Classifying Poor Postures of the Neck and Spine in Computer Work by Using Image and Skeleton Analysis

Abstract

When using a desktop computer, people tend to adopt postures that are detrimental to their bodies, such as text neck and the L-posture of leaning forward with their buttocks out and their shoulders against the backrest of the chair. These two postures cause chronic problems by bending the cervical and thoracic spines and can have detrimental effects on the body. While there have been many studies on text neck posture, there were limited studies on classifying these two postures together, and there are limitations to the accuracy of their classification. To address these limitations, we propose an algorithm for classifying good posture, text neck posture, and L-posture, the latter two of which may negatively affect the body when using a desktop computer. The proposed algorithm utilizes a skeleton algorithm to calculate angles from images of the user’s lateral posture, and then classifies the three postures based on the angle values. If there is sufficient space next to the computer, the method can be implemented anywhere, and classification can be performed at low cost. The experimental results showed a high accuracy rate of 97.06% and an F1-score of 95.23%; the L posture was classified with 100% accuracy.