As the main health threat to the elderly living alone and performing indoor activities, falls have attracted great attention from institutions and society. Currently, fall detection systems are mainly based on wear sensors, environmental sensors, and computer vision, which need to be worn or require complex equipment construction. However, they have limitations and will interfere with the daily life of the elderly. On the basis of the indoor propagation theory of wireless signals, this paper proposes a conceptual verification module using Wi-Fi signals to identify human fall behavior. The module can detect falls without invading privacy and affecting human comfort and has the advantages of noninvasive, robustness, universality, and low price. The module combines digital signal processing technology and machine learning technology. This paper analyzes and processes the channel state information (CSI) data of wireless signals, and the local outlier factor algorithm is used to find the abnormal CSI sequence. The support vector machine and extreme gradient boosting algorithms are used for classification, recognition, and comparative research. Experimental results show that the average accuracy of fall detection based on wireless sensing is more than 90%. This work has important social significance in ensuring the safety of the elderly.