Apparatus and method for estimating height using walking information

Abstract

The present disclosure relates to an apparatus and a method for estimating a user's height by using walking information, wherein the user's height may be estimated by collecting walking information of the user and applying the walking information of the user to a height estimation model. The operation of collecting the walking information of the user comprises the operations of: acquiring inertial information, atmospheric pressure information, and GPS information; determining whether it is in a state in which the walking information of the user can be accurately collected; and when it is determined, as a result of the determination, that the walking information of the user can be accurately collected, estimating a step count, walking speed consistency, undulation information indicating vertical movement that occurs during walking, and a stride length by using at least one of the inertial information, the atmospheric pressure information, and the GPS information.

Description

Device and method for estimating height using gait information

[0001] The following embodiments relate to a device and method for estimating height using gait information.

[0002] Wearables and smart devices are now becoming crucial tools for monitoring users' health and daily activities, and most health-related solutions fundamentally require users' height information.

[0003] If a user's height information isn't updated properly—for example, if a growing adolescent hasn't updated their height information for a long period of time—the accuracy of health-related algorithms will inevitably decline as height changes. Height, in particular, can be a crucial indicator for monitoring the development of growing adolescents, and height measurement plays a crucial role in providing personalized services for medical and health care.

[0004] According to various embodiments disclosed in this document, a device and method for estimating height using gait information can be provided.

[0005] A method for estimating height according to one embodiment may include an operation of collecting gait information of a user; and an operation of applying the gait information of the user to a height estimation model to estimate the height of the user.

[0006] An electronic device according to one embodiment includes one or more processors; and a memory storing instructions, wherein the instructions, when executed by the one or more processors, cause the electronic device to perform the following operations: collecting gait information of a user; and applying the gait information of the user to a height estimation model to estimate the height of the user.

[0007] FIG. 1 is a diagram illustrating a configuration of an electronic device according to one embodiment.

[0008] FIG. 2 is a diagram illustrating the operation of a processor of an electronic device according to one embodiment.

[0009] FIG. 3 is a flowchart schematically illustrating a flow for estimating height using gait information according to one embodiment.

[0010] Figure 4 is a flowchart illustrating a flow for collecting gait information according to one embodiment.

[0011] FIG. 5 is a flowchart illustrating a process of learning a height estimation model using gait information and estimating height using the learned height estimation model according to one embodiment.

[0012] FIG. 6 is a drawing illustrating a situation when a user is standing and walking according to one embodiment.

[0013] FIG. 7 is a diagram illustrating data on leg length and height according to one embodiment.

[0014] Figure 8 is a diagram illustrating a simulation example of learning a height estimation model according to one embodiment.

[0015] FIG. 9 is a diagram illustrating an example of providing a notification when an electronic device detects that a user's height has grown according to an embodiment.

[0016] FIG. 10 is a diagram illustrating an example of a guidance message output to obtain accurate walking information in an electronic device according to one embodiment.

[0017] FIG. 11 is a diagram illustrating an example of providing a monthly growth trend in a graph format based on a user's height estimate in an electronic device according to one embodiment.

[0018] FIG. 12 is a diagram illustrating an example of analyzing a user's growth trend in an electronic device according to one embodiment and providing a guide based on the analyzed results.

[0019] FIG. 13 is a diagram illustrating a schematic structure of an electronic device according to one embodiment.

[0020] FIG. 14 is a diagram illustrating an electronic device within a network environment according to one embodiment.

[0021] Hereinafter, embodiments are described in detail with reference to the attached drawings. However, the embodiments may be modified in various ways, and the scope of the patent application is not limited or restricted by these embodiments. It should be understood that all modifications, equivalents, or alternatives to the embodiments are included within the scope of the patent application.

[0022] The terms used in the examples are for illustrative purposes only and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as "comprise" or "have" are intended to indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but should be understood to not preclude the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0023] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments pertain. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0024] In addition, when describing with reference to the attached drawings, identical components will be assigned the same reference numerals regardless of the drawing numbers, and redundant descriptions thereof will be omitted. When describing embodiments, if a detailed description of a related known technology is judged to unnecessarily obscure the gist of the embodiment, the detailed description will be omitted.

[0025] Additionally, terms such as first, second, A, B, (a), (b), etc. may be used to describe components of the embodiments. These terms are only intended to distinguish the components from other components, and the nature, order, or sequence of the components are not limited by the terms. When a component is described as being "connected," "coupled," or "connected" to another component, it should be understood that the component may be directly connected or connected to the other component, but another component may also be "connected," "coupled," or "connected" between each component.

[0026] Components included in one embodiment and components with common functions will be described using the same names in other embodiments. Unless otherwise stated, the descriptions given in one embodiment may also apply to other embodiments, and detailed descriptions will be omitted to the extent of overlap.

[0027] Hereinafter, a device and method for estimating height using gait information according to an embodiment of the present invention will be described in detail with reference to the attached FIGS. 1 to 14.

[0028] FIG. 1 is a diagram illustrating a configuration of an electronic device according to one embodiment.

[0029] Referring to FIG. 1, an electronic device (100) may be configured to include a processor (110), an inertial sensor (120), a pressure sensor (130), a GPS (Global Positioning System) sensor (140), a memory (150), a display (160), and an interface (170).

[0030] The inertial sensor (120) includes an acceleration sensor and a gyro sensor, and can acquire sensor data including 3-axis acceleration data and 3-axis gyro data.

[0031] The barometer (130) measures the atmospheric pressure of the surrounding air and can use the change in barometric pressure to calculate altitude and provide barometric pressure information.

[0032] The GPS sensor (140) can collect GPS information. The GPS information can include latitude, longitude, altitude, speed, direction, time, and satellite information. The satellite information can include information on satellite reception strength and satellite location.

[0033] The memory (150) can store various data used by at least one component of the electronic device (100). The data may include, for example, input data or output data for software and commands related thereto. The memory (150) may include volatile memory or non-volatile memory.

[0034] The display (160) can visually provide information to an external party (e.g., a user) of the electronic device (100). In the present disclosure, the display (160) can output an estimated height and analyze the change trend of the height to output a guide according to the change in height.

