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Raw DataSet source

https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones Human Activity Recognition Using Smartphones Dataset Version 1.0 Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto. Smartlab - Non Linear Complex Systems Laboratory DITEN - Università degli Studi di Genova. Via Opera Pia 11A, I-16145, Genoa, Italy. activityrecognition@smartlab.ws www.smartlab.ws

Data Set Information:

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 was 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. See 'features_info.txt' for more details.

For each record 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.
  • Its activity label.
  • An identifier of the subject who carried out the experiment.

The dataset includes the following files

  • 'README.txt'
  • 'features_info.txt': Shows information about the variables used on the feature vector.
  • 'features.txt': List of all features.
  • 'activity_labels.txt': Links the class labels with their activity name.
  • 'train/X_train.txt': Training set.
  • 'train/y_train.txt': Training labels.
  • 'test/X_test.txt': Test set.
  • 'test/y_test.txt': Test labels.

The following files are available for the train and test data. Their descriptions are equivalent.

  • 'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.
  • 'train/Inertial Signals/total_acc_x_train.txt': The acceleration signal from the smartphone accelerometer X axis in standard gravity units 'g'. Every row shows a 128 element vector. The same description applies for the 'total_acc_x_train.txt' and 'total_acc_z_train.txt' files for the Y and Z axis.
  • 'train/Inertial Signals/body_acc_x_train.txt': The body acceleration signal obtained by subtracting the gravity from the total acceleration.
  • 'train/Inertial Signals/body_gyro_x_train.txt': The angular velocity vector measured by the gyroscope for each window sample. The units are radians/second.

Transformations or work that the script performed to clean up the data

  • Step 1 - You need download the raw datasource and extract. You need pass the directory name like this "UCI HAR Dataset/".
  • Step 2 - Read and combine subjects ("train/subject_train.txt"; "test/subject_test.txt").
  • Step 3 - Read, combine ("train/y_train.txt"; "test/y_test.txt") and combined rows decode by activity labels ("activity_labels.txt").
  • Step 4 - Read, combine ("test/X_test.txt"; "train/X_train.txt") and set column names for subjects ("features.txt").
  • Step 5 - Combine the whole datasets.
  • Step 6 - Extract from dataset only the required columns like "-mean()" and "-std()".
  • Step 7 - Compute the means, grouped by activity/subject.
  • Step 8 - Writing the result to ";" separated txt file.

Data after Transformation

For more info check the raw dataset informations! activity LAYING; SITTING; STANDING; WALKING; WALKING_DOWNSTAIRS; WALKING_UPSTAIRS

subject 1...30

average of each variable for each activity and each subject tBodyAcc-mean()-X tBodyAcc-mean()-Y tBodyAcc-mean()-Z tBodyAcc-std()-X tBodyAcc-std()-Y tBodyAcc-std()-Z tGravityAcc-mean()-X tGravityAcc-mean()-Y tGravityAcc-mean()-Z tGravityAcc-std()-X tGravityAcc-std()-Y tGravityAcc-std()-Z tBodyAccJerk-mean()-X tBodyAccJerk-mean()-Y tBodyAccJerk-mean()-Z tBodyAccJerk-std()-X tBodyAccJerk-std()-Y tBodyAccJerk-std()-Z tBodyGyro-mean()-X tBodyGyro-mean()-Y tBodyGyro-mean()-Z tBodyGyro-std()-X tBodyGyro-std()-Y tBodyGyro-std()-Z tBodyGyroJerk-mean()-X tBodyGyroJerk-mean()-Y tBodyGyroJerk-mean()-Z tBodyGyroJerk-std()-X tBodyGyroJerk-std()-Y tBodyGyroJerk-std()-Z tBodyAccMag-mean() tBodyAccMag-std() tGravityAccMag-mean() tGravityAccMag-std() tBodyAccJerkMag-mean() tBodyAccJerkMag-std() tBodyGyroMag-mean() tBodyGyroMag-std() tBodyGyroJerkMag-mean() tBodyGyroJerkMag-std() fBodyAcc-mean()-X fBodyAcc-mean()-Y fBodyAcc-mean()-Z fBodyAcc-std()-X fBodyAcc-std()-Y fBodyAcc-std()-Z fBodyAccJerk-mean()-X fBodyAccJerk-mean()-Y fBodyAccJerk-mean()-Z fBodyAccJerk-std()-X fBodyAccJerk-std()-Y fBodyAccJerk-std()-Z fBodyGyro-mean()-X fBodyGyro-mean()-Y fBodyGyro-mean()-Z fBodyGyro-std()-X fBodyGyro-std()-Y fBodyGyro-std()-Z fBodyAccMag-mean() fBodyAccMag-std() fBodyBodyAccJerkMag-mean() fBodyBodyAccJerkMag-std() fBodyBodyGyroMag-mean() fBodyBodyGyroMag-std() fBodyBodyGyroJerkMag-mean() fBodyBodyGyroJerkMag-std()