A body fat scale and its control method

Abstract

The present application provides a body fat scale and a control method therefor. The body fat scale includes an identity recognition unit and a control unit. The identity recognition unit includes a first fingerprint recognition module and a second fingerprint recognition module. Among them, the first fingerprint recognition module includes a first fingerprint recognition panel with an adjustable angle; the second fingerprint recognition module includes a second fingerprint recognition panel having a first inclination angle with respect to the vertical plane. The angles of each module conform to the natural angles of the user's fingers contacting the body fat scale, improving the user experience. The control unit is configured to determine a target fingerprint recognition module adapted to the user; in response to the user's fingerprint input operation through the target fingerprint recognition module, determine a first identifier corresponding to the first fingerprint image; based on the first identifier, establish a matching relationship between the first initial data and the basic data corresponding to the first identifier. During the user's use process, learn the user's usage habits, etc., to adapt to the operation characteristics of different users and quickly and accurately determine the user's identity.

Description

Technical Field

[0001] The application relates to the field of electronic measurement, and in particular to a body fat scale and a control method thereof. Background Art

[0002] As people's living standards improve, they're increasingly focused on health and body shape management. In daily life, users use body fat scales to manage their health and figure, helping them understand their bodies, such as weight, body fat percentage, BMI, visceral fat, and muscle mass.

[0003] In related technologies, body fat scales use communication methods such as Bluetooth and Wi-Fi to establish connections with terminal devices such as smartphones and tablets, thereby enabling collaborative operation with applications on these terminal devices. During this process, users need to enter basic data such as height, gender, and age into the application; the body fat scale is equipped with pressure sensors and electrodes to measure the pressure and electrical impedance values generated when the user stands on the scale. Users need to manually select their basic data on the body fat scale's operating interface or through the supporting application to achieve a matching association between the user's basic data and the measured data, and then carry out subsequent data processing and determination of indicator data.

[0004] However, in traditional usage scenarios, when a body fat scale serves multiple users, the manual selection of basic data for matching may result in an inability to accurately identify the user, which in turn leads to inaccurate indicator data and analysis, resulting in a poor user experience. Summary of the invention

[0005] The present application provides a body fat scale and a control method thereof, which can quickly and accurately determine the identity of a user during use.

[0006] In a first aspect, a body fat scale is provided, comprising an identification unit, a measurement unit, a control unit, and a wireless communication unit. The control unit is communicatively connected to the identification unit, the measurement unit, and the wireless communication unit. The identification unit comprises a first fingerprint recognition module and a second fingerprint recognition module. The first fingerprint recognition module is disposed on the first surface of the body fat scale and comprises a first fingerprint recognition panel with an adjustable angle and a first sensor. The second fingerprint recognition module is disposed on the side of the body fat scale and comprises a second fingerprint recognition panel and a second sensor. The second fingerprint recognition panel has a first inclination angle with respect to the vertical plane. The measurement unit comprises an electrode module and a pressure sensor module.

[0007] The control unit is configured to: when the number of times the body fat scale is used reaches a preset number and it is recognized that the user is in a stable state on the body fat scale, obtain the user's first initial data, where the first initial data includes bioimpedance data and pressure distribution data; input the first initial data, historical usage data, and user preference analysis data into a preset recognition model to determine the target fingerprint recognition module suitable for the user; in response to the user entering a fingerprint operation through the target fingerprint recognition module, obtain the user's first fingerprint image and determine the first identifier corresponding to the first fingerprint image, where the target fingerprint recognition module is the first fingerprint recognition module or the second fingerprint recognition module; based on the first identifier, establish a matching relationship between the first initial data and the basic data corresponding to the first identifier, and determine the user's index data.

[0008] The body fat scale is equipped with multiple fingerprint recognition modules, and the layout design fully considers ergonomics and usage scenarios. Some fingerprint recognition modules are set on the surface of the body fat scale, and some fingerprint recognition modules are hidden on the side of the body fat scale, providing more choices for users with different usage habits. Also, during the process of the user using the body fat scale, it continuously learns the user's usage habits, user preference analysis data, etc., to adapt to the operation characteristics of different users, and guides the user to enter a fingerprint through the target fingerprint recognition module among the multiple fingerprint recognition modules, thereby quickly and accurately determining the user's identity.

[0009] In a possible implementation manner, the first fingerprint recognition module further includes a universal connection mechanism, and the universal connection mechanism is used to movably connect the first fingerprint recognition panel to the body fat scale. The first fingerprint recognition panel is provided with a first sensor, and the second fingerprint recognition panel is provided with a second sensor.

[0010] By setting a universal connection mechanism in the first fingerprint recognition module, the first fingerprint recognition panel becomes an adjustable-angle panel. The user can manually rotate the first fingerprint recognition panel in the first fingerprint recognition module to adjust the angle to meet the user's input of fingerprint images in different gestures or by different users through the first fingerprint recognition panel.

[0011] In a possible implementation manner, the universal connection mechanism further includes an angle sensor and a motor. After determining the target fingerprint recognition module suitable for the user, the control unit is further configured to: if the target fingerprint recognition module is the first fingerprint recognition module, analyze the historical usage data to determine the target angle of the first fingerprint recognition panel, where the historical usage data includes the usage frequency of the first fingerprint recognition module and the initial angle corresponding to the first fingerprint recognition panel obtained through the angle sensor when the user enters a fingerprint; control the motor to adjust the angle of the first fingerprint recognition panel to the target angle.

[0012] It should be understood that the fingerprint recognition panel is driven by a motor-driven universal joint mechanism to rotate, realizing automatic angle adjustment. This structure can achieve multi-angle adjustment within a wide range, meeting the needs of different users (such as different heights and usage habits) to input fingerprints in a comfortable posture. Moreover, during use, before the user inputs fingerprints, the fingerprint recognition panel in the corresponding fingerprint recognition module can be adjusted to a suitable angle.

[0013] In a possible implementation, after the control unit responds to the user's fingerprint input operation through the target fingerprint recognition module and obtains the user's first fingerprint image, it is further configured to: perform recognition and analysis on the first fingerprint image based on the user information stored in the body fat scale; if the first fingerprint image is not recognized, perform a matching degree analysis on the features of the first fingerprint image with the fingerprint features already collected by the first fingerprint recognition module and the second fingerprint recognition module respectively to obtain the corresponding similarity; and use the fingerprint recognition module with the highest similarity as the updated target fingerprint recognition module.

[0014] In the process of recognizing and analyzing the first fingerprint image after it is obtained, there may still be unrecognized situations. To improve the fault tolerance rate of the solution and enhance the user experience, in the case where the first fingerprint image is not recognized, the user is guided to input fingerprints through other fingerprint recognition modules by analysis.

[0015] In a possible implementation, when the number of times the body fat scale is used has not reached the preset number and it is recognized that the user is in a stable state on the body fat scale, the control unit is further configured to: obtain the user's second fingerprint image and second initial data, where the second fingerprint image is an image collected by the first fingerprint recognition module and / or the second fingerprint recognition module; analyze the second fingerprint image and the second initial data to obtain the user's historical usage data and user preference analysis data; and optimize the preset recognition model based on the second fingerprint image, the second initial data, the historical usage data, and the user preference analysis data to obtain an optimized preset recognition model.

[0016] In the daily use scenario of the body fat scale, the intelligence of the body fat scale will gradually progress as the number of times the user uses it increases. Whenever the user completes a measurement, the body fat scale will collect the second fingerprint image to accurately identify the user's identity. The body fat scale uses the second fingerprint image, the second initial data, the historical usage data, and the user preference analysis data as input data to comprehensively optimize the preset recognition model. In continuous iterative operations, the preset recognition model gradually adjusts its internal parameters to improve the recognition accuracy of the user's individual characteristics and behavior habits, thereby obtaining an optimized preset recognition model. Furthermore, in the subsequent use process, a more accurate target fingerprint recognition model is provided for the user, providing personalized services for the user.

[0017] In a possible implementation, the first fingerprint recognition module further includes a first indicator light, and the second fingerprint recognition module further includes a second indicator light; after the control unit determines the target fingerprint recognition module suitable for the user, it is further configured to: generate a prompt message, where the prompt message is used to instruct the user to input a fingerprint through the target fingerprint recognition module. When the prompt message indicates that the first fingerprint recognition module is the target fingerprint recognition module, drive the first indicator light to emit light based on a preset mode; when the prompt message indicates that the second fingerprint recognition module is the target fingerprint recognition module, drive the first indicator light to emit light based on a preset mode; where the preset mode includes a constant-on mode or a flashing mode.

[0018] The generated prompt message is used to guide the user to input a fingerprint through the target fingerprint recognition module. Among them, the prompt message can be presented in different forms to achieve the purpose of prompting the user.

[0019] In a possible implementation, the first fingerprint recognition module further includes a first LED light source, and the first sensor includes an optical fingerprint sensor and a capacitive fingerprint sensor; the control unit is further configured to: obtain the user's heart rate data, where the heart rate data is used to obtain the user's historical usage data, optimize a preset recognition model, and input it into the preset recognition model to determine the target fingerprint recognition module suitable for the user.

