication gesture, based on a sample of the set authentication gesture. The processor 330 can use a probability mass function to quantify the degree to which the signal obtained during authentication is close to the signal for the collected samples. The processor 330 may determine whether the signal obtained during authentication is the user's signal or a signal of a person other than the user based on the numerical score.

The graph in FIG. 5 shows the probability mass function for matching a user gesture with a gesture based on the characteristics of the user gesture signal. The first function 510 may refer to a probability mass function that matches the target user's gesture. The second function 520 may refer to a probability mass function in which another user's gesture matches the gesture. In the graph of FIG. 5, the closer it is to the center or peak, the higher the probability that the user's gesture matches the gesture. That is, the closer the probability of the target user's gesture is to the median 512 of the first function 510, the higher the probability of matching the target user may be. The variance of the probability mass function can be determined based on the variability of the gesture signal. It may be difficult for users to always perform gestures in the same environment due to changes in the external environment. For example, the processor 330 may detect signals corresponding to the gesture differently even if the user performs the same gesture depending on the wearing state or location of the electronic device. This volatility can increase the variance of the probability distribution and cause the probability distribution to spread out.

According to one embodiment, the processor 330 may divide the input signal into discrete zones using the probability distribution mean and standard deviation and determine a score. For example, in the first function 510, the mean of the user probability distribution is

, the variance is

With this said, the score s for the feature value x of the first function 510 is as follows.

if

<

,s=

else if

<

,s=

else if

<

,s=

At this time, r(r1<r2<r3) means a constant for setting the area from the center of the probability distribution. a(a1>a2>a3) means the score for each area of each probability distribution. The final score (St) for user identification for the scores obtained from each feature can be expressed as follows.

means the weight for each feature, and U means the effective feature group used for user identification. Weights for each feature and effective feature groups may vary for each user or each gesture. Effective features refer to signal characteristics that can be used for user identification. The electronic device 300 may select valid features through the training set. The electronic device 300 may derive a probability mass function for each user for the same gesture. However, the method of calculating all distances between people may cause errors due to the influence of specific samples that deviate from the average. Additionally, the method of calculating all distances between people has the problem of increasing the amount of calculation. To prevent this problem, the electronic device 300 can select valid features and determine a sample that is the target of calculation. The electronic device 300 can derive a probability mass function using the gesture samples collected through this process. The electronic device 300 may determine a user identification score for the input signal using a probability mass function. The electronic device 300 may complete user identification and authentication based on the user identification score exceeding a specified level.

According to one embodiment, the processor 330 performs accurate user identification in an environment where the probability distribution of the target user and the probability distribution function of other users are distinguished while the variance of the target user's probability distribution is not large. can do. The relatively smaller the overlapping area between probability distribution functions, the higher the probability of distinguishing between the target user's probability distribution function and another user's probability distribution function. For example, when the difference between the median value 512 of the first function 510 and the median value 522 of the second function 520 exceeds a certain level, the first function 510 and the second function 520 The relatively smaller the overlapping portion 530, the higher the probability of distinguishing between the target user's probability distribution function and another user's probability distribution function. The processor 330 may determine a user identification score for the input signal using this calculation process and probability distribution function. Processor 330 may determine a match for a target user based on the user's identification score exceeding a specified level. Based on a determination that the processor 330 matches the target user, the processor 330 may terminate the authentication process and unlock the security lock, or perform mid-channel authentication to allow the external device to connect. The operations that the processor 330 can perform are not limited to this, and various other operations that require user authentication can be performed.

The operations described with reference to FIG. 6 may be implemented based on instructions that can be stored in a computer recording medium or memory (e.g., memory 320 in FIG. 3). The illustrated method 600 can be executed by the electronic device previously described with reference to FIGS. 1 to 5 (e.g., the electronic device 300 of FIG. 3), and technical features described above will be omitted below. The order of each operation in FIG. 6 may be changed, some operations may be omitted, and some operations may be performed simultaneously.

In operation 610, the processor (e.g., processor 330 in FIG. 3) outputs a signal requesting an authentication gesture and may detect signals corresponding to the user's first and second gestures using a sensor.

