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@article{174230,
        author = {S.Kalaiselvan},
        title = {IOT Enabled Fall Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3022-3026},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174230},
        abstract = {According to statistics, falls are the primary cause of injury or death for the elderly over 65 years old.  About 30% of the elderly over 65 years old fall every year.  Along with the increase in the elderly fall accidents each year, it is urgent to find a fast and effective fall detection method to help the elderly fall. The reason for falling is that the center of gravity of the human body is not stable or symmetry breaking and the body cannot keep balance. To solve the above problem, in this paper, we propose an approach for reorganization of accidental falls based on the symmetry principle. We extract the skeleton information of the human body by Open Pose and identify the fall through three critical parameters: speed of descent at the center of the hip joint, the human body centerline angle with the ground, and width-to height ratio of the human body external rectangular. Unlike previous studies that have just investigated falling behavior, we consider the standing up of people after falls. This method has 97% success rate to recognize the fall down behavior.},
        keywords = {Deep Learning, Fall Detection, Open Pose, Pose Estimation},
        month = {March},
        }