KR20230052779A - Apparatus and method for weight data clibration - Google Patents

Abstract

Disclosed are an apparatus for correcting weight data and a method thereof. According to an embodiment of the present invention, the apparatus for correcting weight data can accurately correct and display net weight. According to the embodiment of the present invention, the apparatus for correcting weight data includes a processor and a memory storing instructions executed by the processor. When the processor executes the instructions, the processor can execute: a motion of receiving and storing first weight data on a user measured by a weight measuring device, from the weight measuring device connected to an electronic device; a motion of determining and storing a first state parameter of the user corresponding to the measured first weight data; a motion of receiving and storing measured second weight data on the user; a motion of determining and storing a second state parameter of the user corresponding to the second weight data; a motion of determining a weight correction value based on a comparison result between the first state parameter and the second state parameter; and a motion of estimating the net weight of the user by applying the weight correction value to the second weight data.

Description

Apparatus and method for correcting weight data {APPARATUS AND METHOD FOR WEIGHT DATA CLIBRATION}

Various embodiments of the present invention relate to weight data calibration techniques.

Weight, which can be easily measured in daily life, is one of the important indicators that can infer health conditions. Various types of devices are being developed so that this weight can be measured several times in more various places. For example, it is being developed to measure weight multiple times by using a space where various users stay, such as weight measurement through a footrest at the entrance of a front door, a shower booth or a chair.

The user's weight may be measured several times in various spaces, but the accuracy and consistency of the measured weight may vary depending on the user's condition at the time of measurement. For example, in an environment where more and more users use various wearable devices (eg, smart watches, smart bands, earphones, smart glasses, smart rings) and portable devices (eg, smart phones, tablets), weight loss Whenever measuring, weight may cause measurement error. For consistent weight measurement, it is inconvenient from a usability point of view if factors that affect weight measurement must be removed and measured at every measurement.

According to the weight correction method and apparatus according to an embodiment, weight may be corrected by identifying factors affecting weight measurement.

An electronic device for correcting weight data according to an embodiment includes a processor and a memory storing instructions to be executed by the processor, and when the instructions are executed by the processor, the processor: An operation of receiving and storing first weight data of the user measured by the weight measurement device from a weight measuring device connected to the electronic device, determining and storing a first state parameter of the user corresponding to the measured first weight data operation of receiving and storing the measured second weight data of the user, determining and storing a second state parameter of the user corresponding to the second weight data, and storing the first state parameter and the second state parameter. An operation of determining a weight correction value based on a comparison result between parameters and an operation of estimating a net weight of the user by applying the weight correction value to the second weight data may be performed.

A weight data correction method according to an embodiment includes an operation of receiving and storing first weight data of the user measured by the weight measuring device from a weight measuring device connected to an electronic device; Determining and storing the corresponding first state parameter of the user, receiving and storing the measured second weight data of the user, determining the second state parameter of the user corresponding to the second weight data, Storing, determining a weight correction value based on a comparison result between the first state parameter and the second state parameter, and applying the weight correction value to the second weight data weight) may be included.

A weight correction method and apparatus according to an embodiment stores state parameters that can affect weight change according to a user's weight measurement situation, and when measuring weight thereafter, the past measured values and state parameters and current user's changed values are stored together. By comparing the state parameter values, the net weight can be more accurately corrected and displayed.

1 is a block diagram of an electronic device in a network environment according to various embodiments.
2 is a block diagram of an electronic device according to an exemplary embodiment.
3 is a block diagram of a body weight measuring device according to an embodiment.
4 is a flowchart of a weight data correction method according to an exemplary embodiment.
5 is a diagram for explaining a method of estimating weights of state parameters according to an exemplary embodiment.
6 is a diagram for explaining an estimated net weight based on estimated weights of state parameters according to an exemplary embodiment.
7 is a diagram for explaining a weight trend chart according to an exemplary embodiment.
8 is a flowchart illustrating a method of correcting a measured weight by detecting a wearable device worn by a user and a mobile device possessed by the user.
9 is a diagram for explaining a body composition data correction method according to an exemplary embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. In the description with reference to the accompanying drawings, the same reference numerals are given to the same components regardless of reference numerals, and overlapping descriptions thereof will be omitted.

1 is a block diagram of an electronic device 101 within a network environment 100, according to various embodiments. Referring to FIG. 1 , in a network environment 100, an electronic device 101 communicates with an electronic device 102 through a first network 198 (eg, a short-range wireless communication network) or through a second network 199. It may communicate with at least one of the electronic device 104 or the server 108 through (eg, 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 an 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, 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 the antenna module 197 may be included. In some embodiments, in the electronic device 101, at least one of these components (eg, the connection terminal 178) may be omitted or one or more other components may be added. In some embodiments, some of these components (eg, sensor module 176, camera module 180, or antenna module 197) are integrated into a single component (eg, display module 160). It can be.

The processor 120, for example, executes software (eg, the program 140) to cause at least one other component (eg, hardware or software component) of the electronic device 101 connected to the processor 120. It can control and perform various data processing or calculations. According to one embodiment, as at least part of data processing or operation, the processor 120 transfers instructions or data received from other components (e.g., sensor module 176 or communication module 190) to volatile memory 132. , processing commands or data stored in the volatile memory 132 , and storing resultant data in the non-volatile memory 134 . According to one embodiment, the processor 120 may include a main processor 121 (eg, a central processing unit or an application processor) or a secondary processor 123 (eg, a graphic processing unit, a neural network processing unit ( NPU: neural processing unit (NPU), image signal processor, sensor hub processor, or communication processor). For example, when the electronic device 101 includes the main processor 121 and the auxiliary processor 123, the auxiliary processor 123 may use less power than the main processor 121 or be set to be specialized for a designated function. can The secondary processor 123 may be implemented separately from or as part of the main processor 121 .

