In this project we are extending the application of commercial WiFi routers to enable sensing. Our project is a continuation of the works done from the sponsor side, and we are guided by Dr. Perry Wang and Mr. Jet Yu from MERL throughout the project. We connected routers via AI mesh to build a WiFi testbed that is configured as transmitters. Then we used either another router or phone as the sniffer router to capture network packets. From the packets we can extract the Channel State Information(CSI) to understand how the wave propagates from the 5GHz band. From there we gathered multiple datasets and processed this with different machine learning algorithms. This process was done iteratively as we explored different methods of data collection and variations to build a robust dataset. Then we applied preprocessing techniques from MERL along with different machine learning algorithms to increase accuracy throughout the course of our project. Our project allows us to predict the user’s location and pose within our testbed. For example in one experiment we have identified five different locations in a room along with three unique poses the user performs while operating their smartphone. In another experiment, our testbed was able to determine the number of people in a room. This small concept can later be expanded to produce more data points within a larger testbed. Making this technology attractive to companies who need solutions to security, localization, pose recognition and more. To put it succinctly, our results allow us to determine the predefined location and movement of the user and/or users within the testbed with a certain degree of accuracy.