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Code Book

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

Data Source

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.

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

Transformation details

There are 5 parts:

  1. Merges the training and the test sets to create one 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 activity names.
  5. Creates a second, independent tidy data set with the average of each variable for each activity and each subject.

How run_analysis.R implements the above steps:

  • Require reshapre2 and data.table librareis.
  • Load both test and train data
  • Load the features and activity labels.
  • Extract the mean and standard deviation column names and data.
  • Process the data. There are two parts processing test and train data respectively.
  • Merge data set.

Variables

ID Fields

  • subject - The participant ("subject") ID
  • activity - The label of the activity performed when the corresponding measurements were taken

Activity Labels

  • WALKING (value 1)
  • WALKING_UPSTAIRS (value 2)
  • WALKING_DOWNSTAIRS (value 3)
  • SITTING (value 4)
  • STANDING (value 5)
  • LAYING (value 6)

Activities Vector

c(1, 2, 3, 4, 5, 6)

Activity Names Vector

c("WALKING", "WALKING_UPSTAIRS", "WALKING_DOWNSTAIRS", "SITTING", "STANDING", "LAYING")

Extracted Feature Fields

  • tBodyAcc-mean()-X (column 1)
  • tBodyAcc-mean()-Y (column 2)
  • tBodyAcc-mean()-Z (column 3)
  • tBodyAcc-std()-X (column 4)
  • tBodyAcc-std()-Y (column 5)
  • tBodyAcc-std()-Z (column 6)
  • tGravityAcc-mean()-X (column 41)
  • tGravityAcc-mean()-Y (column 42)
  • tGravityAcc-mean()-Z (column 43)
  • tGravityAcc-std()-X (column 44)
  • tGravityAcc-std()-Y (column 45)
  • tGravityAcc-std()-Z (column 46)
  • tBodyAccJerk-mean()-X (column 81)
  • tBodyAccJerk-mean()-Y (column 82)
  • tBodyAccJerk-mean()-Z (column 83)
  • tBodyAccJerk-std()-X (column 84)
  • tBodyAccJerk-std()-Y (column 85)
  • tBodyAccJerk-std()-Z (column 86)
  • tBodyGyro-mean()-X (column 121)
  • tBodyGyro-mean()-Y (column 122)
  • tBodyGyro-mean()-Z (column 123)
  • tBodyGyro-std()-X (column 124)
  • tBodyGyro-std()-Y (column 125)
  • tBodyGyro-std()-Z (column 126)
  • tBodyGyroJerk-mean()-X (column 161)
  • tBodyGyroJerk-mean()-Y (column 162)
  • tBodyGyroJerk-mean()-Z (column 163)
  • tBodyGyroJerk-std()-X (column 164)
  • tBodyGyroJerk-std()-Y (column 165)
  • tBodyGyroJerk-std()-Z (column 166)
  • tBodyAccMag-mean() (column 201)
  • tBodyAccMag-std() (column 202)
  • tGravityAccMag-mean() (column 214)
  • tGravityAccMag-std() (column 215)
  • tBodyAccJerkMag-mean() (column 227)
  • tBodyAccJerkMag-std() (column 228)
  • tBodyGyroMag-mean() (column 240)
  • tBodyGyroMag-std() (column 241)
  • tBodyGyroJerkMag-mean() (column 253)
  • tBodyGyroJerkMag-std() (column 254)
  • fBodyAcc-mean()-X (column 266)
  • fBodyAcc-mean()-Y (column 267)
  • fBodyAcc-mean()-Z (column 268)
  • fBodyAcc-std()-X (column 269)
  • fBodyAcc-std()-Y (column 270)
  • fBodyAcc-std()-Z (column 271)
  • fBodyAccJerk-mean()-X (column 345)
  • fBodyAccJerk-mean()-Y (column 346)
  • fBodyAccJerk-mean()-Z (column 347)
  • fBodyAccJerk-std()-X (column 348)
  • fBodyAccJerk-std()-Y (column 349)
  • fBodyAccJerk-std()-Z (column 350)
  • fBodyGyro-mean()-X (column 424)
  • fBodyGyro-mean()-Y (column 425)
  • fBodyGyro-mean()-Z (column 426)
  • fBodyGyro-std()-X (column 427)
  • fBodyGyro-std()-Y (column 428)
  • fBodyGyro-std()-Z (column 429)
  • fBodyAccMag-mean() (column 503)
  • fBodyAccMag-std() (column 504)
  • fBodyBodyAccJerkMag-mean() (column 516)
  • fBodyBodyAccJerkMag-std() (column 517)
  • fBodyBodyGyroMag-mean() (column 529)
  • fBodyBodyGyroMag-std() (column 530)
  • fBodyBodyGyroJerkMag-mean() (column 542)
  • fBodyBodyGyroJerkMag-std() (column 543)

Extracted Features Vector

c(1, 2, 3, 4, 5, 6, 41, 42, 43, 44, 45, 46, 81, 82, 83, 84, 85, 86, 121, 122, 123, 124, 125, 126, 161, 162, 163, 164, 165, 166, 201, 202, 214, 215, 227, 228, 240, 241, 253, 254, 266, 267, 268, 269, 270, 271, 345, 346, 347, 348, 349, 350, 424, 425, 426, 427, 428, 429, 503, 504, 516, 517, 529, 530, 542, 543)

Extracted Feature Names Vector

c("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()")