Accidental falls are one of the major safety problems that cause the injury and death of the elderly over 65 years old in China. In the face of the lack of accuracy of existing fall detection technologies in dealing with complex environments such as object occlusion and illumination changes, this study based on the improved algorithm of YOLOv8, this study introduces a multi-head attention mechanism to improve the accuracy, robustness, universality and scalability of fall detection. The main goal of this work is to improve the YOLOv8 model's structural adjustments, which include adding a multi-head attention mechanism to improve the model's capacity to identify important characteristics. This method enables the model to learn several features of the input data in separate representational subspaces at the same time, leading to more accurate fall behavior identification and localization. The modified model was tested in several fall scenarios, including ones with varying illumination and shade levels, in the experimental section. This technique outperforms the original YOLOv8 model, achieving 79.4% mAP@0.5 on the dataset, according to a comparison of performance benchmarks. In summary, YOLOv8's multi-head attention method adds to the fall detection algorithm's detection accuracy while simultaneously.