Body fat monitoring management device and monitoring management method thereof

Abstract

The invention discloses a body fat monitoring management system and a monitoring management method thereof, various information such as the height, the weight, the gender and the body fat rate of a customer entering a fitness center is collected and input to a processor unit, and the processor unit provides an accurate and effective training plan for the customer, so that the training efficiency is improved, the body fat rate change of each training can be monitored in real time, and the user experience is improved. And the exercise efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of body fat monitoring; in particular, it relates to a body fat monitoring management system and a monitoring management method thereof. BACKGROUND

[0002] With the improvement of living standards, people pay more attention to their health and body management. Current fitness centers guide customers to exercise through fitness trainers and simple body fat scales. The monitoring and management accuracy of body fat is low, and the effect of fitness cannot be accurately monitored. The change of customer body fat rate cannot provide accurate and effective training plans. SUMMARY

[0003] The present application aims to overcome the shortcomings of the prior art and provide a body fat monitoring management system and a monitoring management method thereof to achieve the purpose of scientifically and efficiently detecting and managing body fat rate.

[0004] To solve the above problems, the technical scheme adopted by the present application is: a body fat monitoring management device, the system mainly comprises: an image acquisition module, a height measurement module, a body weight measurement module, a keyboard input module, a bionic electrode acquisition module, a processor unit, and a fitness device, which are arranged in a fitness center. The image acquisition module, the height measurement module, the body weight measurement module, the keyboard input module, and the bionic electrode acquisition module are connected to the input end of the processor unit through a wireless network, and the output end of the processor unit is connected to a display and voice module. The bionic electrode module is arranged on various fitness devices, and the processor unit is provided with a NEXT neural network module.

[0005] Further, the image acquisition module, the height measurement module, and the body weight measurement module are arranged at the entrance of the fitness center; the bionic electrode module is a two-group body fat detection component, which is a first body fat detection component and a second body fat detection component, and both groups of body fat detection components are composed of four electrodes. The first body fat detection component is arranged at the bottom of the fitness device, and the second body fat detection component is arranged at the upper part of the fitness device. The first body fat detection component and the second body fat detection component are respectively composed of four electrodes.

[0006] Further, the four electrodes of the first body fat detection component are symmetrically arranged, two of which are arranged at the position where the left foot contacts the fitness device, and the other two are arranged at the position where the right foot contacts the fitness device. The four electrodes of the second body fat detection component are also symmetrically arranged, two of which are arranged at the position where the left hand contacts, and the other two are arranged at the position where the right hand contacts.

[0007] A body fat monitoring management method, comprising: Step 1, when the customer enters the fitness center, the gender information, height information and weight information of the target customer are collected; the customer information is displayed at the front desk; Step 2, the front desk confirms the above information to the customer, and judges whether the customer is a historical customer or a new customer; a new customer file is established, the above collected information is confirmed and the customer age information is input; the historical customer file is called for the historical customer; Step 3; the customer file is input into the NEXT neural network model in the processor unit, and the NEXT neural network model outputs a predicted training plan; Step 4; the customer exercises according to the predicted training plan, and the bionic electrode acquisition module collects the body fat rate before training; Step 5; the bionic electrode acquisition module collects the body fat rate after the customer trains; the NEXT neural network model outputs a new predicted training plan according to the above information.

[0008] Further; the NEXT neural network model in the processor unit is obtained by inputting the initial model training data set of the customer's gender information, height information and weight information into the initial model to obtain the prediction model.

[0009] Further; the prediction model inputs the customer's age, height, weight, pre-exercise body fat rate, body fat rate, customer exercise information and customer training body fat change data into the prediction model before training, and classifies the customer information.

[0010] Further; the processor unit receives the body fat data after the customer exercises and compares it with the body fat data before the exercise to make a judgment, and prompts the customer to correct the training through the display voice module.

[0011] Further; when obtaining the customer information, the obtained customer information is desensitized, and the customer's name and identity information are deleted to realize the desensitization of the customer data.

