Electronic device comprising barometric pressure sensor, and barometric pressure sensor data calibration method therefor

Abstract

An electronic device according to one embodiment may comprise: an inertial sensor; a barometric pressure sensor; a memory for storing executable instructions; and a processor for executing the instructions by accessing the memory. The electronic device can be configured to: store, in the memory, a barometric pressure offset estimation model according to a positioning characteristic; acquire a barometric pressure sensor signal from the barometric pressure sensor; acquire an inertial sensor signal from the inertial sensor; if a fall or impact event is detected on the basis of the inertial sensor signal, identify a first section in which there is no movement of the electronic device in the inertial sensor signal before the fall or impact event is detected; and remodel the barometric pressure offset estimation model stored in the memory on the basis of reference barometric pressure and reference acceleration extracted from the first section.

Description

Electronic device including a barometric pressure sensor and method for calibrating barometric pressure sensor data thereof

[0001] The present invention relates to an electronic device including a barometric pressure sensor and a method for calibrating barometric pressure sensor data.

[0002] Electronic devices include sensors that detect (or measure) various types of information (or data) and can provide various related services (e.g., health care) based on the sensing data acquired from the sensors. For example, an electronic device may include a barometric pressure sensor. The barometric pressure sensor can measure barometric pressure by measuring the flow of air flowing through a hole exposed to the outside of the electronic device.

[0003] Meanwhile, among the functional elements of electronic devices, waterproofing may be an essential function required for using electronic devices (e.g., wearable electronic devices) in water, such as while swimming.

[0004] In the case of a pressure sensor, there is a hole exposed to the outside of the electronic device to operate the pressure sensor, so it is implemented by applying a waterproof gel to the sensing part for waterproof function.

[0005] The above information may be provided as background art to aid in understanding the present disclosure. No claim or determination is made as to whether any of the above-described matters constitute prior art related to the present disclosure.

[0006] However, a pressure sensor with a waterproof function (hereinafter, "waterproof pressure sensor") may experience changes in gravity depending on the attitude (or tilt) angle of the electronic device due to the application of waterproof gel, which may result in pressure sensor errors. The pressure sensor error due to the application of waterproof gel tends to increase as the attitude or tilt angle of the electronic device increases.

[0007] Barometric pressure sensor errors can cause fall detection algorithms to misrecognize a fall situation or cause algorithms that predict the user's altitude to malfunction.

[0008] In addition, it is difficult to precisely correct the air pressure sensor error caused by the change in gravity due to the application of waterproof gel in hardware, so a method to supplement or improve this may be required.

[0009] According to various embodiments, when implementing a pressure sensor with a waterproof function, the pressure sensor can be calibrated more accurately by remodeling the pressure offset estimation model and then predicting the pressure compensation value whenever an impact or fall event occurs.

[0010] The technical challenges addressed in this document are not limited to the aforementioned technical challenges. Additional technical challenges not mentioned herein will be readily apparent to those skilled in the art from the description below. However, the challenges addressed in this disclosure are not limited to the aforementioned challenges and may be broadly expanded upon without departing from the spirit and scope of this disclosure.

[0011] An electronic device according to one embodiment may include an inertial sensor. An electronic device according to one embodiment may include an air pressure sensor. An electronic device according to one embodiment may include a memory storing executable instructions and a processor accessing the memory to execute the instructions. The processor according to one embodiment may be configured to store an air pressure offset estimation model according to attitude characteristics of the electronic device in the memory. The processor according to one embodiment may be configured to obtain an air pressure sensor signal from the air pressure sensor. The processor according to one embodiment may be configured to obtain an inertial sensor signal from the inertial sensor. When a fall or impact event is detected based on the inertial sensor signal, the processor according to one embodiment may be configured to identify a first section in which there is no movement of the electronic device in the inertial sensor signal prior to detection of the fall or impact event. The processor according to one embodiment may be configured to remodel the air pressure offset estimation model stored in the memory based on a reference air pressure and a reference acceleration extracted from the first section. In one embodiment, the processor may be configured to identify a second period of no electronic device movement in an inertial sensor signal after the detection of the fall or impact event. In one embodiment, the processor may be configured to estimate a pressure correction value of the pressure sensor by applying the pressure and acceleration extracted from the identified second period as input values ​​of the remodeled pressure offset estimation model. In one embodiment, the processor may be configured to reflect the pressure correction value in the pressure sensor signal after the detection of the fall or impact event to obtain corrected pressure data.

[0012] A method for calibrating barometric sensor data of an electronic device including a barometric sensor according to one embodiment may include storing a barometric offset estimation model according to attitude characteristics of the electronic device in a memory. A method for calibrating barometric sensor data according to one embodiment may include obtaining a barometric sensor signal from the barometric sensor and obtaining an inertial sensor signal from the inertial sensor. A method for calibrating barometric sensor data according to one embodiment may include, when a fall or impact event is detected based on the inertial sensor signal, identifying a first section in which there is no movement of the electronic device in an inertial sensor signal before detection of the fall or impact event. A method for calibrating barometric sensor data according to one embodiment may include remodeling a barometric offset estimation model stored in the memory based on a reference barometric pressure and a reference acceleration extracted from the first section. A method for calibrating barometric sensor data according to one embodiment may include identifying a second section in which there is no movement of the electronic device in an inertial sensor signal after detection of the fall or impact event. A method for calibrating barometric pressure sensor data according to one embodiment may include an operation of estimating a barometric pressure correction value of the barometric pressure sensor by applying the barometric pressure and acceleration extracted from the second section identified above as input values ​​of the remodeled barometric pressure offset estimation model. A method for calibrating barometric pressure sensor data according to one embodiment may further include an operation of reflecting the barometric pressure correction value into a barometric pressure sensor signal after detecting the fall or impact event to obtain calibrated barometric pressure data.

[0013] The electronic device of the present disclosure may include a computer-readable recording medium having recorded thereon a program for implementing a method of calibrating a pressure sensor having a waterproof function.

