Wi-Fi Monostatic Full Duplex Human Sensing

Dataset Overview URL: https://huggingface.co/datasets/JessicaSanson/Wi-Fi_Monostatic_Human_Sensing_CSI_Intel Scope notice: This is a sample dataset released to accompany the paper and enable reproducibility of the reported results. It contains a small number of recordings (3 sessions, 2 subjects, 2 devices). For algorithm validation, signal processing research, or replication of the paper's Range-Filtered Doppler Spectrum method, the data is sufficient as-is. Human Presence Detection via Wi-Fi Range-Filtered Doppler Spectrum — Sample Dataset Paper: Human Presence Detection via Wi-Fi Range-Filtered Doppler Spectrum on Commodity Laptops Authors: Jessica Bartholdy Sanson, Rahul C. Shah, Valerio Frascolla Year: 2026 Venue: IEEE PerCom 2026 — WiSense Workshop (Workshop on Wireless Sensing for Smart Spaces and Beyond) This dataset contains Channel State Information (CSI) recordings for Human Presence Detection (HPD) — capturing macro-level human movement (walking, approach/leave, breathing at rest). The data is collected using monostatic full-duplex Wi-Fi sensing on commercial off-the-shelf (COTS) laptops — no external sensors, no dedicated transmitter, no hardware modification of any kind. Unlike bistatic datasets that require a separate transmitter (e.g., a router) and receiver, here a single unmodified laptop simultaneously transmits and receives by sharing the Local Oscillator and baseband processing, using the device's own self-interference as the sensing signal. CSI is read directly from the built-in NIC. License Creative Commons Attribution 4.0 — Copyright (c) 2026 Intel Corporation Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation, to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies, subject to the above copyright notice and this permission notice appearing in all copies. Ethics and Privacy Informed consent obtained from all participants Full GDPR compliance Anonymized participant IDs No PII, video, or audio recordings