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    "description": "**Author**: enata Cristina Barros Madeo (Madeo\",\"R. C. B.)  Priscilla Koch Wagner (Wagner\",\"P. K.)  Sarajane Marques Peres (Peres\",\"S. M.)  {renata.si\",\"priscilla.wagner\",\"sarajane} at usp.br  http:\/\/each.uspnet.usp.br\/sarajane\/  \n**Source**: UCI \n**Please cite**: Please refer to the Machine Learning Repository's citation policy. \nAdditionally, the authors require a citation to one or more publications from those cited as relevant papers.  \n\nSource:\n\nCreators: \nRenata Cristina Barros Madeo (Madeo, R. C. B.) \nPriscilla Koch Wagner (Wagner, P. K.) \nSarajane Marques Peres (Peres, S. M.) \n{renata.si, priscilla.wagner, sarajane} at usp.br \nhttp:\/\/each.uspnet.usp.br\/sarajane\/ \n\nDonor: \nUniversity of Sao Paulo - Brazil\n\n\nData Set Information:\n\nThe dataset is composed by features extracted from 7 videos with people gesticulating, aiming at studying Gesture Phase Segmentation. \nEach video is represented by two files: a raw file, which contains the position of hands, wrists, head and spine of the user in each frame; and a processed file, which contains velocity and acceleration of hands and wrists. See the data set description for more information on the dataset.\n\n\nAttribute Information:\n\nRaw files: 18 numeric attributes (double), a timestamp and a class attribute (nominal). \nProcessed files: 32 numeric attributes (double) and a class attribute (nominal). \nA feature vector with up to 50 numeric attributes can be generated with the two files mentioned above.\n\n\nRelevant Papers:\n\n1. Madeo, R. C. B. ; Lima, C. A. M. ; PERES, S. M. . Gesture Unit Segmentation using Support Vector Machines: Segmenting \nGestures from Rest Positions. In: Symposium on Applied Computing (SAC), 2013, Coimbra. Proceedings of the 28th Annual \nACM Symposium on Applied Computing (SAC), 2013. p. 46-52. \n* In this paper, the videos A1 and A2 were studied. \n\n2. Wagner, P. K. ; PERES, S. M. ; Madeo, R. C. B. ; Lima, C. A. M. ; Freitas, F. A. . Gesture Unit Segmentation Using \nSpatial-Temporal Information and Machine Learning. In: 27th Florida Artificial Intelligence Research Society Conference \n(FLAIRS), 2014, Pensacola Beach. Proceedings of the 27th Florida Artificial Intelligence Research Society Conference \n(FLAIRS). Palo Alto : The AAAI Press, 2014. p. 101-106. \n* In this paper, the videos A1, A2, A3, B1, B3, C1 and C3 were studied. \n\n3. Madeo, R. C. B.. Support Vector Machines and Gesture Analysis: incorporating temporal aspects (in Portuguese). Master \nThesis - Universidade de Sao Paulo, Sao Paulo Researcher Foundation. 2013. \n* In this document, the videos named B1 and B3 in the document correspond to videos C1 and C3 in this dataset. Only \nfive videos were explored in this document: A1, A2, A3, C1 and C3. \n\n4. Wagner, P. K. ; Madeo, R. C. B. ; PERES, S. M. ; Lima, C. A. M. . Segmentaçao de Unidades Gestuais com Multilayer \nPerceptrons (in Portuguese). In: Encontro Nacional de Inteligencia Artificial e Computacional (ENIAC), 2013, Fortaleza. \nAnais do X Encontro Nacional de Inteligencia Artificial e Computacional (ENIAC), 2013. \n* In this paper, the videos A1, A2 and A3 were studied.\n\n\n\nCitation Request:\n\nPlease refer to the Machine Learning Repository's citation policy. \nAdditionally, the authors require a citation to one or more publications from those cited as relevant papers.",
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