[0020] Since more input data can cover more features and variation situations, the preset recognition model can learn more complex and subtle patterns and rules, reduce the risk of overfitting, and improve the generalization ability on unknown data to improve the accuracy of the preset recognition model. Therefore, without affecting the fingerprint recognition function, a dedicated heart rate signal processing algorithm can be added to the control unit to filter, amplify, extract features, etc. from the collected photoplethysmogram signal, calculate the user's heart rate data, and use the heart rate data as the data for training, optimizing, and using the preset model.

[0021] In a possible implementation, the body fat scale includes a handle and a scale body; the identity recognition unit further includes a face recognition module, the first fingerprint recognition module and the face recognition module are arranged on the second surface of the handle, and the second fingerprint recognition module is arranged on the side of the scale body.

[0022] Equipping multiple fingerprint recognition modules in a body fat scale with a detachable handle and scale body, with some fingerprint recognition modules arranged on the surface of the handle and some hidden on the side of the scale body, provides more choices for users with different usage habits. This design ensures that no matter in what posture the user steps on the body fat scale, regardless of their height, hand shape or finger characteristics, they can conveniently and comfortably place their fingers on one of the fingerprint recognition modules, greatly improving the convenience of obtaining fingerprint images and optimizing the user experience.

[0023] In a possible implementation, the first sensor includes an optical fingerprint sensor and a capacitive fingerprint sensor, and the second sensor includes an optical fingerprint sensor and a capacitive fingerprint sensor.

[0024] The first sensor and the second sensor, as fusion sensors, combine the optical fingerprint sensor and the capacitive fingerprint sensor; this fusion of multiple sensors can not only improve the speed of fingerprint recognition but also enhance the accuracy, enabling quick and accurate acquisition of fingerprint images even when the finger has slight stains or is wet.

[0025] In a second aspect, a control method for a body fat scale is provided, which is applied to the body fat scale in any one of the first aspects. The control method includes: when the usage times of the body fat scale reach a preset number and it is recognized that the user is in a stable state on the body fat scale, obtaining the user's first initial data, where the first initial data includes bioimpedance data and pressure distribution data; inputting the first initial data, historical usage data, and user preference analysis data into a preset recognition model to determine a target fingerprint recognition module suitable for the user; in response to the user entering a fingerprint operation through the target fingerprint recognition module, obtaining the user's first fingerprint image and determining a first identifier corresponding to the first fingerprint image, where the target fingerprint recognition module is the first fingerprint recognition module or the second fingerprint recognition module; based on the first identifier, establishing a matching relationship between the first initial data and the basic data corresponding to the first identifier, and determining the user's index data.

[0026] In a third aspect, a control device is provided, including a unit for executing the control method of the body fat scale in any one of the second aspects. The control device can be a body fat scale or a chip inside the body fat scale.

[0027] In a fourth aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is run by the control device, the control device is caused to execute the control method of the body fat scale in any one of the second aspects.

[0028] In a fifth aspect, a computer program product is provided. The computer program product includes: a computer program, and when the computer program is run by the control device, the control device is caused to execute the control method of the body fat scale in any one of the second aspects.

[0029] It can be understood that the beneficial effects of the above second to fifth aspects can be referred to the relevant descriptions in the first aspect above, and will not be elaborated here. Description of the Drawings

[0030] Figure 1 Shows a schematic diagram of a body fat scale and the communication between the body fat scale and a terminal device;

[0031] Figure 2Shows a schematic diagram of the system architecture of a body fat scale provided by an embodiment of the present application;

[0032] Figure 3 Shows a schematic diagram of the structure of a body fat scale provided by an embodiment of the present application;

[0033] Figure 4 Shows a schematic diagram of the structure of another body fat scale provided by an embodiment of the present application;

[0034] Figure 5 Shows Figure 4 A schematic diagram of the body fat scale in another form;

[0035] Figure 6 Shows a schematic diagram of fingerprint entry through the first fingerprint recognition module in the body fat scale provided by an embodiment of the present application;

[0036] Figure 7 Shows a schematic diagram of fingerprint entry through the first fingerprint recognition module in the body fat scale provided by an embodiment of the present application;

[0037] Figure 8 Shows a schematic diagram of the first fingerprint recognition module in a body fat scale provided by an embodiment of the present application;

[0038] Figure 9 Shows a schematic diagram of the structure of another body fat scale provided by an embodiment of the present application;

[0039] Figure 10 Shows a schematic diagram of the flow of a control method for a body fat scale provided by an embodiment of the present application;

[0040] Figure 11 Shows a schematic diagram of the analysis flow in a control method for a body fat scale provided by an embodiment of the present application;

[0041] Figure 12 Shows a schematic diagram of the control device of the body fat scale provided by an embodiment of the present application;

[0042] Figure 13 Shows a schematic diagram of the structure of a body fat scale provided by the present application. Detailed implementation manners

[0043] The technical solutions in the embodiments of the present application will be described below in conjunction with the accompanying drawings in the embodiments of the present application. Among them, in the description of the embodiments of the present application, unless otherwise specified, " / " means "or". For example, A / B may mean A or B. The "and / or" herein is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B may mean: A exists alone, A and B exist simultaneously, and B exists alone. The terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first", "second", and "third" may explicitly or implicitly include one or more of such features.

[0044] For the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0045] With the improvement of people's living standards, people pay more and more attention to health and body management. In daily life, users manage their health and body through a body fat scale, which helps people understand their bodies, such as weight, body fat percentage, visceral fat, muscle mass, etc.

[0046] A body fat scale is usually provided with pressure sensors and electrode pads for measuring multiple data. When a user steps on the body fat scale, the weight of the body is evenly applied to the surface of the scale body. The pressure sensor is deformed under the action of pressure, causing the resistance or capacitance value to change. This electrical change is converted into an electrical signal, and after being amplified, filtered, etc., the user's weight data is finally presented in digital form. Also, when the user's feet, or feet and hands, touch the electrode pads of the body fat scale, the electrode pads apply a small safe current to the user's body, and this current will circulate in the body through the user's tissues and body fluids. Since adipose tissue and muscle tissue have different impedances to the current, the electrode pads can detect the impedance changes generated when the current conducts in different tissues of the user and obtain the impedance value.

[0047] In the related art, the body fat scale uses communication means such as Bluetooth and Wi-Fi to establish a connection with terminal devices such as smartphones and tablets, and then realizes collaborative work with the application programs on these terminal devices. The application programs on the terminal devices are used to collect the basic data of the user, such as height, gender, age, etc., and transmit these basic data to the body fat scale for storage.

[0048] The body fat scale in the related art will be described below in conjunction with the accompanying drawings.Figure 1 shows a schematic diagram of a body fat scale and the communication between the body fat scale and a terminal device; as Figure 1 shown in (a) of [reference], the body fat scale can be a four - electrode body fat scale. The four - electrode body fat scale includes a measurement unit, a display unit, a wireless communication unit, and a power supply unit. Among them, the electrode module in the measurement unit includes four electrode plates, which are arranged on the surface of the scale body of the four - electrode body fat scale; as Figure 1 shown in (b) of [reference], the body fat scale can also be an eight - electrode body fat scale. The eight - electrode body fat scale includes a measurement unit, a display unit, a wireless communication unit, and a power supply unit. Among them, the electrode module in the measurement unit includes eight electrode plates, where four electrode plates are arranged on the surface of the scale body of the eight - electrode body fat scale, and the other four electrode plates are arranged on the surface of the handle of the eight - electrode body fat scale. It should also be understood that the number of electrode plates of the body fat scale is not limited.

[0049] It should be understood that the electrode plates can be external electrode plates or internal electrode plates; as Figure 1 shown in (b) of [reference], the electrode plates on the handle are external electrode plates, as Figure 1 shown in (a) of [reference], Figure 1 shown in (b) of [reference], the electrode plates on the scale body are internal electrode plates.

[0050] As Figure 1 shown in (c) of [reference], taking the eight - electrode body fat scale as an example, the eight - electrode body fat scale is communicatively connected to a mobile phone through wireless communication methods such as Bluetooth and Wi - Fi. When the eight - electrode body fat scale is used for the first time, through application A on the mobile phone, a communication connection is established between the mobile phone and the eight - electrode body fat scale, and the basic data of user X, the basic data of user Y, etc. are entered into application A. When the eight - electrode body fat scale is used again, it receives the basic data of each user transmitted by the mobile phone and stores them in the memory in the eight - electrode body fat scale.

[0051] The body fat scale combines the measured weight data, impedance value and other measurement data with the received basic data, and then uses a specific algorithm to accurately calculate multiple index data such as the user's body fat percentage, moisture content, muscle mass, bone mass, etc. These data are of extremely important reference value for people to understand their own health status, formulate reasonable fitness plans, adjust diet structures, etc., and help users manage their health more scientifically.