[0014] A computer-readable recording medium storing a computer program for executing a method for calibrating barometric sensor data of an electronic device including a barometric sensor according to one embodiment comprises the steps of: storing a barometric offset estimation model according to a posture characteristic of the electronic device in the memory; obtaining a barometric sensor signal from the barometric sensor; obtaining an inertial sensor signal from the inertial sensor; when a fall or impact event is detected based on the inertial sensor signal, identifying a first section in which there is no movement of the electronic device in the inertial sensor signal before the detection of the fall or impact event; remodeling the barometric offset estimation model stored in the memory based on a reference barometric pressure and a reference acceleration extracted from the first section; identifying a second section in which there is no movement of the electronic device in the inertial sensor signal after the detection of the fall or impact event; applying the barometric pressure and acceleration extracted from the identified second section as input values ​​of the remodeled barometric offset estimation model to estimate a barometric correction value of the barometric sensor; and reflecting the barometric correction value in the barometric pressure sensor signal after the detection of the fall or impact event to obtain corrected barometric data. Can be recorded.

[0015] Electronic devices, methods and recording media according to various embodiments can reduce input sensor errors due to waterproof gel application by calibrating a pressure sensor based on an impact or fall situation to predict a more accurate pressure compensation value.

[0016] Electronic devices, methods and recording media according to various embodiments can reduce air pressure sensor errors due to waterproof gel roads and prevent malfunctions due to fall situation detection algorithms or altitude measurement algorithms.

[0017] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly understood by a person having ordinary skill in the art to which the present disclosure belongs from the description below.

[0018] In connection with the description of the drawings, the same or similar reference numerals may be used for the same or similar components.

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

[0020] FIG. 2A is a schematic diagram illustrating an example of an electronic device configuration according to one embodiment.

[0021] FIG. 2b is a diagram illustrating an example of the structure of a waterproof pressure sensor according to one embodiment.

[0022] FIG. 3 illustrates a method for calibrating barometric sensor data of an electronic device according to one embodiment.

[0023] FIG. 4 illustrates graphs of inertial sensor and barometric pressure sensor signals of an electronic device according to one embodiment.

[0024] FIG. 5 illustrates a method for modeling a pressure offset estimation model of an electronic device according to one embodiment.

[0025] FIG. 6 illustrates a method for calibrating barometric sensor data of an electronic device according to one embodiment.

[0026] FIG. 7 illustrates a user interface screen related to pressure sensor calibration of an electronic device according to one embodiment.

[0027] FIGS. 8 to 10 illustrate graphs of inertial sensor and barometric sensor signals in various event situations according to various embodiments.

[0028] An electronic device according to an embodiment disclosed in this document may take various forms. The electronic device may include, for example, a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a home appliance. The electronic device according to this document is not limited to the aforementioned devices.

[0029] FIG. 1 is a block diagram of an electronic device within a network environment, according to one embodiment.

[0030] Referring to FIG. 1, in a network environment (100), an electronic device (101) may communicate with an electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with at least one of an electronic device (104) or a server (108) via a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include a processor (120), a memory (130), an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a connection terminal (178), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)).

[0031] The processor (120) may, for example, execute software (e.g., a program (140)) to control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) and perform various data processing or calculations. According to one embodiment, as at least a part of the data processing or calculations, the processor (120) may store commands or data received from other components (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the commands or data stored in the volatile memory (132), and store result data in a non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or a secondary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor)) that can operate independently or together therewith. For example, if the electronic device (101) includes a main processor (121) and a secondary processor (123), the secondary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a specified function. The secondary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0032] The auxiliary processor (123) may control at least a portion of functions or states associated with at least one component (e.g., a display module (160), a sensor module (176), or a communication module (190)) of the electronic device (101), for example, on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). In one embodiment, the auxiliary processor (123) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, in the electronic device (101) itself where artificial intelligence is performed, or can be performed through a separate server (e.g., server (108)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model can include multiple artificial neural network layers.The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0033] The memory (130) can store various data used by at least one component (e.g., processor (120) or sensor module (176)) of the electronic device (101). The data can include, for example, software (e.g., program (140)) and input data or output data for commands related thereto. The memory (130) can include volatile memory (132) or non-volatile memory (134).

[0034] The program (140) may be stored as software in the memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0035] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0036] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. In one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0037] The display module (160) can visually provide information to an external party (e.g., a user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. In one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0038] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), output sound through the sound output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0039] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0040] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). In one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0041] The connection terminal (178) may include a connector through which the electronic device (101) may be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0042] A haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. In one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0043] The camera module (180) can capture still images and videos. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0044] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented, for example, as at least a part of a power management integrated circuit (PMIC).

[0045] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0046] The communication module (190) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (194) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (104) via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0047] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) can support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC realization.

[0048] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). In one embodiment, the antenna module (197) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). In one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (198) or the second network (199), may be selected from the plurality of antennas by, for example, the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device through the selected at least one antenna. In some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0049] In one embodiment, the antenna module (197) may form a mmWave antenna module. In one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high-frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high-frequency band.

[0050] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0051] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0052] FIG. 2a is a schematic diagram illustrating an example of a configuration of an electronic device according to one embodiment, and FIG. 2b is a diagram illustrating an example of a structure of a waterproof pressure sensor according to one embodiment.

[0053] Referring to FIG. 2A, an electronic device (101) according to one embodiment may include a display (210) (e.g., a display module (160)), a pressure sensor (220) (e.g., a sensor module (176) of FIG. 1), an inertial sensor (230) (e.g., a sensor module (176) of FIG. 1), a processor (120), and a memory (130).

[0054] An electronic device (101) according to one embodiment may be a device that can be worn on a user's body, such as a wearable electronic device, but is not limited thereto.

