To address the challenges of balancing detection accuracy and response speed in vision-based fall detection methods，as well as the limitations posed by a single experimental environment，a lightweight fall detection algorithm based on an improved YOLOv11n，termed MWA-YOLO，was proposed. The algorithm incorporated a lightweight channel attention mechanism mixed local channel attention（MLCA） into the C3K2 module to enhance channel feature representation and mitigate information loss due to channel splitting. Additionally，the WIoU loss function was introduced to replace the original CIoU，improving bounding box localization accuracy in complex environments. Several standard convolutional layers in the network were substituted with the advanced downsampling ADown module to bolster the model’s ability to capture relevant features while maintaining its lightweight characteristics. Experimental results demonstrate that MWA-YOLO achieves an improvement in the mAP@0.5 metric from 92.3% to 93.5% when compared to YOLOv11n，with a model size of only 2.2×10⁶ parameters and 6.0×10⁹ FLOPs. This makes the proposed method suitable for deployment on hardware with limited computational resources，making it ideal for fall detection in real-life scenarios.