Detecting and treating older adults who fall in an environment without others is essential. Millimeter-wave radar sensors do not have the disadvantage of invading user privacy like cameras, nor do they require users to wear them in real-time like wearable devices. Actual samples of older adults fall are difficult to collect, and it is unethical to require older adults to fall repeatedly to collect data. In addition, different body types and action patterns will inevitably reduce the model’s performance when new users use the model. In this paper, we constructed a fall detection model based on anomaly detection. The model is trained only using non-fall samples and detects falls as abnormal actions. The proposed model uses a domain generalization architecture based on domain feature alignment to extract domain-invariant features of the model, thereby improving the model’s generalization ability. In addition, we introduced the idea of denoising learning into the feature extractor and feature predictor to improve the model’s anti-interference ability. We conducted sufficient experiments to explore the effectiveness of the proposed method. When tested with new domain data, the proposed model has a true positive rate of 96.12%, a false positive rate of 0.97%, and an area under the receiver operating characteristic of 0.9979.