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Data Dictionary

Human Activity Recognition Using Smartphones Data Set - Programming Assignment

The script run_analyis.R generates the two dataframes data and data2 in the workspace of R (see the README.md for details) (data2 also is stored in the average_data.txt in this repo). This code book contains information about

  1. The experiment the data come from.
  2. The way the original data are processed by run_analysis.R to obtain the dataframes data and data2.
  3. The variables and the data in data and data2.

1. The Experiment and the original data

For full information about the experiment go to:

http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

Here are the data for the project:

https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

Here are the most important informations about the experiment from the above website resp. the UCI HAR Dataset/README.txt and the UCI HAR Dataset/features_info.txt (contained in the above archive):

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 features selected for this database come from the accelerometer and gyroscope 3-axial raw signals tAcc-XYZ and tGyro-XYZ. These time domain signals (prefix 't' to denote time) were captured at a constant rate of 50 Hz. Then they were filtered using a median filter and a 3rd order low pass Butterworth filter with a corner frequency of 20 Hz to remove noise. Similarly, the acceleration signal was then separated into body and gravity acceleration signals (tBodyAcc-XYZ and tGravityAcc-XYZ) using another low pass Butterworth filter with a corner frequency of 0.3 Hz.
Subsequently, the body linear acceleration and angular velocity were derived in time to obtain Jerk signals (tBodyAccJerk-XYZ and tBodyGyroJerk-XYZ). Also the magnitude of these three-dimensional signals were calculated using the Euclidean norm (tBodyAccMag, tGravityAccMag, tBodyAccJerkMag, tBodyGyroMag, tBodyGyroJerkMag).
Finally a Fast Fourier Transform (FFT) was applied to some of these signals producing fBodyAcc-XYZ, fBodyAccJerk-XYZ, fBodyGyro-XYZ, fBodyAccJerkMag, fBodyGyroMag, fBodyGyroJerkMag. (Note the 'f' to indicate frequency domain signals).
These signals were used to estimate variables of the feature vector for each pattern: '-XYZ' is used to denote 3-axial signals in the X, Y and Z directions.

  • tBodyAcc-XYZ
  • tGravityAcc-XYZ
  • tBodyAccJerk-XYZ
  • tBodyGyro-XYZ
  • tBodyGyroJerk-XYZ
  • tBodyAccMag
  • tGravityAccMag
  • tBodyAccJerkMag
  • tBodyGyroMag
  • tBodyGyroJerkMag
  • fBodyAcc-XYZ
  • fBodyAccJerk-XYZ
  • fBodyGyro-XYZ
  • fBodyAccMag
  • fBodyAccJerkMag
  • fBodyGyroMag
  • fBodyGyroJerkMag

The set of variables that were estimated from these signals are:

  • mean(): Mean value
  • std(): Standard deviation
  • mad(): Median absolute deviation
  • max(): Largest value in array
  • min(): Smallest value in array
  • sma(): Signal magnitude area
  • energy(): Energy measure. Sum of the squares divided by the number of values.
  • iqr(): Interquartile range
  • entropy(): Signal entropy
  • arCoeff(): Autorregresion coefficients with Burg order equal to 4
  • correlation(): correlation coefficient between two signals
  • maxInds(): index of the frequency component with largest magnitude
  • meanFreq(): Weighted average of the frequency components to obtain a mean frequency
  • skewness(): skewness of the frequency domain signal
  • kurtosis(): kurtosis of the frequency domain signal
  • bandsEnergy(): Energy of a frequency interval within the 64 bins of the FFT of each window.
  • angle(): Angle between to vectors.

Additional vectors obtained by averaging the signals in a signal window sample. These are used on the angle() variable:

  • gravityMean
  • tBodyAccMean
  • tBodyAccJerkMean
  • tBodyGyroMean
  • tBodyGyroJerkMean

The complete list of variables of each feature vector is available in UCI HAR Dataset/features.txt

2. Processing of the data

The data described in 1. are processed by the script run_analysis.R contained in this repo. For details about the implementation see the run_analysis.R script itself which contains many comments.

Which files are used

Of the data provided in the above zip archive the following files are used:

  • UCI HAR Dataset/test/subject_test.txt: subject labels of the test subjects
  • UCI HAR Dataset/test/y_test.txt: activity labels of the test subjects
  • UCI HAR Dataset/test/X_test.txt: records of the test subjects
  • UCI HAR Dataset/train/subject_train.txt: subject labels of the train subjects
  • UCI HAR Dataset/train/y_train.txt: activity labels of the train subjects
  • UCI HAR Dataset/train/X_train.txt: records of the train subjects
  • UCI HAR Dataset/features.txt: names of the feature labels
  • UCI HAR Dataset/activity_labels.txt: names of the activity labels

These files are stored in dataframes by run_analysis.R.

Extracting the variables describing mean and standard deviation

Of all 561 variables provided in the records only those 79 variables are kept which describe either means or standard deviations. Note that we also keep the variables of the type meanFreq. The other variables are removed from the dataframes.

Putting the data together and providing descriptive names for the variables and the activity labels

  • The data of the test subjects and the train subjects are joined.
  • The variable containing the subject lables is renamed to subject.
  • The variable containing the activity labels is renamed to activity.
  • The variables containing the records are renamed to the names provided in the features.txt.
  • These data are joined to one dataframe data.
  • The variable containig the subject lables is made into a factor with descriptive labels provided by activity_lables.txt.
  • The variable containing the subject lables is made into a factor.

Averaging the data in view of test subjects and activities

Using data a second dataframe data2 is constructed which contains the average of each of the 79 variables for each activity and each subject.

3.The variables in the dataframes

Both dataframes data and data2 contain the same variables. Whereas data contains 10299 single records, data2 contains the average of each of the 79 numerical variables for each activity and each subject.

  • subject
    Factor with 30 levels representing the thirty test volunteers
    1-30

  • activity
    Factor with 6 levels representing the activities performed by the subjects
    WALKING
    WALKING_UPSTAIRS
    WALKING_DOWNSTAIRS
    SITTING
    STANDING
    LAYING

The remaining 79 variables contain numerical test data ('mean/std' indicates that there exists one variable for the mean and a different variable for the standard deviation of this quantity; 'XYZ' vindicates that there exists three separate variables for the X, Y, and Z-direction describing the quantity). All variables are normalized and bounded within [-1,1].

  • tBodyAcc-mean/std()-XYZ
  • tGravityAcc-mean/std()-XYZ
  • tBodyAccJerk-mean/std()-XYZ
  • tBodyGyro-mean/std()-XYZ
  • tBodyGyroJerk-mean/std()-XYZ
  • tBodyAccMag-mean/std()
  • tGravityAccMag-mean/std()
  • tBodyAccJerkMag-mean/std()
  • tBodyGyroMag-mean/std()
  • tBodyGyroJerkMag-mean/std()
  • fBodyAcc-mean/std()-XYZ
  • fBodyAcc-meanFreq()-XYZ
  • fBodyAccJerk-mean/std()-XYZ
  • fBodyAccJerk-meanFreq()-XYZ
  • fBodyGyro-mean/std()-XYZ
  • fBodyGyro-meanFreq()-XYZ
  • fBodyAccMag-mean()
  • fBodyAccMag-std()
  • fBodyAccMag-meanFreq()
  • fBodyBodyAccJerkMag-mean/std()
  • fBodyBodyAccJerkMag-meanFreq()
  • fBodyBodyGyroMag-mean/std()
  • fBodyBodyGyroMag-meanFreq()
  • fBodyBodyGyroJerkMag-mean/std()
  • fBodyBodyGyroJerkMag-meanFreq()