WiFi-based human sensing has shown remarkable potential to detect sign language gestures in a non-intrusive manner. However, most previous works focus on American Sign Language detection, ignoring applications in other widely used languages such as Bangla Sign Language. There also remains a lack of collection of sign language gestures for Activities of Daily Life (ADL) necessary for instructing children with Autism Spectrum Disorder (ASD).
This dataset contains 20,000 packets of wireless channel measurements collected between two simulated devices, Alice and Bob, using the Vienna 5G Link Level Simulator. The dataset captures channel state information (CSI), signal magnitude, and phase variations under four different wireless environments: Indoor Mobile (IME), Indoor Static (ISE), Outdoor Mobile (OME), and Outdoor Static (OSE), with corresponding correlation values of 0.65, 0.82, 0.66, and 0.63, respectively.
This dataset mentioned in the article "Environment Independent Gait Recognition Based on Wi-Fi Signals". This dataset was collected using a pair of Wi-Fi transceivers gathering channel state information of human walking, with the transmitter featuring an omnidirectional antenna and the receiver having three omnidirectional antennas. Data was collected in four indoor environments, where eight users walked along 24 directions. For specific environments and directions arrangements, please refer to the article. Each user walked ten times in each direction.
Channel frequency response (CFR) dataset used for "In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks" paper (IEEE Transactions on Microwave Theory and Techniques, in print, doi: 10.1109/TMTT.2023.3256532, preprint link: https://arxiv.org/abs/2303.04470).
Since the CFR of the wireless channel, which is also known as the channel state information (CSI) in many cellular or Wi-Fi positioning literature, contains the angle and delay information of both the direct path and reflected paths, we believe the following research fields can benefit from this dataset:
With the rapid deployment of indoor Wi-Fi networks, Channel State Information (CSI) has been used for device-free occupant activity recognition. However, various environmental factors interfere with the stable propagation of Wi-Fi signals indoors, which causes temporal variation of CSI data. In this study, we investigated temporal CSI variation in a real-world housing environment and its impact on learning-based occupant activity recognition.
Abstract—Network slicing (NwS) is one of the main technologies
in the h-generation of mobile communication and
beyond (5G+). One of the important challenges in the NwS
is information uncertainty which mainly involves demand
and channel state information (CSI). Demand uncertainty is
divided into three types: number of users requests, amount
of bandwidth, and requested virtual network functions workloads.
Moreover, the CSI uncertainty is modeled by three
methods: worst-case, probabilistic, and hybrid. In this paper,
Using Wi-Fi IEEE 802.11 standard, radio frequency waves are mainly used for communication on various devices such as mobile phones, laptops, and smart televisions. Apart from communication applications, the recent research in wireless technology has turned Wi-Fi into other exploration possibilities such as human activity recognition (HAR). HAR is a field of study that aims to predict motion and movement made by a person or even several people.