According to one embodiment, the processor 330 may request the user to register a gesture to be used for authentication during initial setup for user identification and authentication. The processor 330 may guide the registration of gestures by combining gestures. Gestures may generally refer to standardized gestures that anyone can easily perform. Gestures can be, for example, open-clench-open (OCO), pinch between the thumb and index finger, slice, knockdown, or circle with the index finger. It may include at least one of the following. This is just an example, and the type of gesture may not be limited to this.

According to one embodiment, the processor 330 sets a gesture to be used for user identification as a combination of a plurality of gestures and can obtain more information on signal patterns generated while the user performs a plurality of gestures. For example, the processor 330 can extract user characteristics from the signal waveform of a gesture, a signal waveform between gestures, or a signal waveform of a connection operation, and improve user identification performance. Additionally, the processor 330 can strengthen security by increasing the complexity of authentication by setting a gesture to be used for user identification using a combination of a plurality of gestures. For example, when there are 10 types of gestures that can be set, the processor 330 can create a total of 1000 (=10^3) combinations using 3 gestures among them. Alternatively, when there are five types of gestures that can be set, the processor 330 can create a total of 125 (=5^3) combinations using three of these gestures. The processor 330 can enhance security by increasing the types and number of combinations of gestures.

According to one embodiment, when the user performs a gesture corresponding to a request signal (eg, vibration), the processor 330 may detect the signal through a plurality of sensors. The processor 330 may determine whether the gestures performed by the user match the gesture corresponding to the request signal (eg, vibration) based on the signal pattern. The processor 330 may perform a user identification process based on the match between the gestures performed by the user and the gesture corresponding to the request signal (eg, vibration).

According to one embodiment, the signal waveform between the first gesture and the second gesture may include a first section exceeding a specified period among sections in which the magnitude of the signal movement is less than a specified level. For example, the first section may mean a resting period (e.g., a resting period 415 in FIG. 4). The processor 330 may determine user characteristics based on the location and frequency of the first section.

In operation 620, the processor 330 may determine a plurality of gestures corresponding to the output signal and check whether the gestures performed by the user match the plurality of gestures corresponding to the output signal.

According to one embodiment, the processor 330 may determine whether the order of gestures performed by the user matches the order of a plurality of gestures corresponding to a signal (eg, vibration) output on the mapping data. For example, if the order of a plurality of gestures corresponding to a signal (e.g., vibration) output on the mapping data is G1, G4, G3, the processor 330 determines that the order of the gestures performed by the user is G1, G4, G3. You can determine whether it matches.

According to one embodiment, the processor 330 performs the gestures again based on the fact that the order of the gestures performed by the user does not match the order of the plurality of gestures corresponding to the signal (e.g., vibration) output on the mapping data. Information indicating that something can be displayed.

According to one embodiment, the processor 330 determines whether the order of gestures performed by the user matches the order of combination of a plurality of gestures that the user must perform, and whether the individual operations of the gestures performed by the user are individually selected among the plurality of gestures. Scores can be given based on whether the gesture matches the action.

According to one embodiment, the processor 330 may determine that the user's gesture is appropriate based on the assigned score exceeding a specified level. Additionally, the processor 330 may determine that the user's gesture is not appropriate based on the score given being below (or below) a specified level. The processor 330 may provide the user with a randomly determined vibration again based on the fact that the given score is below (or less than) a specified level and request the user to perform a plurality of gestures corresponding to the vibration.

In operation 640, the processor 330 determines the signal waveform of the first gesture performed by the user, the signal waveform of the second gesture, the signal waveform between the first gesture and the second gesture, or the connection operation of the first gesture and the second gesture. User characteristics may be determined based on at least one of the signal waveforms.

In operation 650, the processor 330 may authenticate the user based on whether the determined user characteristics match user characteristics pre-stored on the memory 320.

According to one embodiment, the processor 330 unlocks the security lock of the electronic device 300 based on the determined user characteristics matching the user characteristics pre-stored on the memory 320 or performs multi-channel authentication to determine the external device 300. connection can be allowed.

According to one embodiment, the electronic device 300 may further include an artificial intelligence learning model. The processor 330 may use an artificial intelligence learning model to learn user characteristics determined based on the location and frequency of the first section on the timeline and determine user characteristics corresponding to the user of the electronic device 300.