The secondary processor 123 may, for example, take the place of the main processor 121 while the main processor 121 is in an inactive (eg, sleep) state, or the main processor 121 is active (eg, running an application). ) state, together with the main processor 121, at least one of the components of the electronic device 101 (eg, the display module 160, the sensor module 176, or the communication module 190) It is possible to control at least some of the related functions or states. According to one embodiment, the auxiliary processor 123 (eg, image signal processor or communication processor) may be implemented as part of other functionally related components (eg, camera module 180 or communication module 190). there is. According to an embodiment, the auxiliary processor 123 (eg, a neural network processing device) may include a hardware structure specialized for processing an artificial intelligence model. AI models can be created through machine learning. Such learning may be performed, for example, in the electronic device 101 itself where the artificial intelligence model is performed, or may be performed through a separate server (eg, the server 108). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning or reinforcement learning, but in the above example Not limited. The artificial intelligence model may include a plurality of artificial neural network layers. Artificial neural networks include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), It may be one of deep Q-networks or a combination of two or more of the foregoing, but is not limited to the foregoing examples. The artificial intelligence model may include, in addition or alternatively, software structures in addition to hardware 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 . The data may include, for example, input data or output data for software (eg, program 140) and commands related thereto. The 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 an application 146 .

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

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

The display module 160 may 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 set to detect a touch or a pressure sensor set to measure the intensity of force generated by the touch.

The audio module 170 may convert sound into an electrical signal or vice versa. 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 connected directly or wirelessly to the electronic device 101 (eg: Sound may be output through the electronic device 102 (eg, a speaker or a headphone).

The sensor module 176 detects an operating state (eg, power or temperature) of the electronic device 101 or an external environmental state (eg, a user state), and generates an electrical signal or data value corresponding to the detected state. can do. According to one embodiment, the sensor module 176 may include, 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 bio sensor, It may include a temperature sensor, humidity sensor, or light sensor.

The interface 177 may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device 101 to 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 may 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 may convert electrical signals into mechanical stimuli (eg, vibration or motion) or electrical stimuli that a user may 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 may 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 may manage power supplied to the electronic device 101 . According to one embodiment, the power management module 188 may be implemented as at least part of a power management integrated circuit (PMIC), for example.

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 cell, a rechargeable secondary cell, or a fuel cell.

The communication module 190 is a direct (eg, wired) communication channel or a wireless communication channel between the electronic device 101 and an external electronic device (eg, the electronic device 102, the electronic device 104, or the server 108). Establishment and communication through the established communication channel may be supported. The communication module 190 may include one or more communication processors that operate independently of the processor 120 (eg, an application processor) and support direct (eg, wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (eg, a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (eg, : a local area network (LAN) communication module or a power line communication module). Among these communication modules, a corresponding communication module is a first network 198 (eg, a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (eg, a legacy communication module). It may communicate with the external electronic device 104 through a cellular network, a 5G network, a next-generation communication network, the Internet, or a telecommunications network such as a computer network (eg, a LAN or a WAN). These various types of communication modules may be integrated as one component (eg, a single chip) or implemented as a plurality of separate components (eg, multiple chips). The wireless communication module 192 uses subscriber information (eg, International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199. The electronic device 101 may be identified or authenticated.

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

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

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

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

According to an 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 the same as or different from the electronic device 101 . According to an embodiment, all or part of operations executed in the electronic device 101 may be executed in one or more external electronic devices among the external electronic devices 102 , 104 , or 108 . For example, when the electronic device 101 needs to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device 101 instead of executing the function or service by itself. Alternatively or additionally, one or more external electronic devices may be requested to perform the function or at least part of the service. One or more external electronic devices receiving the request may execute at least a part of the requested function or service or an additional function or service related to the request, and deliver the execution result to the electronic device 101 . The electronic device 101 may provide the result as at least part of a response to the request as it is or additionally processed. To this end, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an internet of things (IoT) device. 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 (eg, smart home, smart city, smart car, or health care) based on 5G communication technology and IoT-related technology.

2 is a block diagram of an electronic device according to an exemplary embodiment.

Referring to FIG. 2 , an electronic device 200 (eg, the electronic device 101 of FIG. 1 ) according to an embodiment includes a processor 205 (eg, the processor 120 of FIG. 1 ), a communication module 210 (eg, the communication module 190 of FIG. 1), a display 215 (eg, the display module 160 of FIG. 1), a memory 220 (eg, the memory 130 of FIG. 1), a first sensor ( 230) (eg, sensor module 176 of FIG. 1 ) and a second sensor 245 (eg, sensor module 176 of FIG. 1 ).

In one embodiment, the communication module 210 may include a wireless communication module 210 such as Bluetooth or WiFi and a wired communication module 210 such as a USB interface. However, it is not limited thereto and may include various communication modules 210 . For example, the communication module 210 may include a GPS communication module 210 .

In one embodiment, the electronic device 200 may be connected to a weight measurement device (eg, the electronic device 102 or the electronic device 104 of FIG. 1 ) wirelessly or wired. The electronic device 200 may receive weight data measured by the weight measuring device from the weight measuring device. The weight data may be transmitted to the electronic device 200 through a wireless communication method or a wired communication method, or may be transmitted from the weight measurement device to the electronic device 200 via a server.

In one embodiment, the electronic device 200 may display data measured inside or outside the electronic device 200 through the display 215 . The electronic device 200 may display a trend chart representing changes over time in measured data through the display 215 . For example, the electronic device 200 may display the user's weight data and body composition data transmitted from the weight measuring device through the display 215 in the form of numerical values and trend charts.

In one embodiment, the electronic device 200 may include various sensors. For example, the electronic device 200 includes a first sensor 230 and a second sensor 245, as shown in FIG. 2, and a sensor control circuit that controls the first sensor 230 and the second sensor 245. can include The processor 205 may control the first sensor 230 and the second sensor 245 through a sensor control circuit. Although the first sensor 230 and the second sensor 245 are shown as separate configurations for ease of understanding in FIG. 2 , the electronic device 200 may have a structure in which the sensors are connected to a sensor control circuit.

