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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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 |
work_keys_str_mv |
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