[0035] The interface (170) is a device that provides an interface with a user of the electronic device (100) and can receive user input. The user's input of the electronic device (100) may include the user's physical information. In this case, the user's physical information may include at least one of the user's actual height, the user's age, and the user's gender.

[0036] The processor (110) obtains inertial information, barometric information, and GPS information through an inertial sensor (120), a barometric sensor (130), and a GPS sensor (140), collects the user's walking information using the inertial information, barometric information, and GPS information, and estimates the user's height by applying the user's walking information to a height estimation model.

[0037] When the user's height estimation is completed, the processor (110) can provide the user with a notification message notifying that the height estimation is completed.

[0038] Then, a method for estimating height through an electronic device (100) of the present disclosure will be described below with reference to FIGS. 6 to 8.

[0039] The height estimation method using an electronic device (100), which is a portable terminal, is basically based on features extracted during walking, and extracts step count, walking regularity, undulation information, which is information on up-and-down movements occurring during walking, and stride as gait information. In order to extract such gait information, the user's gait must be extracted in a consistent state, and therefore, it is necessary that the posture of the electronic device (100) does not shake significantly. Accordingly, when the user walks while carrying the electronic device (100), the carrying posture may be limited to a hand-fixed posture or a case where the electronic device (100) is located in a top pocket or a back pocket of pants.

[0040] FIG. 6 is a drawing illustrating a situation when a user is standing and walking according to one embodiment.

[0041] Referring to Figure 6, it can be seen that, unlike when the user is standing, when walking, the legs are spread apart and an up-and-down movement occurs equal to the height u. In Figure 6, L represents the user's leg length, D represents stride, and u represents undulation.

[0042] Unduration can be estimated using the following <Mathematical Formula 1>.

[0043]

[0044] Here, a n is the vertical acceleration, V n is the vertical speed, and θ1 can represent the first correction value.

[0045] <Mathematical expression 1> is the vertical acceleration (a n ) and integrate the vertical speed (V n ) and calculate the vertical speed (V) to consider the change in height due to the movement of ascent and descent that occurs during walking. n ) is integrated and the result is divided by 2 to estimate the undulation, which is the average height change.

[0046] Additionally, since the correlation between undulation and leg length may vary for each user, a correction value of θ1 may be applied. Here, θ1 can be viewed as the first correction value according to walking habits.

[0047] The stride can be estimated using the following <Mathematical Formula 2>.

[0048]

[0049] Here, D represents stride information, Distance_GPS represents the distance traveled over a certain period of time, Step_Count represents the number of steps taken over a certain period of time, L represents the user's leg length, and u represents undulation.

[0050] The user's movement distance over a certain period of time can be obtained through GPS information, and the user's number of steps during that period can be obtained through inertial sensor information.

[0051] The electronic device (100) can estimate the user's stride information by dividing the distance traveled by the number of steps. This stride information can be converted into equations for undulation (u) and leg length (L), and the equation for leg length (L) can be organized as shown in <Mathematical Formula 3> below. In other words, the leg length can be estimated using undulation (u) and stride information as shown in <Mathematical Formula 3> below.

[0052]

[0053] Here, D represents stride information, L represents the user's leg length, and u represents undulation.

[0054]

[0055] FIG. 7 is a diagram illustrating data on leg length and height according to one embodiment.

[0056] Referring to Figure 7, the points represent height data for leg length, and the graph represented by the diagonal line is an example of a height model estimated based on the points.

[0057] The leg length information based on the undulation and stride information can be modeled using a relationship as in <Mathematical Formula 4> below through regression analysis, and the user's height can be estimated through this.

[0058]

[0059] Here, h is the user's height, *?* is a constant term, and each independent variable (l i1 … ) represents the regression coefficient corresponding to the independent variable. That is, it indicates the size of the influence of each independent variable on height. ε is the error term or residual, which means the difference between the actual value and the value predicted by the regression model. l i1 is an independent variable used to predict height.

[0060] However, for users whose height estimation model is close to the height estimation model, the height may be calculated accurately, whereas for users whose height estimation model deviates from the height estimation model, the height may be calculated inaccurately. That is, the graph above shows that a certain amount of height error may occur depending on the leg length, and this can be seen as an error that may appear due to a difference in the ratio between the leg length and the height. Accordingly, in the present disclosure, the accuracy of height estimation can be further improved by applying the second correction value (θ2) as in <Mathematical Formula 5> below.

[0061]

[0062] Here, is improved kidney information.

[0063] In summary, the user height estimation model can be modeled as shown in <Mathematical Formula 6> below through regression analysis of the estimated leg length and user height based on the undulation and stride information. Here, θ1 and θ2 are parameters for walking habits and body proportions, respectively.

[0064]

[0065] In this disclosure, a height estimation model is trained using a generative AI, the Large Health Model (LHM). The LHM is a sophisticated AI model that processes diverse sensor data and performs health-related data prediction and analysis based on this data. It can effectively learn and adjust high-dimensional parameters to predict specific health outcomes. The LHM model's structure can be configured to enable multimodal data processing, and the training process can include various steps, from data purification to model parameter adjustment and performance evaluation.

[0066] To train the height estimation model described above, the user must initially input height information measured directly at least once. This is to improve the height estimation accuracy of the height estimation model.

[0067] LHM can set the parameters (θ1, θ2) as in <Mathematical Formula 8> below so that <Mathematical Formula 7> below is established by reporting the initial height information directly input by the user as the actual value for the height estimation model above.

[0068]

[0069] Here, H in is the height information entered directly by the user.

[0070]

[0071] Here, W is the height estimation parameter vector to be corrected, and θ1 and θ2 are the height estimation parameters for gait habits and body proportions, respectively. The estimated height information ( ) and the height information entered by the user ( ) is the error objective function for It can be defined as , and the height estimation parameter can be optimized by estimating W that minimizes the output value of this objective function.

[0072] Figure 8 is a diagram illustrating a simulation example of learning a height estimation model according to one embodiment.

[0073] Referring to FIG. 8, the upper graph (810) represents raw data for the three axes of acceleration during walking, and the lower graph (820) is a graph representing undulation calculated from an acceleration sensor.