In one embodiment, the first sensor 230 may be an optical sensor for biometric measurement. The light source 235 may include N (N is a natural number) LEDs having various wavelengths. In the case of the green wavelength, it is the most frequently used wavelength band for measuring the heart rate, and may have the advantage of being resistant to noise as it penetrates shallowly into the skin. In the case of a red wavelength, there may be an advantage of being able to measure the heart rate more accurately by penetrating the skin relatively deeply. By using the IR wavelength band, more biometric information such as heart rate and SpO2 can be obtained along with the red wavelength. The first sensor 230 may measure a tendency of blood sugar by further using a blue wavelength band. The first sensor 230 may obtain more biometric information as various LED wavelength bands are used. The light source 235 may emit light of various wavelengths and may include at least one emitter for each wavelength.

In one embodiment, the photo detector 240 may include at least one photo diode. The first sensor 230 may include at least one photodetector 240 . The first sensor 230 may include a plurality of photodetectors 240 disposed at different distances from the light source 235 .

In one embodiment, the sensor control circuit may include a sensor drive controller that controls the sensors and an analog digital converter (ADC). The sensor driving controller may include an emitter controller driving the emitter and a detector controller driving the light detector 240 .

The sensor control circuitry can function as an analog frontend (AFE). The AFE may include an amplifier that amplifies values of the LED drivers and the photodetector 240, an ADC that converts an analog value output from the photodetector 240 into a digital value, and a controller that controls the LED driver and the ADC. The signal input through the photodetector 240 is transmitted to the processor 205 through a filter and ADC, and the value of the corresponding signal is extracted as a biometric information value to be measured through an algorithm and displayed to the user or stored for related applications. It can be transmitted to a 3rd party.

In one embodiment, the second sensor 245 may include a weight measurement sensor for measuring the user's weight. In one embodiment, the second sensor 245 is an electrocardiogram (ECG), skin resistance response (GSR), electroencephalogram (EEG), and bioelectrical impedance (BIA) in addition to an optical sensor for biometric measurement of the first sensor 230 Electrodes for measuring the same bio-signals may be included.

In one embodiment, the second sensor 245 may include various sensors such as an acceleration sensor, a proximity sensor, a gyro sensor, a temperature (body temperature) sensor, and an iris sensor for determining the state of the user of the electronic device 200. .

In one embodiment, the second sensor 245 may include various sensors such as a temperature sensor, a humidity sensor, an illuminance sensor, a TOF sensor, and a UWB sensor for determining the user's external environment.

In one embodiment, the second sensor 245 may further include various sensors such as a gas sensor and a fine dust sensor.

In one embodiment, the processor 205 may register and manage a weight measuring device that may be linked with the electronic device 200 . The processor 205 may display the weight data and body composition data received from the weight measuring device in a trend chart.

In one embodiment, the processor 205 is a sound quantity measuring device (eg, a cup-shaped sound quantity measuring device) that can be interlocked with the electronic device 200 (eg, the electronic device 102 of FIG. 1 or the electronic device ( 104)) may receive information on the amount of water intake of the user.

In one embodiment, the processor 205 may be configured to be a mobile device capable of interoperating with the electronic device 200 (eg, electronic device 102 or electronic device 104 of FIG. 1 ) and a wearable device (eg, the electronic device 104 of FIG. 1 ). Device 102 or electronic device 104 may sense and receive contextual information about the user's condition that may affect the user's weight measurement. The context information may include, for example, information about a mobile device owned by the user, a wearable device worn by the user, and clothes worn by the user. In one embodiment, a mobile device and a wearable device may include the components of the electronic device 200 .

In one embodiment, the processor 205 may receive information on the user's exercise amount from a mobile device and a wearable device that may work with the electronic device 200 .

3 is a block diagram of a body weight measuring device according to an embodiment.

Referring to FIG. 3 , a body weight measurement device 300 according to an embodiment may include a processor 305, a display 310, a communication module 315, and a second sensor 320.

In one embodiment, the second sensor 320 may include a weight measurement sensor for measuring the user's weight. In one embodiment, the second sensor 320 is an electrocardiogram (ECG), skin resistance response (GSR), brain wave (EEG), and bioelectrical impedance (BIA) in addition to an optical sensor for biometric measurement of the first sensor 230 Electrodes for measuring the same bio-signals may be included.

In one embodiment, the second sensor 320 may include various sensors such as an acceleration sensor, a proximity sensor, a gyro sensor, a temperature (body temperature) sensor, and an iris sensor for determining the state of the user of the electronic device 200. .

In one embodiment, the second sensor 320 may include various sensors such as a temperature sensor, a humidity sensor, an illuminance sensor, a TOF sensor, and a UWB sensor for determining the user's external environment.

In one embodiment, the second sensor 320 may further include various sensors such as a gas sensor and a fine dust sensor.

In one embodiment, the communication module 315 may include a wireless communication module such as Bluetooth or WiFi and a wired communication module such as a USB interface. However, it is not limited thereto and may include various communication modules. For example, the communication module 315 may include a GPS communication module.

In an embodiment, the processor 305 may measure the user's weight and body composition using the second sensor 320 and transmit the measured weight data and body composition data to the electronic device of FIG. 1 through the communication module 315. there is.

In one embodiment, the processor 305 may display the measured weight data and body composition data through the display 310 .

4 is a flowchart of a weight data correction method according to an exemplary embodiment.

Weight, which can be easily measured in daily life, is one of the important indicators that can infer health conditions, and maintaining an appropriate weight is important for maintaining a healthy life. In recent years, various types of devices have been developed to measure this weight several times in more various places. For example, weight measurement devices 300 are being developed to measure weight through a footstool at the entrance of a front door, or to measure weight multiple times by utilizing a space where various users stay, such as a shower booth or a chair.

The user may measure the user's weight several times in various spaces using the weight measurement devices 300, but the accuracy and consistency of the measured weight may vary depending on the user's condition at the time of measurement. For example, in an environment where more and more users use various wearable devices (eg, smart watches, smart bands, earphones, smart glasses, smart rings) and portable devices (eg, smart phones, tablets), weight loss Whenever a measurement is performed, a measurement error may occur due to the weight of a wearable device or a portable device used by a user.

In order to reduce the measurement error, it may be inconvenient from the viewpoint of usability for the user to take off or put down the device currently worn or carried and measure it every time. In addition, when some applications are linked with the weight measurement device 300, which has a function of providing a real-time median value (expressing the amount of change in weight during measurement through UI) during weight measurement, measurement while looking at the screen while holding a smartphone or wearable device However, at this time, an error may occur in the measured weight depending on the device being carried.

