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title CodeBook
author SP
date April 8, 2019

This CodeBook describes the variables, data, and transformations for the TidyDataSet.txt and TidyDataSetMeanStdDev.txt data files derived for the final project for the Getting and Cleaning Data class from the Data Science series of classes. This codebook extends the codebook provided for the UCI HAR Dataset, included below as an addendum.

The dataset TidyDataSet.txt contains all the available data from the individual HAR datasets, combined into one dataset. The names of the data have been kept relatively the same as in the UCI HAR data sets and are as follows:

DataSet: Text strings indicating whether data is from "Test" or "Train" samples

Subject: Combined data from "subject_test.txt" and "subject_train.txt" files. The data indicates which individual the data is for.

X_Data: Combined data from "X_test.txt" and "X_train.txt" files. The data indicates relative measurement times.

Y_Data: Combined data from "Y_test.txt" and "Y_train.txt" files. The data indicates activity.

Body_Acc_X: Combined data from "body_acc_x_test.txt" and "body_acc_x_train.txt" files. The data indicates
body acceleration in the x dimension.

Body_Acc_Y: Combined data from "body_acc_y_test.txt" and "body_acc_y_train.txt" files. The data indicates
body acceleration in the y dimension.

Body_Acc_Z: Combined data from "body_acc_z_test.txt" and "body_acc_z_train.txt" files. The data indicates body acceleration in the z dimension.

Body_Gyro_X: Combined data from "body_gyro_x_test.txt" and "body_gyro_x_train.txt" files. The data indicates rotational acceleration in the x dimension.

Body_Gyro_Y: Combined data from "body_gyro_y_test.txt" and "body_gyro_y_train.txt" files. The data indicates rotational acceleration in the y dimension.

Body_Gyro_z: Combined data from "body_gyro_z_test.txt" and "body_gyro_z_train.txt" files. The data indicates rotational acceleration in the z dimension.

Total_Acc_X: Combined data from "total_acc_x_test.txt" and "total_acc_x_train.txt" files. The data indicates total acceleration in the x dimension.

Total_Acc_Y: Combined data from "total_acc_y_test.txt" and "total_acc_y_train.txt" files. The data indicates total acceleration in the y dimension.

Total_Acc_Z: Combined data from "total_acc_z_test.txt" and "total_acc_z_train.txt" files. The data indicates the total acceleration in the z dimension.

The dataset TidyDataSetMeanStdDev.txt contains all the mean and standard deviation values for the data from the TidyDataSet.txt referenced above and its data is as follows:

DataSet: Integer field indicating whether data is from "Test" (1) or "Train" (2) samples.

Subject: Integer field indicating which individual the data is for.

Activity: String field indicating activity the measurements are for.

Body_Acc_X_Mean: Numeric field indicating the mean of the Body_Acc_X data.

Body_Acc_X_StdDev: Numeric field indicating the standard deviation of the Body_Acc_X data.

Body_Acc_Y_Mean: Numeric field indicating the mean of the Body_Acc_Y data.

Body_Acc_Y_StdDev: Numeric field indicating the standard deviation of the Body_Acc_Y data.

Body_Acc_Z_Mean: Numeric field indicating the mean of the Body_Acc_Z data.

Body_Acc_Z_StdDev: Numeric field indicating the standard deviation of the Body_Acc_Z data.

Body_Gyro_X_Mean: Numeric field indicating the mean of the Body_Gyro_X data.

Body_Gyro_X_StdDev: Numeric field indicating the standard deviation of the Body_Gyro_X data.

Body_Gyro_Y_Mean: Numeric field indicating the mean of the Body_Gyro_Y data.

Body_Gyro_Y_StdDev: Numeric field indicating the standard deviation of the Body_Gyro_Y data.

Body_Gyro_Z_Mean: Numeric field indicating the mean of the Body_Gyro_Z data.

Body_Gyro_Z_StdDev: Numeric field indicating the standard deviation of the Body_Gyro_Z data.

Total_Acc_X_Mean: Numeric field indicating the mean of the Total_Acc_X data.

Total_Acc_X_StdDev: Numeric field indicating the standard deviation of the Total_Acc_X data.

Total_Acc_Y_Mean: Numeric field indicating the mean of the Total_Acc_Y data.

Total_Acc_Y_StdDev: Numeric field indicating the standard deviation of the Total_Acc_Y data.

Total_Acc_Z_Mean: Numeric field indicating the mean of the Total_Acc_Z data.

Total_Acc_Z_StdDev: Numeric field indicating the standard deviation of the Total_Acc_Z data.

References:

[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

Addendum - UCI HAR Dataset CodeBook:

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 - Universita degli Studi di Genova. Via Opera Pia 11A, I-16145, Genoa, Italy. activityrecognition@smartlab.ws www.smartlab.ws

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 s eparated 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.

Notes:

  • Features are normalized and bounded within [-1,1].
  • Each feature vector is a row on the text file.

For more information about this dataset contact: activityrecognition@smartlab.ws

License:

Use of this dataset in publications must be acknowledged by referencing the following publication [1]

This dataset is distributed AS-IS and no responsibility implied or explicit can be addressed to the authors or their institutions for its use or misuse. Any commercial use is prohibited.

Jorge L. Reyes-Ortiz, Alessandro Ghio, Luca Oneto, Davide Anguita. November 2012.