Data
har

har

active ARFF Publicly available Visibility: public Uploaded 22-05-2015 by Rafael G. Mantovani
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Author: Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto and Xavier Parra Source: [UCI](https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones) Please cite: Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013. Human Activity Recognition Human Activity Recognition (HAR) database built from the recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. This dataset version contains all the training and testing examples provided in the original data repository. The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers were selected for generating the training data and 30% the test data. The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low-frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain. ### Attribute Information For each record in the dataset it is provided: * Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration. * Triaxial Angular velocity from the gyroscope. * A 561-feature vector with time and frequency domain variables. * It's activity label. ### Relevant Papers Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012 Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge L. Reyes-Ortiz. Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic. Journal of Universal Computer Science. Special Issue in Ambient Assisted Living: Home Care. Volume 19, Issue 9. May 2013 Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. 4th International Workshop of Ambient Assisted Living, IWAAL 2012, Vitoria-Gasteiz, Spain, December 3-5, 2012. Proceedings. Lecture Notes in Computer Science 2012, pp 216-223. Jorge Luis Reyes-Ortiz, Alessandro Ghio, Xavier Parra-Llanas, Davide Anguita, Joan Cabestany, Andreu Català. Human Activity and Motion Disorder Recognition: Towards Smarter Interactive Cognitive Environments. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.

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0 missing
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0 missing
V509numeric7211 unique values
0 missing
V254numeric9795 unique values
0 missing
V510numeric9872 unique values
0 missing
V255numeric9669 unique values
0 missing
V511numeric5275 unique values
0 missing
V256numeric7190 unique values
0 missing
V512numeric30 unique values
0 missing

62 properties

10299
Number of instances (rows) of the dataset.
562
Number of attributes (columns) of the dataset.
6
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.
561
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0.26
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.85
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
373.43
Maximum kurtosis among attributes of the numeric type.
-0.98
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
8.9
Third quartile of kurtosis among attributes of the numeric type.
0.83
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
99.82
Percentage of numeric attributes.
-0.1
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
6
The minimal number of distinct values among attributes of the nominal type.
0.18
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
6
The maximum number of distinct values among attributes of the nominal type.
-3.49
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
2.42
Third quartile of skewness among attributes of the numeric type.
14.03
Maximum skewness among attributes of the numeric type.
0.04
Minimum standard deviation of attributes of the numeric type.
-0.27
First quartile of kurtosis among attributes of the numeric type.
0.36
Third quartile of standard deviation of attributes of the numeric type.
0.75
Maximum standard deviation of attributes of the numeric type.
13.65
Percentage of instances belonging to the least frequent class.
-0.87
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.
1406
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
13.66
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
0.15
First quartile of skewness among attributes of the numeric type.
-0.51
Mean of means among attributes of the numeric type.
0.19
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.
2.58
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.
0.59
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.05
Number of attributes divided by the number of instances.
6
Average number of distinct values among the attributes of the nominal type.
-0.66
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.
1.64
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
18.88
Percentage of instances belonging to the most frequent class.
0.28
Mean standard deviation of attributes of the numeric type.
0.87
Second quartile (Median) of skewness among attributes of the numeric type.
1944
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

32 tasks

9306 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
32 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature:
0 runs - target_feature: Class
1310 runs - target_feature: Class
1301 runs - target_feature: Class
1300 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1297 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
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