An apparatus and method for compensating weight data according to an embodiment stores a state parameter that can affect a weight change according to a user's weight measurement situation together with weight data, and when weight is measured thereafter, a past measured weight data value and By comparing the state parameter with the changed state parameter of the current user, the net weight can be more accurately corrected and displayed.

An apparatus and method for compensating body weight data according to an embodiment may estimate and display a consistent net weight even when a user's condition changes in an environment in which weight may be repeatedly or continuously collected in various spaces.

Referring to FIG. 4 , in operation 405 , the electronic device 200 may receive and store first weight data of the user measured by the weight measuring device 300 . In operation 410, the electronic device 200 may determine and store a first state parameter corresponding to the received first weight data.

In one embodiment, state parameters are factors that affect the user's measured weight, and may represent the user's state when weight is measured. For example, the state parameter may correspond to a wearable device worn by the user when measuring weight, a mobile device possessed by the user, the amount of food intake, water intake, and exercise amount of the user. However, it is not limited thereto, and various state parameters may be determined as needed. For example, the state parameter may be determined for each type of wearable device such as a smart watch, wireless earphone, smart glasses, and smart belt.

In one embodiment, the state parameter may be determined by detecting a state of the user that affects weight data when the user's weight data is measured. The electronic device 200 may determine a state parameter corresponding to the detected state of the user. For example, when the user wears the wearable device when the first weight data is measured, the first state parameter may be a state parameter for the wearable device.

In an embodiment, the electronic device 200 may determine a plurality of state parameters when there are a plurality of factors influencing the measured weight of the user. For example, when the user wears two wearable devices when measuring the first weight data, the electronic device 200 may determine first state parameters corresponding to the two wearable devices.

In an embodiment, the electronic device 200 may determine a state parameter by distinguishing each wearable device even if a user wears two or more wearable devices of the same type. For example, when a user wears two smart watches of the same model, the electronic device 200 may determine different state parameters for each smart watch using identification information of each smart watch.

state parameter wear a wearable watch wearing wearable earphones Smartphone food intake hydration increment 70g 10g 300g 500g 30g correlation (+) (+) (+) (+) (+)

Referring to Table 1, examples of state parameters affecting body weight are represented. In Table 1, since the measured weight of the user increases as the number of wearable devices worn by the user increases, each wearable device may be a state parameter that directly affects weight measurement. Food intake and water intake can also be condition parameters that affect weight gain.

In operation 415, the electronic device 200 may receive and store the measured second weight data of the user from the weight measuring device 300. In operation 420, the electronic device 200 may determine and store a second state parameter corresponding to the second weight data. In operation 420, as in operation 410, when the second weight data is measured, a user's condition affecting the second weight data is detected, and a second state parameter corresponding to the detected user's condition is detected. It can be determined by determining the state parameter.

In operation 425, the electronic device 200 may compare the first state parameter and the second state parameter. The electronic device 200 may determine a weight correction value based on a comparison result between the first state parameter and the second state parameter.

In an embodiment, the electronic device 200 may estimate the weight of the second state parameter based on a comparison result between the first state parameter and the second state parameter. In one embodiment, the electronic device 200 may store the estimated weight for the state parameter. The electronic device 200 may determine a weight correction value based on the weight of the estimated second state parameter.

In an embodiment, when estimating the weight of the state parameter, the electronic device 200 may estimate the weight of the state parameter using all state parameters stored before the state parameter. For example, in order to estimate the weight of the third state parameter corresponding to the third weight data, the third state parameter is compared with the first state parameter and the second state parameter stored before the third state parameter to estimate the weight of the third state parameter. weight can be estimated.

In an embodiment, the operation of determining the state parameter and estimating the weight of the state parameter may be performed whenever the electronic device 200 receives weight data from the weight measuring device 300 . For example, when the electronic device 200 receives the second weight data, it determines a second state parameter corresponding to the second weight data, and determines the weight of the second state parameter based on the comparison result with the first state parameter. estimation, and when the electronic device 200 receives the third weight data, determines a third state parameter corresponding to the third weight data, and compares the first state parameter, the second state parameter, and the third state parameter. Based on the result, the weight of the third state parameter can be estimated.

A process of determining the weight correction value in operation 425 will be described in detail below with reference to FIG. 5 .

In operation 430, the electronic device 200 may estimate the user's net weight by applying the determined weight correction value to the second weight data. In one embodiment, the electronic device 200 may display the estimated net weight through the display 215 of the electronic device 200 .

In one embodiment, when the user's weight is continuously measured, the electronic device 200 may correct the continuous weight data in real time and display the continuous net weight change of the user through the display 215 .

In another embodiment, when measuring the weight data (eg, any one of the first weight data and the second weight data), the electronic device 200 detects a state of the user affecting the weight data, a state corresponding to the state You can prompt the user to enter the weight of the parameter. When the user receives the notification and inputs the weight of the corresponding state parameter, the electronic device 200 may estimate the weight as the weight of the corresponding state parameter. The electronic device 200 may determine a weight correction value based on the estimated weight of the corresponding state parameter.

For example, when the electronic device 200 detects that the user is wearing a wearable watch that affects the weight when measuring the first weight data, the electronic device 200 outputs a user interface through the display 215 of the electronic device 200, , a notification can be output to input the weight of the wearable watch through the corresponding interface. The electronic device 200 may estimate the input weight as the weight of the corresponding state parameter. The electronic device 200 may determine a weight correction value for the first weight data based on the estimated weight of the corresponding state parameter.

In another embodiment, when measuring the weight data, the electronic device 200 may transmit a notification to the user to remove the condition parameter affecting the weight data, if the user's condition affecting the weight data is detected. For example, when it is detected that the user is wearing a wearable watch that affects weight, a notification to measure weight while not wearing the wearable watch is delivered to the user through the display 215 of the electronic device 200. can When the user removes the detected state parameter and measures the weight again, the electronic device 200 may estimate the weight as the user's net weight.