[0052] It should be noted that the body fat scales in related technologies do not have the technical ability to accurately identify users and cannot automatically determine the identity of the current user. When the body fat scale serves multiple users, the user needs to manually select the basic data of the user on the operation interface of the body fat scale or through a supporting application program to achieve the matching and association of the user's basic data and measurement data, and then carry out subsequent data processing and determination of index data.

[0053] In actual application scenarios, due to the lack of accurate user identification technology, manual selection operations increase the complexity of user use, reduce the use efficiency, and affect the user experience. Moreover, it is easy to cause user operation errors. Once the wrong information is selected, the matching between the measurement data and the user will be incorrect, affecting the accuracy and availability of the data.

[0054] In view of this, the embodiments of the present application provide a body fat scale and its control method. The body fat scale includes an identity recognition unit, a measurement unit, a control unit, and a wireless communication unit. Among them, the identity recognition unit includes a first fingerprint recognition module and a second fingerprint recognition module. The first fingerprint recognition module includes a first fingerprint recognition panel with an adjustable angle; the second fingerprint recognition module includes a second fingerprint recognition panel with a first inclination angle with respect to the vertical plane. The angles of each module conform to the natural angles of the user's fingers contacting the body fat scale, enhancing the user experience. The control unit is configured to obtain the user's first initial data when the number of times the body fat scale is used reaches a preset number and the user is recognized to be in a stable state on the body fat scale; input the first initial data, as well as the corresponding historical usage data and user preference analysis data, into a preset recognition model to determine the target fingerprint recognition module suitable for the user; in response to the user entering a fingerprint operation through the target fingerprint recognition module, obtain the user's first fingerprint image and determine the first identifier corresponding to the first fingerprint image; based on the first identifier, establish a matching relationship between the first initial data and the basic data corresponding to the first identifier, and learn the user's usage habits during the user's use process, etc., to adapt to the operation characteristics of different users and quickly and accurately determine the user's identity.

[0055] The following combines Figures 2 to 9 to describe in detail the body fat scale provided by the embodiments of the present application, and illustrate the embodiments of the present application through the following multiple exemplary embodiments.

[0056] Figure 2 shows a schematic diagram of the system architecture of a body fat scale provided by the embodiments of the present application. As Figure 2 shown, the body fat scale 10 includes a control unit 11, a measurement unit 12, an identity recognition unit 13, a storage unit 14, a display unit 15, a power supply unit 16, and a wireless communication unit 17. The control unit 11 is communicatively connected to the measurement unit 12, the identity recognition unit 13, the storage unit 14, the display unit 15, the power supply unit 16, and the wireless communication unit 17. Among them, the measurement unit 12 includes an electrode module 12A, a pressure sensor module 12B, etc.; the identity recognition unit 13 includes a first fingerprint recognition module 13A, a second fingerprint recognition module 13B, a face recognition module 13C, etc.

[0057] It should be noted that Figure 2The connection relationship between the units shown is only a schematic illustration and does not constitute a limitation on the connection relationship between the units in the body fat scale 10. In other embodiments of the present application, the body fat scale 10 may include Figure 2 More or fewer components than those shown, or the body fat scale 10 may include Figure 2 A combination of some of the components shown, or the body fat scale 10 may include Figure 2 Subassemblies of some of the components shown. Figure 2 The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0058] The control unit 11 may include one or more processors, for example, the control unit may include a central processing unit (CPU), a graphics processing unit (GPU), etc. The different processors may be independent devices or integrated into one processor.

[0059] The control unit can be the nerve center and command center of the body fat scale 10. The control unit can generate an operation control signal based on the instruction operation code and the timing signal to complete the control of fetching and executing instructions.

[0060] The measuring unit 12 includes an electrode module 12A and a pressure sensor module 12B. The measuring unit 12 is mainly used to measure measurement data such as pressure value and electrical impedance value generated when a user stands on the scale.

[0061] The electrode module 12A mainly uses bioelectrical impedance analysis (BIA) technology to measure the user's body composition data such as fat content. The electrode module 12A includes multiple electrode pads, for example, four electrode pads or eight electrode pads.

[0062] The electrodes transmit a weak, safe electrical current to the user. Different tissues and components (such as muscle, fat, and water) have different resistances to this current. Fat tissue has a higher resistance, while muscle tissue and water have a relatively lower resistance. After the current passes through the user, the electrodes receive feedback and calculate the user's electrical impedance based on how the current is conducted through the user.

[0063] The pressure sensor module 12B is used to measure the stable pressure distribution data after the user steps on the body fat scale, and then obtain the corresponding pressure value.

[0064] In some embodiments, in order to increase the accuracy of pressure value measurement, the pressure sensor module 12B includes a plurality of pressure sensors, which are evenly arranged. It can be used to accurately obtain the pressure distribution data of the user, and can also detect the pressure distribution when the user measures on the main body, so as to deduce the center of gravity position of the user, which is used to characterize the body balance state and posture habit of the user, thereby improving the measurement accuracy.

[0065] The identity recognition unit 13 includes a first fingerprint recognition module 13A and a second fingerprint recognition module 13B. Both the first fingerprint recognition module 13A and the second fingerprint recognition module 13B are used to collect the fingerprint images of the user, which is convenient for the identification of the user's identity.

[0066] It should be understood that in the embodiments of the present application, the number of the first fingerprint recognition modules 13A can be one or more, and the number of the second fingerprint recognition modules 13B can also be one or more. The embodiments of the present application do not limit this.

[0067] The body fat scale provided by the embodiments of the present application is equipped with a plurality of fingerprint recognition modules. The layout design fully considers ergonomics and usage scenarios. Some fingerprint recognition modules are arranged on the surface of the body fat scale, and some fingerprint recognition modules are hidden on the side of the body fat scale, providing more choices for users with different usage habits. This design ensures that no matter in what posture the user steps on the body fat scale, regardless of their height, hand shape or finger characteristics, they can conveniently and comfortably place their fingers on one of the fingerprint recognition modules, greatly improving the convenience of obtaining fingerprint images and optimizing the user experience.

[0068] The storage unit 14 is used to store the user's basic data, measurement data, user habits, preset recognition models, control algorithms, etc.

[0069] The display unit 15 is mainly provided with a display. The display is used to display the user's basic data, measurement data, index data, etc., and can also display the corresponding curve analysis diagrams of each data. Among them, the display can be a color display. For example, a thin film transistor liquid crystal display (Thin Film Transistor Liquid Crystal Display), also known as a TFT color display.

[0070] Figure 3 The structural schematic diagram of a body fat scale provided by the embodiments of the present application is shown; as Figure 3 As shown, the body fat scale is a four-electrode body fat scale 100. It should be understood that the four-electrode body fat scale 100 includes a control unit, an identity recognition unit 110, a measurement unit, a display unit, a storage unit, a wireless communication unit and a power supply unit, etc.; among them, the ground electrode module in the measurement unit includes four electrode plates, which are arranged on the first surface 130 of the scale body in the four-electrode body fat scale 100.

[0071] The identification unit 110 includes a first fingerprint identification module 111 and a second fingerprint identification module 112. The first fingerprint identification module 111 is disposed on the first surface 130 of the body fat scale and includes a first fingerprint identification panel, which is an angle-adjustable panel. The second fingerprint identification module 112 is disposed on the side of the body fat scale and includes a second fingerprint identification panel, which has a first inclination angle with the vertical plane.

[0072] It should be understood that the plumb plane is the plane perpendicular to the horizontal plane, that is, the plane on which the plumb line lies. It should be understood that any object that is perpendicular to the ground, like a plumb line, is in the plumb direction. The plane that is perpendicular to the horizontal plane and the plane that is inclined to the front and side are also plumb planes.

[0073] Figure 4 FIG. 1 shows a structural diagram of another body fat scale provided in an embodiment of the present application; FIG. Figure 4 As shown, the body fat scale is an eight-electrode body fat scale 200 , and the eight-electrode body fat scale 200 is structurally divided into a handle 210 and a scale body 220 .

[0074] The eight-electrode body fat scale 200 is divided into units, including a control unit, an identification unit, a measurement unit, a display unit, a storage unit, a wireless communication unit and a power supply unit; wherein, the electrode module in the measurement unit includes eight electrode sheets, four of which are arranged on the surface of the scale body 220, and the other four electrode sheets are arranged on the surface of the handle 210.

[0075] The identification unit includes a first fingerprint identification module 211 and a second fingerprint identification module 221. The first fingerprint identification module 211 is arranged on the surface of the handle. It should be understood that when the eight-electrode body fat scale 200 is in Figure 4 In the illustrated configuration, the surface of the scale body 220 and the surface of the handle 210 are on the same horizontal plane. This also means that the first fingerprint recognition module 211 is located on the first surface of the body fat scale. The first fingerprint recognition module 211 includes a first fingerprint recognition panel, which is an adjustable angle panel. The second fingerprint recognition module 221 is located on the side of the scale body and includes a second fingerprint recognition panel, which has a first inclination angle with the vertical plane.

