In recent years, with the rapid developments in science and technology which have taken place, wireless signals have evolved from a simple communication medium to something which can be used for environmentally aware tools. Many researchers have applied wireless signals to the research fields of human perception and human behavior recognition. At present, vision-based and sensor-based action recognition represent two mainstream methods. However, the former is sensitive to the levels of light available and the environment, and the latter requires users to wear (or deploy) devices which may cause the user inconvenience. Action recognition systems based on wireless signals can avoid these difficulties, and at low cost. Wi-Fi signals have been used to realize keystroke recognition, gesture recognition, and simple human behavior recognition. Inspired by this and the urgent need for the implementation of indoor somatosensory games and other application requirements which include human motion recognition, this paper presents a Table Tennis action recognition system based on Wi-Fi signals, which is termed WiTT. In family environment, Wi-Fi signals is always seriously affected by other wireless signals and environmental factors. WiTT uses discrete wavelet decomposition and support vector machines and other techniques, and it achieves a greater than 96.34% detection rate in detecting table tennis actions and a 90.33% recognition accuracy in classifying 6 different table tennis actions.