Dynamic Tracking and Real-Time Fall Detection Based on Intelligent Image Analysis with Convolutional Neural Network

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Abstract
As many countries face rapid population aging, the supply of manpower for caregiving falls far short of the increasing demand for care. Therefore, if the care system can continuously recognize and track the care recipient and, at the first sign of a fall, promptly analyze the image to accurately assess the circumstances of the fall, it would be highly critical. This study integrates the mobility of drones in conjunction with the Dlib HOG algorithm and intelligent fall posture analysis, aiming to achieve real-time tracking of care recipients. Additionally, the study improves and enhances the real-time multi-person action analysis feature of OpenPose to enhance its analytical capabilities for various fall scenarios, enabling accurate analysis of the approximate real-time situation when a care recipient falls. In the experimental results, the system’s identification accuracy for four fall directions is higher than that of Google Teachable Machine’s Pose Project training model. Particularly, there is the significant improvement in identifying backward falls, with the identification accuracy increasing from 70.35% to 95%. Furthermore, the identification accuracy for forward and leftward falls also increases by nearly 14%. Therefore, the experimental results demonstrate that the improved identification accuracy for the four fall directions in different scenarios exceeds 95%.

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