Data
GesturePhaseSegmentationProcessed

GesturePhaseSegmentationProcessed

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  • concept_drift OpenML-CC18 OpenML100 study_123 study_14 study_34 study_98 study_99
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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](https://archive.ics.uci.edu/ml/datasets/gesture+phase+segmentation) Please cite: Please refer to the [Machine Learning Repository's citation policy](https://archive.ics.uci.edu/ml/citation_policy.html). Additionally, the authors require a citation to one or more publications from those cited as relevant papers. Creators: 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/ Donor: University of Sao Paulo - Brazil Data Set Information: The dataset is composed by features extracted from 7 videos with people gesticulating, aiming at studying Gesture Phase Segmentation. Each 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. 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. This is the processed data set with the following feature description: Processed files: 1. Vectorial velocity of left hand (x coordinate) 2. Vectorial velocity of left hand (y coordinate) 3. Vectorial velocity of left hand (z coordinate) 4. Vectorial velocity of right hand (x coordinate) 5. Vectorial velocity of right hand (y coordinate) 6. Vectorial velocity of right hand (z coordinate) 7. Vectorial velocity of left wrist (x coordinate) 8. Vectorial velocity of left wrist (y coordinate) 9. Vectorial velocity of left wrist (z coordinate) 10. Vectorial velocity of right wrist (x coordinate) 11. Vectorial velocity of right wrist (y coordinate) 12. Vectorial velocity of right wrist (z coordinate) 13. Vectorial acceleration of left hand (x coordinate) 14. Vectorial acceleration of left hand (y coordinate) 15. Vectorial acceleration of left hand (z coordinate) 16. Vectorial acceleration of right hand (x coordinate) 17. Vectorial acceleration of right hand (y coordinate) 18. Vectorial acceleration of right hand (z coordinate) 19. Vectorial acceleration of left wrist (x coordinate) 20. Vectorial acceleration of left wrist (y coordinate) 21. Vectorial acceleration of left wrist (z coordinate) 22. Vectorial acceleration of right wrist (x coordinate) 23. Vectorial acceleration of right wrist (y coordinate) 24. Vectorial acceleration of right wrist (z coordinate) 25. Scalar velocity of left hand 26. Scalar velocity of right hand 27. Scalar velocity of left wrist 28. Scalar velocity of right wrist 29. Scalar velocity of left hand 30. Scalar velocity of right hand 31. Scalar velocity of left wrist 32. Scalar velocity of right wrist 33. phase: - D (rest position, from portuguese "descanso") - P (preparation) - S (stroke) - H (hold) - R (retraction) 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. Citation Request: Please refer to the Machine Learning Repository's citation policy. Additionally, the authors require a citation to one or more publications from those cited as relevant papers.

33 features

Phase (target)nominal5 unique values
0 missing
X1numeric9822 unique values
0 missing
X2numeric9826 unique values
0 missing
X3numeric9625 unique values
0 missing
X4numeric9810 unique values
0 missing
X5numeric9840 unique values
0 missing
X6numeric9703 unique values
0 missing
X7numeric9816 unique values
0 missing
X8numeric9810 unique values
0 missing
X9numeric9595 unique values
0 missing
X10numeric9817 unique values
0 missing
X11numeric9827 unique values
0 missing
X12numeric9664 unique values
0 missing
X13numeric9466 unique values
0 missing
X14numeric9489 unique values
0 missing
X15numeric8395 unique values
0 missing
X16numeric9557 unique values
0 missing
X17numeric9584 unique values
0 missing
X18numeric8724 unique values
0 missing
X19numeric9425 unique values
0 missing
X20numeric9380 unique values
0 missing
X21numeric8304 unique values
0 missing
X22numeric9473 unique values
0 missing
X23numeric9533 unique values
0 missing
X24numeric8714 unique values
0 missing
X25numeric9831 unique values
0 missing
X26numeric9844 unique values
0 missing
X27numeric9824 unique values
0 missing
X28numeric9827 unique values
0 missing
X29numeric9509 unique values
0 missing
X30numeric9635 unique values
0 missing
X31numeric9493 unique values
0 missing
X32numeric9576 unique values
0 missing

62 properties

9873
Number of instances (rows) of the dataset.
33
Number of attributes (columns) of the dataset.
5
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
32
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
7.6
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
193.9
Maximum kurtosis among attributes of the numeric type.
-0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
52.89
Third quartile of kurtosis among attributes of the numeric type.
0.01
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
96.97
Percentage of numeric attributes.
0
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
5
The minimal number of distinct values among attributes of the nominal type.
3.03
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
5
The maximum number of distinct values among attributes of the nominal type.
-1.88
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
2.37
Third quartile of skewness among attributes of the numeric type.
7.17
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
10.69
First quartile of kurtosis among attributes of the numeric type.
0.01
Third quartile of standard deviation of attributes of the numeric type.
0.01
Maximum standard deviation of attributes of the numeric type.
10.11
Percentage of instances belonging to the least frequent class.
-0
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
998
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
47.82
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
-0.53
First quartile of skewness among attributes of the numeric type.
0
Mean of means among attributes of the numeric type.
0
First quartile of standard deviation of attributes of the numeric type.
0.96
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
2.19
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
32.23
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
5
Average number of distinct values among the attributes of the nominal type.
0
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.95
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
29.88
Percentage of instances belonging to the most frequent class.
0.01
Mean standard deviation of attributes of the numeric type.
0.25
Second quartile (Median) of skewness among attributes of the numeric type.
2950
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

27 tasks

11556 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Phase
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Phase
0 runs - estimation_procedure: 33% Holdout set - target_feature: Phase
44 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Phase
0 runs - target_feature: Phase
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