The dataset is based on data from [1], downloaded from
https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
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 wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz have been captured. The experiments have been video-recorded to label the data manually.
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.
[1] 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
Tidy data set is generated by running "run_analysis.R" Script
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subject - range of values [1:30] . ID of the person praticipating in the experiment.
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activityClass - Name of activity performed by subject
It is one of the LAYING, SITTING, STANDING, WALKING, WALKING_DOWNSTAIRS, WALKING_UPSTAIRS (activity.Class had been transformed from range[1:6] to actual activity labels)
Rest of the columns correspond to averaged variables describing mean/std measurements for each activity for each subject. Names of this variable are changed from the old ones for better readability according to Google R style guide. Brackets,"-" are removed and Capital letters are used wherever required. Those variables are...
"subject" "activityClass" "tBodyAccMeanX" "tBodyAccMeanY" "tBodyAccMeanZ" "tBodyAccStdX" "tBodyAccStdY" "tBodyAccStdZ" "tGravityAccMeanX" "tGravityAccMeanY" "tGravityAccMeanZ" "tGravityAccStdX" "tGravityAccStdY" "tGravityAccStdZ" "tBodyAccJerkMeanX" "tBodyAccJerkMeanY" "tBodyAccJerkMeanZ" "tBodyAccJerkStdX" "tBodyAccJerkStdY" "tBodyAccJerkStdZ" "tBodyGyroMeanX" "tBodyGyroMeanY" "tBodyGyroMeanZ" "tBodyGyroStdX" "tBodyGyroStdY" "tBodyGyroStdZ" "tBodyGyroJerkMeanX" "tBodyGyroJerkMeanY" "tBodyGyroJerkMeanZ" "tBodyGyroJerkStdX" "tBodyGyroJerkStdY" "tBodyGyroJerkStdZ" "tBodyAccMagMean" "tBodyAccMagStd" "tGravityAccMagMean" "tGravityAccMagStd" "tBodyAccJerkMagMean" "tBodyAccJerkMagStd" "tBodyGyroMagMean" "tBodyGyroMagStd" "tBodyGyroJerkMagMean" "tBodyGyroJerkMagStd" "fBodyAccMeanX" "fBodyAccMeanY" "fBodyAccMeanZ" "fBodyAccStdX" "fBodyAccStdY" "fBodyAccStdZ" "fBodyAccJerkMeanX" "fBodyAccJerkMeanY" "fBodyAccJerkMeanZ" "fBodyAccJerkStdX" "fBodyAccJerkStdY" "fBodyAccJerkStdZ" "fBodyGyroMeanX" "fBodyGyroMeanY" "fBodyGyroMeanZ" "fBodyGyroStdX" "fBodyGyroStdY" "fBodyGyroStdZ" "fBodyAccMagMean" "fBodyAccMagStd" "fBodyBodyAccJerkMagMean" "fBodyBodyAccJerkMagStd" "fBodyBodyGyroMagMean" "fBodyBodyGyroMagStd" "fBodyBodyGyroJerkMagMean" "fBodyBodyGyroJerkMagStd"
[1] 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