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A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments
The aim of this paper is to present a dataset for Wi-Fi-based human activity recognition. The dataset is comprised of five experiments performed by 30 different subjects in three different indoor environments. The experiments performed in the first two environments are of a line-of-sight (LOS) natur...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7704290/ https://www.ncbi.nlm.nih.gov/pubmed/33299909 http://dx.doi.org/10.1016/j.dib.2020.106534 |
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author | Alsaify, Baha’ A. Almazari, Mahmoud M. Alazrai, Rami Daoud, Mohammad I. |
author_facet | Alsaify, Baha’ A. Almazari, Mahmoud M. Alazrai, Rami Daoud, Mohammad I. |
author_sort | Alsaify, Baha’ A. |
collection | PubMed |
description | The aim of this paper is to present a dataset for Wi-Fi-based human activity recognition. The dataset is comprised of five experiments performed by 30 different subjects in three different indoor environments. The experiments performed in the first two environments are of a line-of-sight (LOS) nature, while the experiments performed in the third environment are of a non-line-of-sight (NLOS) nature. Each subject performed 20 trials for each of the experiments which makes the overall number of recorded trials in the dataset equals to 3000 trials (30 subjects × 5 experiments × 20 trials). To record the data, we used the channel state information (CSI) tool [1] to capture the exchanged Wi-Fi packets between a Wi-Fi transmitter and receiver. The utilized transmitter and receiver are retrofitted with the Intel 5300 network interface card which enabled us to capture the CSI values that are contained in the recorded transmissions. Unlike other publicly available human activity datasets, this dataset provides researchers with the ability to test their developed methodologies on both LOS and NLOS environments, in addition to many different variations of human movements, such as walking, falling, turning, and pen pick up from the ground. |
format | Online Article Text |
id | pubmed-7704290 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-77042902020-12-08 A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments Alsaify, Baha’ A. Almazari, Mahmoud M. Alazrai, Rami Daoud, Mohammad I. Data Brief Data Article The aim of this paper is to present a dataset for Wi-Fi-based human activity recognition. The dataset is comprised of five experiments performed by 30 different subjects in three different indoor environments. The experiments performed in the first two environments are of a line-of-sight (LOS) nature, while the experiments performed in the third environment are of a non-line-of-sight (NLOS) nature. Each subject performed 20 trials for each of the experiments which makes the overall number of recorded trials in the dataset equals to 3000 trials (30 subjects × 5 experiments × 20 trials). To record the data, we used the channel state information (CSI) tool [1] to capture the exchanged Wi-Fi packets between a Wi-Fi transmitter and receiver. The utilized transmitter and receiver are retrofitted with the Intel 5300 network interface card which enabled us to capture the CSI values that are contained in the recorded transmissions. Unlike other publicly available human activity datasets, this dataset provides researchers with the ability to test their developed methodologies on both LOS and NLOS environments, in addition to many different variations of human movements, such as walking, falling, turning, and pen pick up from the ground. Elsevier 2020-11-18 /pmc/articles/PMC7704290/ /pubmed/33299909 http://dx.doi.org/10.1016/j.dib.2020.106534 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Alsaify, Baha’ A. Almazari, Mahmoud M. Alazrai, Rami Daoud, Mohammad I. A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments |
title | A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments |
title_full | A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments |
title_fullStr | A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments |
title_full_unstemmed | A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments |
title_short | A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments |
title_sort | dataset for wi-fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7704290/ https://www.ncbi.nlm.nih.gov/pubmed/33299909 http://dx.doi.org/10.1016/j.dib.2020.106534 |
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