[0055] The display (210) may correspond to the display module illustrated in FIG. 1. The display (210) may, under the control of the processor (120), visually provide various information (e.g., a user interface screen) and / or various result information processed for sensing data (e.g., pressure data and / or inertial data) related to the barometric sensor (220) or the inertial sensor (230).

[0056] The barometric pressure sensor (220) can detect changes in barometric pressure through a hole exposed to the outside of the electronic device (101), convert the changes in barometric pressure into a digital signal (hereinafter, barometric pressure data), and transmit the digital signal to the processor (120). The processor (120) can obtain barometric pressure data from the barometric pressure sensor (220) and store the barometric pressure data in the memory (130).

[0057] According to one embodiment, the pressure sensor (220) may be implemented as a waterproof pressure sensor including a waterproof structure (e.g., waterproof gel, waterproof member). The pressure sensor 190 (220) may be formed, for example, with a structure as shown in FIG. 2B. FIG. 2B illustrates an example of a pressure sensor (220) including a waterproof structure, but is not limited thereto. The pressure sensor (220) may be disposed in a space between a first housing (2201) and a second housing (2002). The pressure sensor (220) may include a flexible substrate (221) and a sealing structure (222). The sealing structure (222) may be configured to seal between the inner wall of the bracket (228) and the pressure sensor (220) by coming into close contact with the inner wall of the bracket (228). In some cases, a waterproof gel may be applied in the direction of the flexible substrate (221) and the sealing structure (222). The bracket (228) can support components mounted on the electronic device (101) (e.g., a circuit board (223), a support plate (224), a battery, a display, etc.). A portion of the bracket (228) can be formed with a structure including a duct that introduces external air into an opening area (225) through a hole. Electronic components such as a processor (120), a memory (130), a communication module (190), an interface, or a connection terminal can be arranged on the circuit board (223). The barometric pressure sensor (220) is electrically connected to the circuit board (223) and can transmit barometric pressure data to the processor (120) arranged on the circuit board (223). The processor (120) can also determine whether the electronic device (101) is submerged based on a barometric pressure change amount greater than a certain value measured by the barometric pressure sensor (220). A waterproof member (227) can be placed between the bracket (228) and the support plate (224). The waterproof member (227) can block water that has entered the opening area (225) from the outside through the hole from passing into the internal space (226) of the electronic device (101).

[0058] The inertial sensor (230) can detect a change in movement according to the posture of the electronic device (101), convert the movement change signal into a digital signal (hereinafter, inertial data) and transmit it to the processor (120). The inertial data may include acceleration data and gyro data, but is not limited thereto. The processor (120) can obtain inertial data (e.g., acceleration data, gyro data) from the inertial sensor (230) and store the inertial data in the memory (130).

[0059] According to one embodiment, the inertial sensor (230) may include various types of sensors for measuring angular velocity and linear acceleration. The inertial sensor (230) may include at least one of a 3-axis accelerometer, a gyroscope, a magnetometer sensor, a tilt sensor, a motion sensor, and / or an inertial measurement device. For example, the inertial sensor (230) may extract angular information such as pitch, roll, and yaw using a gyro sensor, and track the direction of velocity (jump, movement speed) using an acceleration sensor. In some cases, when the inertial sensor (230) includes a geomagnetic sensor, the geomagnetic sensor may be used to track the magnetic field value of the Earth to track the direction of movement.

[0060] The memory (130) can store various instructions that can be performed by the processor (120). Such instructions can include arithmetic and logical operations, data movement, or control commands such as input / output that can be recognized by the processor (120).

[0061] The processor (120) controls the operation of the display (210), the barometric pressure sensor (220) or / and the inertial sensor (230), and can control the operation of a barometric pressure sensor calibration algorithm (or program, process) by converting sensing data (e.g., barometric pressure data, inertial data) obtained from the barometric pressure sensor (220) or / and the inertial sensor (230) into barometric pressure.

[0062] There is no limitation to the computational and data processing functions that the processor (120) can implement on the electronic device (101). However, below, the pressure sensor calibration operations for determining the timing (or conditions) for remodeling the pressure offset estimation model based on inertial data and reducing the pressure sensor error range by remodeling the pressure offset estimation model will be described. The operations of the processor (120) described below can be performed by loading instructions stored in the memory (130).

[0063] An electronic device (101) according to one embodiment includes an inertial sensor (230), an air pressure sensor (220), a memory (130) storing executable instructions, and a processor (120) accessing the memory (130) to execute the instructions, wherein the processor (120) stores an air pressure offset estimation model according to the attitude characteristics of the electronic device in the memory, obtains an air pressure sensor signal from the air pressure sensor, obtains an inertial sensor signal from the inertial sensor, and, when a fall or impact event is detected based on the inertial sensor signal, identifies a first section in which there is no movement of the electronic device in the inertial sensor signal before the detection of the fall or impact event, remodels the air pressure offset estimation model stored in the memory based on a reference air pressure and a reference acceleration extracted from the first section, identifies a second section in which there is no movement of the electronic device in the inertial sensor signal after the detection of the fall or impact event, and uses the air pressure and acceleration extracted from the identified second section as input values ​​of the remodeled air pressure offset estimation model. It can be set to estimate the pressure compensation value of the pressure sensor by applying the pressure compensation value and to obtain corrected pressure data by reflecting the pressure compensation value in the pressure sensor signal after detecting the fall or impact event.

[0064] According to one embodiment, the pressure sensor may be characterized as a waterproof pressure sensor having a waterproof gel applied to a sensing portion.

[0065] According to one embodiment, the processor (120) may be further configured to detect a fall or impact event when the inertial sensor signal exceeds a preset threshold or an impact of a certain level or higher occurs.

[0066] According to one embodiment, the inertial sensor data may include a three-axis acceleration signal and a gyro signal.

[0067] According to one embodiment, the processor (120) may be further configured to determine a section in which the signal vector magnitude (SVM) signal and the gyro signal for the acceleration 3-axis signal are included within a certain threshold range for a certain period of time or a section in which the signal fluctuation exists within an error range for a certain period of time as a first section in which there is no movement of the electronic device.

