Human Sitting Postures Classification Based on Angular Features with Fuzzy-Logic Labeling

Abstract

Musculoskeletal disorders (MSDs) are generally associated with sitting postures. Assessing and ensuring healthy sitting posture are indispensable aspects of reducing the occurrence of MSDs. This study aims to develop a system that allows office workers' body postures to be contactless and recognized by different classification methods while sitting on a chair and can be used for health applications. Five different sitting body postures have been determined within the scope of medical and health literature studies and relevant standards. Thirty subjects were asked to sit in these body postures for 30 seconds. While the subjects were sitting, skeleton point position defined as a pose data of the subjects were obtained from the Kinect device simultaneously. Five angles that are thought to distinguish sitting positions according to different joint positions were determined and calculated. The angle values that can take in the standard sitting position in the literature have been determined. According to these values, the angle values in other postures were determined. A rule-based fuzzy inference system was designed using angle values for labeling sitting posture data. Angle values were calculated to classify the labeled depth values, and an artificial neural network classifier was designed according to these angle values. As a result, five different sitting body postures were classified with KNN (K-Nearest Neighbours) and Neural Network (NN), respectively, with 98.9% and 97% overall accuracy values. The study was compared with other studies in the literature. In this context, a high-performance system design that can improve healthy sitting behaviors for office workers that can be used in both health applications and robot vision is presented.