To solve the problems of insufficient feature extraction,a single fall detection method,and weak real-time performance of traditional fall detection algorithms,an improved YOLOv8 fall detection algorithm combined with human skeleton key points is proposed.First,the backbone network of YOLOv8 is replaced by a ShuffleNetV2 network,and the mixed attention mechanism(Shuffle Attention,SA) is added in the neck,so that the model can extract the behavioral characteristics better and realize the static posture matching of a human body.Second,by analyzing the information on position change of skeletal key points,the decline speed of the center of mass,the angle speed between the trunk and the ground and height-to-width ratio of the body are taken as the basis of the fall behavior to improve the accuracy of fall judgment.Experimental results show that the algorithmic accuracy,F1 value,and mAP50 value on COCO Key Points datasets are 78.3%,67.9%,and 70.0% respectively,that the algorithmic accuracy is 95.85%,92.8% and 96.52% on UR Fall Detection,Fall Detection Datasets and self-built datasets,and that the proposed algorithm outperforms the traditional algorithm in distinguishing daily life behavior and falling behavior.