Hereinafter, a method of estimating the weight of a state parameter will be described with reference to FIG. 5 .

5 is a diagram for explaining a method of estimating weights of state parameters according to an exemplary embodiment.

Referring to FIG. 5 , first weight data 505, second weight data 515, third weight data 525 measured in chronological order, and first state parameters 510 and 513 corresponding to each weight data , second state parameters 520 and 523 and third state parameters 530 are shown.

In an embodiment, the user may measure the first weight data 505 while wearing wireless earphones and carrying a mobile device. The electronic device 200 may detect a user's condition when measuring weight. The electronic device 200 may determine and store state parameters 510 corresponding to the sensed wearable device and mobile device.

For example, the electronic device 200 receives values obtained through sensors of a wearable device and a mobile device connected to the electronic device 200 and determines the wearable device worn by the user and the wearable device carried by the user when measuring the first weight data 505 . It can detect mobile devices. When the first weight data 505 is measured, the electronic device 200 may determine and store first state parameters 510 corresponding to the wireless earphone detected as being worn by the user and the mobile device detected as possessed by the user. there is.

In an embodiment, the electronic device 200 may receive information about the amount of food intake, water intake, and exercise of the user when the first weight data 505 is measured. Information about the user's food intake, water intake, and exercise may be input by the user through an application running on the electronic device 200 . Alternatively, information on the user's food intake, water intake, and exercise amount may be measured using smart devices such as smart containers, smart cups, and smart shoes, and the electronic device 200 may determine the user's food intake, water intake, and water intake from the smart devices. Data on intake and exercise may be received. The electronic device 200 may determine and store first state parameters 513 corresponding to the amount of food intake, water intake, and exercise of the user.

In an embodiment, the user may measure the second weight data 515 while wearing a smart watch and wireless earphones and carrying a mobile device. The electronic device 200 may detect the user's state when measuring the second weight data 515 . The electronic device 200 includes a smart watch detected as being worn by the user when the second weight data 515 is measured, a wireless earphone, a mobile device detected as possessed by the user, and corresponding to the user's food intake, water intake, and exercise amount. The second state parameters 520 and 523 may be determined and stored.

In an embodiment, the electronic device 200 may compare the first state parameters 510 and 513 and the second state parameters 520 and 523 to determine the weight correction value. 5, when the first weight data 505 is 64.7 kg and the second weight data 515 is 65 kg when all state parameters are recognized, it can be estimated that the weight of the state parameter corresponding to the smart watch is 300 g. there is. In the same way, weights of other state parameters can be estimated, and the accuracy of the estimated weights of state parameters can be made more accurate through continuous data accumulation. For example, the electronic device 200 may estimate the weight of the state parameter of the mobile device using a statistical method when the weight estimated for the state parameter of the mobile device is stored for several times.

In one embodiment, information on the amount of food intake, water intake, and exercise of the user may be input by the user through an application running on the electronic device 200 . Alternatively, information on the user's food intake, water intake, and exercise amount may be measured using smart devices such as smart containers, smart cups, and smart shoes, and the electronic device 200 may determine the user's food intake, water intake, and water intake from the smart devices. Data on intake and exercise may be received. The electronic device 200 measures the user's food intake, water intake, and exercise amount when the second weight data 515 is measured based on the user's food intake, water intake, and exercise data input by the user or received from smart devices. The weight of the state parameters 523 corresponding to can be estimated.

The electronic device 200 may determine a weight correction value for the second weight data 515 based on the estimated weight of the second state parameters 520 and 523 . The electronic device 200 may estimate the user's net weight by applying the determined weight correction value to the second weight data 515 .

In one embodiment, the user may measure the third weight data 525 while wearing the smart watch and carrying the mobile device. The electronic device 200 may detect the user's state when the third weight data 525 is measured. The electronic device 200 is in a third state corresponding to the smart watch detected as worn by the user when the third weight data 525 is measured, the mobile device detected as possessed by the user, the user's food intake, water intake, and exercise amount. Parameters 530 may be determined and stored.

In an embodiment, the electronic device 200 may compare the first state parameters 510 and 513, the second state parameters 520 and 523, and the third state parameter 530 to determine the weight compensation value. In an embodiment, when the weight of a state parameter corresponding to the third state parameter 530 is estimated and stored prior to the third state parameter 530, the electronic device 200 determines a weight correction value using the corresponding weight. can For example, 300 g estimated for the second state parameter of the smart watch may be estimated as the weight of the third state parameter of the smart watch.

In one embodiment, the weight of the state parameter may be input by a user through the electronic device 200 . In an embodiment, the electronic device 200, if there is a weight previously known or estimated and stored for the wearable device detected as being worn by the user and the mobile device detected as being possessed by the user, the electronic device 200 calculates the corresponding weight for each wearable device. and the weight of the state parameter corresponding to the mobile device. For example, for the third state parameter of the smart watch, the electronic device 200, when a previously known weight of a smart watch of a corresponding model is stored in the electronic device 200, the electronic device 200 determines the weight of the smart watch. It can be estimated by the weight of the three-state parameter.

The electronic device 200 may determine a weight correction value for the third weight data 525 based on the estimated weight of the third state parameters. The electronic device 200 may estimate the user's net weight by applying the determined weight correction value to the third weight data 525 .

In FIG. 5, only the state parameters corresponding to the smart watch, mobile device, wireless earphone, food intake, water intake, and exercise amount are shown, but the type of state parameter can be determined in a variety of ways as needed, and various state parameters are It can be determined based on the detected information. For example, the electronic device 200 may use a camera to capture a picture of the user during weight measurement, and determine a state parameter for clothes worn by the user based on the captured image.

6 is a diagram for explaining an estimated net weight based on estimated weights of state parameters according to an exemplary embodiment.

Referring to FIG. 6 , the first weight data 605, the second weight data 610, and the third weight data 615 received by the electronic device 200 and state parameters 625, 630, 635) and estimated net weight 620 are shown.

