@inproceedings{aae0885846064b158e7d35da72006cdd,
title = "Human Activity Recognition Using Wi-Fi CSI",
abstract = "Wi-Fi signals were originally developed with a focus on communication. However, beyond communication applications, Wi-Fi signals have been recently studied as a possible powerful tool for human sensing applications. In this sense, we present in this paper an original approach for obtaining human activity recognition (HAR) through the use of commercial Wi-Fi devices. Using our proposal, it is possible to infer the position of a monitored person in an indoor environment (room). To achieve this, we clean and process the amplitude of the channel state information (CSI) data collected from the Wi-Fi channel. We selected and evaluated five different classification algorithms to infer the subjects position and compare their performance. The proposed method was evaluated on a dataset of Wi-Fi CSI data collected from 125 participants. The proposed system is trained with the data collected while a person performs a variety of activities in a room. For the scenario and dataset considered in this study, the results showed that the Random Forest algorithm had the best performance for all tests, reaching an accuracy of 93.03\% on average.",
keywords = "Channel state information, CSI, HAR, human activity recognition, Wi-Fi",
author = "Egberto Caballero and Iandra Galdino and Soto, \{Julio C.H.\} and Ramos, \{Taiane C.\} and Raphael Guerra and D{\'e}bora Muchaluat-Saade and C{\'e}lio Albuquerque",
note = "Publisher Copyright: {\textcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.; 17th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2023 ; Conference date: 27-11-2023 Through 29-11-2023",
year = "2024",
doi = "10.1007/978-3-031-59717-6\_21",
language = "Ingl{\'e}s",
isbn = "9783031597169",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "309--321",
editor = "Dario Salvi and \{Van Gorp\}, Pieter and Shah, \{Syed Ahmar\}",
booktitle = "Pervasive Computing Technologies for Healthcare - 17th EAI International Conference, PervasiveHealth 2023, Proceedings",
address = "Alemania",
}