A multi-sensor dataset of human-human handover

The article describes a multi-sensor dataset of human-human handovers composed of over 1000 recordings collected from 18 volunteers. The recordings refer to 76 test configurations, which consider different volunteer׳s starting positions and roles, objects to pass and motion strategies. In all experi...

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Detalles Bibliográficos
Autores principales: Carfì, Alessandro, Foglino, Francesco, Bruno, Barbara, Mastrogiovanni, Fulvio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Engineering
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6297854/
https://www.ncbi.nlm.nih.gov/pubmed/30581913
http://dx.doi.org/10.1016/j.dib.2018.11.110
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author Carfì, Alessandro
Foglino, Francesco
Bruno, Barbara
Mastrogiovanni, Fulvio
author_facet Carfì, Alessandro
Foglino, Francesco
Bruno, Barbara
Mastrogiovanni, Fulvio
author_sort Carfì, Alessandro
collection PubMed
description The article describes a multi-sensor dataset of human-human handovers composed of over 1000 recordings collected from 18 volunteers. The recordings refer to 76 test configurations, which consider different volunteer׳s starting positions and roles, objects to pass and motion strategies. In all experiments, we acquire 6-axis inertial data from two smartwatches, the 15-joint skeleton model of one volunteer with an RGB-D camera and the upper-body model of both persons using a total of 20 motion capture markers. The recordings are annotated with videos and questionnaires about the perceived characteristics of the handover.
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spelling pubmed-62978542018-12-21 A multi-sensor dataset of human-human handover Carfì, Alessandro Foglino, Francesco Bruno, Barbara Mastrogiovanni, Fulvio Data Brief Engineering The article describes a multi-sensor dataset of human-human handovers composed of over 1000 recordings collected from 18 volunteers. The recordings refer to 76 test configurations, which consider different volunteer׳s starting positions and roles, objects to pass and motion strategies. In all experiments, we acquire 6-axis inertial data from two smartwatches, the 15-joint skeleton model of one volunteer with an RGB-D camera and the upper-body model of both persons using a total of 20 motion capture markers. The recordings are annotated with videos and questionnaires about the perceived characteristics of the handover. Elsevier 2018-11-27 /pmc/articles/PMC6297854/ /pubmed/30581913 http://dx.doi.org/10.1016/j.dib.2018.11.110 Text en © 2018 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Engineering
Carfì, Alessandro
Foglino, Francesco
Bruno, Barbara
Mastrogiovanni, Fulvio
A multi-sensor dataset of human-human handover
title A multi-sensor dataset of human-human handover
title_full A multi-sensor dataset of human-human handover
title_fullStr A multi-sensor dataset of human-human handover
title_full_unstemmed A multi-sensor dataset of human-human handover
title_short A multi-sensor dataset of human-human handover
title_sort multi-sensor dataset of human-human handover
topic Engineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6297854/
https://www.ncbi.nlm.nih.gov/pubmed/30581913
http://dx.doi.org/10.1016/j.dib.2018.11.110
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