In order to detect the elderly fall high-risk behaviors quickly and accurately on the low-power and low-cost hardware platform， an abnormal behavior detection algorithm based on AlphaPose optimization model was proposed. Firstly， the pedestrian target detection model and pose estimation model were optimized to accelerate the human target detection and pose joint point reasoning. Then， the image coordinate data of human pose joint points were computed rapidly through the optimized AlphaPose model. Finally， the relationship between the head joint point linear velocity and the crotch joint linear velocity at the moment the human body falls was calculated， as well as the change of the angle between the midperpendicular of the torso and X-axis of the image， were calculated to determine the occurrence of the fall. The proposed algorithm was deployed to the Jetson Nano embedded development board， and compared with several main fall detection algorithms based on human pose at present： YOLO （You Only Look Once）v3+Pose， YOLOv4+Pose， YOLOv5+Pose， trt_pose and NanoDet+Pose. Experimental results show that on the used embedded platform when the image resolution is 320×240， the proposed algorithm has the detection frame rate of 8.83 frame/s and the accuracy of 0.913， which are both better than those of the algorithms compared above. The proposed algorithm has relatively high real-time performance and accuracy， and can timely detect the occurrence of the elderly fall behaviors.