A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels

TL;DR

This paper introduces a new, diverse Wi-Fi CSI dataset over 80 MHz channels, enabling improved development of human sensing algorithms that are robust across various environments and hardware configurations.

Contribution

The authors provide a large, diverse dataset of Wi-Fi channel measurements on 80 MHz bands, addressing limitations of previous datasets with narrow bandwidths and limited domain diversity.

Findings

Dataset contains over 13 hours of CSI data from 13 subjects.

Includes measurements in different environments, days, and hardware setups.

Data collected with blocked direct path and in a semi-anechoic chamber.

Abstract

In the last years, several machine learning-based techniques have been proposed to monitor human movements from Wi-Fi channel readings. However, the development of domain-adaptive algorithms that robustly work across different environments is still an open problem, whose solution requires large datasets characterized by strong domain diversity, in terms of environments, persons and Wi-Fi hardware. To date, the few public datasets available are mostly obsolete - as obtained via Wi-Fi devices operating on 20 or 40 MHz bands - and contain little or no domain diversity, thus dramatically limiting the advancements in the design of sensing algorithms. The present contribution aims to fill this gap by providing a dataset of IEEE 802.11ac channel measurements over an 80 MHz bandwidth channel featuring notable domain diversity, through measurement campaigns that involved thirteen subjects across different environments, days, and with different hardware. Novel experimental data is provided by blocking the direct path between the transmitter and the monitor, and collecting measurements in a semi-anechoic chamber (no multi-path fading). Overall, the dataset - available on IEEE DataPort [1] - contains more than thirteen hours of channel state information readings (23.6 GB), allowing researchers to test activity/identity recognition and people counting algorithms.