Wi-Fi sensing
This dataset offers both Channel State Information (CSI) and Beamforming Feedback Information (BFI) data for human activity classification, featuring 20 distinct activities performed by three subjects across three environments. Collected in both line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, this dataset enables researchers to explore the complementary roles of CSI and BFI in activity recognition and environmental characterization.
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This dataset enables advanced Wi-Fi sensing applications, including multi-subject monitoring for home surveillance, remote healthcare, and entertainment. It focuses on Beamforming Feedback Information (BFI) as a proxy for Channel State Information (CSI), eliminating the need for firmware modifications and enabling single-capture data collection across multiple channels between an access point (AP) and stations (STAs).
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This dataset contains Wi-Fi sensing data using Channel State Information (CSI) for various sleep disturbance parameters, from respiratory disturbances, to motion-based disturbances from posture shifts, leg restlessness and confusional arousals.The Wi-Fi CSI data was collected using the Wi-Fi module on the ESP32 Microcontroller units using the esp32-csi-tool.The Wi-Fi CSI respiratory disturbance data is accompanied by respiration belt data taken with the Wi-Fi measurements simultaneously using the Neulog NUL-236 respiration belt logger as ground truth.
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622 Views
Recent advances in Wi-Fi sensing have ushered in a plethora of pervasive applications in home surveillance, remote healthcare, road safety, and home entertainment, among others. Most of the existing works are limited to the activity classification of a single human subject at a given time. Conversely, a more realistic scenario is to achieve simultaneous, multi-subject activity classification. The first key challenge in that context is that the number of classes grows exponentially with the number of subjects and activities.
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750 Views
The dataset includes channel frequency response (CFR) data collected through an IEEE 802.11ax device for human activity recognition. This is the first dataset for Wi-Fi sensing with the IEEE 802.11ax standard which is the most updated Wi-Fi version available in commercial devices. The dataset has been collected within a single environment considering a single person as the purpose of the study was to evaluate the impact of communication parameters on the performance of sensing algorithms.
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2739 Views
The complete description of the dataset can be found at: https://arxiv.org/abs/2305.03170
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8067 Views