"CSI-BFI-HAR: Datasets for Human Activity Recognition with Integrated Sensing and Communications in Wi-Fi"

"Wi-Fi sensing has emerged as a promising non-intrusive approach for human activity recognition (HAR), leveraging the widespread availability of Wi-Fi-enabled devices. While machine learning tools help extract meaningful features from complex wireless propagation patterns, developing robust and domain-adaptive models remains challenging without diverse datasets. To address this, we release two comprehensive datasets containing Channel State Information (CSI) and Beamforming Feedback Information (BFI), collected using commercial IEEE 802.11ac devices across multiple days in both LoS and NLoS scenarios. The first dataset includes 20 activities performed by six subjects across six setups. The second captures simultaneous multi-subject activities, with three individuals performing distinct actions concurrently. Notably, this is the first public HAR dataset to include corresponding BFI measurements"

Wi-Fi感知作为一种极具前景的非侵入式人类活动识别（Human Activity Recognition，简称HAR）方案，依托已普及的支持Wi-Fi功能的设备而得以快速发展。尽管机器学习工具可从复杂的无线传播模式中提取有效特征，但在缺乏多样化数据集的情况下，构建鲁棒且具备领域自适应能力的模型仍面临诸多挑战。为解决这一痛点，我们发布了两套涵盖信道状态信息（Channel State Information，缩写CSI）与波束赋形反馈信息（Beamforming Feedback Information，缩写BFI）的综合数据集。数据集采用商用IEEE 802.11ac标准设备采集，采集周期覆盖多日，且同时涵盖视距（Line of Sight，缩写LoS）与非视距（Non-Line of Sight，缩写NLoS）两类部署场景。第一套数据集包含6名受试者在6种实验布置下完成的20类人类活动数据；第二套数据集聚焦多受试者同步活动场景，包含3名个体同时执行不同动作的采集样本。值得注意的是，本数据集是首个公开的、包含配套波束赋形反馈信息测量数据的人类活动识别数据集。

搜集汇总

背景与挑战

背景概述

该数据集是用于Wi-Fi基于人类活动识别（HAR）的研究，包含两个子集：单主体数据集和多主体数据集，分别记录了20种不同活动和多主体同时进行的活动。数据集首次公开了包含CSI和BFI测量的HAR数据，总数据量约为500GB的CSI和700GB的BFI，适用于开发鲁棒和领域自适应的传感算法。