HIGH PERFORMANCE THROUGH WALL HUMAN ACTIVITY RECOGNITION USING WIFI

Abstract

ABSTRACT: Passive human activity recognition without requiring a device is crucial in various fields, including smart homes, health care, and identification. However, current systems for human activity recognition require a dedicated device, or they need to be more suitable for scenarios where signals are transmitted through walls. To address this challenge, we propose a system for device-free, passive recognition of human activity that utilizes CSI-based Wi-Fi signals and does not require any dedicated devices. The proposed approach uses two techniques to distinguish different human activities. First, we introduce an opposite robust method to eliminate the influence of the background environment on correlation extraction and to obtain the correlation between human activity and its resulting changes in channel state information values. Second, we propose a normalized variance sliding windows algorithm to segment the time of human action from the waveforms, which can differentiate human actions' start and end times. We also implemented a model CSI based using Nexmon with an LSTM algorithm with commodity Wi-Fi devices and evaluated it in several environments. Our experimental results demonstrate that we achieve an average accuracy of 95% when signals pass through concrete walls.