[0068] According to one embodiment, the processor may be further configured to extract a representative value of the pressure sensor signals and a representative value of the acceleration signal within the first section as the reference pressure and the reference acceleration.

[0069] According to one embodiment, the processor (120) may be configured to smooth the obtained pressure sensor signal by applying a low pass filter (LPF), and to extract a representative value of the pressure sensor signals based on the smoothed pressure sensor signal.

[0070] According to one embodiment, the processor (120) may be configured to repeatedly collect acceleration and air pressure data for each posture of the electronic device, calculate a model between the collected independent variables and dependent variables, measure and analyze the fit, model the air pressure offset estimation model, and store the modeled air pressure offset estimation model in the memory.

[0071] According to one embodiment, the processor (120) may be configured to remodel the pressure offset estimation model whenever the fall or impact event is detected.

[0072] FIG. 3 illustrates a method for calibrating barometric sensor data of an electronic device according to one embodiment, and FIG. 4 illustrates graphs of inertial sensor and barometric sensor signals of an electronic device according to one embodiment.

[0073] In the following examples, the operations may be performed sequentially, but are not necessarily sequential. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0074] Referring to FIG. 3, the processor (120) of the electronic device (101) according to one embodiment may store a pressure offset estimation model in the memory (130) in operation 310. The pressure offset estimation model may be generated by repeatedly collecting acceleration and pressure data for each posture of the electronic device (101) during manufacturing of the electronic device (101) and analyzing / modeling the collected data.

[0075] The pressure offset estimation model can predict (or output, obtain) a pressure compensation value by inputting pressure and acceleration signals and using a mathematical formula to derive parameters that minimize the pressure error. The modeling operation of the pressure offset estimation model is described in Fig. 5.

[0076] In operation 320, the processor (120) may obtain a pressure sensor signal from the pressure sensor (220) and an inertial sensor signal from the inertial sensor (230). The inertial sensor signal may include an acceleration signal and a gyro signal.

[0077] For example, FIG. 4 may be a diagram that graphs the inertial sensor signals and the barometric pressure signal sensor when the electronic device is rotated about the x-axis and y-axis in a posture in which the front of the electronic device faces the sky. FIG. 4 <401> is a 3-axis acceleration sensor graph, and may mean an acceleration x-axis signal (410), an acceleration y-axis signal (411), and a change in an acceleration z-axis signal (412) according to the posture of the electronic device (101). FIG. 4 <403> is a gyro sensor graph and may mean a change in the gyro signal (430) according to the attitude of the electronic device (101). Fig. 4 <402> is a graph of the sum magnitude (hereinafter, SVM (signal vector magnitude)) signal for the 3-axis acceleration signal, and is shown in Fig. 4. <404> is a barometric pressure sensor graph.

[0078] In operation 330, the processor (120) can remove noise by smoothing the pressure sensor signal using a smoothing filter. The smoothing filter can use an LPF (Low Pass Filter) and a digital filter, but is not limited thereto.

[0079] In the case of a barometric pressure sensor signal, since minute changes (or errors) may occur even in sections where there is no movement of the electronic device due to the characteristics of the sensor, the electronic device (101) may generate a smoothed signal through a smoothing filter. For example, in FIG. 4 <404> Some are barometric sensor graphs, showing changes in the smoothed signal (440) depending on the attitude of the electronic device (101).

[0080] In some embodiments, operation 330 may be omitted. For example, operation 330 may be omitted when the noise error level in the barometric pressure sensor signal is low (or when the barometric pressure sensor performance is good).

[0081] In operation 340, the processor (120) can detect (or determine, confirm) a first section (d) without electronic device movement based on an inertial sensor signal, and extract reference pressure (A) and reference acceleration (B) information in the first section (d). For convenience of explanation, the first section (d) <404> As shown in the graph, <401> <402> and <403> Even in sections, the first section can be the same.

[0082] According to one embodiment, the processor (120) may obtain a signal vector magnitude (SVM) value for a collected three-axis (e.g., x-axis, y-axis, z-axis) acceleration signal, and analyze the SVM signal and the gyro signal to detect the first section (d). The SVM value may refer to a value used to process the output value of the three-axis acceleration sensor as a single representative value without considering the rotational component included therein.

[0083] For example, in Fig. 4 <402> is an acceleration SVM graph, <401> It may be a change in the SVM signal (420) that represents the change in the sum of the acceleration 3-axis signals shown in .

[0084] The processor (120) can determine a section in which the SVM signal (420) and the gyro signal (430) are within a certain threshold range for a certain period of time or a section in which the signal fluctuation exists within an error range for a certain period of time as a first section (d) in which there is no movement of the electronic device. For example, in FIG. 4 <402> Graph and <403> Using the graph, the SVM value and the gyro signal can be determined as a section within a certain critical range for a certain period of time or as a first section (d) within which the signal fluctuation for a certain period of time is within an error range.

[0085] According to one embodiment, the processor (120) may divide the section into a predetermined unit and detect a first section (d) for each divided section, if the section in which the SVM value and the gyro signal are included within a predetermined threshold range for a predetermined period of time or the section in which the signal fluctuation exists within an error range for a predetermined period of time is a long period exceeding a set threshold time.

[0086] When the first section (d) is detected, the processor (120) can extract the reference pressure (A) and reference acceleration (B) information in the first section (d). The processor (120) of FIG. 4 <404> As illustrated, the pressure sensor signal in the first section (d) is verified, and the representative value of the verified pressure sensor signal is extracted and used as reference pressure (A) information, and the acceleration sensor signal in the first section is verified, and the representative value of the verified acceleration sensor signal is used as reference acceleration (B) information. For example, the representative value may be the median or mean in each section, but another value representing the section may also be used.

