BODY MASS INDEX

ABSTRACT:-

            A person’s weight status will have profound effects on their life, from psychological status to longevity and income. At the societal level, “fat shame” and alternative forms of “sizeism” are a growing problem, while rising fat levels are a growing problem. For these reasons, researchers from different backgrounds have an interest in finding fleshiness from all angles. For information, a person in the past could give their exact body mass index (BMI) themselves, or they could see a doctor to have it measured. However, in this article we show that Computer Vision typically inferred a person’s BMI from image. This tool we unleashed will help advance research into the social aspects related to body weight. body mass index BMI could be a measure of the body fat supported by your weight relative to your height this calculation mainly applies to men and women over the age of twenty your BMI tells the life insurance underwriter whether or not you are maintaining a healthy weight according to the authority the correlation between a high BMI and the possibility of developing dangerous health problems is powerful obese people are at increased risk for many diseases and health problems so during this project we tend to determine the supported human facial body mass index and suggest insurance policy quotes. System also prescribes how people can maintain a proper diet to be healthy and stay in shape. Based on the result of the machine learning algorithm prediction, the android application provides people with the prescribed form such as a gym, proper nutrition, medical consultation, etc. People can choose the respective module according to their fitness level.

OBJECTIVES OF THE PROJECT:-

• Take images from the user via android application.
• To detect faces we have to crop the face region.
• Then we use face semantic segmentation to obtain each face region mask.
• Face region mask multiply element with convolutional feature map to obtain high weight to different face regions.
• Then we apply max pooling to fetch max information from the image.
• That max pooling applies on each mask convolution.
• Finally we feed all the data to our model which predicts the BMI.
• Predicted BMI is fed to a recommended algorithm which suggests quotes of policies.

MODULES:

• User Registration: User have to register to become a part of this app.
• User Login: User have to login itself to check own BMI.
• Home Page: User has to select one image from gallery and upload it on server. Server send the BMI level of that image and display on the next page of that app.
• Gym location: User can view the nearest gym location on the Google map.
• Diet videos: User can view the diet videos.
• Diet chart: User can view the diet charts.
• Insurance policy: User can view the Insurance policy suggested based on the his BMI.

HARDWARE AND SOFTWARE REQUIREMENTS

HARDWARE:-

• Processor: Intel Core i3 or more.
• RAM: 4GB or more.
• Hard disk: 250 GB or more.

SOFTWARE:-

• Operating System : Windows 10, 7, 8.
• Python
• Anaconda
• Spyder, Jupyter notebook, Flask.
• MYSQL