An IOT sensor was presented to detect human motionvital signs with Wi-Fi signals. It mainly combines MIMO beamforming, injection-locking radarmachine learning techniques to recognizetrack physicalphysiological activities for health status monitoring. As a proof of concept for pandemic prevention, the presented sensor aims to inspect whether someone is exhibiting signssymptoms of COVID-19 infection within the Wi-Fi coverage area, such as fatigue, cough, shortness of breathirregular heart rhythm,even to detect sudden death.

We develop an asynchronous framework across userkernel spaces for deep learning applications on the improvement of Wi-Fi performanceimplement it in the driver of commodity Intel Wi-Fi cards. Under this framework, we apply deep reinforcement learning to developing an intelligent rate adaptation (RA) algorithm (DRL-RA), which can achieve the highest throughput in varying channel conditions given many rate options of current Wi-Fi technologies. Its on-learning capability can learn how to efficiently approach the best rate from the experiences of its common usage patternenvironment.