[0074] As can be seen in the graphs (810, 820), although the user is walking at a constant speed, there may be some deviation in the undulation, and therefore, the undulation can be calculated as an average value for the section in which the user walked at a constant speed and a certain number of steps or more.

[0075] The results of estimating the user height using the initial height estimation model for the example in Fig. 8 above are as follows. Since model learning has not yet occurred in the initial stage of the height estimation model, the θ1 and θ2 parameters can be set to the initial value 1. In this example, the undulation was calculated as the average of 500 steps, and the stride information was calculated by dividing the number of steps by the GPS movement distance, and these were input to the height estimation model to obtain the height estimation value. As a result of estimating the height using the height estimation model before learning, the user height was estimated to be 174.1936 (cm), as shown below.

[0076] When the user's actual height is 176.0 (cm), if the height estimation model is trained and the parameters θ1 and θ2 are corrected, the values ​​θ1 = 1.007109 and θ2 = 1.014473 can be obtained.

[0077] After training the height estimation model and adjusting parameters θ1 and θ2, more accurate height estimation is possible. After training the height estimation model, when the user walked 500 steps again, the height estimation result was estimated to be 176.1969 (cm).

[0078] Before the height estimation model was trained, an error of about 1.8 (cm) occurred, but after the model was trained, an error of about 0.2 (cm) occurred. The height estimation model may not have been completely trained once. After the user inputs the initial height information, the height information may be valid as the actual measurement value within a certain period of time. Therefore, if there is additional valid gait data collected within the certain period, the height estimation model can be retrained using the above method and the model can be updated, and through this, the estimation accuracy of the height estimation model can be gradually improved.

[0079]

[0080] Then, more specific operation of the processor (110) will be described below with reference to FIG. 2.

[0081] FIG. 2 is a diagram illustrating the operation of a processor of an electronic device according to one embodiment.

[0082] Referring to FIG. 2, in operation 210, when the processor (110) receives inertial information and air pressure information, it performs preprocessing on the sensor data using a filter such as a low pass filter (LPF) and a high pass filter (HPF) to remove noise from the sensor data.

[0083] In operation 212, the processor (110) can determine an analysis time zone, which is a time to analyze gait information through activity pattern analysis based on preprocessed inertial information, preprocessed air pressure information, and accumulated and stored gait information. When the analysis time zone is set, the processor (110) can operate to estimate height only during the analysis time zone. However, if the processor (110) repeatedly determines a preset number of times that the collected inertial information, air pressure information, and GPS information during the set analysis time zone is not in a suitable state for collecting gait information, the analysis time zone can be set again. At this time, the analysis time zone can be set by the processor (110) analyzing the user's activity pattern, or the user can directly set a specific time.

[0084] In operation 214, the processor (110) can use preprocessed inertial information and preprocessed air pressure information to determine whether the walking environment is flat, a slope, or stairs.

[0085] In operation 216, the processor (110) can check the GPS signal strength from the GPS information and determine whether the GPS signal strength exceeds a reference value.

[0086] In operation 220, the processor (110) determines whether the state is suitable for collecting gait information by using at least one of preprocessed inertial information, preprocessed air pressure information, and GPS information, and if the state is suitable for collecting gait information, the user's gait information including the number of steps (222), gait constant speed (224), undulation information (226), which is information on up-and-down movements occurring during walking, and stride length (228) can be estimated. Since methods for estimating gait information are not limited to methods using inertial information, air pressure information, and GPS information, if gait information can be estimated through other methods, these can be applied to the present disclosure. For example, the processor (110) can measure the user's stride by linking with a separate GCT (gait cycle time) sensor. Here, the GCT is an important parameter utilized in gait analysis by measuring the gait cycle time, which means the time from when one foot touches the ground to when the same foot touches the ground again, and the GCT sensor can measure the GCT with a sensor that can be installed on a shoe.

[0087] Meanwhile, in the 220 motion, the processor (110) can estimate the stride length using only inertial information if the GPS information is inaccurate. The processor (110) collects stride length data according to the walking frequency through experiments, models the collected data using a regression model to create a stride length model, obtains the walking frequency when the user walks, inputs it to the stride length model, and obtains the stride length as an output.

[0088] In operation 220, the processor (110) can use preprocessed inertial information, preprocessed air pressure information, and GPS information to check whether hand swing, walking, walking speed, walking environment, and GPS field are available to check whether walking information can be collected.

[0089] In the 220 motion, the processor (110) can determine that the user's walking information can be accurately collected if the user is not swinging his / her hand, is walking, the walking speed is constant within a preset range, the walking environment is flat, and the GPS electric field is greater than a preset strength.

[0090] In operation 220, the processor (110) can use inertial information to determine whether a hand is swinging, use at least one of the inertial information and GPS information to determine whether walking is taking place, use at least one of the inertial information and GPS information to check the walking speed, use at least one of the inertial information and GPS information to check whether the walking environment is flat, a slope, or stairs, and use at least one of the air pressure information and GPS information to check the GPS electric field to check the accuracy of the GPS signal.

[0091] In operation 230, the processor (110) can learn a height estimation model through operation 232, apply a height estimation model through operation 234, or analyze a growth trend of height through operation 236 using an artificial intelligence model / engine. At this time, the artificial intelligence model / engine may be a rule-based artificial intelligence model or a generative artificial intelligence model. The artificial intelligence model / engine may be a large health model (LHM), which is a generative artificial intelligence model based on large-scale health data.

[0092] In operation 232, the processor (110) accumulates and stores gait information, and when the amount of data of the accumulated gait information has been collected to a level that can be learned, the processor (110) requests and receives user's body information using an artificial intelligence model / engine, learns to estimate the user's height using the user's body information, number of steps, gait speed, undulation information, and stride length, and updates the existing height estimation model with the learned height estimation model. At this time, the user's body information may include at least one of the user's actual height, the user's age, and the user's gender. At this time, when the learning or updating of the height estimation model is completed, the processor (110) may output a guidance message to the user notifying that the learning or updating of the height estimation model is completed.

[0093] In operation 234, the processor (110) inputs gait information including the number of steps (222), gait speed (224), undulation information (226), and stride length (228) into the height estimation model, and can confirm the user's estimated height information as the output of the height estimation model.

