Application of Artificial Neural Network and Background Subtraction for Determining Body Mass Index (BMI) in Android Devices Using Bluetooth

Abstract

Abstract-Body mass index (BMI) is one of the different ways to monitor the health of a person. It is based on the height and weight of the person. This study aims to compute for the BMI using an Android tablet by obtaining the height of the person by using a camera and measuring the weight of the person by using a weighing scale or load cell. The height of the person was estimated by applying background subtraction to the image captured and applying different processes such as getting the vanishing point and applying Artificial Neural Network. The weight was measured by using Wheatstone bridge load cell configuration and sending the value to the computer by using Gizduino microcontroller and Bluetooth technology after the amplification using AD623 instrumentation amplifier. The application will process the images and read the measured values and show the BMI of the person. The study met all the objectives needed and further studies will be needed to improve the design project.Index Terms-Artificial neural network, bluetooth, body mass index, vanishing point, wheatstone bridge load cell.

Ensemble Learning for Skeleton-Based Body Mass Index Classification

“…BMI prediction/classification based on full-body person images has also been studied [11][12][13]. For example, Bipembi et al [11] proposed a method for obtaining the BMI of an individual from their full-body silhouette image.…”

“…According to their results, the SVM outperformed the other algorithms and achieved an accuracy of approximately 72.16%. Madariaga et al [13] proposed the calculation of human height from full-body images using the vanishing point and camera height information. For body weight calculation, a sensor called the Wheatstone bridge load cell was used.…”

“…In particular, since most of the features used in [8][9][10] can be extracted from only front-view face images without any occlusions, the feasibility and effectiveness of these methods for real-world application still need to be demonstrated. Some studies on BMI classification have been conducted using human full-body images [11][12][13]. In these studies, each individual was asked to stand up straight while their full-body image was taken.…”

“…In addition, the image was captured from a frontal view. Most of the features proposed in [11][12][13] can be obtained from only frontal full-body images. Therefore, in a real-world situation, if the methods proposed in [11][12][13] are used for BMI classification, it is necessary to have the cooperation of an individual to enable the extraction of features from their frontal full-body images.…”