[0076] For the sake of convenience, the following embodiments of this application all take the eight-electrode body fat scale as an example. Figure 5 Show Figure 4 A schematic diagram of a body fat scale in another form, such as Figure 5As shown, the handle 210 and the scale body 220 in the eight - electrode body fat scale 200 are connected by a retractable cable, and the retractable cable is used for structural connection and communication connection. It should be understood that in Figure 5 the state shown, the surfaces of the scale body 220 and the handle 210 are not on the same horizontal plane, but are respectively in two intersecting planes. Further, the user can hold both sides of the handle 210 with both hands and raise the handle to the front or chest; at this time, the surface of the handle 210 with the display screen is used as the first surface, and the surface of the scale body 220 is used as the second surface.

[0077] It should be understood that in order to make the first fingerprint recognition panel an adjustable - angle panel, the first fingerprint recognition module further includes a universal connection mechanism. The universal connection mechanism is used to movably connect the first fingerprint recognition panel to the body fat scale, and the first fingerprint recognition panel is provided with a first sensor.

[0078] Figure 6 The figure shows a schematic diagram of fingerprint entry through the first fingerprint recognition module in the body fat scale provided by the embodiment of the present application. Taking the user's left thumb for fingerprint image entry as an example, as Figure 6 shown in (a) of the figure, the user's left thumb faces the first surface of the handle 210. At this time, the first fingerprint recognition panel is relatively parallel to the first surface; as Figure 6 shown in (b) of the figure, the user's left thumb faces the side of the handle 210 that is away from the display. At this time, there is a second inclination angle between the first fingerprint recognition panel and the first surface, which is convenient for the user to enter the fingerprint image. That is to say, by adding a universal connection mechanism in the first fingerprint recognition module, the angle adjustment of the first fingerprint recognition panel in the vertical angle is realized.

[0079] It should be understood that different users have different dominant hands. Some users are used to using their left hands, and some users are used to using their right hands. Therefore, the universal connection mechanism in the first fingerprint recognition module can also adjust the left - right adjustment of the first fingerprint recognition panel in the horizontal direction.

[0080] Figure 7 The figure shows a schematic diagram of fingerprint entry through the first fingerprint recognition module in the body fat scale provided by the embodiment of the present application. Taking the user's right thumb for fingerprint image entry as an example, as Figure 7 shown in (a) of the figure, the user's right thumb faces the first surface of the handle 210. At this time, the first fingerprint recognition panel is relatively parallel to the first surface; as Figure 7 shown in (b) of the figure, the user's right thumb faces the side of the handle 210 that is away from the display. At this time, there is a second inclination angle between the first fingerprint recognition panel and the first surface, which is convenient for the user to enter the fingerprint image. That is to say, by adding a universal connection mechanism in the first fingerprint recognition module, the angle adjustment of the first fingerprint recognition panel in the vertical angle is realized.

[0081] It should be understood that the user can manually rotate the fingerprint recognition panel (ie, the first fingerprint recognition panel or the second fingerprint recognition panel) in each fingerprint recognition module (ie, the first fingerprint recognition module or the second fingerprint recognition module) to adjust it to an angle suitable for the user.

[0082] Among them, the universal connection mechanism can be one of the structures such as a universal shaft, a spherical joint structure, a cross-axis universal joint, and a ball cage universal joint. The universal connection structure enables the first fingerprint recognition panel to rotate relatively freely within a larger angle range to meet the needs of users to input fingerprint images through the first fingerprint recognition panel under different gestures. Different users can enter fingerprint images through the first fingerprint recognition panel and the corresponding first sensor at a comfortable angle for their fingers.

[0083] Figure 8 A schematic diagram showing a first fingerprint recognition module in a body fat scale provided by an embodiment of the present application is shown. Figure 5 The cross-sectional view at HH' is as follows: Figure 8 As shown, the first fingerprint recognition module 211 includes a first fingerprint recognition panel 211A and a spherical joint structure 211C. The spherical joint structure 211C is used to connect the first fingerprint recognition panel 211A with an adjustable angle in the body fat scale, that is, the spherical joint structure 211C is used to connect the first fingerprint recognition panel 211A with an adjustable angle in the handle.

[0084] It should be understood that the first fingerprint recognition module can also include a fixed base, a rotating shaft, and a first fingerprint recognition panel; the fixed base is used to fix the entire device on the body fat scale, and the rotating shaft connects the fixed base and the fingerprint recognition panel body so that the panel can rotate around the axis.

[0085] In some embodiments, the universal connection mechanism further includes an angle sensor, which is used to accurately measure and feedback the rotation angle of the first fingerprint recognition panel.

[0086] In some embodiments, the universal joint mechanism further includes an angle sensor and a motor, wherein the angle sensor is used to accurately measure and provide feedback on the rotation angle of the first fingerprint recognition panel, and the motor is used to adjust the universal joint mechanism to rotate according to the angle command issued by the control unit.

[0087] It should be understood that the fingerprint recognition panel is rotated by the motor-driven universal connection mechanism to achieve automatic angle adjustment. This structure can achieve multi-angle adjustment within a wide range to meet the needs of different users (such as different heights, different usage habits, etc.) to enter fingerprints in a comfortable posture.

[0088] In the embodiment of the present application, the first fingerprint recognition panel is designed to be angle-adjustable so that the first fingerprint recognition module is arranged at Figure 3The surface of the body fat scale shown, or the surface of the handle in the body fat scale shown in Figure 4 can allow users to easily place their fingers on the fingerprint recognition module, reducing the difficulty and time of operation and improving the efficiency of obtaining fingerprint images.

[0089] As Figure 3 、 Figure 4 shown, the second fingerprint recognition module is arranged on the side of the body fat scale or the side face of the weighing body in the body fat scale. For the convenience of users, there is a first inclination angle between the second fingerprint recognition panel in the second fingerprint recognition module and the vertical plane. It should be understood that the first inclination angle can be set to incline towards the center of the body fat scale or the weighing body, which is convenient for the inclination angle when the user's finger naturally hangs down and the finger is bent inward. Similarly, it can allow users to easily place their fingers on the fingerprint recognition module, reducing the difficulty and time of operation and improving the efficiency of obtaining fingerprint images.

[0090] In some embodiments, the first inclination angle can also be set to incline towards a direction outside the body fat scale or the weighing body. For users who are young and / or short in height, it can also allow users to easily place their fingers on the fingerprint recognition module, reducing the difficulty and time of operation and improving the efficiency of obtaining fingerprint images.

[0091] Among them, a first sensor 211B is arranged in the first fingerprint recognition panel 211A. It should be understood that the first sensor 211B can be an optical fingerprint sensor or a capacitive fingerprint sensor.

[0092] In some embodiments, in order to improve the recognition speed and accuracy of fingerprint images, the first sensor is a fusion sensor, including an optical fingerprint sensor and a capacitive fingerprint sensor, combining the optical fingerprint sensor and the capacitive fingerprint sensor. The optical fingerprint sensor can quickly obtain the overall image of the fingerprint, while the capacitive fingerprint sensor can more precisely capture the detailed features of the fingerprint, such as the ridges and valleys of the fingerprint; combining multiple sensors can not only improve the speed of fingerprint recognition but also enhance the accuracy. Even when the finger has slight stains or is wet, it can quickly and accurately obtain fingerprint images.

[0093] In some embodiments, the body fat scale can also obtain environmental information (such as temperature, humidity, light intensity, etc.) through an application program on a terminal device. Therefore, an adaptive fusion algorithm can be adopted to dynamically adjust the weights of the data of the two sensors according to different environmental conditions (such as humidity, light intensity) and user fingerprint characteristics (such as dryness and wetness, clarity of fingerprint patterns). For example, in an environment with higher humidity, increase the weight of the data of the capacitive fingerprint sensor because the image quality of the optical fingerprint sensor may decline in a humid environment. By continuously adjusting the weights, the fused fingerprint features can be more accurate, improving the recognition accuracy.

[0094] The first fingerprint recognition module further includes a first indicator light, and the first indicator light is disposed on the first surface of the body fat scale. When the first indicator light emits light in a preset mode, it is used to remind the user that the corresponding first fingerprint recognition module is the most suitable fingerprint recognition module for use. By keeping the first indicator light on or flashing, the user is guided to use this module to input fingerprints.

[0095] For example, the first indicator light can be set on the periphery of the first fingerprint recognition panel, or can be set on the periphery of the first fingerprint recognition module on the first surface; the form of the first indicator light can be annular, dot-shaped, strip-shaped, etc.; the embodiments of the present application do not limit the position and form of the first indicator light.

[0096] Figure 9 The structural schematic diagram of another body fat scale provided by the embodiments of the present application is shown, as Figure 9 shown, the first indicator light 211D of the first fingerprint recognition module 211 is set on the periphery of the first fingerprint recognition module 211 and is in a ring shape.

[0097] In some embodiments, the first fingerprint recognition module further includes an LED light source. The LED light source emits red light and infrared light. These lights penetrate the skin and blood vessels. When the blood flows, oxyhemoglobin and deoxyhemoglobin will absorb these two lights in different proportions. After the light passes through the blood vessels and is partially absorbed, it is received by the first sensor in the first fingerprint recognition module. Furthermore, by optimizing the signal acquisition and processing circuit of the first sensor, the change of the pulse wave and the absorption degree of the light can be measured.

