Device-free wireless sensing has recently attracted a lot of attention thanks to its non-intrusive and sensor-free nature. Contrary to the traditional sensor-based and wearable sensing, wireless sensing does not need any sensors but leverages the signal distortions and machine learning algorithms for sensing. Moreover, wireless signals can propagate through walls which allows sensing to be performed even in non-line-of sight (NLOS) scenarios which increases the sensing coverage over camera-based systems. Different types of wireless signals have been employed for sensing including WiFi, RFID, mmWave, UWB, and acoustic signals. As wireless signals bounce off of physical objects within the environment such as static objects like walls or furniture as well as any humans in the environment, their characteristics (e.g., amplitude, phase) change uniquely. This then provides an opportunity to sense the environment and obtain contextual information (e.g., recognizing the motion) through a fine-grained analysis of signal variations. Wireless sensing has been considered in various applications including but not limited to localization, human activity and gesture recognition, gait estimation, fall detection, respiration monitoring and crowd counting.