[0087] For example, in Fig. 4 <404> Some of the graphs show the change in signal (450) extracted from the pressure representative value for the first section (d) where there is no movement.

[0088] According to some embodiments, the processor (120) may detect a first section of stillness even when a user wearing the electronic device is walking. For example, the ground contact time (GCT) refers to the time during which the feet of a user wearing the electronic device are in contact with the ground while walking, and thus, even if the electronic device recognizes that the user is walking, it may detect the first section of stillness by detecting the GCT section.

[0089] In operation 350, the processor (120) can determine whether a fall or impact event (or a situation requiring pressure compensation) is detected. For example, the processor (120) can detect a fall or impact event when an inertial sensor signal exceeds a preset threshold or an impact exceeding a certain level occurs. As another example, the processor (120) can detect a situation requiring pressure compensation when an opposite movement situation, such as a step-up to a step-down, occurs during stair movement or floor recognition based on a pressure sensor signal.

[0090] In a 360 motion, the processor (120) can detect (or determine, confirm) a second section of no electronic device movement based on an inertial sensor signal after a fall or impact event occurs, and extract pressure (A') and acceleration (B') information corresponding to the second section.

[0091] The processor (120) can determine a section in which the SVM value and the gyro signal after a fall or impact event are within a certain critical range for a certain period of time or a section in which signal fluctuation exists within an error range for a certain period of time as a second section in which there is no movement of the electronic device. The processor (120) can check a pressure sensor signal section corresponding to the second section, extract a representative value of the checked pressure sensor section, and use the extracted value as pressure (A') information, and check an acceleration sensor signal section corresponding to the second section, and use the representative value of the checked acceleration signal section as acceleration (B') information.

[0092] In operation 370, the processor (120) may remodel the pressure offset estimation model stored in the memory using the reference pressure (A) and reference acceleration (B) information extracted before the occurrence of a fall or impact event.

[0093] In operation 380, the processor (120) may estimate (or predict / produce) a pressure compensation value (or offset value) by applying the pressure (A') and acceleration (B') signals of the pressure compensation section after a fall or impact event occurs as inputs of a remodeled pressure offset estimation model. The pressure compensation value (or offset value) may be a value that adjusts whether the origin of the pressure sensor deviates from 0 and is biased + or - depending on the difference between the pressure value measured by the pressure sensor and the reference signal.

[0094] In operation 390, the processor (120) can obtain calibrated pressure data by applying the estimated pressure correction value (C) to the estimated pressure (A') in the pressure correction section.

[0095] FIG. 5 illustrates a method for modeling a pressure offset estimation model of an electronic device according to one embodiment.

[0096] Referring to FIG. 5, an electronic device according to one embodiment can model and store a pressure sensor offset estimation model when manufacturing or setting the electronic device (101).

[0097] In operation 510, the electronic device (101) can repeatedly collect acceleration and air pressure data for each posture of the electronic device (101). In operation 520, the electronic device (101) can obtain a model between two variables, an independent variable x and a dependent variable y, among the collected data to measure the goodness of fit. Here, the independent variable x means acceleration x-axis data, acceleration y-axis data, acceleration z-axis data, and air pressure data, and the dependent variable y can mean an air pressure correction value (or offset value). In operation 530, the electronic device (101) can perform regression analysis based on the goodness of fit measurement, and in operation 540, can model an air pressure offset estimation model.

[0098] For example, the electronic device (101) can model a model that receives barometric pressure and acceleration signals and predicts (or outputs, obtains) a barometric pressure correction value using a mathematical formula that obtains parameters that minimize barometric pressure errors.

[0099] Meanwhile, the modeling method of the pressure offset estimation model can be understood as a known technology and can be replaced with a predictable modeling method by those skilled in the art.

[0100] FIG. 6 illustrates a method for calibrating barometric sensor data of an electronic device according to one embodiment.

[0101] In the following examples, the operations may be performed sequentially, but are not necessarily sequential. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0102] Referring to FIG. 6, the processor (120) of the electronic device (101) according to one embodiment may obtain a pressure sensor signal from the pressure sensor (220) and an inertial sensor signal from the inertial sensor (230) in operation 610. The inertial sensor signal may include an acceleration signal and a gyro signal.

[0103] The processor (120) can store the barometric pressure sensor signal transmitted from the barometric pressure sensor (220) and the inertial sensor signal transmitted from the inertial sensor (230) in the memory (130).

[0104] In operation 620, the processor (120) may determine whether a fall or impact event (or a situation requiring air pressure compensation) has been detected. For example, the processor (120) may detect a fall or impact event when an inertial sensor signal exceeds a preset threshold or an impact exceeding a certain level occurs.

[0105] In operation 630, the processor (120) can identify a first section in which there is no movement of the electronic device prior to event detection, and extract reference pressure (A) and reference acceleration (B) information of the first section.

[0106] According to one embodiment, the processor (120) may obtain a three-axis sum magnitude (hereinafter, SVM (signal vector magnitude)) value for the collected three-axis (e.g., x-axis, y-axis, z-axis) acceleration signals, and analyze the SVM signal and the gyro signal to detect a first section. The processor (120) may determine a section in which the SVM signal (420) and the gyro signal (430) are within a certain threshold range for a certain period of time or a section in which signal fluctuation exists within an error range for a certain period of time as a first section in which there is no movement of the electronic device. When the first section is detected, the processor (120) may extract reference pressure (A) and reference acceleration (B) information in the first section. For example, the processor (120) can check a pressure sensor signal corresponding to the first section, extract a representative value of the checked pressure sensor signal, and use it as reference pressure (A) information, check an acceleration sensor signal corresponding to the first section, and use a representative value of the checked acceleration sensor signal as reference acceleration (B) information.

[0107] According to some embodiments, the processor (120) may detect a first section of stillness even when a user wearing the electronic device is walking. For example, the ground contact time (GCT) refers to the time during which the feet of a user wearing the electronic device are in contact with the ground while walking, and thus, even if the electronic device recognizes that the user is walking, it may detect the first section of stillness by detecting the GCT section.

