Comparative Analysis of Human Pose Estimation Methods with Fall Detection and Smart Alarming System

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Rahul Patil, Prashant Ahire, G. B. Sambare, Yashwant Dongre, Aarti S. Gaikwad, Nishant Ashtekar, Sushant Chaudhari, Shailendra Chaudhari, Abhijit Gadhave


The population of old age peoples are increasing day by day and it will grow up by 31% of today’s population till 2031. In many cases the old age people are prone to fall. So, old age peoples can help in various aspect by using advanced technology. We already know that fall discovery is very much critical part of senior care as fall pose a threat to their health. To this challenge, this discussion and study proposes a fall detection model comprising three crucial phases: Pose Estimation, Fall Detection, and Smart Alerting System. Using computer vision model similar as OpenPose, PoseNet, MoveNet and YOLOv7. By the help of this models, we directly track the act of senior individualities in real time and identifies fall incidents grounded by abnormal changes in posture. When fall detect, the WhatsApp Alert message with fall detected frame and phone call will be received by Caregiver or their family members to take urgent actions. In case of fall, one can prevent from severe injuries which could be caused due to delay in mediation. These proposed models will enhance the safety of old age population.

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