[0012] The body fat monitoring management device and the monitoring management method thereof provided by the application can quickly and accurately plan reasonable exercise selection and exercise time for customers, accurately provide the optimal training plan, improve the training efficiency, can monitor the body fat rate change of each training in real time, and improve the exercise efficiency.

[0013] In order to make the above-mentioned purpose, effect and characteristics of the application more specific, the following is described with reference to the accompanying drawings: DETAILED DESCRIPTION Figure 1 is a system block diagram of an embodiment of the application; Figure 2 is a flowchart of the application; In Figures 1-2The system mainly comprises an image acquisition module 1, a height measurement module 2, a weight measurement module 3, a keyboard input module 4, a bionic electrode acquisition module 5, a processor unit 6 and a display voice module 7. DETAILED DESCRIPTION

[0014] As shown in FIG. 1, which is a system block diagram of the preferred embodiment of the present application, the system of the present application mainly comprises an image acquisition module 1, a height measurement module 2, a weight measurement module 3, a keyboard input module 4, a bionic electrode acquisition module 5, a processor unit 6 and a display voice module 7. The image acquisition module 1, the height measurement module 2, the weight measurement module 3, the keyboard input module 4 and the bionic electrode acquisition module 5 are connected to the input end of the processor unit 6 through a wireless network, and the output end of the processor unit 6 is connected to the display voice module 7.

[0015] In the present embodiment, the system is arranged in a fitness center. The image acquisition module 1 and the height measurement module 2 are arranged at the entrance of the fitness center. The image acquisition module 1 adopts a camera. When a customer enters the fitness center, the camera acquires the image of the customer to identify the gender of the customer. The height measurement module 2 arranged at both sides of the entrance measures the height of the customer. The weight measurement module 3 is arranged on the ground at the entrance to measure the weight information of the customer. After the customer information is confirmed, the front desk staff member confirms the above information with the customer and inputs the age information of the customer. After the above information is confirmed, the information is input to the processor unit 6 for information processing.

[0016] The bionic electrode monitoring module 5 is arranged on the fitness equipment in the fitness center, such as a treadmill, an elliptical machine, a stationary bicycle and the like. The bionic electrode acquisition module is two sets of body fat detection assemblies, namely a first body fat detection assembly and a second body fat detection assembly. Both sets of body fat detection assemblies are composed of four electrodes. The first body fat detection assembly is arranged at the bottom of the fitness equipment to contact and measure the position of the feet of the customer. The second body fat detection assembly is arranged at the upper part of the fitness equipment to contact and measure the position of the hands of the customer. The first body fat detection assembly and the second body fat detection assembly are each composed of four electrodes. The electrodes of the first body fat detection assembly and the second body fat detection assembly measure the voltage of the human body by releasing high-frequency micro-current. The voltage signal of the human body is converted into the resistance signal of the human body. The first body fat detection assembly and the second body fat detection assembly generate the upper limb resistance signal and the lower limb resistance signal of the human body, respectively. Then the signals are input to the processor unit through wireless transmission. The impedance difference is obtained by using the electrical characteristic difference of biological tissues and through Ohm's law and frequency domain analysis. Then the body fat content of the human body is obtained in combination with the weight, height and other data of the customer.

[0017] The four electrodes of the first body fat detection assembly are symmetrically arranged, two of which are arranged at positions contacted by the left foot, and the other two are arranged at positions contacted by the right foot, and the four electrodes of the second body fat detection assembly are also symmetrically arranged, two of which are arranged at positions contacted by the left hand, and the other two are arranged at positions contacted by the right hand.

