Wi-Fi Signal-Based Through-Wall Sensing for Human Presence and Fall Detection Using ESP32 Module

Device-free Wi-Fi sensing has gained much attention due to its simplicity, low cost, and it requires no additional hardware. The main advantages of Wi-Fi sensing are, it is unobtrusive, can operate through walls, work without lighting, is ubiquitous, and does not require users to carry any additional wearable devices. These Wi-Fi signals can be exploited to detect the event and recognize various human activities. This paper proposes a device-free Wi-Fi sensing system to track human presence and fall activity detection across the wall using the Channel State Information (CSI) values extracted from the received Wi-Fi signals. The received signal characteristics changes with the presence of the human beings, and their activities influence the signal propagation, resulting from reflection and scattering. The activities can be recognized by analyzing the CSI values corresponding to different subcarriers of the received signal. CSI values contain fine grain information such as amplitude and phase to achieve better sensing accuracy with a unique pattern that can be observed corresponding to each activity. We developed the transmitter and receiver hardware modules together with the necessary software for capturing the CSI from Wi-Fi signals using low-power and low-cost ESP32 module. Multiple experiments are conducted for human presence and fall detection activities in indoor environments. The proposed model finds the best time window size to detect the activities instead of considering the usual fixed time window size. This helped to increase the detection accuracy. The results show the effectiveness of the proposed method with accurate event detection.