"CSI Dataset for WiFi Based Human Detection "

"This dataset presents time-series Channel State Information (CSI) data collected using a Raspberry Pi 4 Model B equipped with the Broadcom BCM43455c0 WiFi chipset and the Nexmon CSI extraction toolchain. Operating on the 2.4 GHz frequency band with a 20 MHz channel bandwidth, the system passively captures raw CSI packets from a predefined access point within a controlled indoor environment. The primary objective of this dataset is to enable real-time indoor occupancy detection through passive WiFi sensing, leveraging the pervasive nature of wireless signals and cost-effective embedded hardware.The dataset includes a comprehensive range of occupancy scenarios, such as an empty room, single-person presence (both static and dynamic), and multiple-person occupancy, reflecting realistic environmental conditions. Each CSI frame contains high-resolution amplitude and phase data across 64 subcarriers. Ground truth labels specifying occupancy states (Occupied \/ Not Occupied) are provided to facilitate supervised learning and time-series analysis. Alongside raw CSI data in .pcap format, the dataset includes pre-processed .csv files, Python scripts for real-time packet capture, wavelet-based denoising using Discrete Wavelet Transform (DWT), and Doppler-based motion feature extraction to enhance signal interpretation.This publicly available dataset is a valuable resource for researchers and practitioners working in the fields of wireless sensing, signal processing, and machine learning. It enables development and benchmarking of models for occupancy detection, activity recognition, device-free localization, and smart building automation. Designed with a focus on real-time performance and privacy-preserving sensing, the dataset stands out for its accessibility, reproducibility, and relevance in advancing intelligent indoor environment applications using open-source tools and low-cost hardware platforms."

本数据集包含时序信道状态信息（Channel State Information, CSI）数据，采集自搭载博通（Broadcom）BCM43455c0 WiFi芯片组与Nexmon CSI提取工具链的树莓派4 Model B开发板。本系统工作于2.4 GHz频段、信道带宽为20 MHz，在受控室内环境中被动捕获来自预设接入点的原始CSI数据包。本数据集的核心目标是依托无线信号的普适性与低成本嵌入式硬件，通过被动WiFi感知实现实时室内人员占用检测。本数据集涵盖了贴合真实室内环境的多类典型人员占用场景，包括空房间、单人在场（含静态与动态两种状态）以及多人占用。每帧CSI数据均包含64个子载波的高分辨率幅度与相位信息。数据集附带标注了人员占用状态（占用/未占用）的真实标签，可用于监督学习与时序数据分析任务。除.pcap格式的原始CSI数据外，本数据集还包含预处理后的.csv文件、用于实时数据包捕获的Python脚本、基于离散小波变换（Discrete Wavelet Transform, DWT）的小波去噪代码，以及用于提升信号可解释性的多普勒运动特征提取代码。本公开数据集为无线感知、信号处理与机器学习领域的科研人员与工程实践者提供了极具价值的研究资源，可用于开发并基准测试人员占用检测、活动识别、无设备定位以及智能建筑自动化相关的模型。本数据集以实时性能与隐私保护感知为设计核心，凭借其易用性、可复现性，以及基于开源工具与低成本硬件平台推进智能室内环境应用发展的适用性，具备显著的研究优势。