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Dataset from PPG wireless sensor for activity monitoring

We introduce a dataset to provide insights about the photoplethysmography (PPG) signal captured from the wrist in presence of motion artifacts and the accelerometer signal, simultaneously acquired from the same wrist. The data presented were collected by the electronics research team of the Departme...

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Detalles Bibliográficos
Autores principales: Biagetti, Giorgio, Crippa, Paolo, Falaschetti, Laura, Saraceni, Leonardo, Tiranti, Andrea, Turchetti, Claudio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6971339/
https://www.ncbi.nlm.nih.gov/pubmed/31989005
http://dx.doi.org/10.1016/j.dib.2019.105044
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author Biagetti, Giorgio
Crippa, Paolo
Falaschetti, Laura
Saraceni, Leonardo
Tiranti, Andrea
Turchetti, Claudio
author_facet Biagetti, Giorgio
Crippa, Paolo
Falaschetti, Laura
Saraceni, Leonardo
Tiranti, Andrea
Turchetti, Claudio
author_sort Biagetti, Giorgio
collection PubMed
description We introduce a dataset to provide insights about the photoplethysmography (PPG) signal captured from the wrist in presence of motion artifacts and the accelerometer signal, simultaneously acquired from the same wrist. The data presented were collected by the electronics research team of the Department of Information Engineering, Polytechnic University of Marche, Ancona, Italy. This article describes data recorded from 7 subjects and includes 105 PPG signals (15 for each subject) and the corresponding 105 tri-axial accelerometer signals measured with a sampling frequency of 400 Hz. These data can be reused for testing machine learning algorithms for human activity recognition.
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spelling pubmed-69713392020-01-27 Dataset from PPG wireless sensor for activity monitoring Biagetti, Giorgio Crippa, Paolo Falaschetti, Laura Saraceni, Leonardo Tiranti, Andrea Turchetti, Claudio Data Brief Engineering We introduce a dataset to provide insights about the photoplethysmography (PPG) signal captured from the wrist in presence of motion artifacts and the accelerometer signal, simultaneously acquired from the same wrist. The data presented were collected by the electronics research team of the Department of Information Engineering, Polytechnic University of Marche, Ancona, Italy. This article describes data recorded from 7 subjects and includes 105 PPG signals (15 for each subject) and the corresponding 105 tri-axial accelerometer signals measured with a sampling frequency of 400 Hz. These data can be reused for testing machine learning algorithms for human activity recognition. Elsevier 2019-12-23 /pmc/articles/PMC6971339/ /pubmed/31989005 http://dx.doi.org/10.1016/j.dib.2019.105044 Text en © 2019 The Authors 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 Engineering
Biagetti, Giorgio
Crippa, Paolo
Falaschetti, Laura
Saraceni, Leonardo
Tiranti, Andrea
Turchetti, Claudio
Dataset from PPG wireless sensor for activity monitoring
title Dataset from PPG wireless sensor for activity monitoring
title_full Dataset from PPG wireless sensor for activity monitoring
title_fullStr Dataset from PPG wireless sensor for activity monitoring
title_full_unstemmed Dataset from PPG wireless sensor for activity monitoring
title_short Dataset from PPG wireless sensor for activity monitoring
title_sort dataset from ppg wireless sensor for activity monitoring
topic Engineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6971339/
https://www.ncbi.nlm.nih.gov/pubmed/31989005
http://dx.doi.org/10.1016/j.dib.2019.105044
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