Background: People often get hurt at work because they sit or stand in ways that are not good for their bodies. We usually use methods such as RULA and REBA to determine whether someone is sitting or standing in a way that could hurt them. These methods are not always accurate, and we cannot use them to check on people all the time. Therefore, we need to use computers and other digital tools to understand what is going on. This is important for ensuring that people are healthy and safe at work, which is one of the things the United Nations discusses in its Sustainable Development Goals, the one about being healthy. Objectives: We examined what other researchers have written about using technology to check if people are sitting or standing in ways that could hurt them. Methods: This study employed a scoping review design following the PRISMA-ScR guidelines by searching studies from the PubMed, Scopus, and SAGE databases published within the last five years to identify digital technologies used for occupational posture risk assessment related to musculoskeletal disorders. Results: We found that digital technology, such as sensors, computer vision, and machine learning, can be used to check whether people are sitting or standing in ways that could hurt them. This technology is very good at determining if someone is in a position, and it can do it quickly and without people having to do it by hand. This technology can be used in many types of jobs, such as working in a factory, on a farm, or in a hospital. Conclusion: Digital technologies such as inertial sensors, sensor fusion, computer vision, and deep learning demonstrate strong potential for improving occupational posture risk assessment through more objective, accurate, and real-time ergonomic monitoring, thereby supporting the prevention of work-related musculoskeletal disorders in various occupational sectors.
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