[0108] In operation 640, the processor (120) can remodel the pressure offset estimation model stored in the memory using the reference pressure (A) and reference acceleration (B) information extracted before the occurrence of a fall or impact event.

[0109] In operation 650, the processor (120) can detect (or determine, confirm) a second section in which there is no movement of the electronic device after detecting a fall event, and extract pressure (A') and acceleration (B') information corresponding to the second section.

[0110] The processor (120) can determine a section in which the SVM value and the gyro signal after a fall or impact event are within a certain critical range for a certain period of time or a section in which signal fluctuation exists within an error range for a certain period of time as a second section in which there is no movement of the electronic device. The processor (120) can check a pressure sensor signal section corresponding to the second section, extract a representative value of the checked pressure sensor section, and use the extracted value as pressure (A') information, and check an acceleration sensor signal section corresponding to the second section, and use the representative value of the checked acceleration signal section as acceleration (B') information.

[0111] In operation 660, the processor (120) can estimate (or predict / produce) a pressure compensation value (or offset value) by applying the pressure (A') and acceleration (B') signals of the pressure compensation section after a fall or impact event occurs as inputs of a remodeled pressure offset estimation model.

[0112] In operation 670, the processor (120) can obtain calibrated pressure data by applying an estimated pressure correction value (C) to the pressure (A') of the second section.

[0113] FIG. 7 illustrates a user interface screen related to pressure sensor calibration of an electronic device according to one embodiment.

[0114] Referring to FIG. 7, an electronic device according to one embodiment may support an on / off function for setting a pressure sensor calibration function. For example, the electronic device may display a pressure sensor calibration setting menu (720) on a setting screen (710). The pressure sensor calibration setting menu (720) may include a toggle item (730) for performing or not performing pressure sensor calibration in the event of an impact or falling flower event. A user may use the toggle item (730) to enable or disable the pressure sensor calibration function.

[0115] FIGS. 8 to 10 illustrate graphs of inertial sensor and barometric sensor signals in various event situations according to various embodiments.

[0116] Figure 8 shows changes in inertial sensor and barometric sensor signals in a situation where an impact event occurs while a user is walking on a flat surface. <801> is the collected 3-axis (e.g. x-axis (810), y-axis (811), z-axis (812)) acceleration signal, <802> is the SVM (signal vector magnitude) signal (820) for the 3-axis acceleration signal, <803> is the gyro signal (830), <804> represents a smoothed pressure sensor signal (840) before engagement and a pressure sensor signal (850) after correction. The electronic device (101) can identify the impact occurrence time (860) based on the SVM (signal vector magnitude) signal (820) for the collected three-axis (e.g., x-axis (810), y-axis (811), z-axis (812)) acceleration signal and the gyro signal (830). Here, the impact occurrence time (860) may mean the time of detection of a fall or impact event. The electronic device (101) can identify a first section (865) in which there is no movement before the impact occurrence time (860), and extract the reference pressure and reference acceleration in the first section to remodel the pressure offset estimation model. Thereafter, the electronic device (101) can correct the pressure data after the impact occurrence time (860) using the remodeled pressure offset estimation model. In the situation of Figure 8, it was calculated that the user lost about 122.1 cm in height due to the fall. <804> As shown in , it can be confirmed that the pressure sensor signal (580) after pressure correction is predicted to be about 71.45 cm lower in altitude compared to the pressure sensor signal (840) before correction.

[0117] Figure 9 shows changes in inertial sensor and barometric sensor signals in a situation where a user wearing an electronic device makes a golf swing. <901> is the collected 3-axis (e.g. x-axis (910), y-axis (911), z-axis (912)) acceleration signal, <902> is a signal vector magnitude (SVM) signal (920) for a three-axis acceleration signal, <903> is the gyro signal (930), <904> represents a smoothed pressure sensor signal (940) before correction and a pressure sensor signal (950) after correction. The electronic device (101) can identify the impact occurrence time (960) (e.g., the fall or impact event detection time) based on the signal vector magnitude (SVM) signal (920) and the gyro signal (930). The electronic device (101) can identify a first section (965) in which there is no movement before the impact occurrence time (960), extract a reference pressure and reference acceleration in the first section (965), remodel the pressure offset estimation model, and correct the pressure data after the impact occurrence time (960) using the remodeled pressure offset estimation model. In the case of the event situation of FIG. 9, it can be confirmed that the pressure sensor signal (940) before pressure correction was calculated to have decreased in altitude by about 37.97 cm, while the pressure sensor signal (950) after pressure correction was predicted to have increased in altitude by about 68.8 cm.

[0118] Figure 10 shows changes in inertial sensor and barometric sensor signals in a situation where a user wearing an electronic device falls from a bed. <1001> is the collected 3-axis (e.g. x-axis (1010), y-axis (1011), z-axis (1012)) acceleration signal, <1002> is the SVM (signal vector magnitude) signal (1020) for the 3-axis acceleration signal, <1003> is the gyro signal (1030), <1004> represents a smoothed pressure sensor signal (1040) before correction and a pressure sensor signal (1050) after correction. The electronic device (101) can identify the time of impact occurrence (1060) (e.g., the time of detection of a fall or impact event) based on the signal vector magnitude (SVM) signal (1020) and the gyro signal (1030). The electronic device (101) can identify a first section (1065) in which there is no movement before the time of impact occurrence (1060), extract a reference pressure and reference acceleration in the first section (1065), remodel the pressure offset estimation model, and correct the pressure data after the time of impact occurrence (1060) using the remodeled pressure offset estimation model. In the event situation of Fig. 10, it can be confirmed that the pressure sensor signal (1040) before pressure correction was calculated to have increased in altitude by approximately 33.56 cm, while the pressure sensor signal (1050) after pressure correction was predicted to have decreased in altitude by approximately 57.65 cm, even though the user fell from the bed.