According to one embodiment, the processor is based on at least one of the signal waveforms of the first gesture and the second gesture, the signal waveform between the first gesture and the second gesture, or the signal waveform of the connecting operation of the first gesture and the second gesture. Based on the determined user characteristics matching user characteristics pre-stored in memory, the electronic device may be unlocked securely or multi-channel authentication may be performed to allow connection of an external device.

According to one embodiment, the processor displays mapping data in which an authentication request signal and a plurality of gestures are mapped to the user, and outputs a signal requesting an authentication gesture based on the characteristics of the authentication request signal included in the mapping data. After that, the signal corresponding to the first gesture and the second gesture is detected using a sensor, a plurality of gestures corresponding to the signal are determined on the mapping data using a preset offset, and the gestures performed by the user are determined. It is determined whether the order matches the order of a plurality of gestures corresponding to the identification information of the signal with an offset applied to the identification information of the output signal, and the authentication request signal may include a vibration form.

According to one embodiment, the processor performs gestures again based on the fact that the order of gestures performed by the user does not match the order of a plurality of gestures corresponding to the vibration identification information with an offset applied in the output vibration identification information. Information indicating that something can be displayed.

According to one embodiment, the processor determines whether the order of gestures performed by the user matches the combination order of a plurality of gestures that the user must perform, and whether the individual actions of the gestures performed by the user are the actions of the individual gestures among the plurality of gestures. A score can be assigned to the user's authentication actions based on whether they match or not.

According to one embodiment, the processor may perform authentication of the user based on the score exceeding a specified level, and may not perform authentication of the user based on the score being below (or below) a specified level.

According to one embodiment, the processor provides the user with a randomly determined vibration again based on the score being below (or below) a specified level, and a plurality of pieces corresponding to the identification information of the vibration applied with an offset from the identification information of the output vibration. You can be asked to perform gestures.

According to one embodiment, the processor may determine a combination of a plurality of gestures to be performed by the user on the mapping data based on a number corresponding to the vibration and a specific number corresponding to the offset.

According to one embodiment, the signal waveform between the first gesture and the second gesture may include a first section exceeding a specified period among sections where the magnitude of the signal movement is less than a specified level. The processor may determine user characteristics based on the signal waveform of the first gesture, the signal waveform of the second gesture, and the location and frequency of the first section on the timeline.

According to one embodiment, the electronic device further includes an artificial intelligence learning model, and the processor uses the artificial intelligence learning model to learn user characteristics determined based on the location and frequency of the first section on the timeline, and the electronic device User characteristics corresponding to the user can be determined.

According to one embodiment, the method of performing authentication of an electronic device further includes searching for a combination of a plurality of gestures to be performed by the user on mapping data based on a preset offset and a randomly determined request signal, and searching for a combination of a plurality of gestures that the user must perform. The signal may include a form of vibration.

According to one embodiment, a method of performing authentication of an electronic device is such that the order of gestures performed by the user matches the combination order of a plurality of gestures that the user must perform, and the individual actions of the gestures performed by the user are among the plurality of gestures. It may further include performing authentication of the user based on matching the actions of individual gestures.

Claims

In electronic devices,

A sensor that detects movement of the electronic device;

memory; and

Includes a processor,

The processor is

Outputs a signal requesting a preset authentication gesture and detects signals corresponding to the user's first and second gestures using a sensor,

Check whether the degree to which signals corresponding to the first gesture and the second gesture match signals corresponding to a plurality of preset gestures exceeds a specified level,

Based on the signal corresponding to the first gesture and the second gesture matching the plurality of gestures by exceeding a specified level, the signal waveforms of the first gesture and the second gesture, the first gesture and the second gesture Determine user characteristics based on at least one of a signal waveform between two gestures or a signal waveform of a connecting operation of the first gesture and the second gesture,

An electronic device that performs authentication of the user based on whether the determined user characteristics match user characteristics pre-stored in the memory.
According to clause 1,

The processor is

A user determined based on at least one of the signal waveforms of the first gesture and the second gesture, the signal waveform between the first gesture and the second gesture, or the signal waveform of the connecting operation of the first gesture and the second gesture. Based on the characteristics matching user characteristics pre-stored in the memory

An electronic device that allows connection of an external device by unlocking the security of the electronic device or performing multi-channel authentication.
According to clause 1,