[0094] In operation 240, the processor (110) can provide the estimated height information confirmed in operation 234 to the user.

[0095] In operation 236, the processor (110) can analyze the accumulated and stored changes in the user's height. At this time, the growth trend can be monitored and analyzed on a monthly basis, and information such as the height growth rate, expected adult height, and current position relative to the growth curve can be provided.

[0096] In operation 250, the processor (110) can provide guidance based on growth trend analysis. At this time, the guidance can recommend a nutritional intake plan and diet to ensure adequate intake of key nutrients (calcium, protein, iron, etc.) appropriate for growth and age, or recommend an exercise plan and activities, including stretching to stimulate growth plates and jumping activities that can aid in height growth, depending on the user's physical growth status. Additionally, if the user's growth pattern deviates from the typical range, personalized services can be provided, such as suggesting consultation with a growth clinic specialist for early diagnosis and treatment.

[0097]

[0098] Hereinafter, the method according to the present disclosure configured as above will be described with reference to the drawings below.

[0099] FIG. 3 is a flowchart schematically illustrating a flow for estimating height using gait information according to one embodiment.

[0100] Referring to FIG. 3, in operation 310, the electronic device (100) can collect the user's gait information. More specifically, a method for collecting the user's gait information will be described with reference to FIG. 4.

[0101] In the 320 motion, the electronic device (100) can estimate the user's height by applying the user's gait information to a height estimation model.

[0102]

[0103] Figure 4 is a flowchart illustrating a flow for collecting gait information according to one embodiment.

[0104] Referring to FIG. 4, in operation 410, the electronic device (100) can obtain inertial information, barometric information, and GPS information through the inertial sensor (120), barometric sensor (130), and GPS sensor (140) included in the electronic device (100).

[0105] In operation 420, the electronic device (100) can check whether the current electronic device (100) is in a state suitable for collecting gait information.

[0106] In operation 420, the electronic device (100) can use inertial information, air pressure information, and GPS information to check whether hand swing, walking, walking speed, walking environment, and GPS electric field to determine whether walking information can be collected. The electronic device (100) can use inertial information to determine whether hand swing is performed, use at least one of the inertial information and the GPS information to determine whether walking is performed, use at least one of the inertial information and the GPS information to confirm walking speed, use at least one of the air pressure information and the GPS information to determine whether the walking environment is flat ground, a slope, or stairs, and use GPS information to check the GPS electric field to confirm the accuracy of the GPS signal.

[0107] In the 420 motion, the electronic device (100) can be judged to be in a state where it can accurately collect the user's walking information if the user is not swinging his / her hand, is walking, the walking speed is constant within a preset range, the walking environment is flat, and the GPS electric field is above a preset strength.

[0108] In the 430 motion, the electronic device (100) can estimate the number of steps, walking speed, undulation information, which is information on up-and-down movements occurring during walking, and stride length using at least one of inertial information, air pressure information, and GPS information.

[0109]

[0110] FIG. 5 is a flowchart illustrating a process of learning a height estimation model using gait information and estimating height using the learned height estimation model according to one embodiment.

[0111] Referring to FIG. 5, in operation 510, the electronic device (100) can obtain inertial information, barometric information, and GPS information through the inertial sensor (120), barometric sensor (130), and GPS sensor (140) included in the electronic device (100).

[0112] In operation 520, the electronic device (100) can determine an analysis time zone, which is the time to analyze gait information through activity pattern analysis based on accumulated gait information. For example, if a user goes to school at 8:00 AM, carries a cell phone in his / her pocket during the school commute, and the moving path is flat and the GPS field is determined to be good, the electronic device (100) can set a certain period of time from 8:00 AM as the analysis time zone and perform height estimation daily or at certain daily intervals.

[0113] In operation 530, the electronic device (100) can check whether the current electronic device (100) is in a state suitable for collecting gait information.

[0114] In operation 530, the electronic device (100) can use inertial information, air pressure information, and GPS information to check whether hand swing, walking, walking speed, walking environment, and GPS electric field to determine whether walking information can be collected. The electronic device (100) can use inertial information to determine whether hand swing is performed, use at least one of the inertial information and the GPS information to determine whether walking is performed, use at least one of the inertial information and the GPS information to confirm walking speed, use at least one of the air pressure information and the GPS information to determine whether the walking environment is flat ground, a slope, or stairs, and use GPS information to check the GPS electric field to confirm the accuracy of the GPS signal.

[0115] In the 530 motion, the electronic device (100) can be determined to be in a state where it can accurately collect the user's walking information if the user is not swinging his / her hand, is walking, the walking speed is constant within a preset range, the walking environment is flat, and the GPS electric field is above a preset strength.

[0116] In operation 540, the electronic device (100) can estimate the user's gait information using at least one of inertial information, air pressure information, and GPS information. The user's gait information may include the number of steps, walking speed, undulation information (up-and-down movement information generated during walking), and stride length. The electronic device (100) can accumulate and store the estimated gait information for the purpose of learning a height estimation model.

[0117] In operation 550, the electronic device (100) can check whether the amount of data of walking information accumulated in operation 540 has been collected to a level that can be learned.

[0118] If the amount of data of accumulated walking information is not enough to be learned as a result of the confirmation of operation 550, the electronic device (100) can return to operation 510 and repeat a series of operations.

[0119] If the amount of data of accumulated walking information is sufficient to enable learning as a result of the verification of operation 550, the electronic device (100) can learn a height estimation model in operation 552.

[0120] In operation 552, when learning a height estimation model, the electronic device (100) may request and receive user body information, and learn to estimate the user's height using the user's body information, step count, walking speed, undulation information, and stride length. At this time, the learning may be performed using generative artificial intelligence. In addition, the user's body information may include at least one of the user's actual height, the user's age, and the user's gender.

[0121] In operation 554, the electronic device (100) can update the existing height estimation model with a learned height estimation model.

[0122] In operation 560, the electronic device (100) can determine whether height estimation is possible by checking whether a height estimation model generated through learning exists.

[0123] As a result of the verification of operation 560, if a height estimation model exists and height estimation is not possible, the electronic device (100) can return to operation 510 and repeat a series of operations.