As described in FIG. 5 , the net weight 620 may be estimated by applying a weight correction value to each weight data. In FIG. 6 , a net weight 620 is also shown for weight data first received by the electronic device 200 . The electronic device 200 cannot compare the weight of the initially determined first state parameter 625 with other state parameters to estimate the weight, but the user input information on the mobile device and the wearable device and the user input or smart devices The weight of the first state parameter 625 may be estimated based on the data of the user's food intake, water intake, and exercise amount received from .

The electronic device 200 may determine a weight correction value based on the weight of the estimated first state parameter 625 . The electronic device 200 may estimate the net weight 620 by applying the determined weight correction value to the first weight data 605 .

In an embodiment, the electronic device 200 re-estimates the weight of the first state parameter 625 using other weight data received after the first weight data and a state parameter corresponding to the corresponding weight data, and The weight correction value applied to the weight data can be re-determined. For example, the weight of the first state parameter 625 is re-estimated using the second weight data 610, the second state parameter 630, the third weight data 615, and the third state parameter 635. And, a weight correction value for the first weight data may be determined again. The electronic device 200 may re-estimate the pure weight 620 by applying the determined weight correction value to the first weight data 605 .

In another embodiment, when the electronic device 200 cannot estimate the weight of the first state parameter 625 , the first weight data 605 may be displayed as the pure weight 620 .

In one embodiment, the electronic device 200 may display weight data and net weight 620 on a display in the form of a trend chart as shown in FIG. 6 . Even if weight information is measured several times through a trend chart as shown in FIG. 6, the user can consistently estimate the pure weight from which error factors are removed.

7 is a diagram for explaining a weight trend chart according to an exemplary embodiment.

Referring to FIG. 7 , the electronic device 200 determines the state parameters 720, 725, and 730 corresponding to the state of the user detected during the time between the measurement of the first weight data 705 and the second weight data 715. to estimate the net weight 710 by correcting the first weight data 705 in real time, and display the measured weight data and the estimated net weight 710 as a trend chart 700 over time as shown in FIG. 7 there is.

In an embodiment, the electronic device 200 may receive the first weight data 705 and estimate the user's net weight 710 from the first weight data 705 . After receiving the first weight data 705, the electronic device 200 may detect occurrence of events related to food intake, water intake, and exercise.

For example, the electronic device 200 may receive information about food intake, water intake, and exercise amount from an application running on the electronic device 200 . Information about the user's food intake, water intake, and exercise may be input by the user through an application. Alternatively, information on the user's food intake, water intake, and exercise amount may be measured using smart devices such as smart containers, smart cups, and smart shoes, and the electronic device 200 may determine the user's food intake, water intake, and water intake from the smart devices. Data on intake and exercise may be received.

In an embodiment, the electronic device 200 determines state parameters 720 , 725 , and 730 for the event when the occurrence of events related to food intake, water intake, and exercise is detected, and the determined state parameter 720 , 725, 730) can estimate the weight to determine the weight correction value. The electronic device 200 may estimate the user's net weight 710 by applying the determined weight correction value to the first weight data 705 .

In one embodiment, the electronic device 200 estimates the net weight 710 according to the occurrence of events related to food intake, water intake, and exercise, thereby calculating the net weight 710 of the user even for a section in which the user did not measure the weight. may be represented by a trend chart 700.

In one embodiment, the electronic device 200 may output the trend chart 700 as shown in FIG. 7 through the display of the electronic device 200 . The user can check the trend chart 700 through the display, continuously observe and manage the trend of the user's own weight change, and can estimate the current expected weight when an event related to weight is introduced even if the user does not measure the weight. there is.

8 is a flowchart illustrating a method of correcting a measured weight by detecting a wearable device worn by a user and a mobile device possessed by the user.

Referring to FIG. 8 , when the user intermittently measures the user's weight through the weight measurement device, the electronic device 200 detects the possession and wearing state of the mobile and wearable device, which is a state parameter affecting the weight, A method of correcting data by considering the weight of a device carried and worn in the measured weight is shown.

In operation 805, the electronic device 200 may communicate with the weight measurement device by wire or wirelessly.

In operation 810, the electronic device 200 may detect a user's carrying state of the mobile device and wearing state of the wearable device. The carrying state of the mobile device and the wearing state of the wearable device may be detected using sensor data measured through sensors of the mobile device and the wearable device. In an embodiment, the electronic device 200 may determine a holding state of the mobile device and a wearing state of the wearable device based on sensor data received from the mobile device and the wearable device. In another embodiment, a mobile device and a wearable device may detect a carrying state and a wearing state of a corresponding device based on data measured by a sensor of each device and transmit the detection result to the electronic device 200 .

For example, when a user touches the touch screen of a mobile device or wearable device, when a specific switch of the mobile device or wearable device is pressed or a certain pressure is detected from a pressure sensor, a connection part such as a band or belt of the wearable device are in contact with each other, when the measurement value of the gyro sensor of the mobile device and wearable device satisfies the set condition, when the weight measurement situation is recognized from the image captured through the camera of the mobile device and wearable device, the mobile device and wearable In cases such as when the user's biosignal is measured through the sensor of the device and when it is determined that the measured distance between mobile devices and wearable devices is close through BLE (bluetooth low energy) RSSI (received signal strength indicator) It may be determined that the user has a mobile device or wears a wearable device. However, this is only an example, and the carrying state of the mobile device and the wearing state of the wearable device may be detected in various ways as needed.

In operation 815, the weight measuring device may measure the weight of the user. In operation 820, the electronic device 200 may receive weight data of the user from the weight measuring device.

In operation 825, the electronic device 200 may determine whether the user carries a mobile device or wears a wearable device when measuring weight based on the detection result of operation 810. When it is determined that the user carries a mobile device or wears a wearable device when measuring weight, the electronic device 200, in operation 830, determines the weight of state parameters corresponding to the mobile device carried by the user and the wearable device worn by the user. Based on this, a weight correction value may be determined, and the user's net weight may be estimated by applying the determined weight correction value to weight data.

In one embodiment, information managed for each model regarding the weight of the mobile device and the wearable device may be stored in the electronic device 200 or may be received and stored from a server. The electronic device 200 may determine a weight correction value based on the stored information.

