Classification Algorithm for Sitting Postures Using Weighted Random Forest

Abstract

The increasing use of computers has led to a significant rise in neck and back disorders caused by poor sitting posture. While various posture analysis methods have been proposed to mitigate these issues, existing approaches are often limited by constrained data acquisition environments, low accuracy, and restricted posture classification capabilities. In this paper, we propose a method for classifying sitting postures that negatively impact health. By capturing front-facing images and detecting the coordinates and angles of the face and shoulders, our method utilises a random forest algorithm for posture classification. As a result of the experiment, the proposed approach achieved high performance with an accuracy, TPR, FPR, and F1-score of 0.983, 0.988, 0.004, and 0.983, respectively, outperforming previous studies.