Data
GesturePhaseSegmentationRAW

GesturePhaseSegmentationRAW

active ARFF Publicly available Visibility: public Uploaded 17-02-2016 by Hilda Fabiola Bernard
0 likes downloaded by 7 people , 9 total downloads 0 issues 0 downvotes

Problem validating uploaded description file: XML does not correspond to XSD schema. ,Problem validating uploaded description file: XML does not correspond to XSD schema. ,Problem validating uploaded description file: XML does not correspond to XSD schema. ,Problem validating uploaded description file: XML does not correspond to XSD schema. ,Problem validating uploaded description file: XML does not correspond to XSD schema. ,Problem validating uploaded description file: XML does not correspond to XSD schema. ,Problem validating uploaded description file: XML does not correspond to XSD schema. ,Problem validating uploaded description file: XML does not correspond to XSD schema. ,Problem validating uploaded description file: XML does not correspond to XSD schema. ,Problem validating uploaded description file: XML does not correspond to XSD schema. ,Problem validating uploaded description file: XML does not correspond to XSD schema. ,Problem validating uploaded description file: XML does not correspond to XSD s
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Renata 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/ Source: UCI Please cite: Gesture Unit Segmentation using Support Vector Machines: Segmenting Gestures from Rest Positions. In: Symposium on Applied Computing (SAC), 2013, Coimbra. Proceedings of the 28th Annual ACM Symposium on Applied Computing (SAC), 2013. p. 46-52. Data Set Information: The dataset is composed by features extracted from 7 videos with people gesticulating, aiming at studying Gesture Phase Segmentation. It contains velocity and acceleration of hands and wrists. Attribute Information: Raw files: 18 numeric attributes (double), a timestamp and a class attribute (nominal). Processed files: 32 numeric attributes (double) and a class attribute (nominal). A feature vector with up to 50 numeric attributes can be generated with the two files mentioned above. Relevant Papers: 1. Madeo, R. C. B. ; Lima, C. A. M. ; PERES, S. M. . Gesture Unit Segmentation using Support Vector Machines: Segmenting Gestures from Rest Positions. In: Symposium on Applied Computing (SAC), 2013, Coimbra. Proceedings of the 28th Annual ACM Symposium on Applied Computing (SAC), 2013. p. 46-52. In this paper, the videos A1 and A2 were studied. 2. Wagner, P. K. ; PERES, S. M. ; Madeo, R. C. B. ; Lima, C. A. M. ; Freitas, F. A. . Gesture Unit Segmentation Using Spatial-Temporal Information and Machine Learning. In: 27th Florida Artificial Intelligence Research Society Conference (FLAIRS), 2014, Pensacola Beach. Proceedings of the 27th Florida Artificial Intelligence Research Society Conference (FLAIRS). Palo Alto : The AAAI Press, 2014. p. 101-106. In this paper, the videos A1, A2, A3, B1, B3, C1 and C3 were studied. 3. Madeo, R. C. B.. Support Vector Machines and Gesture Analysis: incorporating temporal aspects (in Portuguese). Master Thesis - Universidade de Sao Paulo, Sao Paulo Researcher Foundation. 2013. In this document, the videos named B1 and B3 in the document correspond to videos C1 and C3 in this dataset. Only five videos were explored in this document: A1, A2, A3, C1 and C3. 4. Wagner, P. K. ; Madeo, R. C. B. ; PERES, S. M. ; Lima, C. A. M. . Segmentaçao de Unidades Gestuais com Multilayer Perceptrons (in Portuguese). In: Encontro Nacional de Inteligencia Artificial e Computacional (ENIAC), 2013, Fortaleza. Anais do X Encontro Nacional de Inteligencia Artificial e Computacional (ENIAC), 2013. In this paper, the videos A1, A2 and A3 were studied.

0 features

Data features are not analyzed yet. Refresh the page in a few minutes.

0 properties

Data properties are not analyzed yet. Refresh the page in a few minutes.

5 tasks

40 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: phase
34 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: phase
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task