Area human sensing via ambient Wi-Fi signals

Indoor human wireless sensing is crucial for many applications such as smart homes and security monitoring. It needs to understand both the number of people and their activities, which is often difficult, especially in large spaces. This paper proposes a combined people number counting and action recognition method by using the channel state information (CSI) of Wi-Fi signal, which aims to simultaneously estimate the number of people and their actions in wireless sensing applications. First, a jump region removal algorithm is developed to calibrate the phase difference. Second, a cumulative sliding variance algorithm is designed for the detection of moving targets in the environment. Then a multi-dimensional feature is formed by the amplitude, frequency domain, and phase difference of CSI, and based on it, a two-level classifier based on the SVM algorithm is used to estimate the number of people and their actions. Experimental results show that the average accuracy of the system for the people counting is 93.5%, and the activity recognition rate of one-person and two-person is 99.6% and 94.6% respectively.