Activity recognition plays a key role in health management and security. Traditional approaches are based on vision or wearables, which only work under the line of sight (LOS) or require the targets to carry dedicated devices. As human bodies and their movements have influences on WiFi propagation, this paper proposes the recognition of human activities by analyzing the channel state information (CSI) from the WiFi physical layer. The method requires only the commodity: WiFi transmitters and receivers that can operate through a wall, under LOS and non-line of sight (NLOS), while the targets are not required to carry dedicated devices. After collecting CSI, the discrete wavelet transform is applied to reduce the noise, followed by outlier detection based on the local outlier factor to extract the activity segment. Activity recognition is fulfilled by using the bi-directional long short-term memory that takes the sequential features into consideration. Experiments in through-the-wall environments achieve recognition accuracy >95％ for six common activities, such as standing up, squatting down, walking, running, jumping, and falling, outperforming existing work in this field.