[0098] Inside the first fingerprint recognition panel of the first fingerprint recognition module, an LED that emits red light and infrared light is integrated as the light source for heart rate measurement.

[0099] It should be understood that the first sensor includes an optical fingerprint sensor and a capacitive fingerprint sensor, that is, the first fingerprint recognition module has a photoelectric detection function for capturing fingerprint images. After adding the LED light source, the existing photoelectric sensor can be used to detect the absorption and reflection changes of red light and infrared light when passing through the finger blood vessels, and then obtain the photoplethysmogram signal related to the heart rate.

[0100] It should be understood that the second fingerprint recognition panel is provided with a second sensor, and the second sensor can be an optical fingerprint sensor or a capacitive fingerprint sensor; the second sensor can also include an optical fingerprint sensor and a capacitive fingerprint sensor.

[0101] The second fingerprint recognition module further includes a second indicator light. When the second indicator light emits light in a preset mode, it is used to remind the user that the corresponding second fingerprint recognition module is the most suitable fingerprint recognition module for use. By keeping the second indicator light on or flashing, the user is guided to use this module to input fingerprints. The embodiments of the present application do not limit the position and form of the second indicator light.

[0102] It should be understood that the second fingerprint recognition module is arranged on the side of the body fat scale and is not easily noticed from the user's perspective (the perspective of looking down). Therefore, the second indicator light is arranged on the surface of the body fat scale and corresponds to the second fingerprint recognition module on the side. As Figure 9 shown, the second indicator light 221A in the second fingerprint recognition module 221 is arranged on the surface of the scale body, which is convenient for the user to view and is connected to the second fingerprint recognition module, showing a strip shape.

[0103] In some embodiments, the second fingerprint recognition module further includes an LED light source. For the description of the LED light source in the first fingerprint module, reference can be made to the above, and details will not be elaborated here.

[0104] In some embodiments, a circle of second fingerprint recognition modules can be arranged around the body fat scale, and each second fingerprint recognition module is correspondingly provided with a second indicator light. When it is determined that the target fingerprint recognition module is one of the second fingerprint recognition modules, the corresponding second indicator light will automatically light up to guide the user to quickly place the finger in the correct position, so as to obtain the fingerprint image more quickly. At the same time, different colors of lights can be set according to different users to increase the personalized experience.

[0105] It should be understood that an LED light source is added to each fingerprint recognition module to monitor the user's heart rate. There are also differences in the heart rates of different users. Therefore, on the premise of not affecting the fingerprint recognition function, adding the monitoring of the user's heart rate data can be used to obtain the user's historical usage data and can also be used to optimize the preset recognition model, etc.

[0106] In some other embodiments, the identity recognition unit further includes a face recognition module. The face recognition module is arranged on the surface of the handle, and a camera is arranged at the top of the display in the handle, which can accurately capture the features of the user's face. For example, the camera can utilize infrared fill light technology to clearly and accurately capture the features of the user's face even in a dim environment.

[0107] When the user stands on the body fat scale, the camera is started to obtain the user's face image. The face recognition algorithm carried in the control unit has been deeply optimized and can quickly recognize the user's identity, forming a double insurance with the fingerprint recognition module, greatly improving the accuracy and convenience of identity recognition. Whether the user is sleepy in the morning or sweating profusely after exercise, the face recognition module can work stably, bringing a smooth use experience to the user and easily realizing accurate data matching when multiple users share the body fat scale.

[0108] The following will be combined with Figures 10 to 11 to describe in detail the control method of the body fat scale provided by the embodiments of the present application. It should be noted that if there are substantially the same results, the method of the present application does not depend on Figures 10 to 11It is limited to the shown process sequence. An embodiment of the present application also provides a control method for a body fat scale, which is applied to the above-mentioned body fat scale. Figure 10 The flowchart of a control method for a body fat scale provided by an embodiment of the present application is shown, as Figure 10 shown, the control method of the body fat scale includes the following steps:

[0109] S310. When the number of times the body fat scale is used reaches a preset number and it is recognized that the user is in a stable state on the body fat scale, obtain the first initial data of the user.

[0110] Among them, the first initial data includes bioimpedance data and pressure distribution data. It should be understood that the bioimpedance data is obtained through the electrode module, and the pressure distribution data is obtained through the pressure sensor module.

[0111] The preset first number is determined by the R & D personnel of the body fat scale during the R & D process. For example, the preset number can be 10 times, 20 times. It should be understood that the preset number will be controlled to be as small as possible while meeting the learning ability to improve the user experience.

[0112] It should be understood that from the first use until the number of uses reaches the preset number, the body fat scale is in the learning mode. By learning different users of the body fat scale, historical usage data and user preference analysis data of the user are obtained.

[0113] A first-use guidance process is set in the body fat scale. When the body fat scale is used for the first time, through the application on the mobile phone, a communication connection between the mobile phone and the eight-electrode body fat scale is established, and the user is required to input basic data such as gender, age, height, etc. in the application for better subsequent data matching and analysis. In addition, the user is prompted to use each fingerprint recognition module in turn to input fingerprint images, record the user's historical usage data such as fingerprint images and the corresponding module usage situations, and analyze the features in the fingerprint images collected by different fingerprint recognition modules, such as fingerprint image clarity, fingerprint patterns, the number of feature points, etc. For example, on a body fat scale with three fingerprint recognition modules (for example, a first fingerprint recognition module and two second fingerprint recognition modules), the user inputs fingerprint images according to the prompt on each fingerprint recognition module, and a total of 3 fingerprint images are input, and the features in each collected fingerprint image are analyzed.

[0114] During the second to the Nth (the preset number is N + 1 times) use of the body fat scale, the usage frequency of the fingerprint recognition module is recorded through the historical usage data, etc., which is convenient for learning which fingerprint recognition module the user is used to using for fingerprint input, the user's pressure distribution data, the user's heart rate data, etc. during the subsequent optimization of the preset recognition model.

[0115] It should be understood that each sensor in each fingerprint recognition module is initialized before each use of the body fat scale. For example, for an optical fingerprint sensor, parameters such as exposure time and image resolution are set; for a capacitive fingerprint sensor, the induction capacitance range is calibrated, etc.

[0116] In some embodiments, the preset duration of using the body fat scale can be determined by the R & D personnel of the body fat scale. For example, the preset duration can be one week or one month.

[0117] It should be noted that the stable state of the user on the body fat scale is determined by evenly distributing multiple pressure sensors inside the body fat scale. When the user steps on the body fat scale, the pressure sensors start to work. If within a certain period of time (such as 1 - 3 seconds), the pressure values detected by each pressure sensor change within a very small range (for example, the fluctuation does not exceed 5% up and down), and the overall pressure distribution presents a relatively uniform and stable state, it can be determined that the user's posture is stable. For example, when the user stands steadily on both feet, the pressure distribution data measured by the pressure sensors on the left and right sides and the front and back positions are stable, indicating that the user's posture is stable.

[0118] In some embodiments, an acceleration sensor can also be embedded in the body fat scale to sense the acceleration changes in all directions of the body fat scale. When the user stands on the scale, the acceleration sensor collects data in real time. When the acceleration changes in the X, Y, and Z axes all approach zero and last for a certain period of time (such as 1 - 2 seconds), it means that the body fat scale is in a stationary state, and it can be determined that the user is in a stable state. For example, the user's body no longer shakes, the body fat scale has no displacement and tilt, and the data of the acceleration sensor tends to be stable.

[0119] In other embodiments, the data of the pressure sensor and the acceleration sensor can be fused, analyzing both the stability of the pressure distribution data and combining the acceleration change situation for weighted calculation and logical judgment. Through in-depth analysis of multi-source data by a preset algorithm, it can more accurately determine whether the user is in a stable state, effectively avoiding misjudgment that may occur with a single sensor and ensuring the accuracy of the measurement results.

[0120] S320: Input the first initial data, historical usage data, and user preference analysis data into a preset recognition model to determine the target fingerprint recognition module suitable for the user.

[0121] It should be understood that the historical usage data and user preference analysis data are obtained, analyzed, and stored when the number of uses of the body fat scale has not reached the preset quantity.

[0122] Therefore, after obtaining the first initial data, the first initial data, historical usage data, and user preference analysis data are jointly used as input data and input into a preset recognition model, and a target fingerprint recognition module adapted to the user can be obtained. The target fingerprint recognition module is the first fingerprint recognition module or the second fingerprint recognition module.

[0123] It should be understood that the preset recognition model in step 320 is a trained model and is optimized by the usage data corresponding to the number of user usages not reaching the preset quantity.

[0124] The preset recognition model is a machine learning model, and algorithms such as decision trees and neural networks can be selected for construction. Taking the neural network model as an example, it consists of an input layer, a hidden layer, and an output layer. The input layer is responsible for receiving the first initial data, historical usage data, and user preference analysis data. These data undergo complex operations and feature extraction by a large number of neurons in the hidden layer, and finally, the result of the target fingerprint recognition module adapted to the user is output in the output layer.