[0018] As shown in Figure 2 A body fat detection management method, comprising the following steps: when a customer enters a fitness center, collecting gender information, height information, and weight information of the target customer; displaying the customer information at the front desk; The front desk confirms the above information with the customer, judges whether the customer is a historical customer or a new customer, establishes a new customer file for the new customer, confirms the collected information and inputs the customer's age information, and retrieves the historical data of the historical customer; The processor unit is provided with a NEST neural network model, which has an initial model and a prediction model, the initial model is based on the training plan given by the coach experience, the prediction model inputs the customer's age, height, weight, pre-exercise body fat rate, subcutaneous fat rate, and other information, the body fat change rate of the customer on different training equipment, and other information into the initial model for training to obtain a predicted training plan; the predicted training plan provides a training plan for the customer within a training period, including a training plan subdivided to each day, the customer's selection of fitness equipment each day, the training time of each fitness equipment, and the predicted training body fat change data.

[0019] The prediction model inputs the customer's age, height, weight, pre-exercise body fat rate, subcutaneous fat rate, customer exercise information, and customer training body fat change data into the prediction model before training, and classifies the customer information.

[0020] When the customer enters the fitness center, the processor unit 6 gives a predicted training plan according to the customer's gender information, height information, weight information, age information, etc.; the customer selects fitness equipment and training time according to the predicted training plan and starts exercising, according to the customer's selection of fitness equipment, the bionic electrode detection module arranged on various fitness equipment detects and records the customer's body fat data before exercise according to the customer's contact with the first body fat detection assembly and the second detection assembly, and transmits it to the processor unit; When the customer finishes exercising, the bionic electrode detection module 5 detects and records the customer's body fat data after exercise according to the customer's contact with the first body fat detection assembly and the second detection assembly, and transmits it to the processor unit; the processor unit judges and stores the customer's training time, selection of fitness equipment, and body fat rate change before and after exercise, and inputs the information into the initial model for training to improve the accuracy of the prediction model.

[0021] The processor unit judges the customer according to the detection record, evaluates the customer's training plan in this stage, and outputs a predicted training plan.

[0022] After the customer enters the fitness center, the camera collects the customer's information and sends it to the processor unit to determine whether the customer is a new customer or a historical customer. If it is a new customer, a new customer file is established. The height measurement module measures the customer's height information, the weight detection module measures the customer's weight information, the front desk guides the customer to input the customer's age information, and the customer's expected exercise time and expected body fat change information. The above information is sent to the NEXT neural network model in the processor unit, and the NEXT neural network model outputs a predicted training plan. The customer exercises according to the predicted training plan. The bionic electrode collection module collects the customer's body fat rate before and after exercise and inputs it into the NEXT neural network model. The processor unit stores and judges the customer's exercise effect and corrects the later training plan. If it is a historical customer, the processor unit formulates a predicted training plan according to the customer's weight information and previous training preference information. At the same time, the customer exercises according to the predicted training plan. The bionic electrode collection module collects the customer's body fat rate before and after exercise and inputs it into the NEXT neural network model. The processor unit stores and judges the customer's exercise effect and corrects the later training plan. At the same time, the processor unit also outputs a customer exercise data report, including the difference in body fat change during the actual exercise process according to the training plan, the preference of fitness equipment selection, and the exercise duration, etc. Exercise data, the processor unit outputs to the display voice module, which is convenient for the customer to compare. During the customer's exercise process, the processor unit receives the comparison between the customer's post-exercise body fat data and pre-exercise body fat data, and prompts the customer to correct the exercise through the display voice module. If the customer's body fat change does not meet the expected exercise at the end of the day, the customer is prompted to increase the training intensity or training duration. If the customer's body fat change exceeds the expected exercise, the customer is reminded to appropriately reduce the training intensity or training duration.

[0023] The predicted training plan includes the training duration, the expected body fat change rate, the detailed training duration per day, the selected fitness equipment type per day, the training duration of each fitness equipment, and the body fat rate change after the training of each fitness equipment per day, to facilitate the customer's intuitive understanding and fine monitoring and management.

[0024] The NEXT neural network model is obtained by inputting the initial model with the above customer gender information, height information, weight information, etc. Data set to train the prediction model.

[0025] When obtaining customer information, the obtained customer information is desensitized, the customer's name and identity information are deleted, the customer data is desensitized, and the security and privacy of the data in the collection, transmission, storage, and sharing processes are ensured.