[0124] As a result of the verification of operation 560, if a height estimation model exists and height estimation is possible, the electronic device (100) can estimate the height using the height estimation model in operation 562. More specifically, the electronic device (100) can input gait information including the number of steps, gait speed, undulation information, and stride length estimated in operation 540 into the height estimation model, and can confirm the user's estimated height information as the output of the height estimation model and provide it to the user.

[0125] In operation 564, the electronic device (100) can analyze the accumulated and stored change trends of the user's height and provide the user with a guide according to the height change. At this time, the growth trend can be monitored and analyzed on a monthly basis, and information such as the height growth rate, expected adult height, and current position compared to the growth curve can be provided. In addition, the guide can recommend a nutritional intake plan and diet to ensure that the user can consume the appropriate major nutrients (calcium, protein, iron, etc.) appropriate for growth and age, or can recommend an exercise plan and activity that includes stretching to stimulate the growth plate and jumping activities that can help with height growth depending on the user's physical growth status. In addition, if the user's growth pattern deviates from the general range, personalized services such as suggesting a consultation with a growth clinic expert to enable early diagnosis and treatment can be provided.

[0126] At this time, actions 550 and 560 are performed separately and can also be performed simultaneously in parallel.

[0127]

[0128] FIG. 9 is a diagram illustrating an example of providing a notification when an electronic device detects that a user's height has grown according to an embodiment.

[0129] Referring to FIG. 9, the electronic device (100) may inform the user that the height has grown through a first guidance message (910) and may inquire whether to confirm the user's estimated height through a second guidance message (920).

[0130] Additionally, the electronic device (100) may request the user's height information through a third guidance message (930). The user's height information collected in this manner may be used to learn a height estimation model.

[0131]

[0132] FIG. 10 is a diagram illustrating an example of a guidance message output to obtain accurate walking information in an electronic device according to one embodiment.

[0133] Referring to FIG. 10, the electronic device (100) can identify the user's behavioral pattern based on inertial information and GPS information, and can provide the user with a guidance message (1010) while walking in order to better collect information for height estimation regarding the user's repeated walking situations at a specific time.

[0134] That is, if the electronic device (100) determines that it is capable of accurately collecting the user's gait information and that it is capable of accurately collecting the user's gait information by only modifying the hand swing, it can provide a guidance message (1010) to guide the user to collect accurate gait information.

[0135]

[0136] FIG. 11 is a diagram illustrating an example of providing a monthly growth trend in a graph format based on a user's height estimate in an electronic device according to one embodiment.

[0137] Referring to FIG. 11, the electronic device (100) may output a guidance message (1110) that provides a monthly growth trend in a graph format based on the user's height estimate. At this time, the electronic device (100) may calculate and provide the user's height growth rate compared to the previous year. In addition, the electronic device (100) may analyze the user's growth trend and growth pattern to predict the expected future height and provide the expected height at the end of growth.

[0138] When a user inputs actual height information, the electronic device (100) may display a correction value as indicated by a bold line in the guidance message (1110). In addition, the electronic device (100) may retrain the height estimation model at the time actual height information is input to estimate more accurate height information.

[0139]

[0140] FIG. 12 is a diagram illustrating an example of analyzing a user's growth trend in an electronic device according to one embodiment and providing a guide based on the analyzed results.

[0141] Referring to FIG. 12, the electronic device (100) can provide a user with a guidance message (1210) including guide information based on growth trend analysis.

[0142] The electronic device (100) may analyze the user's growth trend and recommend a nutritional intake plan and diet so that the user can consume the appropriate amount of major nutrients (calcium, protein, iron, etc.) suitable for the user's growth and age, or may recommend an exercise plan and activity including stretching that can stimulate the growth plate and jumping activities that can help with height growth through a guidance message (1210) depending on the user's physical growth status.

[0143] Additionally, the electronic device (100) can provide personalized services, such as suggesting a consultation with a growth clinic expert to enable early diagnosis and treatment when the user's growth pattern is outside the normal range, through a guidance message (1210).

[0144]

[0145] FIG. 13 is a diagram illustrating a schematic structure of an electronic device according to one embodiment.

[0146] Referring to FIG. 13, an electronic device (1300) may be configured to include a processor (1310) and a memory (1320).

[0147] The memory (1320) can store various data used by at least one component (e.g., the processor (1310)) of the electronic device (1300). The data can include, for example, input data or output data for software and commands related thereto. The memory (1320) can include volatile memory or non-volatile memory. In this case, the memory (1320) can have a configuration corresponding to the memory (130) of FIG. 1.

[0148] The processor (1310) can control the overall operation of the electronic device (1300). In addition, the processor (1310) can obtain inertial information, air pressure information, and GPS information, collect the user's gait information using the inertial information, air pressure information, and GPS information, receive the user's body information, learn a height estimation model that estimates the user's height using the gait information and the user's body information, and estimate the user's height by applying the user's gait information to the learned height estimation model.

[0149] At this time, the processor (1310) may have a configuration corresponding to the processor (110) of FIGS. 1 and 2.

[0150]

[0151] FIG. 14 is a diagram illustrating an electronic device within a network environment according to one embodiment.

[0152] Referring to FIG. 14, in a network environment (1400), an electronic device (1401) may communicate with an electronic device (1402) via a first network (1498) (e.g., a short-range wireless communication network), or may communicate with an electronic device (1404) or a server (1408) via a second network (1499) (e.g., a long-range wireless communication network). In one embodiment, the electronic device (1401) may communicate with the electronic device (1404) via the server (1408). According to one embodiment, the electronic device (1401) may include a processor (1420), a memory (1430), an input module (1450), an audio output module (1455), a display module (1460), an audio module (1470), a sensor module (1476), an interface (1477), a connection terminal (1478), a haptic module (1479), a camera module (1480), a power management module (1488), a battery (1489), a communication module (1490), a subscriber identification module (1496), or an antenna module (1497). In some embodiments, the electronic device (1401) may omit at least one of these components (e.g., the connection terminal (1478)), or may have one or more other components added. In some embodiments, some of these components (e.g., sensor module (1476), camera module (1480), or antenna module (1497)) may be integrated into a single component (e.g., display module (1460)).

