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The frequent occurrence of falls among the elderly has attracted the attention of numerous research institutions and scholars at home and abroad as an important obstacle on the path to a healthy global aging population. Injuries associated with falls pose a great challenge to the elderly in maintaining the normal function of the organism, which seriously threatens the safety of the elderly's lives. Therefore, real-time fall detection based on deep learning is crucial for the health monitoring and rescue of the elderly. To address the problems of large model size, poor timeliness, and inability to accurately recognize multi-target poses in existing pose recognition methods, a lightweight and improved OpenPose real-time fall detection algorithm is proposed to replace the VGG-19 feature extraction network with a lightweight MobileNet network. The experimental results show that the proposed algorithm effectively reduces the model size and computational volume, and has good real-time performance and robustness, which meets the application requirements of real-time multi-person pose detection.
Li et al. (Wed,) studied this question.