HUMAN MOVEMENT IDENTIFICATION USING WI-FI WITH CHANNEL STATE INFORMATION
DOI:
https://doi.org/10.47820/recima21.v6i11.6992Keywords:
Channel State Information, Artificial Intelligence, ProcessingAbstract
This research describes the development of a system for the identification of movements using physical information from the Wi-Fi signal, such as amplitude and phase, known as Channel State Information (CSI). Data is captured in a controlled environment using a Raspberry Pi 4 microcomputer with custom firmware and components for the emission and transmission of Wi-Fi signals. Subsequently, the data is classified using Machine Learning models, such as SVM, Random Forest, and KNN. The experimental results demonstrate the capability of the system to detect movements, indicating that CSI is a non-intrusive alternative for movement identification, superior to traditional monitoring methods using images or audio.
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