Aiming at the problem of limited computing power and storage space of edge computing devices, a lightweight human fall detection algorithm based on YOLOX was proposed. Firstly, Ghost module in GhostNet is used to reduce the redundancy of convolution parameters in Neck and Prediction layers in YOLOX. Secondly, the coordinate attention mechanism is added to the Neck layer to enhance the ability of key information extraction and reduce the influence of background noise. Finally, aiming at the problem of insufficient detection ability of lightweight model detection head, an auxiliary head module is introduced to strengthen the learning ability of lightweight detection head. The performance of the proposed model is tested by the algorithm and the experimental results are run on the edge computing end of NVIDIA Jetson Xavier NX. The results show that the mAP@05 of the proposed model reaches 849%, and the model size is 256 MB. Compared with the YOLOX model, only a small amount of reasoning speed is sacrificed to improve the detection accuracy of 4.6% and reduce the model size of 25.4%. In addition, compared with some mainstream object detection algorithms, the proposed model also has certain advantages. These results show that the proposed model can better meet the requirements of lightweight and accuracy of the model for edge computing equipment in human fall detection.