[0026] The application can give the optimal training plan according to the training preference, training time, age, body fat, height of the customer, improve the training efficiency, and can monitor the body fat rate change of each training in real time.

[0027] It should be noted that the terms "comprising", "including", or any other variant thereof are intended to cover a non-exclusive inclusion, such that processes, methods, articles, or apparatuses that comprise a list of elements are not limited to those elements, but can also include other elements not expressly listed, or can also include elements inherent in such processes, methods, articles, or apparatuses. Without further limitation, an element defined by the phrase "comprising a" does not exclude the existence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0028] The above only describes the embodiments of the application and is not intended to limit the application. The application can have various changes and modifications for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the application shall be included in the scope of claims of the application.

Claims

1. A body fat monitoring management apparatus characterized by comprising: The system mainly comprises: an image acquisition module (1), a height measuring module (2), a weight measuring module (3), a keyboard input module (4), a bionic electrode acquisition module (5), a processor unit (6), various fitness equipment, the image acquisition module (1), the height measuring module (2), the weight measuring module (3), the keyboard input module (4) and the bionic electrode acquisition module (5) are connected with the input end of the processor unit (6) through a wireless network, the output end of the processor unit (6) is connected with a display voice module (7), the bionic electrode module (5) is arranged on the various fitness equipment, and the processor unit is provided with a NEXT neural network module.

2. The body fat monitoring management apparatus according to claim 1, wherein: The image acquisition module (1), the height measuring module (2) and the weight measuring module (3) are arranged at the entrance of the fitness center; the bionic electrode module (5) is a two-group body fat detection assembly, which is a first body fat detection assembly and a second body fat detection assembly, and both groups of body fat detection assemblies are composed of four electrodes; the first body fat detection assembly is arranged at the bottom of the fitness equipment, and the second body fat detection assembly is arranged at the upper part of the fitness equipment; the first body fat detection assembly and the second body fat detection assembly are respectively composed of four electrodes.

3. The body fat monitoring management apparatus according to claim 2, wherein: The four electrodes of the first body fat detection assembly are symmetrically arranged, two of which are arranged at the positions contacted by the left foot, and the other two are arranged at the positions contacted by the right foot; the four electrodes of the second body fat detection assembly are also symmetrically arranged, two of which are arranged at the positions contacted by the left hand, and the other two are arranged at the positions contacted by the right hand.

4. A monitoring management method of the body fat monitoring management apparatus according to the above claim, characterized by: It comprises: Step 1: when a customer enters the fitness center, the gender information, height information and weight information of the target customer are collected; The customer information is displayed at the front desk; Step 2: the front desk confirms the above information of the customer, judges whether the customer is a historical customer or a new customer, establishes a new customer file for the new customer, confirms the above collected information and inputs the age information of the customer, and calls the historical customer file for the historical customer; Step 3: the customer file is input into the NEXT neural network model in the processor unit, and the NEXT neural network model outputs a predicted training plan; Step 4: the customer exercises according to the predicted training plan, and the bionic electrode acquisition module collects the body fat rate before training; Step 5: after the customer trains, the bionic electrode acquisition module collects the body fat rate after training; the NEXT neural network model outputs a new predicted training plan according to the above information.

5. The body fat monitoring management method according to claim 4, wherein: The NEXT neural network model in the processor unit is obtained by inputting the initial model training of the above customer gender information, height information, weight information and other data sets.

6. The body fat monitoring management method according to claim 5, wherein: The prediction model inputs the age, height, weight, pre-exercise body fat rate, surface body fat rate, customer exercise information and customer training body fat change data into the prediction model before training, and classifies the customer information.

7. The method of claim 4, wherein: The processor unit receives the body fat data after the customer exercises and compares the body fat data before the customer exercises to judge, and prompts the customer to correct the training through the display voice module.

8. The body fat monitoring management method according to claim 4 or 5, characterized by: In the process of obtaining customer information, the obtained customer information is desensitized, the name and identity information of the customer are deleted, and the customer data is desensitized.

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