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.

Submissions will be made using EDAS.

Manuscripts submitted for consideration should not have been already published elsewhere and should not be under review or submitted for review elsewhere during the consideration period. Manuscripts must be written in English, are limited to 6 pages, single spacing, double column, and must strictly adhere to the IEEE template format available here. All accepted papers will be included and indexed in the IEEE Digital Library (IEEE Xplore), showing their affiliation with IEEE WoWMoM. There will be no separate workshop registration, as one single registration will cover both conference and workshops participation. At least one author of each accepted paper is required to attend and present his/her work at the workshop. Papers must be submitted electronically through EDAS.