[0125] The body fat scale learns the user's usage habits, fingerprint placement methods, and fingerprint entry preferences, etc. through the preset recognition model, and automatically adjusts the parameters and strategies of the preset recognition model to adapt to the operation characteristics of different users, so as to obtain the user's fingerprint image more quickly and accurately in subsequent use.

[0126] The preset recognition model deployed on the body fat scale is a trained model, and its input data includes at least the first initial data, historical usage data, and user preference analysis data, and may also include heart rate data, etc.

[0127] During the research and development of the body fat scale, a large number of samples containing various types of data and the corresponding target fingerprint recognition module annotations are collected. The sample data is input into the preset recognition model, and the preset recognition model processes the input data according to the rules of the selected algorithm and outputs a prediction result. Then, the prediction result is compared with the actual annotation, and the difference between the two is measured by calculating the loss function. Based on the loss function value, the connection weights of the neurons in each layer of the preset recognition model are adjusted using the backpropagation algorithm to reduce the loss value. After multiple rounds of iterative training, the preset recognition model is continuously optimized until satisfactory accuracy, recall, and other indicators are achieved on the validation set, so that the preset recognition model can be used to determine the target fingerprint recognition module adapted to the user.

[0128] It should be understood that it is optimized by the usage data corresponding to the number of user usages not reaching the preset quantity; that is to say, when the number of usages of the body fat scale does not reach the preset quantity and it is recognized that the user is in a stable state on the body fat scale, the model is optimized. Before 310, the control method of the body fat scale further includes the following (1)-(3):

[0129] (1) Obtain the user's second fingerprint image and second initial data, where the second fingerprint image is an image collected by the first fingerprint recognition module and / or the second fingerprint recognition module.

[0130] It should be understood that if it is the first time to use the body fat scale, the second fingerprint image includes the image collected by the first fingerprint recognition module and the image collected by the second fingerprint recognition module.

[0131] If it is the second time to the Nth time (the preset number of times is N + 1 times) to use the body fat scale, the second fingerprint image includes the image collected by the first fingerprint recognition module and / or the image collected by the second fingerprint recognition module.

[0132] For the convenience of distinction, the measurement data obtained by the measurement unit when the number of times of using the body fat scale does not reach the preset quantity is called the second initial data; the measurement data obtained by the measurement unit when the number of times of using the body fat scale reaches the preset quantity is called the first initial data.

[0133] Therefore, the second initial data also includes bioimpedance data and pressure distribution data. It should be understood that the bioimpedance data is obtained by the electrode module, and the pressure distribution data is obtained by the pressure sensor module.

[0134] (2) Analyze the second fingerprint image and the second initial data to obtain the user's historical usage data and user preference analysis data.

[0135] In the case where the number of times of using the body fat scale does not reach the preset quantity, optimize the preset recognition model. Therefore, in this process, it is necessary to analyze the second fingerprint image and the second initial data: First, record the corresponding fingerprint recognition module, usage time, etc. for each use, and extract the features of the second fingerprint image to identify information such as the clarity of the user's unique fingerprint image, fingerprint pattern, and the number of feature points; it is convenient to optimize the preset recognition model to be more suitable for the user through the second fingerprint image. Furthermore, obtain the user's historical usage data and user preference analysis data.

[0136] Among them, by associating the current second fingerprint image with information such as the second initial data and the corresponding fingerprint recognition module, comparing, integrating, and statistically analyzing it with the data accumulated by the same fingerprint recognition in the past, the user's historical usage data can be obtained, covering information such as the usage frequency in different time periods.

[0137] During multiple uses by the user, various preference settings during the use process are recorded, such as the commonly used fingerprint recognition module, whether the user is accustomed to automatic angle adjustment, etc. The data obtained each time is deeply integrated with the stored historical usage data. Through the comparison and correlation analysis of data at different stages, the body fat scale can further understand the user's behavior patterns and preferences, analyze the selection tendencies of body fat scale functions, fingerprint recognition modules, etc., improve the user preference analysis data, and thus obtain comprehensive and detailed user preference analysis data.

[0138] (3) Optimize the preset recognition model based on the second fingerprint image, second initial data, historical usage data, and user preference analysis data to obtain an optimized preset recognition model.

[0139] In the daily usage scenario of the body fat scale, the intelligence of the body fat scale will gradually progress as the number of user uses increases. Whenever the user completes a measurement, the body fat scale will collect a second fingerprint image to accurately identify the user's identity.

[0140] The body fat scale uses the second fingerprint image, second initial data, historical usage data, and user preference analysis data as input data to comprehensively optimize the preset recognition model. In continuous iterative operations, the preset recognition model gradually adjusts its internal parameters to improve the recognition accuracy of the user's individual characteristics and behavior habits, thereby obtaining an optimized preset recognition model. Subsequently, during the subsequent use process, a more accurate target fingerprint recognition model is provided for the user to use, providing personalized services for the user.

[0141] In some embodiments, after the control unit determines the target fingerprint recognition module suitable for the user, based on the target fingerprint recognition module, a prompt message is generated, and the prompt message is used to instruct the user to enter a fingerprint through the target fingerprint recognition module. It should be understood that the prompt message can be presented in different forms to achieve the purpose of prompting the user.

[0142] In some embodiments, when the prompt message indicates that the first fingerprint recognition module is the target fingerprint recognition module, the first indicator light is driven to emit light based on a preset mode; when the prompt message indicates that the second fingerprint recognition module is the target fingerprint recognition module, the first indicator light is driven to emit light based on a preset mode; wherein, the preset mode includes a constant light mode or a flashing mode.

[0143] In some embodiments, the body fat scale further includes a voice unit, including a speaker. The prompt information can also be used to remind the user to place their finger on the specified target fingerprint recognition module in a voice manner, and guide the user on how to correctly place their finger to obtain the best recognition effect. For users with poor eyesight or those who are not familiar with the operation, the voice prompt can greatly improve the convenience of obtaining fingerprint images. In some other embodiments, the voice unit further includes a microphone, and voice interaction with the user is realized by adding a corresponding voice recognition model to the control unit.

[0144] In some embodiments, "The XX fingerprint recognition area is the best usage area" is displayed on the display of the body fat scale or the corresponding application. It should be understood that the XX fingerprint recognition area here corresponds to the target fingerprint recognition module on the body fat scale, and the text prompting the user is easy to understand.

[0145] It should be understood that during the use of the preset recognition model, it will continuously learn and optimize, constantly learning the changes in the user's fingerprint features, usage habits, preferences, etc., dynamically optimizing the preset recognition model, and updating the recommended results of the best fingerprint recognition module.

[0146] S330. In response to the user's fingerprint entry operation through the target fingerprint recognition module, obtain the user's first fingerprint image and determine the first identifier corresponding to the first fingerprint image.

[0147] Among them, the target fingerprint recognition module is the first fingerprint recognition module or the second fingerprint recognition module, and the user is guided to enter the fingerprint image through the target fingerprint recognition module.

[0148] It should be noted that the first fingerprint image is identified and analyzed based on the user information stored in the body fat scale. If the first fingerprint image is recognized, determine the first identifier corresponding to the first fingerprint image.

[0149] It should be understood that the terminal device will send the user's basic data to the body fat scale and match it with the second fingerprint image obtained when the number of uses has not reached the preset quantity, and store it in the body fat scale; in order to facilitate the distinction of different users, an identifier can be set for each user. When it is recognized that the first fingerprint image belongs to a stored user, use the identifier of this user as the first identifier corresponding to the first fingerprint image.

[0150] In some embodiments, after the first fingerprint image is entered, during the process of recognizing the first fingerprint image, the user can be informed of the recognition status in different forms. For example, a progress bar is displayed on the display of the body fat scale, or a push voice prompt "Recognizing fingerprint, please wait" is given through the application. If the first fingerprint image is recognized successfully, "Recognition successful, measuring index data" can be displayed; if the recognition fails, specific reason prompts can also be given, such as "Fingerprint is blurred, please re-place" or "This fingerprint is not registered, please register first".

[0151] S340. Based on the first identifier, establish a matching relationship between the first initial data and the basic data corresponding to the first identifier.

[0152] It should be understood that the first identifier is used to identify the user. Furthermore, based on the first identifier, a matching relationship between the first initial data and the basic data corresponding to the first identifier can be established, facilitating subsequent data analysis and the determination of indicator data.

[0153] S350. Based on the first initial data and the basic data corresponding to the first identifier, determine the indicator data of the user.

[0154] For the control method of the body fat scale provided by the embodiments of the present application, when the usage times of the body fat scale reach the preset number and it is recognized that the user is in a stable state on the body fat scale, obtain the first initial data of the user; input the first initial data, the corresponding historical usage data, and the user preference analysis data into a preset recognition model to determine the target fingerprint recognition module suitable for the user; in response to the user entering a fingerprint operation through the target fingerprint recognition module, obtain the first fingerprint image of the user and determine the first identifier corresponding to the first fingerprint image; based on the first identifier, establish a matching relationship between the first initial data and the basic data corresponding to the first identifier, learn the user's usage habits, etc. during the user's use process to adapt to the operation characteristics of different users and quickly and accurately determine the user identity.

