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CodeBook.MD

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This is the codebook for the files.

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.

  • 'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.

The initial file contained the following variables

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.

The analysis that was performed in the data are as follows

  • All the files were read into the program.
  • The datas were merged into a single data frame
  • Appropriate lables and variable names were assigned
  • The mean and std variables were decoded
  • The data was grouped according to subject and activity
  • A summary data frame was created which contains the average of all the grouped data

The final file contains the following variables

  • subject_id - The id of the test subject
  • activity - Contains the name of the activity that the test subjects were performing

The rest of the variables contains the mean of the initial data

  • 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
  • fBodyAcc-meanFreq-X
  • fBodyAcc-meanFreq-Y
  • fBodyAcc-meanFreq-Z
  • fBodyAccJerk-mean-X
  • fBodyAccJerk-mean-Y
  • fBodyAccJerk-mean-Z
  • fBodyAccJerk-std-X
  • fBodyAccJerk-std-Y
  • fBodyAccJerk-std-Z
  • fBodyAccJerk-meanFreq-X
  • fBodyAccJerk-meanFreq-Y
  • fBodyAccJerk-meanFreq-Z
  • fBodyGyro-mean-X
  • fBodyGyro-mean-Y
  • fBodyGyro-mean-Z
  • fBodyGyro-std-X
  • fBodyGyro-std-Y
  • fBodyGyro-std-Z
  • fBodyGyro-meanFreq-X
  • fBodyGyro-meanFreq-Y
  • fBodyGyro-meanFreq-Z
  • fBodyAccMag-mean
  • fBodyAccMag-std
  • fBodyAccMag-meanFreq
  • fBodyBodyAccJerkMag-mean
  • fBodyBodyAccJerkMag-std
  • fBodyBodyAccJerkMag-meanFreq
  • fBodyBodyGyroMag-mean
  • fBodyBodyGyroMag-std
  • fBodyBodyGyroMag-meanFreq
  • fBodyBodyGyroJerkMag-mean
  • fBodyBodyGyroJerkMag-std
  • fBodyBodyGyroJerkMag-meanFreq