Research on the Detection Algorithm of Elderly Falling Behavior Based on AlphaPose Model

Abstract

This study aims to address the challenge of quickly and accurately detecting high-risk behaviors such as falls in elderly people in hardware environments with limited power consumption and cost. Therefore, this article proposes an optimized behavior anomaly detection algorithm based on the AlphaPose model. The algorithm first improved the human target detection and pose estimation models to achieve faster pedestrian detection and pose joint point inference; Then, using the adjusted AlphaPose model, efficiently obtain the image coordinate data of each joint point in the human body; Finally, by analyzing the relationship between the linear velocities of the human head and hip joints, as well as the changes in the angle between the vertical line of the human center and the X-axis of the image, it is determined whether a fall event has occurred. This study deployed the proposed algorithm on the JetsonNano embedded development board and compared its performance with mainstream fall detection algorithms based on human posture, including YOLOv3+Pose, YOLOv4+Pose, YOLOv5+Pose, trtpose, and NanoDet+Pose. The experimental results show that on the set embedded platform, when the image resolution is 320×240, this algorithm achieves a detection rate of 8.83 frames per second and an accuracy of 0.913, both of which are superior to the compared algorithms. In summary, this algorithm has high real-time performance and accuracy, and can effectively identify the falling behavior of elderly people in a timely manner.