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Code Book

This code book describes the variables, the data, and any transformations or work performed to clean up the data.

Source of the Data:

Dataset Information

(adapted from the README.txt file in the Dataset package)

Human Activity Recognition Using Smartphones Dataset

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.

The Data Package

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.

In particular, files contained in the train and in the test (sub)folders contains data that have been made available for training and testing respectively. However, since their description is the same, only training data will be described in the following:

  • train/subject_train.txt: Each row refers to one (out of thirty) subject who performed the activity for each window sample. Therefore, values range 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'. Each row shows a 128 element vector. The same description applies for the total_acc_x_train.txt and for the 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. These values are expressed in radians-per-second.

Data Manipulation details

The run_analysis.R script performs the following operations to clean and transform the data: 0. Read the dataset 1. Merges the training and the test sets to create one single data set. 2. Extracts only the measurements on the mean and standard deviation for each measurement. 3. Uses descriptive activity names to name the activities in the data set 4. Appropriately labels the data set with descriptive variable names. 5. Creates a second, independent tidy data set with the average of each variable for each activity and each subject.

Merges the training and the test sets to create one single data set.

  • train/X_train.txt & test/X_test.txt: this results in a 10299x561 data frame, as reported in the original description ("Number of Instances: 10299" and "Number of Attributes: 561")

  • train/subject_train.txt & test/subject_test.txt: this results in a 10299 x 1 data frame with subject IDs,

  • train/y_train.txt & test/y_test.txt: this results in a 10299 x 1 data frame (as well) with activity IDs.

Extracts only the measurements on the mean and standard deviation for each measurement.

To perform this step, the script reads the file features.txt, and extracts only the measurements of the mean and the standard deviation of each measurement/example.

This results in a 10299 x 66 data frame, where 66 out of 561 features are selected and filtered in this step.

All measurements correspond to numeric (real) numbers in the range (-1, 1).

Uses descriptive activity names to name the activities in the data set

In this step, the script reads the file activity_labels.txt, and applies descriptive activity names to name the activities in the data set, namely:

  • WALKING
  • WALKING_UPSTAIRS
  • WALKING_DOWNSTAIRS
  • SITTING
  • STANDING
  • LAYING

Appropriately labels the data set with descriptive variable names.

The script properly labels the dataset with descriptive names: all feature names and activity names are converted to , underscores and brackets ("(", ")") are removed.

Finally, all the data are merged into a single 10299x68 data frame corresponding to:

  • 10299x1 data frame of subject IDs;
  • 10299x1 data frame of activity labels;
  • 10299x68 data frame of features.

Subject IDs are integers with values in range [1, 30].

Names of the attributes corresponds to:

  • "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-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"

The result is saved as merged_and_cleaned_dataset.txt.

Create the Tidy Dataset

Finally, the script creates a second, and independent tidy dataset with the average of each measurement for each activity and each subject.

The result is saved in the tidy_dataset_with_average_values.txt file, containing a 180x68 data frame, resulting from 30 subjects and 6 activities (thus 180 rows, w/ averages).

Again, the data frame contains:

  • subject IDs in the 1st column;
  • activity labels in the 2nd column;
  • the average of features in the next 66 columns.

NOTE: Please note that data in the tidy dataset are grouped by subject.