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Radar human breathing dataset for applications of ambient assisted living and search and rescue operations
This dataset consists of signatures of human vital signs that are recorded by ultrawideband radar and lidar sensors. The data acquisition scene considers the human posture models(supine/lateral/facedown), different radar antenna angles towards the human, various set of distances and operational rada...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10694063/ http://dx.doi.org/10.1016/j.dib.2023.109757 |
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author | Eren, Cansu Karamzadeh, Saeid Kartal, Mesut |
author_facet | Eren, Cansu Karamzadeh, Saeid Kartal, Mesut |
author_sort | Eren, Cansu |
collection | PubMed |
description | This dataset consists of signatures of human vital signs that are recorded by ultrawideband radar and lidar sensors. The data acquisition scene considers the human posture models(supine/lateral/facedown), different radar antenna angles towards the human, various set of distances and operational radar characteristics (bandwidth selection/mean power). The raw data files of lidar&radar and processed data files are presented separately in the data repository. The lidar sensor is chosen as a reference sensor. There are 432 data records, and each data scene's trial number is eight. There is a homogeneous wooden table to mimic clutter while forming a dataset. Thus, this dataset covers applications of search and rescue operations, sleep monitoring, and ambient assisted living (AAL) applications. |
format | Online Article Text |
id | pubmed-10694063 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-106940632023-12-05 Radar human breathing dataset for applications of ambient assisted living and search and rescue operations Eren, Cansu Karamzadeh, Saeid Kartal, Mesut Data Brief Data Article This dataset consists of signatures of human vital signs that are recorded by ultrawideband radar and lidar sensors. The data acquisition scene considers the human posture models(supine/lateral/facedown), different radar antenna angles towards the human, various set of distances and operational radar characteristics (bandwidth selection/mean power). The raw data files of lidar&radar and processed data files are presented separately in the data repository. The lidar sensor is chosen as a reference sensor. There are 432 data records, and each data scene's trial number is eight. There is a homogeneous wooden table to mimic clutter while forming a dataset. Thus, this dataset covers applications of search and rescue operations, sleep monitoring, and ambient assisted living (AAL) applications. Elsevier 2023-11-10 /pmc/articles/PMC10694063/ http://dx.doi.org/10.1016/j.dib.2023.109757 Text en © 2023 The Author(s) https://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 Eren, Cansu Karamzadeh, Saeid Kartal, Mesut Radar human breathing dataset for applications of ambient assisted living and search and rescue operations |
title | Radar human breathing dataset for applications of ambient assisted living and search and rescue operations |
title_full | Radar human breathing dataset for applications of ambient assisted living and search and rescue operations |
title_fullStr | Radar human breathing dataset for applications of ambient assisted living and search and rescue operations |
title_full_unstemmed | Radar human breathing dataset for applications of ambient assisted living and search and rescue operations |
title_short | Radar human breathing dataset for applications of ambient assisted living and search and rescue operations |
title_sort | radar human breathing dataset for applications of ambient assisted living and search and rescue operations |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10694063/ http://dx.doi.org/10.1016/j.dib.2023.109757 |
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