[0119] Various embodiments can estimate the pressure offset more accurately by remodeling the pressure offset estimation model with data prior to the event occurrence in various fall or impact event situations, and then performing pressure sensor calibration, and can correct the pressure data with the estimated subscription offset.

[0120] Various embodiments can prevent misrecognition of recognition actions related to pressure data (e.g., floor recognition algorithm / motion recognition algorithm) by remodeling the pressure offset state model in each fall or impact event situation, and can provide more accurate and diverse information to the user by utilizing sophisticated pressure data.

[0121] A method for calibrating barometric sensor data of an electronic device including a barometric sensor according to one embodiment may further include: storing a barometric offset estimation model according to a posture characteristic of the electronic device in a memory; acquiring a barometric sensor signal from the barometric sensor and acquiring an inertial sensor signal from the inertial sensor; when a fall or impact event is detected based on the inertial sensor signal, identifying a first section in which there is no movement of the electronic device in an inertial sensor signal before detection of the fall or impact event; remodeling the barometric offset estimation model stored in the memory based on a reference barometric pressure and a reference acceleration extracted from the first section; identifying a second section in which there is no movement of the electronic device in an inertial sensor signal after detection of the fall or impact event; applying the barometric pressure and acceleration extracted from the identified second section as input values ​​of the remodeled barometric offset estimation model to estimate a barometric correction value of the barometric sensor; and reflecting the barometric correction value in a barometric sensor signal after detection of the fall or impact event to obtain corrected barometric data.

[0122] According to one embodiment, the pressure sensor may be characterized as a waterproof pressure sensor having a waterproof gel applied to a sensing portion.

[0123] According to one embodiment, the inertial sensor data includes a 3-axis acceleration signal and a gyro signal, and the operation of confirming the first section may further include an operation of detecting a situation in which a fall or impact event has occurred when the inertial sensor signal exceeds a preset threshold or an impact of a predetermined level or higher has occurred, and an operation of determining a section in which a signal vector magnitude (SVM) signal and a gyro signal for the 3-axis acceleration signal are included within a predetermined threshold range for a predetermined period of time or a section in which a signal fluctuation exists within an error range for a predetermined period of time as a first section in which there is no movement of the electronic device.

[0124] An operation of remodeling the pressure offset estimation model stored in the memory according to one embodiment may further include an operation of applying a low pass filter (LPF) to the acquired pressure sensor signal to smooth it, and an operation of extracting a representative value of the smoothed pressure sensor signals and a representative value of an acceleration signal within the first section as the reference pressure and the reference acceleration.

[0125] The operation of storing the pressure offset estimation model in the memory according to one embodiment may further include an operation of repeatedly collecting acceleration and pressure data for each posture of the electronic device, calculating a model between the collected independent variables and dependent variables, measuring and analyzing the goodness of fit, and modeling the pressure offset estimation model.

[0126] The operation of remodeling the pressure offset estimation model according to one embodiment may be characterized by remodeling the pressure offset estimation model each time the fall or impact event is detected.

[0127] A computer-readable recording medium storing a computer program for executing a method for calibrating barometric sensor data of an electronic device including a barometric sensor according to one embodiment may include operations of storing a barometric offset estimation model according to a posture characteristic of the electronic device in the memory, obtaining a barometric sensor signal from the barometric sensor, obtaining an inertial sensor signal from the inertial sensor, and, when a fall or impact event is detected based on the inertial sensor signal, identifying a first section in which there is no movement of the electronic device in the inertial sensor signal before the detection of the fall or impact event, remodeling the barometric offset estimation model stored in the memory based on a reference barometric pressure and a reference acceleration extracted from the first section, identifying a second section in which there is no movement of the electronic device in the inertial sensor signal after the detection of the fall or impact event, applying the barometric pressure and acceleration extracted from the identified second section as input values ​​of the remodeled barometric offset estimation model to estimate a barometric correction value of the barometric sensor, and reflecting the barometric correction value in the barometric pressure sensor signal after the detection of the fall or impact event to obtain corrected barometric data.

[0128] According to one embodiment, the inertial sensor data includes a 3-axis acceleration signal and a gyro signal, and the computer program may further include an operation of detecting a situation in which a fall or impact event has occurred when the inertial sensor signal exceeds a preset threshold or an impact of a certain level or higher has occurred, and determining a section in which a signal vector magnitude (SVM) signal and a gyro signal for the 3-axis acceleration signal are included within a certain threshold range for a certain period of time or a section in which a signal fluctuation exists within an error range for a certain period of time as a first section in which there is no movement of the electronic device.

[0129] The computer program according to one embodiment may further include an operation of extracting a representative value of the pressure sensor signals and a representative value of the acceleration signal within the first section as the reference pressure and the reference acceleration.

[0130] The computer program according to one embodiment may further include an operation of applying a low pass filter (LPF) to the acquired pressure sensor signal to smooth it, and extracting a representative value of the pressure sensor signals based on the smoothed pressure sensor signal.

[0131] The computer program according to one embodiment may further include an operation of repeatedly collecting posture-specific acceleration and pressure data of the electronic device, calculating a model between the collected independent variables and dependent variables to measure and analyze the goodness of fit, modeling the pressure offset estimation model, and storing the modeled pressure offset estimation model in the memory.

[0132] It should be understood that the terms used in the embodiments of the document are not intended to limit the technical features described in the document to specific embodiments, but rather to encompass various modifications, equivalents, or alternatives of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly dictates otherwise. In this document, each of the phrases "A or B," "at least one of A and B," "at least one of A or B," "A, B, or C," "at least one of A, B, and C," and "at least one of A, B, or C" may include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0133] The term "module" used in one embodiment of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0134] One embodiment of the present document may be implemented as software (e.g., a program (140)) including one or more instructions stored in a storage medium (e.g., an internal memory (136) or an external memory (138)) readable by a machine (e.g., an electronic device (101)). For example, a processor (e.g., a processor (120)) of the machine (e.g., an electronic device (101)) may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the machine to operate to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0135] According to one embodiment, the method according to one embodiment disclosed in the present document may be provided as a computer program product. The computer program product may be traded between sellers and buyers as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)) or may be provided through an application store (e.g., Play Store). TM ) or directly between two user devices (e.g., smart phones), online distribution (e.g., downloading or uploading). 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 an intermediary server.

