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112 lines
4.9 KiB
Plaintext
Executable File
112 lines
4.9 KiB
Plaintext
Executable File
1. Title: LIBRAS Movement Database
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2. Sources:
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(a) Creators:
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Daniel Baptista Dias (Dias, D.B.)
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Sarajane Marques Peres (Peres, S. M.)
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Helton Hideraldo Bíscaro (Bíscaro. H. H.)
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{danielbdias,heltonhb, sarajane} at usp.br
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(b) Donor:
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University of São Paulo
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School of Art, Sciences and Humanities
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São Paulo, SP, Brazil
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http://each.uspnet.usp.br/each/
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(c) Date: November, 2008
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3. Past Usage:
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1. DIAS, D. B.; MADEO, R. C. B.; ROCHA, T.; BÍSCARO, H. H.; PERES, S. M.. Hand Movement Recognition for
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Brazilian Sign Language: A Study Using Distance-Based Neural Networks. In: 2009 International Joint
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Conference on Neural Networks, 2009, Atlanta, GA. Proceedings of 2009 International Joint Conference
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on Neural Networks. Eau Claire, WI, USA : Documation LLC, 2009. p. 697-704.
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Digital Object Identifier 10.1109/IJCNN.2009.5178917
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4. Relevant Information:
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--- LIBRAS, acronym of the Portuguese name "LÍngua BRAsileira de Sinais", is the oficial brazilian sign language.
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--- The dataset (movement_libras) contains 15 classes of 24 instances each, where each class references to a hand
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movement type in LIBRAS. The hand movement is represented as a bidimensional curve performed by the hand in a
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period of time. The curves were obtained from videos of hand movements, with the Libras performance from 4
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different people, during 2 sessions. Each video corresponds to only one hand movement and has about $7$ seconds.
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--- Each video corresponds to a function F in a functions space which is the continual version of the input dataset.
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--- In the video pre-processing, a time normalization is carried out selecting 45 frames from each video, in according
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to an uniform distribution. In each frame, the centroid pixels of the segmented objects (the hand) are found, which
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compose the discrete version of the curve F with 45 points. All curves are normalized in the unitary space.
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--- In order to prepare these movements to be analysed by algorithms, we have carried out a mapping operation, that is,
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each curve F is mapped in a representation with 90 features, with representing the coordinates of movement.
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--- Each instance represents 45 points on a bi-dimensional space, which can be plotted in an ordered way (from 1 through
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45 as the X co-ordinate) in order to draw the path of the movement.
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5. Number of Instances: 360 (24 in each of fifteen classes)
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6. Number of Attributes: 90 numeric (double) and 1 for the class (integer)
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7. Attribute Information:
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1. 1º coordinate abcissa
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2. 1º coordinate ordinate
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3. 2º coordinate abcissa
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4. 2º coordinate ordinate
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...
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89. 45º coordinate abcissa
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90. 45º coordinate ordinate
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91. class:
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-- 1: curved swing
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-- 2: horizontal swing
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-- 3: vertical swing
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-- 4: anti-clockwise arc
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-- 5: clockwise arc
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-- 6: circle
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-- 7: horizontal straight-line
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-- 8: vertical straight-line
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-- 9: horizontal zigzag
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-- 10: vertical zigzag
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-- 11: horizontal wavy
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-- 12: vertical wavy
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-- 13: face-up curve
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-- 14: face-down curve
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-- 15: tremble
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8. Missing Attribute Values: None
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9. Class Distribution: 6.66% for each of 15 classes.
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10. Extra Information
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(a)
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Applying a simple k-nearest neighbors algorithm (with Euclidean distance), it has been obtained the values below:
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--- (%) perc of instance which have been correctly placed in a same group
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--- using the complete dataset: 24 instances per class, 15 classes
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| mean of correct | standard deviate | max of correct / class | min of correct / class
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| grouping (%) | | grouping (%) | grouping (%)
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24-nearest neighbors | 0.3918 | 0.1267 | 0.5556 / 5 | 0.2587 / 10
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neighbors in a ratio = 1.0 | 0.2245 | 0.0979 | 0.3957 / 9 | 0.1181 / 1
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neighbors in a ratio = 2.0 | 0.3514 | 0.1210 | 0.4514 / 9 | 0.2500 / 10
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neighbors in a ratio = 3.0 | 0.3848 | 0.1266 | 0.5347 / 5 | 0.2587 / 10
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(b)
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In order to support the comparisons with the experiments of the paper cited in 3(3.1) (Past Usage),
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we supply you the sub-datasets:
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1. movement_libras_1.data
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2. movement_libras_5.data
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3. movement_libras_8.data
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4. movement_libras_9.data
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5. movement_libras_10.data |