[0153] The processor (1420) may, for example, execute software (e.g., a program (1440)) to control at least one other component (e.g., a hardware or software component) of the electronic device (1401) connected to the processor (1420) and perform various data processing or operations. According to one embodiment, as at least a part of the data processing or operation, the processor (1420) may store a command or data received from another component (e.g., a sensor module (1476) or a communication module (1490)) in a volatile memory (1432), process the command or data stored in the volatile memory (1432), and store the resulting data in a non-volatile memory (1434). At this time, the processor (1420) may have a configuration corresponding to the processor (110) of FIG. 1 and the processor (1310) of FIG. 13.

[0154] According to one embodiment, the processor (1420) may include a main processor (1421) (e.g., a central processing unit or an application processor) or an auxiliary processor (1423) (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 with the main processor (1421). For example, when the electronic device (1401) includes the main processor (1421) and the auxiliary processor (1423), the auxiliary processor (1423) may be configured to use less power than the main processor (1421) or to be specialized for a given function. The auxiliary processor (1423) may be implemented separately from the main processor (1421) or as a part thereof.

[0155] The auxiliary processor (1423) may control at least a portion of functions or states associated with at least one component (e.g., the display module (1460), the sensor module (1476), or the communication module (1490)) of the electronic device (1401), for example, on behalf of the main processor (1421) while the main processor (1421) is in an inactive (e.g., sleep) state, or together with the main processor (1421) while the main processor (1421) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (1423) (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 (1480) or a communication module (1490)). In one embodiment, the auxiliary processor (1423) (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, on the electronic device (1401) where the artificial intelligence is performed, or can be performed through a separate server (e.g., server (1408)). 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.

[0156] The memory (1430) can store various data used by at least one component (e.g., the processor (1420) or the sensor module (1476)) of the electronic device (1401). The data can include, for example, software (e.g., the program (1440)) and input data or output data for commands related thereto. The memory (1430) can include a volatile memory (1432) or a non-volatile memory (1434). In this case, the memory (1430) can have a configuration corresponding to the memory (130) of FIG. 1 and the memory (1320) of FIG. 13.

[0157] The program (1440) may be stored as software in memory (1430) and may include, for example, an operating system (1442), middleware (1444), or an application (1446).

[0158] The input module (1450) can receive commands or data to be used in a component (e.g., processor (1420)) of the electronic device (1401) from an external source (e.g., a user) of the electronic device (1401). The input module (1450) 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). In this case, the input module (1450) can be configured to include the microphone (130) of FIG. 1.

[0159] The audio output module (1455) can output audio signals to the outside of the electronic device (1401). The audio output module (1455) 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.

[0160] The display module (1460) can visually provide information to an external party (e.g., a user) of the electronic device (1401). The display module (1460) may include, for example, a display, a holographic device, or a projector, and a control circuit for controlling the device. According to one embodiment, the display module (1460) 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. In this case, the display module (1460) may have a configuration corresponding to the displayer (160) of FIG. 1.

[0161] The audio module (1470) can convert sound into an electrical signal, or vice versa. According to one embodiment, the audio module (1470) can acquire sound through the input module (1450), output sound through the sound output module (1455), or an external electronic device (e.g., electronic device (1402)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (1401).

[0162] The sensor module (1476) can detect the operating status (e.g., power or temperature) of the electronic device (1401) 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 (1476) may include, for example, a gesture sensor, a gyro sensor, a 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. At this time, the sensor module (1476) may be configured to include the inertial sensor (120), the pressure sensor (130), and the GPS sensor (140) of FIG. 1.

[0163] The interface (1477) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (1401) to an external electronic device (e.g., the electronic device (1402)). In one embodiment, the interface (1477) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface. In this case, the interface (1477) may be configured to include the interface (170) of FIG. 1.

[0164] The connection terminal (1478) may include a connector through which the electronic device (1401) may be physically connected to an external electronic device (e.g., the electronic device (1402)). In one embodiment, the connection terminal (1478) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0165] The haptic module (1479) 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 (1479) may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0166] The camera module (1480) can capture still images and videos. In one embodiment, the camera module (1480) may include one or more lenses, image sensors, image signal processors, or flashes.

[0167] The power management module (1488) can manage the power supplied to the electronic device (1401). According to one embodiment, the power management module (1488) can be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

[0168] A battery (1489) may power at least one component of the electronic device (1401). In one embodiment, the battery (1489) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0169] The communication module (1490) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (1401) and an external electronic device (e.g., electronic device (1402), electronic device (1404), or server (1408)), and the performance of communication through the established communication channel. The communication module (1490) may operate independently from the processor (1420) (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 (1490) may include a wireless communication module (1492) (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 (1494) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, a corresponding communication module can communicate with an external electronic device (1404) via a first network (1498) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (1499) (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 local area network or a wide area network)). 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 (1492) can verify or authenticate the electronic device (1401) within a communication network such as the first network (1498) or the second network (1499) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (1496).

[0170] The wireless communication module (1492) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimizing terminal power and connecting multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency communications (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (1492) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (1492) may support various technologies for securing performance in high-frequency bands, 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 (1492) may support various requirements specified in the electronic device (1401), an external electronic device (e.g., the electronic device (1404)), or a network system (e.g., the second network (1499)). According to one embodiment, the wireless communication module (1492) may support a peak data rate (e.g., 20 Gbps or more) for eMBB implementation, a loss coverage (e.g., 164 dB or less) for mMTC implementation, 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 implementation.

[0171] The antenna module (1497) 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 (1497) 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 (1497) 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 (1498) or the second network (1499), may be selected from the plurality of antennas by, for example, the communication module (1490). A signal or power may be transmitted or received between the communication module (1490) and the external electronic device via the at least one selected 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 (1497).

[0172] According to various embodiments, the antenna module (1497) 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.

[0173] 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)).

