Body Information Analysis based Personal Exercise Management System

ABSTRACT


INTRODUCTION
As the number of consumers for a healthy life increases, a system for periodically checking physical condition and health using a smart phone or a wearable device is being developed. By using this, the past and present state of the user can be known and the service [1][2][3]. However, since existing systems record the user's movements and provide information accordingly, there is a problem that they cannot help users who have not understanding of motion [4][5][6][7][8]. In order to solve this problem, it is required to develop a system for analyzing the user's body information and providing a service accordingly.
In this paper, we analyze user BMI index and BMR value and provide service. The service provided by the proposed system analyzes the BMI index then recommends exercise intensity. Next analyze BMR values and recommend momentum. It also provides a graphical representation of the records of used exercise equipment.
System recommend to user exercise intensity and momentum can select the exercise device and can view the calories consumed in real time and select whether to transmit the exercise amount to the server. Also, records of other users belonging to the same BMI group as the user can be referred to This allows the user to compare other user's select the exercise equipment. Using the proposed system, the user can get more information and exercise more efficiently than existing exercise management system. And user can efficient planning.

DESIGN
This chapter describes the design of the proposed system.  Figure 1 shows the structure of the system and Figure 2 shows the flow chart of the system.  The structure of the system consists of Android, PHP, Java, and server PC. Android, PHP and server PC is interlocked for analyzing the user's body information. The user will be able to enter and check the BMI index and BMR value in the Android application and be informed accordingly. The user's information is transferred to the database and in the integrated database, user information is analyzed and grouped according to BMI index. For visualizing user information, Android, Java, and server PC communicate. We store the exercise equipment used by the user in a personal database, construct a history and analyze it through R programming. The analyzed data is sent to Android via Java socket communication and displayed as a graph. Also, showing the used exercise equipment by users belonging to the same BMI group. So user can compare other users exercise history.

Design of System
He system beginning is to the application run on the Android smartphone. The main functions of the application are login, BMI index check, BMR value check, fitness history reference, exercise equipment selection, motion detection function. A user who has not been registered yet should fill in information such as name, sex, age, ID, password, and momentum for membership registration. Based on the log-in and BMI index confirmation, the recommended exercise intensity is recommended by the first recommendation algorithm, and the second recommendation algorithm is based on the BMR value confirmation. It also graphically displays the exercise equipment that the user has already used, and also graphs the exercise equipment records used by other users belonging to the same BMI group as the user. Based on two recommendation algorithms and exercise history reference, the user is provided with appropriate information for the body. Next, when the user selects the exercise device the exercise device selected as the personal database is stored. The third recommendation algorithm is provides information on commonly used exercise equipment. Also when the user performs the exercise, it displays the consumed calorie value in real time. After the exercise, the user transfers the exercise amount to the server or initializes it according to the user's selection.

Recommendation Algorithm
The proposed system consists of three recommended algorithms. The first recommendation algorithm recommends exercise intensity, the second recommendation algorithm recommends the momentum, and the third recommendation algorithm recommends a exercise mahcine. Figure 3 is a flow chart of the first recommendation algorithm of the fitness system of the proposed system.
The first recommendation algorithm calculates the BMI index of the user. We recommend exercise intensity to the user according to the BMI index. The exercise intensity in the application is expressed as Level. BMI groups will also be divided and recommended for exercise intensity. The correlation between the BMI index and exercise intensity was set based on information in the U-Healthcare field. Figure 4 is a flowchart of the second recommendation algorithm. The second recommendation algorithm recommends the amount of exercise that needs to be carried out in one day, taking into account the BMR of the user and the level of exercise the user has entered. The using Harris-Benedict Equation for BMR value calculates and recommends momentum to the user. Figure 5 is a flowchart of the third recommendation algorithm.
The third recommendation algorithm provides information based on the data of the user's BMI group. When the user selects the exercise machine, the Toast message is displayed to the exercise devices used by other users belonging to the same BMI group. Using the information provided by the application, the user can exercise more efficiently than the services provided by the existing exercise management systems or plan the exercise. It also shows the calories consumed by the user in real time. And it shows the effect of the exercise machine to the user by specifying the improved muscle area when the exercise is performed using the corresponding exercise machine.

IMPLEMENTATION AND REVIEW 3.1. Implementation Environment
The operating systems is Windows 7, CPU i5-4690, and RAM 8G. The smartphones used are Galaxy S5 and Galaxy S6 Edge. The tool used Eclipse to control the smartphone and the server, and APM Setup to connect the PC and the smartphone. Figure 6 shows the screen for analyzing the user's body information. Figure 6. Analysis User Body Information Figure 6 analyzes the height and weight entered by the user and recommends exercise intensity to the user using the Check BMI button. Also, when you press the Check BMR button, you will be informed of the user's metabolism and recommended exercise amount. Figure 7 is a graphical representation of the exercise history. It is also the records of the users belonging to the same BMI group as the user. The user can review and modify the plan with respect to the exercise by referring to the record of the exercise equipment he or she has used and the records of other users.

Review
For exercise management, users should be provided with information about exercise intensity, exercise amount, and exercise equipment. However, existing exercise simply management systems analyze the height and weight of the user and provide the corresponding information.
However, only individual data of a user is analyzed. This analyzed data is not general information. So this problem must solve. For this problem solve we propose system. The proposed system consists of a analyzes the user's BMI index and BMR value and provides corresponding information. After analyzing the BMI index with the first recommendation algorithm, we recommend exercise intensity. The BMR value is recommended after the second recommendation algorithm is analyzed. And the exercise equipment that the user uses is stored in the database and displayed to the user graphically.
Users can refer to exercise equipment used by other users belonging to the same BMI group as the user, so that the user can compare with the records of exercise equipment he / she has used. Table 1 shows the comparison between the existing exercise management systems and the proposed system. Existing exercise management systems provided the function of analyzing the user's body weight and calculating the momentum. Body weight-based information cannot accurately analyze the user's body