Meneghello, Francesca and Garlisi, Domenico and Dal Fabbro, Nicolò and Tinnirello, Ilenia and Rossi, Michele (2022) Wi-Fi channel frequency response database for contactless human activity recognition. [Data Collection]
Collection description
This database collects the channel frequency response (CFR) vectors captured through the Nexmon CSI extraction tool from an Asus RT-AC86U IEEE 802.11ac Wi-Fi router working with a total bandwidth of 80 MHz. The dataset is collected in three different environments, i.e., a bedroom, a living room and a University laboratory, while one person performs one among seven activities of interest within the room. The CFR data for the empty room (E) is also provided. We obtained data from three volunteers (a male, and two females) while they were walking (W) or running (R) around, jumping (J) in place, sitting (L) or standing (S) somewhere in the room, sitting down and standing up (C) continuously, and doing arm gym (H). Each CFR sample results in complex-valued channel information from 242 data sub-channels for each transmit-receive antennas pair. In our experiments, with one transmitter antenna and four at the monitoring device, each sample corresponds to four vectors of 242 complex values. Although the total number of sub-channels at 80 MHz is 256, each antenna vector has 242 components as the CFR is only provided for data sub-channels, namely sub-channels whose indexes are {-122, ..., -2} and {2, ..., 122}, i.e., no CFR value is provided for the control sub-channels. For more information about the setup, please, refer to the related publication. This dataset was used to design and assess the performance of SHARP presented in the article ''SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points'' by Francesca Meneghello, Domenico Garlisi, Nicolò Dal Fabbro, Ilenia Tinnirello, Michele Rossi. The Python source code is available at https://github.com/signetlabdei/SHARP. If you use this dataset, please cite our paper: @misc{meneghello2022SHARP, url = {https://arxiv.org/abs/2103.09924}, author = {Meneghello, Francesca and Garlisi, Domenico and Fabbro, Nicolò Dal and Tinnirello, Ilenia and Rossi, Michele}, title = {Environment and Person Independent Activity Recognition with a Commodity IEEE 802.11ac Access Point}, publisher = {arXiv}, year = {2021} }
| DOI: |  10.25430/researchdata.cab.unipd.it.00000624 |
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| Keywords: | Wi-Fi channel frequency response, human activity recognition | ||||||||||||||||||
| Subjects: | Physical:Sciences and Engineering > Systems and Communication Engineering: Electrical, electronic, communication, optical and systems engineering > Signal processing Physical:Sciences and Engineering > Systems and Communication Engineering: Electrical, electronic, communication, optical and systems engineering > Networks (communication networks, sensor networks, networks of robots, etc.) |
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| Department: | Departments > Dipartimento di Ingegneria dell'Informazione (DEI) | ||||||||||||||||||
| Depositing User: | Francesca Meneghello | ||||||||||||||||||
| Date Deposited: | 02 May 2022 07:32 | ||||||||||||||||||
| Last Modified: | 02 May 2023 06:36 | ||||||||||||||||||
| Creators/Authors: |
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| Type of data: | Database | ||||||||||||||||||
| Research funder: | Italian Ministry of Education, University and Research (MIUR), European Union’s Horizon 2020 programme | ||||||||||||||||||
| Research project title: | Departments of Excellence Law 232/2016, Grants No. 871249 project LOCUS | ||||||||||||||||||
| Collection period: |
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| Resource language: | English | ||||||||||||||||||
| Metadata language: | English | ||||||||||||||||||
| Publisher: | Research Data Unipd | ||||||||||||||||||
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| Date: | 7 April 2022 | ||||||||||||||||||
| Copyright holders: | The Author | ||||||||||||||||||
| URI: | https://researchdata.cab.unipd.it/id/eprint/624 |
orcid.org/0000-0002-9905-0360