[0174] According to one embodiment, commands or data may be transmitted or received between the electronic device (1401) and an external electronic device (1404) via a server (1408) connected to a second network (1499). Each of the external electronic devices (1402 or 1404) may be the same or a different type of device as the electronic device (1401). According to one embodiment, all or part of the operations executed in the electronic device (1401) may be executed in one or more of the external electronic devices (1402, 1404, or 1408). For example, when the electronic device (1401) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (1401) 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 (1401). The electronic device (1401) 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 (1401) may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device (1404) may include an Internet of Things (IoT) device. The server (1408) may be an intelligent server utilizing machine learning and / or a neural network.According to one embodiment, an external electronic device (1404) or server (1408) may be included within the second network (1499). The electronic device (1401) may be applied to intelligent services (e.g., smart homes, smart cities, smart cars, or healthcare) based on 5G communication technology and IoT-related technology.

[0175]

[0176] According to one embodiment, a method for estimating height may include: collecting gait information of a user; and applying the gait information of the user to a height estimation model to estimate the height of the user.

[0177] According to one embodiment, the user's walking information may include the number of steps, walking speed, undulation information, which is information on up-and-down movements occurring during walking, and stride length.

[0178] According to one embodiment, the operation of collecting the user's walking information may include an operation of obtaining inertial information, air pressure information, and GPS information; and an operation of estimating the number of steps, walking speed, undulation information, which is information on up-and-down movements occurring during walking, and stride length using at least one of the inertial information, the air pressure information, and the GPS information.

[0179] According to one embodiment, the operation of collecting the user's gait information may include: an operation of acquiring inertial information, air pressure information, and GPS information; an operation of determining whether the user's gait information can be accurately collected; and, if the determination result indicates that the user's gait information can be accurately collected, an operation of estimating the number of steps, gait speed, undulation information, which is information on up-and-down movements occurring during walking, and stride length using at least one of the inertial information, the air pressure information, and the GPS information.

[0180] According to one embodiment, the operation of determining whether the user's walking information can be accurately collected may include an operation of checking whether the user is swinging his or her hand, whether the user is walking, the walking speed, the walking environment, and the GPS electric field using the inertial information, the air pressure information, and the GPS information; and an operation of determining whether the user's walking information can be accurately collected if the user is not swinging his or her hand, the user is walking, the walking speed is constant within a preset range, the walking environment is flat, and the GPS electric field is greater than a preset strength.

[0181] According to one embodiment, the method for estimating height may further include an action of outputting a notification requesting the user to stop the hand swing if the hand swing is the hand swing, the user is walking, the walking speed is constant within a preset range, the walking environment is flat, and the GPS field is greater than a preset strength.

[0182] According to one embodiment, the operation of collecting the user's walking information may include: an operation of obtaining inertial information, air pressure information, and GPS information; an operation of determining whether the user's walking information can be accurately collected; an operation of analyzing the user's lifestyle pattern to determine a time zone in which the user's walking information can be accurately collected; and an operation of collecting the user's walking information during the determined time zone.

[0183] According to one embodiment, the operation of collecting the user's walking information may include an operation of analyzing the user's lifestyle pattern to determine a time zone in which the user's walking information can be accurately collected; and an operation of outputting a guidance message to guide the user to collect the walking information during the determined time zone.

[0184] According to one embodiment, the height estimation model can output the estimated height of the user when the number of steps, the walking speed, the undulation information, and the stride included in the walking information of the user are input.

[0185] According to one embodiment, a method for estimating height may further include: collecting gait information of the user as learning information for learning the height estimation model; collecting body information of the user as the learning information; and learning the height estimation model using the learning information.

[0186] According to one embodiment, the user's body information may include at least one of the user's actual height, the user's age, and the user's gender.

[0187] According to one embodiment, the operation of learning the height estimation model using the learning information may include an operation of learning to estimate the height of the user using the user's body information, the number of steps, the walking speed, the undulation information, and the stride.

[0188] According to one embodiment, a method for estimating height may further include an operation of accumulating and storing the estimated height of the user; and an operation of analyzing a change trend of the accumulated and stored height of the user to provide guidance according to the change in height.

[0189] According to one embodiment, a computer-readable recording medium stores instructions, which, when executed by one or more processors, cause the computer to perform the following actions: collecting user gait information; and applying the user gait information to a height estimation model to estimate the user's height.

[0190] According to one embodiment, an electronic device includes one or more processors; and a memory storing instructions, which, when executed by the one or more processors, cause the electronic device to perform the following operations: collecting gait information of a user; and applying the gait information of the user to a height estimation model to estimate the height of the user.

[0191] According to one embodiment, the electronic device further includes an inertial sensor for sensing inertial information; a barometric pressure sensor for sensing barometric pressure information; and a GPS sensor for sensing GPS information, wherein the instructions, when executed by the one or more processors, cause the electronic device to perform the following operations: acquiring the inertial information, the barometric pressure information, and the GPS information; determining whether the user's gait information can be accurately collected; and, if the user's gait information can be accurately collected as a result of the determination, collecting the number of steps, gait steadiness, undulation information, which is information on up-and-down movements occurring during walking, and stride length as the user's gait information using at least one of the inertial information, the barometric pressure information, and the GPS information.

[0192] According to one embodiment, the commands, when executed by the one or more processors, may cause the electronic device to perform an operation of checking whether the user is in a state where the user's walking information can be accurately collected, using the inertial information, the air pressure information, and the GPS information to determine whether the user is swinging his or her hand, whether the user is walking, the walking speed, the walking environment, and the GPS field; and an operation of determining whether the user's walking information can be accurately collected if the user is not swinging his or her hand, the user is walking, the walking speed is constant within a preset range, the walking environment is flat, and the GPS field is greater than or equal to a preset strength.

[0193] According to one embodiment, the commands, when executed by the one or more processors, may cause the electronic device to perform the following operations: acquiring the inertial information, the air pressure information, and the GPS information; determining whether the user's gait information can be accurately collected; analyzing the user's lifestyle pattern to determine a time zone in which the user's gait information can be accurately collected; and determining whether the user's gait information is collected in the determined time zone.

[0194] According to one embodiment, the height estimation model can output the estimated height of the user when the number of steps, the walking speed, the undulation information, and the stride included in the walking information of the user are input.