The processor is

Displays mapping data in which an authentication request signal and a plurality of gestures are mapped to the user,

After outputting a signal requesting an authentication gesture based on the characteristics of the authentication request signal included in the mapping data, detecting signals corresponding to the first gesture and the second gesture using a sensor,

Determine a plurality of gestures corresponding to a signal on the mapping data using a preset offset,

Determine whether the order of gestures performed by the user matches the order of a plurality of gestures corresponding to the identification information of the signal to which the offset is applied in the identification information of the output signal,

An electronic device wherein the authentication request signal includes a vibration form.
According to claim 3,

The processor is

Displays information instructing to perform gestures again based on the fact that the order of gestures performed by the user does not match the order of a plurality of gestures corresponding to the vibration identification information to which the offset is applied in the output vibration identification information. electronic device that does.
According to clause 3,

The processor is

Based on whether the order of gestures performed by the user matches the combination order of the plurality of gestures that the user must perform, and whether the individual actions of the gestures performed by the user match the actions of individual gestures among the plurality of gestures, the user An electronic device that assigns points to actions for authentication.
According to clause 5,

The processor is

Perform authentication of the user based on the score exceeding a specified level,

An electronic device that does not perform authentication of the user based on the score being below (or below) a specified level.
According to clause 5,

The processor is

Based on the score being below (or below) a specified level, a randomly determined vibration is provided to the user again, and a plurality of gestures are performed corresponding to the identification information of the vibration to which the offset is applied from the identification information of the output vibration. Electronic devices that require it.
According to clause 3,

The processor is

An electronic device that determines a combination of a plurality of gestures to be performed by a user on the mapping data based on a number corresponding to vibration and a specific number corresponding to the offset.
According to clause 1,

The signal waveform between the first gesture and the second gesture is

Includes a first section exceeding a specified period among sections in which the magnitude of signal movement is less than a specified level,

The processor is

An electronic device that determines user characteristics based on the signal waveform of the first gesture, the signal waveform of the second gesture, and the location and frequency of the first section on a timeline.
According to clause 9,

The electronic device is

It further includes an artificial intelligence learning model,

The processor is

Using the artificial intelligence learning model, learn user characteristics determined based on the location and frequency of the first section on the timeline,

An electronic device that determines user characteristics corresponding to a user of the electronic device.
In a method of performing authentication of an electronic device,

Outputting a signal requesting a preset authentication gesture and detecting signals corresponding to the user's first and second gestures using a sensor;

An operation of checking whether the degree to which signals corresponding to the first gesture and the second gesture match signals corresponding to a plurality of preset gestures exceeds a specified level;

Based on the signal corresponding to the first gesture and the second gesture matching the plurality of gestures, signal waveforms of the first gesture and the second gesture, a signal between the first gesture and the second gesture An operation of determining user characteristics based on at least one of a waveform or a signal waveform of a connecting operation of the first gesture and the second gesture; And

A method comprising performing authentication of the user based on whether the determined user characteristics match user characteristics previously stored in a memory.
According to clause 11,

The method of performing authentication of the electronic device is

Further comprising searching for a combination of a plurality of gestures to be performed by the user on the mapping data based on a preset offset and a randomly determined request signal,

The method of claim 1, wherein the request signal includes a vibration form.
According to clause 12,

The authentication method of the electronic device is such that the order of gestures performed by the user matches the combination order of a plurality of gestures that the user must perform, and the individual actions of the gestures performed by the user are the actions of individual gestures among the plurality of gestures. The method further includes performing authentication of the user based on the match.
According to clause 13,

The method of performing authentication of the electronic device is

Based on the fact that the order of the gestures performed by the user does not match the combination order of the plurality of gestures that the user must perform, and the individual actions of the gestures performed by the user do not match the actions of individual gestures among the plurality of gestures, A method further comprising maintaining a secure lock of an electronic device and displaying information indicating that authentication was incorrect.
According to clause 13,

The authentication method of the electronic device determines whether the order of gestures performed by the user matches the combination order of a plurality of gestures that the user must perform, and whether the individual actions of the gestures performed by the user are the actions of the individual gestures among the plurality of gestures. The method further includes an operation for awarding a score to the user's authentication behavior based on whether it matches.