[0155] In some embodiments, after obtaining the first fingerprint image, during the process of its recognition and analysis, there may still be unrecognized situations. To improve the fault tolerance rate of the solution, user experience, etc. Figure 11 shows a schematic flowchart of the analysis in a control method of a body fat scale provided by the embodiments of the present application, as Figure 11 shown, after step 330 of obtaining the first fingerprint image of the user in response to the user entering a fingerprint operation through the target fingerprint recognition module, the following steps are further included:

[0156] S410. Based on the user information stored in the body fat scale, perform recognition and analysis on the first fingerprint image.

[0157] It should be understood that the terminal device will send the user's basic data to the body fat scale and match it with the second fingerprint image obtained when the usage times do not reach the preset number, and store it in the body fat scale; to facilitate the distinction of different users, an identifier can be set for each user. When it is recognized that the first fingerprint image belongs to a stored user, use the identifier of this user as the first identifier corresponding to the first fingerprint image; when it is recognized that the first fingerprint image does not belong to a stored user, it means that the first fingerprint image is not recognized.

[0158] S420. If the first fingerprint image is not recognized, analyze the matching degree between the features of the first fingerprint image and the fingerprint features collected by the first fingerprint recognition module and the second fingerprint recognition module respectively to obtain the corresponding similarity.

[0159] It should be understood that when the fingerprint image currently entered by the user fails to be recognized, the switching process of the fingerprint recognition module will be started. Quickly retrieve from the storage unit the database containing the user's fingerprint features recorded in the previously optimized preset recognition model. This database stores in detail various feature information of the user when collecting fingerprints in each fingerprint recognition area, including the clarity of fingerprint patterns, the number of feature points, etc.

[0160] Using the similarity matching algorithm, compare the features of the currently unrecognized fingerprint image (i.e., the first fingerprint image) with the fingerprint features collected by each fingerprint recognition module one by one, and calculate the similarity between each fingerprint recognition module and the first fingerprint image. For example, use the Euclidean distance algorithm or the cosine similarity algorithm to quantitatively evaluate the similarity between fingerprint features.

[0161] After calculating the similarity, sort each fingerprint recognition module in descending order of the similarity score to obtain the similarity ranking.

[0162] S430. Take the fingerprint recognition module with the highest similarity as the updated target fingerprint recognition module.

[0163] It should be understood that the fingerprint recognition module with the highest similarity score in the similarity ranking is the fingerprint recognition module that best matches the current user's fingerprint features, and then update the target fingerprint recognition module.

[0164] And generate corresponding prompt information to prompt the user. For example, send a clear prompt to the user through the display of the body fat scale or the connected application. The prompt content can include information about the recommended switched fingerprint recognition module, and can also briefly explain the reason for the recommendation, such as "The fingerprint pattern features of yours match the collected data of the XX fingerprint recognition module the most. It is recommended to switch to the XX fingerprint recognition module for recognition".

[0165] It should be understood that after updating the target support module recognition module, execute the above step 330 again.

[0166] To ensure that the user can conveniently perform the fingerprint recognition module switching operation, corresponding interaction guidelines can also be provided on the prompt interface. For example, generate an image on the application, and this image is used to prompt the user the position corresponding to the target fingerprint recognition module; on the display of the body fat scale, guide the user to place the finger on the recommended fingerprint recognition module through the effect of the indicator light emitting. In addition, the operation data of each fingerprint recognition module switch by the user can also be recorded to facilitate subsequent further optimization of the preset recognition model and continuously improve the recognition efficiency and accuracy.

[0167] It should be understood that in some embodiments, the universal joint mechanism in the first fingerprint recognition module further includes an angle sensor and a motor. At this time, after determining the target fingerprint recognition module suitable for the user in step 320, the following (1)-(3) are further included:

[0168] (1) If the target fingerprint recognition module is the first fingerprint recognition module, analyze the historical usage data to determine the target angle of the first fingerprint recognition panel.

[0169] Among them, the historical usage data includes the usage frequency of the first fingerprint recognition module.

[0170] Referring to the above description of the acquisition and analysis of historical usage data, it will not be elaborated here.

[0171] (2) When the user enters a fingerprint, obtain the initial angle corresponding to the first fingerprint recognition panel through the angle sensor. The angle sensor is used to accurately measure and feedback the rotation angle of the first fingerprint recognition panel.

[0172] (3) Control the motor to adjust the angle of the first fingerprint recognition panel to the target angle.

[0173] For the first fingerprint recognition panel with an adjustable angle, according to the position and angle of the user's fingerprint placement, the panel angle can be adjusted through the motor drive algorithm built in the control unit, that is, the first fingerprint recognition panel corresponding to the first sensor is adjusted to the angle corresponding to the user's habit, so as to make the first sensor fit better with the user's finger.

[0174] In some embodiments, it is also possible to detect the deviation between the placement angle of the user's finger and the current angle of the first fingerprint recognition panel, and adjust the first fingerprint recognition panel accordingly. For example, when it is detected that the deviation between the placement angle of the user's finger and the current angle of the panel exceeds 15°, calculate the angle value that needs to be adjusted, and control the motor to adjust the panel to a suitable angle to improve the fingerprint collection quality and recognition success rate.

[0175] When controlling the motor to adjust the angle of the first fingerprint recognition panel, the user can be informed that the angle adjustment is in progress through the flashing of the first indicator light or voice prompt. For example, issue a voice prompt of "The angle of the fingerprint recognition panel is being adjusted, please keep your finger placement stable", and at the same time, the first indicator light around the fingerprint recognition area flashes quickly to let the user know the current operation.

[0176] The control method of the body fat scale provided by the embodiment of the present application can also drive the fingerprint recognition panel to rotate through the motor-driven universal joint mechanism to achieve automatic angle adjustment. This structure can achieve multi-angle adjustment within a wide range, meeting the needs of different users (such as different heights, different usage habits, etc.) to enter fingerprints in a comfortable posture.

[0177] In some embodiments, the body fat scale can also obtain heart rate data through the LED light source and the first sensor. That is, the first initial data also includes heart rate data, and the second initial data also includes heart rate data. Without affecting the fingerprint recognition function, a dedicated heart rate signal processing algorithm can be added to the control unit to filter, amplify, extract features, etc. from the collected photoplethysmogram signals, and calculate the user's heart rate data.

[0178] It should be understood that the heart rate data can be used to obtain the user's historical usage data.

[0179] It should be understood that for a body fat scale with an LED light source, the corresponding preset recognition model will include heart rate data during the training process. Since more input data can cover more features and variation situations, the preset recognition model can learn more complex and subtle patterns and rules, reduce the risk of overfitting, and improve the generalization ability on unknown data to improve the accuracy of the preset recognition model. Also, during the optimization and use of the preset recognition model, the heart rate data is used as input data to optimize the preset recognition model, so that the optimized preset recognition model can more accurately output the result of the target fingerprint recognition module adapted to the user.

[0180] Therefore, in step 320, the heart rate data is also used as input data and input into the preset recognition model to determine the target fingerprint recognition module adapted to the user.

[0181] In some embodiments, the control method of the body fat scale further includes: when the user's finger approaches the fingerprint recognition module but has not fully contacted, the weak signal captured by the sensor corresponding to the fingerprint recognition module (the first sensor or the second sensor) is preliminarily analyzed to determine whether it is a valid fingerprint, and the relevant recognition preparation work is started in advance, so that the fingerprint image can be obtained and recognized more quickly when the finger fully contacts.

[0182] It should be understood that the above is an example illustration of the application scenario and does not limit the application scenario of the present application in any way.

[0183] It should be understood that the above example illustration is to help those skilled in the art understand the embodiments of the present application, rather than to limit the embodiments of the present application to the specific numerical values or specific scenarios illustrated. Those skilled in the art can obviously make various equivalent modifications or changes according to the above example illustration, and such modifications or changes also fall within the scope of the embodiments of the present application.

[0184] As described above in conjunction with Figures 2 to 11 ,the body fat scale and the control method of the body fat scale in the embodiments of the present application have been described in detail. Next, in conjunction with Figure 12, a device embodiment of the present application will be described in detail. It should be understood that the control device in the embodiments of the present application can execute the control methods of various body fat scales in the foregoing embodiments of the present application, that is, the specific working processes of the following various products can refer to the corresponding processes in the foregoing method embodiments.

[0185] Figure 12 FIG. shows a schematic diagram of a control device for a body fat scale provided by an embodiment of the present application. As Figure 12 shown, the control device 500 of the body fat scale can execute Figures 10 to 11 the control method of the body fat scale shown; the control device 500 of the body fat scale includes an acquisition module 510, an analysis module 520, and an identification module 530, wherein:

[0186] The acquisition module 510 is configured to obtain first initial data of a user when the number of times of using the body fat scale reaches a preset number and it is recognized that the user is in a stable state on the body fat scale. The first initial data includes bioimpedance data and pressure distribution data.

