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Code book

Our analysis have for goal to tidy the dataset from the Human Activity Recognition Using Smartphones Dataset[1].

The original dataset [1]

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.

Feature Selection

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 analysis dataset

Steps of the analysis:

Load the data set:

  • Load each dataset
  • Add the activity labels (using descriptive names) and the subjects to each dataset
  • Name the variable correctly
  • Merges the training and the test sets to create one data set.
  • Extracts only the measurements on the mean and standard deviation for each measurement. Export a tidy data set:
  • From the previously generated data set, create a second, independent tidy data set with the average of each variable for each activity and each subject.
  • Save the tidy data set in a file. ("tidy.txt")

Variables in the final dataset:

All the variables in the tidy data set are:

  • subject, the id of the person performing the test.
  • activity, contain the name of the activity produced. It is a factor with which the ids match the ids use in the original data set.
  • The mean of all the measures of the previsous data set (mean() and std() measures), aggregated by subject and activity.

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