Skip to content

Latest commit

 

History

History
72 lines (62 loc) · 2.87 KB

codebook.md

File metadata and controls

72 lines (62 loc) · 2.87 KB
Raw

Original data

These signals were used to estimate variables of the feature vector for each pattern 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

These measurements are performed multiple times on each subject, and during several activities as stated here:

  • WALKING: subject walking
  • WALKING_UPSTAIRS: subject walking up a set of stairs
  • WALKING_DOWNSTAIRS: subject walking down a set of stairs
  • SITTING: subject sitting
  • STANDING: subject standing
  • LAYING: subject laying down

tidy data

Includes only the mean and std variables. the original data contains repeated measurements for each subject and each activity. In the tidy dataset, the average of measurements per subject, per activity are calculated and reported.

workflow processes

  • training and test data sets are read into tables, and appropriate column names are assigned for subject and activity
  • the tables are merged using rbind command
  • feature labels are read into a table, and columns with words mean or std are selected using the grep command
  • a smaller subset of data from selected columns is created
  • activity labels are read into a table, and are substituded for the activity numbers using mapvalues command
  • variable names are inserted in the smaller data set
  • the melt and dcast commands are used to re-shape the data and populate average values for each variable.
  • the final tidy data set is written into a text file.