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CodeBook.md

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CodeBook.md
Tiago Flores
2017 - 12 - 21
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Original Data

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 complete list of variables of each feature vector is available in 'features.txt'

Transformations

Data are originally divided in training and test data, with variables names in a file called features.txt and also another file called activity_labels.txt which contains activity labels and activity id.

  • Variables names (features.txt)
  • Activity labels (activity_labels.txt)
  • Train Data
    • train/X_train.txt
    • train/subject_train.txt
    • train/y_train.txt
  • Test Data
    • test/X_test.txt
    • test/subject_test.txt
    • test/y_test.txt

  1. 1st transformation step is to load activity_labels.txt into the memory naming its two variables "activityid" and "activityname". The final dataset name of this step is named activity_labels.

  2. 2nd transformation step is to join data from the three files of test data in a single file, naming their variables according to activity_labels.txt to data obtained from subject_test.txt, naming "ydata" the variable from y_test.txt, and naming "subject" the variable from subject_test.txt. The same process is done with train data, resulting in two datasets: "tsxdata" and "trxdata".

  3. 3rd transformation step is to "rbind" the test (tsxdata) and train (trxdata) datasets in a single dataframe. Named "alldata".

  4. 4th tranformation step is to create a dataframe from the previou loaded activity lables data. Named "actlbls".

  5. 5th transformation step is to join dataframes "alldata" and "actlbls" by the respective variables, "ydata" and "activityid", overwriting the "alldata" dataframe with the resulting of join operation.

  6. 6th transformation step is to select only variables which contain "std" and "mean" in their names. "activityname" and "subject" variables are also selected and the result overwrites "alldata" dataframe.

  7. 7th transformation step is to rename variables, removing "(", ")" and "-" characters.

  8. 8th transformation step is to create a independent tidy data set with the average of each variable (evavg_data) for each activity and each subject.

Final Data

The final data has 68 variables:

 [1] "activityname"             "subject"                  "tbodyaccmeanx"            "tbodyaccmeany"           
 [5] "tbodyaccmeanz"            "tbodyaccstdx"             "tbodyaccstdy"             "tbodyaccstdz"            
 [9] "tgravityaccmeanx"         "tgravityaccmeany"         "tgravityaccmeanz"         "tgravityaccstdx"         
[13] "tgravityaccstdy"          "tgravityaccstdz"          "tbodyaccjerkmeanx"        "tbodyaccjerkmeany"       
[17] "tbodyaccjerkmeanz"        "tbodyaccjerkstdx"         "tbodyaccjerkstdy"         "tbodyaccjerkstdz"        
[21] "tbodygyromeanx"           "tbodygyromeany"           "tbodygyromeanz"           "tbodygyrostdx"           
[25] "tbodygyrostdy"            "tbodygyrostdz"            "tbodygyrojerkmeanx"       "tbodygyrojerkmeany"      
[29] "tbodygyrojerkmeanz"       "tbodygyrojerkstdx"        "tbodygyrojerkstdy"        "tbodygyrojerkstdz"       
[33] "tbodyaccmagmean"          "tbodyaccmagstd"           "tgravityaccmagmean"       "tgravityaccmagstd"       
[37] "tbodyaccjerkmagmean"      "tbodyaccjerkmagstd"       "tbodygyromagmean"         "tbodygyromagstd"         
[41] "tbodygyrojerkmagmean"     "tbodygyrojerkmagstd"      "fbodyaccmeanx"            "fbodyaccmeany"           
[45] "fbodyaccmeanz"            "fbodyaccstdx"             "fbodyaccstdy"             "fbodyaccstdz"            
[49] "fbodyaccjerkmeanx"        "fbodyaccjerkmeany"        "fbodyaccjerkmeanz"        "fbodyaccjerkstdx"        
[53] "fbodyaccjerkstdy"         "fbodyaccjerkstdz"         "fbodygyromeanx"           "fbodygyromeany"          
[57] "fbodygyromeanz"           "fbodygyrostdx"            "fbodygyrostdy"            "fbodygyrostdz"           
[61] "fbodyaccmagmean"          "fbodyaccmagstd"           "fbodybodyaccjerkmagmean"  "fbodybodyaccjerkmagstd"  
[65] "fbodybodygyromagmean"     "fbodybodygyromagstd"      "fbodybodygyrojerkmagmean" "fbodybodygyrojerkmagstd"