Data
walking-activity

walking-activity

active ARFF Publicly available Visibility: public Uploaded 26-05-2015 by Rafael G. Mantovani
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Author: P. Casale, O. Pujol, P. Radeva. Source: UCI Please cite: Casale, P. Pujol, O. and Radeva, P. 'Personalization and user verification in wearable systems using biometric walking patterns' Personal and Ubiquitous Computing, 16(5), 563-580, 2012 * Title: User Identification From Walking Activity Data Set * Abstract: The dataset collects data from an Android smartphone positioned in the chest pocket from 22 participants walking in the wild over a predefined path. * Source: Pierluigi Casale, Computer Vision Center, Barcelona, Spain. Email: plcasale '@' ieee.org * Data Set Information: The dataset collects data from an Android smartphone positioned in the chest pocket. Accelerometer Data are collected from 22 participants walking in the wild over a predefined path. The dataset is intended for Activity Recognition research purposes. It provides challenges for identification and authentication of people using motion patterns. --- Sampling frequency of the accelerometer: DELAY_FASTEST with network connections disabled --- Number of Participants: 22 * Attribute Information: --- Data are separated by participant --- Each file contains the following information ---- time-step, x acceleration, y acceleration, z acceleration

5 features

Class (target)nominal22 unique values
0 missing
V1numeric72087 unique values
0 missing
V2numeric756 unique values
0 missing
V3numeric665 unique values
0 missing
V4numeric733 unique values
0 missing

63 properties

149332
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
22
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.
4
Number of numeric attributes.
1
Number of nominal attributes.
0.4
Mean skewness among attributes of the numeric type.
1.71
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
43.99
Mean standard deviation of attributes of the numeric type.
4.66
Second quartile (Median) of means among attributes of the numeric type.
NaN
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
NaN
Minimal entropy among attributes.
NaN
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
14.73
Percentage of instances belonging to the most frequent class.
0.29
Minimum kurtosis among attributes of the numeric type.
0.39
Second quartile (Median) of skewness among attributes of the numeric type.
21991
Number of instances belonging to the most frequent class.
-1.66
Minimum of means among attributes of the numeric type.
0
Percentage of binary attributes.
3.01
DataQuality extracted from Fantail Library
NaN
Maximum entropy among attributes.
NaN
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of instances having missing values.
NaN
Third quartile of entropy among attributes.
2.15
Maximum kurtosis among attributes of the numeric type.
22
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of missing values.
2.06
Third quartile of kurtosis among attributes of the numeric type.
185.56
Maximum of means among attributes of the numeric type.
-0.39
Minimum skewness among attributes of the numeric type.
80
Percentage of numeric attributes.
141.36
Third quartile of means among attributes of the numeric type.
NaN
Maximum mutual information between the nominal attributes and the target attribute.
2.77
DataQuality extracted from Fantail Library
20
Percentage of nominal attributes.
NaN
Third quartile of mutual information between the nominal attributes and the target attribute.
22
The maximum number of distinct values among attributes of the nominal type.
1.22
Maximum skewness among attributes of the numeric type.
0.01
Percentage of instances belonging to the least frequent class.
NaN
First quartile of entropy among attributes.
1.19
DataQuality extracted from Fantail Library
167.16
DataQuality extracted from Fantail Library
911
Number of instances belonging to the least frequent class.
0.63
First quartile of kurtosis among attributes of the numeric type.
126.16
DataQuality extracted from Fantail Library
-1
Average entropy of the attributes.
NaN
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
-1.1
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.
1.47
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
NaN
First quartile of mutual information between the nominal attributes and the target attribute.
48.31
Mean of means among attributes of the numeric type.
-0.37
First quartile of skewness among attributes of the numeric type.
1
Average class difference between consecutive instances.
NaN
Average mutual information between the nominal attributes and the target attribute.
2.8
DataQuality extracted from Fantail Library
3.99
Entropy of the target attribute values.
22
Average number of distinct values among the attributes of the nominal type.
NaN
Second quartile (Median) of entropy among attributes.
0.15
The predictive accuracy obtained by always predicting the majority class.

4 tasks

73 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
Define a new task

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