[0136] According to one embodiment, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to one embodiment, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to one embodiment, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Claims

1. In an electronic device (101), Inertial sensor (230); Barometric pressure sensor (220); A memory (130) storing executable instructions and Includes a processor (120) that accesses the above memory and executes the above instructions, The above processor, A pressure offset estimation model according to the attitude characteristics of the electronic device is stored in the above memory, Obtaining a pressure sensor signal from the above pressure sensor, and obtaining an inertial sensor signal from the above inertial sensor, When a fall or impact event is detected based on the above inertial sensor signal, a first period of no electronic device movement is identified in the inertial sensor signal prior to detection of the fall or impact event, Remodeling the pressure offset estimation model stored in the memory based on the reference pressure and reference acceleration extracted from the first section above, Identify a second period of no electronic device movement in the inertial sensor signal after the detection of the above fall or impact event, The pressure and acceleration extracted from the second section confirmed above are applied as input values ​​to the remodeled pressure offset estimation model to estimate the pressure compensation value of the pressure sensor, An electronic device configured to obtain corrected barometric pressure data by reflecting the above barometric pressure compensation value into the barometric pressure sensor signal after detection of the fall or impact event.

2. In paragraph 1, An electronic device characterized in that the above pressure sensor is a waterproof pressure sensor having a waterproof gel applied to the sensing portion.

3. In paragraph 1, The above processor, An electronic device further configured to detect a fall or impact event when the above inertial sensor signal exceeds a preset threshold or an impact of a certain level or higher occurs.

4. In paragraph 1, The above inertial sensor data includes three-axis acceleration signals and gyro signals, The above processor, An electronic device further configured to determine a section in which the SVM (signal vector magnitude) signal and the gyro signal for the above three-axis acceleration signal are included within a certain critical range for a certain period of time or a section in which the signal fluctuation exists within an error range for a certain period of time as a first section in which there is no movement of the electronic device.

5. In paragraph 4, The above processor, An electronic device further configured to extract representative values ​​of pressure sensor signals and representative values ​​of acceleration signals within the first section as the reference pressure and the reference acceleration.

6. In paragraph 5, The above processor, Apply LPF (low pass filter) to the pressure sensor signal acquired above to smooth it, An electronic device configured to extract a representative value of the pressure sensor signals based on the smoothed pressure sensor signal.

7. In paragraph 6, The above processor, After repeatedly collecting the acceleration and pressure data for each posture of the electronic device, the model between the collected independent and dependent variables is calculated, the goodness of fit is measured and analyzed to model the pressure offset estimation model. An electronic device configured to store the above modeled pressure offset estimation model in the memory.

8. In paragraph 1, The above processor, An electronic device configured to remodel said pressure offset estimation model whenever said fall or impact event is detected.

9. In paragraph 1, Further including a display (210), The above processor, An electronic device controlling the display of said display, including a pressure sensor calibration setting menu and a toggle item for toggling the setting on / off, on the electronic device setting screen.

10. A method for calibrating barometric sensor data of an electronic device including a barometric sensor, An operation of storing a pressure offset estimation model according to the detailed characteristics of an electronic device (101) in a memory (130); An operation of acquiring a pressure sensor signal from a pressure sensor (220) and an inertial sensor signal from an inertial sensor (230); An action of identifying a first period of no electronic device movement in the inertial sensor signal prior to detection of the fall or impact event, when a fall or impact event is detected based on the inertial sensor signal; An operation of remodeling the pressure offset estimation model stored in the memory based on the reference pressure and reference acceleration extracted in the first section; An action of identifying a second period of no electronic device movement in the inertial sensor signal after detection of the above fall or impact event; An operation of estimating a pressure compensation value of the pressure sensor by applying the pressure and acceleration extracted from the second section confirmed above as input values ​​of the remodeled pressure offset estimation model, and A method further comprising the action of obtaining corrected barometric pressure data by reflecting the barometric pressure compensation value to the barometric pressure sensor signal after detecting the fall or impact event.

11. In paragraph 10, A method characterized in that the above pressure sensor is a waterproof pressure sensor having a waterproof gel applied to the sensing portion.

12. In paragraph 10, The above inertial sensor data includes three-axis acceleration signals and gyro signals, The action of checking the above first section is An action to detect a fall or impact event when the above inertial sensor signal exceeds a preset threshold or an impact of a certain level or higher occurs; and A method further comprising an operation of determining a section in which an SVM (signal vector magnitude) signal and a gyro signal for the above three-axis acceleration signal are included within a certain critical range for a certain period of time or a section in which a signal fluctuation exists within an error range for a certain period of time as a first section in which there is no movement of the electronic device.

13. In paragraph 10, The operation of remodeling the pressure offset estimation model stored in the above memory is An operation of smoothing the obtained pressure sensor signal by applying a LPF (low pass filter); A method further comprising an operation of extracting a representative value of smoothed pressure sensor signals and a representative value of an acceleration signal within the first section as the reference pressure and the reference acceleration.

14. In paragraph 10, The operation of storing the above pressure offset estimation model in the memory is as follows: A method further comprising the action of repeatedly collecting posture-specific acceleration and pressure data of the electronic device, calculating a model between the collected independent variables and dependent variables, measuring the goodness of fit, and analyzing the model to model the pressure offset estimation model.

15. In paragraph 10, The operation of remodeling the above pressure offset estimation model is: A method characterized by remodeling the pressure offset estimation model whenever the fall or impact event is detected.

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