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.

翻译：近年来，基于机器学习的Wi-Fi信道读数人体运动监测技术被广泛提出。然而，开发能跨环境鲁棒工作的领域自适应算法仍是开放性问题，其解决需要具有强领域多样性的大规模数据集（涵盖环境、人员及Wi-Fi硬件差异）。目前，公开可用的数据集大多已过时——基于20 MHz或40 MHz频段Wi-Fi设备采集——且缺乏或仅包含少量领域多样性，严重制约了感知算法设计的进展。本研究通过开展跨不同环境、日期及硬件设备，涉及13名受试者的测量活动，提供了80 MHz带宽信道上具有显著领域多样性的IEEE 802.11ac信道测量数据集以填补这一空白。实验通过阻断发射器与监测器之间的直射路径，并在半电波暗室（无多径衰落）中采集数据，获得了新型实验数据。该数据集（见IEEE DataPort[1]）包含超过13小时的信道状态信息读数（23.6 GB），可支持研究人员测试活动/身份识别及人数统计算法。