Circle drawing and tracing dataset for evaluation of fine motor control

We introduce a motion dataset from healthy human subjects (n = 125) performing two fine motor control tasks on a graphic tablet, namely circle drawing and circle tracing. The article reports the methods and materials used to capture the motion data. The method for data acquisition is the same as the...

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Main Authors: Eros Quarta, Riccardo Bravi, Diego Minciacchi, Erez James Cohen
Format: Article
Language:English
Published: Elsevier 2021-04-01
Series:Data in Brief
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2352340921000470
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spelling doaj-c17fb68ddd0742fb8f613e142ae14c4e2021-04-26T05:55:57ZengElsevierData in Brief2352-34092021-04-0135106763Circle drawing and tracing dataset for evaluation of fine motor controlEros Quarta0Riccardo Bravi1Diego Minciacchi2Erez James Cohen3Department of Experimental and Clinical Medicine, Physiological Sciences Section, University of Florence, Viale Morgagni, 63 - 50134 Florence, ItalyDepartment of Experimental and Clinical Medicine, Physiological Sciences Section, University of Florence, Viale Morgagni, 63 - 50134 Florence, ItalyDepartment of Experimental and Clinical Medicine, Physiological Sciences Section, University of Florence, Viale Morgagni, 63 - 50134 Florence, ItalyCorresponding author.; Department of Experimental and Clinical Medicine, Physiological Sciences Section, University of Florence, Viale Morgagni, 63 - 50134 Florence, ItalyWe introduce a motion dataset from healthy human subjects (n = 125) performing two fine motor control tasks on a graphic tablet, namely circle drawing and circle tracing. The article reports the methods and materials used to capture the motion data. The method for data acquisition is the same as the one used to investigate some aspects of fine motor control in healthy subjects in the paper by Cohen et al. (2018) “Precision in drawing and tracing tasks: Different measures for different aspects of fine motor control” (https://doi.org/10.1016/j.humov.2018.08.004) [1]. The dataset shared here contains new raw files of the two-dimensional motion data, as well information on the participants (gender, age, laterality index). These data could be instrumental for assessing other aspects of fine motor control, such as speed-accuracy tradeoff, speed-curvature power law, etc., and/or test machine learning algorithms for e.g., task classification.http://www.sciencedirect.com/science/article/pii/S2352340921000470Fine motor controlDrawingTracingPsychophysics
collection DOAJ
language English
format Article
sources DOAJ
author Eros Quarta
Riccardo Bravi
Diego Minciacchi
Erez James Cohen
spellingShingle Eros Quarta
Riccardo Bravi
Diego Minciacchi
Erez James Cohen
Circle drawing and tracing dataset for evaluation of fine motor control
Data in Brief
Fine motor control
Drawing
Tracing
Psychophysics
author_facet Eros Quarta
Riccardo Bravi
Diego Minciacchi
Erez James Cohen
author_sort Eros Quarta
title Circle drawing and tracing dataset for evaluation of fine motor control
title_short Circle drawing and tracing dataset for evaluation of fine motor control
title_full Circle drawing and tracing dataset for evaluation of fine motor control
title_fullStr Circle drawing and tracing dataset for evaluation of fine motor control
title_full_unstemmed Circle drawing and tracing dataset for evaluation of fine motor control
title_sort circle drawing and tracing dataset for evaluation of fine motor control
publisher Elsevier
series Data in Brief
issn 2352-3409
publishDate 2021-04-01
description We introduce a motion dataset from healthy human subjects (n = 125) performing two fine motor control tasks on a graphic tablet, namely circle drawing and circle tracing. The article reports the methods and materials used to capture the motion data. The method for data acquisition is the same as the one used to investigate some aspects of fine motor control in healthy subjects in the paper by Cohen et al. (2018) “Precision in drawing and tracing tasks: Different measures for different aspects of fine motor control” (https://doi.org/10.1016/j.humov.2018.08.004) [1]. The dataset shared here contains new raw files of the two-dimensional motion data, as well information on the participants (gender, age, laterality index). These data could be instrumental for assessing other aspects of fine motor control, such as speed-accuracy tradeoff, speed-curvature power law, etc., and/or test machine learning algorithms for e.g., task classification.
topic Fine motor control
Drawing
Tracing
Psychophysics
url http://www.sciencedirect.com/science/article/pii/S2352340921000470
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