# Ensemble Learning for Skeleton-Based Body Mass Index Classification
## 1 Introduction  
Over the past several decades, the percentage of the population that is obese has steadily increased. Since obesity can cause various diseases, its prevalence among people has become a major social issue. To identify its adverse effects, many studies have investigated the relationship between obesity and various diseases. According to the results presented in the literature, obesity is related to several diseases such as diabetes [1], high blood pressure [2], hyperlipidemia [3], cholelithiasis [4], hypopnea [5], arthritis [6] and mental disorders [7], meaning that the death rate for the obese is relatively high. The World Health Organization (WHO) defines obesity as excessive fat accumulation that may threaten life. Additionally, the WHO uses the body mass index (BMI) to identify obesity in people. The BMI is one of the factors used to estimate obesity levels and is defined as the ratio of a person's body weight in kilograms to the square of their height in meters (i.e., the unit of the BMI is kg/m\({}^{2}\)). In general, a person is classified as \"overweight\" when their BMI is greater than 25. Traditionally, if a person wants to know their BMI, they must first measure their body weight and height using a weight scale and extensometer, respectively. After this, their BMI can be calculated according to the definition of the BMI above. If no device is used to measure body weight or height, it is impossible to calculate the BMI. To make the acquisition of BMIs simpler, i.e., possible without using such devices, many researchers in the field of computer vision have explored various methods ofBMI prediction/classification. Most have focused on calculating the BMI of an individual from a facial image [8; 9; 10]. In the literature, face image databases built under a controlled environment, such as frontal view, clean background, alignment according to interpupillary distance, etc., have been used. However, in a real-world situation, facial images are generally captured in uncontrolled conditions. For example, the facial image of a person can be taken from the side view as well as the frontal view and can have complex backgrounds. In addition, in some cases, a person can wear a mask which conceals parts of their face. 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. Recently, based on advancements in human pose estimation techniques, the acquisition of three-dimensional (3D) human motion data has become quicker, simpler and more precise [14; 15; 16; 17]. Therefore, many studies have exploited human motion data for various applications. Human action recognition [18; 19; 20; 21], animation generation [22; 23; 24; 25] and gait recognition [26; 27] are prime examples of such research. Compared with conventional facial and full-body image-based BMI classifications, skeleton-based BMI classification has the advantage of being performed without the user's cooperation, with the user only being asked to walk in a usual way. Because the user's gait data can be obtained from the video filmed from various viewpoints as well as a frontal view, the BMI classification process is performed in such a way that the user is unaware of it and does not feel uncomfortable.