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@article{157820,
        author = {Yashesvinee V and Sindhu R and Sushmitha M and Tejaswi U and Nethra H.L.},
        title = {A Study on Vision-based Posture Assessment Tools},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {8},
        pages = {155-158},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=157820},
        abstract = {One of the primary contributors to neck and back issues in the modern world is the increased amount of time individuals assume incorrect or uncomfortable postures during work. Many employees have been forced to spend more time than usual in front of a computer, causing them to work in bad postures for a prolonged time. Workers in various industries are required to perform tasks in awkward positions within their workstation. These workstations may or may not have the qualities required for the worker to be able to position themselves comfortably. This paper focuses on developing a lightweight automatic system that tracks a worker’s posture and alerts them anytime a correction or break is required. A camera can be used to gather input frames which is then sent to a model that analyses the severity and risk associated with a particular posture. The suggested method automatically ascertains a worker’s risk pose by integrating Mediapipe with body assessment tools like Rapid Entire Body Assessment (REBA) and Rapid Upper Limb Assessment (RULA).},
        keywords = {OpenCV, Pose estimation, body posture},
        month = {},
        }