[0195] According to one embodiment, the instructions, when executed by the one or more processors, may cause the electronic device to perform the following operations: collecting the user's gait information as learning information for learning the height estimation model; collecting the user's body information as the learning information; and learning the height estimation model using the learning information.

[0196] According to one embodiment, the instructions, when executed by the one or more processors, may cause the electronic device to perform an operation of learning to estimate the height of the user by using the user's body information, the number of steps, the walking speed, the undulation information, and the stride when learning the height estimation model using the learning information.

[0197]

[0198] The method according to the embodiment may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may store program commands, data files, data structures, etc., singly or in combination. The program commands recorded on the medium may be those specially designed and configured for the embodiment or may be those known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program commands, such as ROMs, RAMs, and flash memories. Examples of the program commands include not only machine language codes generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiment, and vice versa.

[0199] Software may include a computer program, code, instructions, or a combination of one or more of these, and may configure a processing device to perform a desired operation or, independently or collectively, command the processing device. The software and / or data may be stored on any type of machine, component, physical device, virtual equipment, computer storage medium, or device, for interpretation by the processing device or for providing instructions or data to the processing device. The software may also be distributed over networked computer systems, and stored or executed in a distributed manner. The software and data may be stored on one or more computer-readable recording media.

[0200] Although the embodiments described above have been described with limited drawings, those skilled in the art will appreciate that various technical modifications and variations can be applied based on the above. For example, appropriate results can still be achieved even if the described techniques are performed in a different order than described, and / or components of the described systems, structures, devices, circuits, etc. are combined or combined in a different manner than described, or are replaced or substituted with other components or equivalents.

[0201] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims described below.

Claims

1. In the method of estimating height, Actions to collect user's walking information; and An action of estimating the height of the user by applying the user's walking information to a height estimation model. How to include.

2. In paragraph 1, The action of collecting the user's walking information is as follows: Actions to acquire inertial information, barometric information, and GPS information; An action to determine whether the user's walking information can be accurately collected; and If the user's walking information can be accurately collected as a result of the judgment, an operation of estimating the number of steps, walking speed, undulation information, which is information on up-and-down movement occurring during walking, and stride length using at least one of the inertial information, the air pressure information, and the GPS information. How to include.

3. In any one of paragraphs 1 and 2, The action of determining whether the user's walking information can be accurately collected is as follows: An operation of checking whether a hand is swinging, whether a person is walking, walking speed, walking environment, and GPS field using the above inertial information, the above air pressure information, and the above GPS information; and An action that determines that the user's walking information can be accurately collected when the user is not swinging his / her hands, is walking, the walking speed is constant within a preset range, the walking environment is flat, and the GPS field strength is higher than a preset strength. How to include.

4. In any one of paragraphs 1 to 3, An action of outputting a notification requesting the user to stop the hand swing when the user is walking, the walking speed is constant within a preset range, the walking environment is flat, and the GPS electric field is greater than a preset strength. How to include more.

5. In any one of paragraphs 1 to 4, The action of collecting the user's walking information is as follows: An action to analyze the lifestyle pattern of the user and determine a time zone in which the user's walking information can be accurately collected; and Action to output a guidance message that prompts the user to collect walking information during the confirmed time period How to include.

6. In any one of paragraphs 1 to 5, An action of collecting the user's gait information as learning information for learning the height estimation model; An action of collecting the user's physical information as the learning information; and An operation of learning the height estimation model using the above learning information. How to include more.

7. In any one of paragraphs 1 to 6, The above user's physical information is: At least one of the user's actual height, the user's age, and the user's gender method.

8. In any one of paragraphs 1 to 7, The operation of learning the height estimation model using the above learning information is as follows: An operation of learning to estimate the height of the user by using the user's body information, the number of steps, the walking speed, the undulation information, and the stride length. How to include.

9. In any one of paragraphs 1 to 8, An operation of accumulating and storing the estimated height of the user; and An action that analyzes the accumulated and stored changes in the user's height and provides guidance according to the height change. How to include more.

10. In electronic devices, one or more processors; and Memory that stores commands Including, The above instructions, when executed by the one or more processors, cause the electronic device to: Actions to collect user's walking information; and An operation of estimating the height of the user by applying the user's walking information to a height estimation model is performed. device.

11. In paragraph 10, Inertial sensor that senses inertial information; A pressure sensor that senses pressure information; and GPS sensor that senses GPS information Including more, The above instructions, when executed by the one or more processors, cause the electronic device to: An operation of acquiring the above inertial information, the above barometric pressure information and the above GPS information; An action to determine whether the user's walking information can be accurately collected; and If the judgment result is that the user's walking information can be accurately collected, an operation is performed to collect the user's walking information, such as the number of steps, walking speed, undulation information which is information on up-and-down movement occurring during walking, and stride length, using at least one of the inertial information, the air pressure information, and the GPS information. device.

12. In any one of paragraphs 10 to 11, The above instructions, when executed by the one or more processors, cause the electronic device to: When determining whether the user's walking information can be accurately collected, An operation of checking whether a hand is swinging, whether a person is walking, walking speed, walking environment, and GPS field using the above inertial information, the above air pressure information, and the above GPS information; and If the user is not swinging his / her hand, is walking, the walking speed is constant within a preset range, the walking environment is flat, and the GPS field is stronger than a preset strength, an action is performed to determine that the user's walking information can be accurately collected. device.

13. In any one of paragraphs 10 to 12, The above instructions, when executed by the one or more processors, cause the electronic device to: An operation of acquiring the above inertial information, the above barometric pressure information and the above GPS information; An action to determine whether the user's walking information can be accurately collected; An action to analyze the lifestyle pattern of the user and determine a time zone in which the user's walking information can be accurately collected; and Performing a judgment action to collect the user's walking information at the confirmed time zone device.

14. In any one of paragraphs 10 to 13, The above height estimation model is, When the number of steps, the walking speed, the undulation information, and the stride included in the user's walking information are input, the estimated height of the user is output. device.

15. In any one of paragraphs 10 to 14, The above instructions, when executed by the one or more processors, cause the electronic device to: An action of collecting the user's gait information as learning information for learning the height estimation model; An action of collecting the user's physical information as the learning information; and An operation of learning the height estimation model using the above learning information is performed. device.

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