In operation 835, the electronic device 200 may store weight data. If there is an estimated net weight in operation 830, the electronic device 200 may store the estimated net weight together with weight data.

In one embodiment, the electronic device 200 may display the weight data and the estimated net weight through the display 215 of the electronic device 200 . In an embodiment, the electronic device 200 transmits information about a mobile device detected as being possessed by the user and a wearable device detected as being worn when measuring the weight of the electronic device 200 together with weight data and net weight. It can be displayed through the display 215.

9 is a diagram for explaining a body composition data correction method according to an exemplary embodiment.

In an embodiment, the electronic device 200 may acquire or receive body composition data (eg, body fat data) of the user. For example, the electronic device 200 performs bioelectric impedance analysis (BIA) measurement through sensors (eg, the first sensor 230 and the second sensor 245) inside the electronic device 200 to measure body composition of the user. Data may be obtained or body composition data acquired through BIA measurement may be received from the weight measuring device 300 . In another example, the electronic device 200 may receive body composition data from a separate wearable device (eg, smart watch).

In general, weight data and body composition data are coupled to show similar change trends, but when the weight data or BIA measurement values are inaccurate, they may be decoupled and show different change trends. In an embodiment, the electronic device 200 compares the user's weight data and body composition data, and guides the user to perform BIA measurement again when the weight data and body composition data are in a decoupling state. If the measured body composition data is out of expectation even after the user performs the BIA measurement again, the electronic device 200 may correct the body composition data using the state parameter used for correcting the weight data. For example, when there is water intake before weight measurement, the electronic device 200 may correct body composition data based on a state in which water intake is not performed.

In an embodiment, the electronic device 200 may correct body composition data based on the estimated net weight.

In an embodiment, the electronic device 200 may display at least one of the user's weight data, estimated net weight, body composition data, and corrected body composition data as a trend chart over time as shown in FIG. 9 . The electronic device 200 may display a trend chart through the display of the electronic device 200 .

In an embodiment, the electronic device 200 may display a change trend 905 of net weight and a change trend 910 of body composition data estimated based on the received weight data as a continuous line. Through the trend chart of FIG. 9 , the user can consistently estimate the net weight with error factors removed even if weight information is measured several times in various environments.

An electronic device 200 according to an embodiment includes a processor 205 and a memory 220 storing instructions to be executed by the processor 205, when the instructions are executed by the processor 205. , The processor 205 receives the user's first weight data measured by the weight measuring device 300 from the weight measuring device 300 connected to the electronic device 200 (eg, the first weight data 505 of FIG. 5 ). )), determining and storing the user's first state parameters (eg, the first state parameters 510 and 513 of FIG. 5 ) corresponding to the measured first weight data, and the user's measurement operation of receiving and storing the second weight data (eg, second weight data 515 of FIG. 5 ), and second state parameters of the user corresponding to the second weight data (eg, second state parameters of FIG. 5 ( 520 and 523)), determining a weight correction value based on the comparison result between the first state parameter and the second state parameter, and applying the weight correction value to the second weight data to determine the user's pure weight. An operation of estimating a net weight (eg, the net weight 620 of FIG. 6 ) may be performed.

The operation of determining the second state parameter includes an operation of detecting a state of the user affecting the second weight data when the second weight data is measured, and an operation of determining a second state parameter corresponding to the detected state of the user. can include

The operation of determining the weight correction value may include an operation of estimating the weight of the second state parameter based on the comparison result, and an operation of determining the weight correction value based on the estimated weight of the second state parameter.

The operation of estimating the weight of the second state parameter may include estimating the weight of the second state parameter based on the estimated weight of the first state parameter when there is an estimated weight of the first state parameter. there is.

The operation of detecting the state of the user includes the operation of recognizing the wearable device worn by the user, and the operation of estimating the weight of the second state parameter includes the operation of recognizing the weight of the second state parameter based on the information on the recognized wearable device. An operation of estimating the weight may be included.

The operation of detecting the user's state may include an operation of receiving data on the amount of water intake, food intake, and exercise of the user through an application driven by the processor 205 .

The processor 205 may further perform an operation of receiving body composition data of the user from the body weight measurement device 300 and an operation of correcting the body composition data based on the net weight.

The processor 205 may further perform an operation of outputting the second weight data, the second state parameter, and the corrected net weight through the display 215 of the electronic device 200 .

The processor 205 may further perform an operation of storing the net weight and an operation of outputting a change in the net weight of the user over time through the display 215 of the electronic device 200 .

After the net weight is estimated, the processor 205 receives data on the amount of water intake, food intake and exercise amount of the user through an application driven by the processor 205, the received water intake amount, food intake amount and exercise amount. Based on the data for , an operation of correcting the net weight and an operation of outputting the corrected net weight through the display 215 of the electronic device 200 may be further performed.

A weight data correction method according to an embodiment includes an operation of receiving and storing first weight data of a user measured by the weight measuring device 300 from a weight measuring device 300 connected to an electronic device 200, and storing the measured weight data. Determining and storing the first state parameter of the user corresponding to the first weight data, receiving and storing the measured second weight data of the user, determining the second state parameter of the user corresponding to the second weight data and storing it, determining a weight correction value based on the comparison result between the first state parameter and the second state parameter, and estimating the user's net weight by applying the weight correction value to the second weight data action may be included.

The operation of determining the second state parameter includes an operation of detecting a state of the user affecting the second weight data when the second weight data is measured, and an operation of determining a second state parameter corresponding to the detected state of the user. can include

The operation of determining the weight correction value may include an operation of estimating the weight of the second state parameter based on the comparison result, and an operation of determining the weight correction value based on the estimated weight of the second state parameter.

The operation of estimating the weight of the second state parameter may include estimating the weight of the second state parameter based on the estimated weight of the first state parameter when there is an estimated weight of the first state parameter. there is.

The operation of detecting the state of the user includes the operation of recognizing the wearable device worn by the user, and the operation of estimating the weight of the second state parameter includes the operation of recognizing the weight of the second state parameter based on the information on the recognized wearable device. An operation of estimating the weight may be included.

The operation of detecting the user's state may include an operation of receiving data on the amount of water intake, food intake, and exercise of the user through an application driven by the processor 205 .

