However, in a real-world situation, facial images are generally captured in uncontrolled conditions. 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. Most have focused on calculating the BMI of an individual from a facial image. 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 of BMI prediction/classification. If no device is used to measure body weight or height, it is impossible to calculate the BMI. After this, their BMI can be calculated according to the definition of the BMI above. 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. The simulation results demonstrate that the proposed model achieves state-of-the-art performance when compared with benchmark methods. To verify the effectiveness of the proposed model, we evaluated it using a public dataset. To produce final results, the proposed ensemble model utilizes multiple k-nearest neighbor classifiers trained using anthropometric features measured over several different time periods.

This approach enables our proposed ensemble model to obtain robust and accurate BMI classification results. In contrast, we controlled the window used to measure anthropometric features over short/mid/long-term periods. Thus, traditionally, anthropometric features are measured over a long period. Average values are generally calculated for all frames because the length of body parts and the subject height vary over time, as a result of the inaccuracy in pose estimation. Traditionally, anthropometric features, including the average length of each body part and average height, have been utilized for this kind of classification. In this study, we performed skeleton-based body mass index (BMI) classification by developing a unique ensemble learning method for human healthcare.

Favorite text people sq0