Sedentary behavior (SB) has been proved to be an important risk factor for poor health, such as blood pressure and even cancer. However, existing sensor- and vision-based SB detection approaches have limitations on practical usage and privacy concerns, respectively. In this paper, we take the first attempt to develop a device-free SB monitoring and recommendation system namely CareFi, which leverages tremendous information behind WiFi signals to monitor the indoor environment and identify series of activities in SB. We deeply investigate the properties of channel state information and various activities in SB. According to different characteristics of static and dynamic activities, we design a foreground detection method to separate two categories and then adopt discriminative features of wireless signals in the frequency and time domains to recognize them. Besides, we propose an updated strategy to overcome the mutability of environment. We implement CareFi on commercial off-the-shelf WiFi routers and evaluate its performance in both office and home environments. Experimental results demonstrate the robustness and accuracy of our method.