The weight data correction method according to an embodiment may further include an operation of receiving body composition data of the user from the body weight measuring device 300 and an operation of correcting the body composition data based on the net weight.

The weight data correction method according to an embodiment may further include an operation of outputting the second weight data, the second state parameter, and the corrected net weight through the display 215 of the electronic device 200 .

The weight data correction method according to an embodiment may further include storing the net weight and outputting the change over time of the user's net weight through the display 215 of the electronic device 200 .

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

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

The term "module" used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and is interchangeable with terms such as, for example, logic, logical blocks, parts, or circuits. can be used as A module may be an integrally constructed component or a minimal unit of components or a portion 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 this document provide one or more instructions stored in a storage medium (eg, internal memory 136 or external memory 138) readable by a machine (eg, electronic device 101). It may be implemented as software (eg, the program 140) including them. For example, a processor (eg, the processor 120 ) of a device (eg, the electronic device 101 ) may call at least one command among one or more instructions stored from a storage medium and execute it. This enables the device to be operated to perform at least one function according to the at least one command invoked. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-temporary' only means that the storage medium is a tangible device and does not contain a signal (e.g. electromagnetic wave), and this term refers to the case where data is stored semi-permanently in the storage medium. It does not discriminate when it is temporarily stored.

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

According to various embodiments, each component (eg, module or program) of the above-described components may include a single object or a plurality of entities, and some of the plurality of entities may be separately disposed in other components. there is. According to various embodiments, one or more components or operations among the aforementioned corresponding components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of 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 of the plurality of components identically or similarly to those performed by a corresponding component of the plurality of components prior to the integration. . According to various embodiments, the actions performed by a module, program, or other component are executed sequentially, in parallel, iteratively, or heuristically, or one or more of the actions are executed in a different order, or omitted. or one or more other actions may be added.

Claims (20)

In electronic devices,
processor; and
a memory for storing instructions to be executed by the processor;
When the instructions are executed by the processor, the processor:
receiving and storing first weight data of a user measured by the weight measuring device from a weight measuring device connected to the electronic device;
determining and storing a first state parameter of the user corresponding to the measured first weight data;
receiving and storing the measured second weight data of the user;
determining and storing a second state parameter of the user corresponding to the second weight data;
determining a weight correction value based on a comparison result between the first state parameter and the second state parameter; and
Estimating the net weight of the user by applying the weight correction value to the second weight data
An electronic device that performs According to claim 1,
The operation of determining the second state parameter,
detecting a state of the user that affects the second body weight data when the second body weight data is measured;
An operation of determining the second state parameter corresponding to the sensed state of the user
Including, electronic device. According to claim 2,
The operation of determining the weight correction value,
estimating the weight of the second state parameter based on the comparison result; and
An operation of determining the weight correction value based on the weight of the estimated second state parameter
Including, electronic device. According to claim 3,
The operation of estimating the weight of the second state parameter,
If there is an estimated weight for the first state parameter, estimating the weight of the second state parameter based on the estimated weight of the first state parameter.
Including, electronic device. According to claim 3,
The operation of detecting the user's state,
Recognizing the wearable device worn by the user
including,
The operation of estimating the weight of the second state parameter,
estimating the weight of the second state parameter based on the information on the recognized wearable device;
Including, electronic device. According to claim 3,
The operation of detecting the user's state,
Receiving data on the amount of water intake, food intake, and exercise of the user through an application driven by the processor
Including, electronic device. According to claim 1,
the processor,
receiving body composition data of the user from the weight measurement device; and
Correcting the body composition data based on the pure body weight
Further, the electronic device. According to claim 1,
the processor,
Outputting the second weight data, the second state parameter, and the corrected net weight through the display of the electronic device.
Further, the electronic device. According to claim 1,
the processor,
storing the net weight; and
Outputting the change of the user's net weight over time through the display of the electronic device
Further, the electronic device. According to claim 9,
the processor,
receiving data on water intake, food intake, and exercise of the user through an application driven by the processor after the net weight is estimated;
correcting the net weight based on the received data on the amount of water intake, food intake, and exercise; and
Outputting the corrected net weight through a display of the electronic device
Further, the electronic device. A method for correcting measured weight data of a user,
receiving and storing first weight data of the user measured by the weight measuring device from a weight measuring device connected to the electronic device;
determining and storing a first state parameter of the user corresponding to the measured first weight data;
receiving and storing the measured second weight data of the user;
determining and storing a second state parameter of the user corresponding to the second weight data;
determining a weight correction value based on a comparison result between the first state parameter and the second state parameter; and
Estimating the net weight of the user by applying the weight correction value to the second weight data
Including, how. According to claim 11,
The operation of determining the second state parameter,
detecting a state of the user that affects the second body weight data when the second body weight data is measured;
An operation of determining the second state parameter corresponding to the sensed state of the user
Including, how. According to claim 12,
The operation of determining the weight correction value,
estimating the weight of the second state parameter based on the comparison result; and
An operation of determining the weight correction value based on the weight of the estimated second state parameter
Including, how. According to claim 13,
The operation of estimating the weight of the second state parameter,
If there is an estimated weight for the first state parameter, estimating the weight of the second state parameter based on the estimated weight of the first state parameter.
Including, how. According to claim 13,
The operation of detecting the user's state,
Recognizing the wearable device worn by the user
including,
The operation of estimating the weight of the second state parameter,
estimating the weight of the second state parameter based on the information on the recognized wearable device;
Including, how. According to claim 13,
The operation of detecting the user's state,
Receiving data on the amount of water intake, food intake, and exercise of the user through an application driven by the electronic device
Including, how. According to claim 11,
receiving body composition data of the user from the weight measurement device; and
Correcting the body composition data based on the pure body weight
How to include more. According to claim 11,
Outputting the second weight data, the second state parameter, and the corrected net weight through the display of the electronic device.
Further comprising a method. According to claim 11,
storing the net weight; and
Outputting the change of the user's net weight over time through the display of the electronic device
Further comprising a method. A computer readable recording medium storing a computer program for executing the method of any one of claims 11 to 19.

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