Fall detection mostly depends on sensor equipment.The method is highly influenced by equipment and environmental factors,and often can not work well.In addition,vision-based methods are often not effective in terms of real-time and robust. In order to solve these problems,a lightweight fall detection algorithm is proposed with strong robustness and convenient deployment in embedded devices.Taking YOLOv5 as the benchmark model,the lightweight attention mechanism module is firstly integrated to make the network focus on the target area to be identified and enhance the recognition accuracy of the network.Secondly,the model is pruned by the model compression method,which reduces the volume and calculation.Therefore it makes the model lightweight,so as to improve the reasoning speed and facilitate deployment in embedded devices.Finally,knowledge distillation is carried out on the pruned model,which can improve the detection accuracy without increasing the complexity of the model.The experimental results show that compared with the benchmark model, the mAP of this model is increased by 1.7%,the recall is increased by 1.2%,the model volume is reduced by 79.1%,and the floating-point operation is reduced by 70.9%.The proposed model is deployed on the embedded device Jetson Nano,and the detection speed is up to 13.2 frame/s,which basically meets the requirements of real-time fall detection.