A Comprehensive Survey on the Detection and Analysis of Sitting Posture

The COVID pandemic has led to a significant rise in the proportion of people who work from home, frequently without the infrastructure or ergonomic equipment that they require. User’s well-being and health are seriously harmed by improper desk heights, a lack of suitable desktop chairs, and extensive laptop use. Bad posture while sitting is a major contributor to back pain, neck pain, headaches, and discomfort in the spine, which can result in spinal dysfunction and make it challenging to work for extended periods of time. Over time, but never proven to be diminishing, the number of patients with lower back discomfort. Additionally, this kind of illness affects about 20% of the populace, particularly those working in the software sector. The ability to better comprehend human movement and avoid musculoskeletal problems has led to an increase in the importance of posture detection and analysis in recent years. The interest in creating automated systems for posture detection and analysis has grown as high-quality, reasonably priced sensors have become more widely available. Due to their capacity to accurately and efficiently extract complex characteristics from images, deep learning-based techniques have become a promising solution to this issue. The Keras framework is used in this survey study to review the most recent techniques for detecting and analysing sitting position. The responsible people can use the knowledge created here to develop their strategies for more effectively reducing the nation’s back pain challenges.