Leveraging WiFi Channel State Information for Efficient and Reliable Device-Free Human Activity Recognition

Abstract

Device-free human activity recognition (DF-HAR) has attracted considerable attention in recent times owing to its promising applications in healthcare monitoring, smart home, and security systems. WiFi channel state information (CSI) has emerged as a promising approach for DF-HAR, as it provides valuable insights into the changes in the radio signal caused by human movements. In this paper, we explore the use of WiFi CSI for DF-HAR and propose a system that utilizes WiFi CSI to reliably recognize human activities without the need for sensors or wearable devices. Our system leverages the rich information available in WiFi CSI and employs advanced signal processing techniques to achieve high reliability in device-free human activity recognition Our experimental results validate the effectiveness of our proposed approach and its potential for application in various real-world scenarios.