[0187] The analysis module 520 is configured to input the first initial data, historical usage data, and user preference analysis data into a preset identification model to determine a target fingerprint identification module suitable for the user.

[0188] The identification module 530 is configured to, in response to a user's fingerprint entry operation through the target fingerprint identification module, obtain a first fingerprint image of the user and determine a first identifier corresponding to the first fingerprint image, where the target fingerprint identification module is a first fingerprint identification module or a second fingerprint identification module; the identification module 530 is further configured to establish a matching relationship between the first initial data and the basic data corresponding to the first identifier based on the first identifier, and determine the index data of the user.

[0189] Each module of the control device 500 of the body fat scale can respectively execute the corresponding steps in the foregoing method embodiments, so the modules will not be elaborated herein. For details, please refer to the descriptions of the corresponding steps above.

[0190] It should be noted that the control device 500 of the body fat scale is embodied in the form of functional modules. The term "module" here can be implemented in software and / or hardware forms, and no specific limitation is made thereto.

[0191] For example, a "module" can be a software program, a hardware circuit, or a combination of both that implements the above functions. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a dedicated processor, or a group of processors, etc.) for executing one or more software or firmware programs, and a memory, a merged logic circuit, and / or other suitable components that support the described functions.

[0192] Figure 13 The schematic structural diagram of a body fat scale provided by the present application is shown. Figure 13 The dotted line in it indicates that the unit or the module is optional. The body fat scale 600 can be used to implement the control method of the body fat scale described in the above method embodiments.

[0193] The body fat scale 600 includes one or more processors 601, and the one or more processors 601 can support the body fat scale 600 to implement the control method of the body fat scale in the method embodiments. The processor 601 can be a general-purpose processor or a special-purpose processor. For example, the processor 601 can be a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices.

[0194] The processor 601 can be used to control the body fat scale 600, execute software programs, and process the data of the software programs. The body fat scale 600 can also include a communication unit 605 for realizing the input (receiving) and output (sending) of signals.

[0195] The body fat scale 600 may include one or more memories 602, on which there is a program 604. The program 604 can be run by the processor 601 to generate instructions 603, so that the processor 601 executes the control method of the body fat scale described in the above method embodiments according to the instructions 603.

[0196] The present application also provides a computer program product. When the computer program product is executed by the processor 601, it implements the control method of the body fat scale in any method embodiment of the present application. The computer program product can be stored in the memory 602, for example, it is the program 604. After processes such as preprocessing, compilation, assembly, and linking, the program 604 is finally converted into an executable target file that can be executed by the processor 601.

[0197] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a computer, it implements the control method of the body fat scale in any method embodiment of the present application. The computer program can be a high-level language program or an executable target program. The computer-readable storage medium is, for example, the memory 602.

[0198] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for example, the division of units is only a logical function division, and there can be other division methods in actual implementation. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0199] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A body fat scale, characterized in that, It includes an identity recognition unit, a measurement unit, a control unit and a wireless communication unit. The control unit is communicatively connected to the identity recognition unit, the measurement unit and the wireless communication unit, wherein: The identity recognition unit includes a first fingerprint recognition module and a second fingerprint recognition module. Among them, the first fingerprint recognition module is arranged on the first surface of the body fat scale, and the first fingerprint recognition module includes a first fingerprint recognition panel with an adjustable angle and a first sensor; the second fingerprint recognition module is arranged on the side of the body fat scale, and the second fingerprint recognition module includes a second fingerprint recognition panel and a second sensor, and there is a first inclination angle between the second fingerprint recognition panel and the plumb plane; The measurement unit includes an electrode module and a pressure sensor module; The control unit is configured to: When the usage times of the body fat scale reach a preset number and it is recognized that the user is in a stable state on the body fat scale, obtain the first initial data of the user, and the first initial data includes bioimpedance data and pressure distribution data; Input the first initial data, historical usage data, and user preference analysis data into a preset recognition model to determine the target fingerprint recognition module suitable for the user; In response to the user entering a fingerprint operation through the target fingerprint recognition module, obtain the first fingerprint image of the user and determine the first identifier corresponding to the first fingerprint image, wherein the target fingerprint recognition module is the first fingerprint recognition module or the second fingerprint recognition module; Based on the first identifier, establish a matching relationship between the first initial data and the basic data corresponding to the first identifier, and determine the index data of the user.

2. The body fat scale according to claim 1, wherein, The first fingerprint recognition module further includes a universal connection mechanism, and the universal connection mechanism is used to movably connect the first fingerprint recognition panel to the body fat scale. The first fingerprint recognition panel is provided with the first sensor, and the second fingerprint recognition panel is provided with the second sensor.

3. The body fat scale according to claim 2, wherein The universal connection mechanism further includes an angle sensor and a motor. After the control unit determines the target fingerprint recognition module suitable for the user, it is further configured to: If the target fingerprint recognition module is the first fingerprint recognition module, analyze the historical usage data to determine the target angle of the first fingerprint recognition panel, wherein the historical usage data includes the usage frequency of the first fingerprint recognition module and the initial angle corresponding to the first fingerprint recognition panel obtained through the angle sensor when the user enters a fingerprint; Control the motor to adjust the angle of the first fingerprint recognition panel to the target angle.

4. The body fat scale according to claim 2, characterized in that After the control unit obtains the first fingerprint image of the user in response to the user entering a fingerprint operation through the target fingerprint recognition module, it is further configured to: Perform recognition and analysis on the first fingerprint image based on the user information stored in the body fat scale; If the first fingerprint image is not recognized, perform a matching degree analysis on the features of the first fingerprint image with the fingerprint features already collected by the first fingerprint recognition module and the second fingerprint recognition module respectively to obtain the corresponding similarity; The fingerprint recognition module with the highest similarity is used as the updated target fingerprint recognition module.

5. The body fat scale according to claim 1, wherein When the number of times the body fat scale is used has not reached the preset number and it is recognized that the user is in a stable state on the body fat scale, the control unit is further configured to: Obtain a second fingerprint image and second initial data of the user, where the second fingerprint image is an image collected by the first fingerprint recognition module and / or the second fingerprint recognition module; Analyze the second fingerprint image and the second initial data to obtain historical usage data and user preference analysis data of the user; Optimize the preset recognition model based on the second fingerprint image, the second initial data, the historical usage data, and the user preference analysis data to obtain the optimized preset recognition model.

6. The body fat scale according to claim 1, wherein The first fingerprint recognition module further includes a first indicator light, and the second fingerprint recognition module further includes a second indicator light; after the control unit determines the target fingerprint recognition module suitable for the user, it is further configured to: Generate a prompt message, where the prompt message is used to instruct the user to enter a fingerprint through the target fingerprint recognition module; When the prompt message indicates that the first fingerprint recognition module is the target fingerprint recognition module, drive the first indicator light to emit light based on a preset mode; When the prompt message indicates that the second fingerprint recognition module is the target fingerprint recognition module, drive the second indicator light to emit light based on a preset mode; Wherein, the preset mode includes a constant light mode or a flashing mode.

7. The body fat scale according to any one of claims 1 to 6, characterized in that, The first fingerprint recognition module further includes a first LED light source, and the first sensor includes an optical fingerprint sensor and a capacitive fingerprint sensor; The control unit is further configured to: Obtain the heart rate data of the user, where the heart rate data is used to obtain the historical usage data of the user, to optimize the preset recognition model, and to be input into the preset recognition model to determine the target fingerprint recognition module suitable for the user.

8. The body fat scale according to any one of claims 1 to 6, characterized in that, The body fat scale includes a handle and a scale body; The identity recognition unit further includes a face recognition module, the first fingerprint recognition module and the face recognition module are arranged on the second surface of the handle, and the second fingerprint recognition module is arranged on the side of the scale body.

9. The body fat scale according to any one of claims 1 to 6, characterized in that, The first sensor includes an optical fingerprint sensor and a capacitive fingerprint sensor, and the second sensor includes an optical fingerprint sensor and a capacitive fingerprint sensor.

10. A control method for a body fat scale, characterized in that, Applied to the body fat scale according to any one of claims 1 to 9 above, the control method of the body fat scale includes: When the number of times the body fat scale is used reaches the preset number and it is recognized that the user is in a stable state on the body fat scale, obtain the first initial data of the user, where the first initial data includes bioimpedance data and pressure distribution data; Input the first initial data, historical usage data, and user preference analysis data into the preset recognition model to determine the target fingerprint recognition module suitable for the user; In response to the user's fingerprint entry operation through the target fingerprint recognition module, obtain the first fingerprint image of the user and determine the first identifier corresponding to the first fingerprint image, where the target fingerprint recognition module is the first fingerprint recognition module or the second fingerprint recognition module; Based on the first identifier, establish a matching relationship between the first initial data and the basic data corresponding to the first identifier, and determine the index data of the user.

Images