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#Data Dictionary for tidy data set.

##Subject Integer values from the set [1:30] representing the 30 test subjects

##Activity Character string representing the 6 activities performed by the test subjects. Theses are:- WALKING WALKING_UPSTAIRS WALKING_DOWNSTAIRS SITTING STANDING LAYING

##mean-value Numeric value, mean() of the corresponding feature. Because each feature is normalized on [-1,1], this value is dimensionless

##Variable Character string representing one of the feature variables that was normalized to a dimensionless value. These are taken from the following set. "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()" "angle(tBodyAccMean,gravity)" "angle(tBodyAccJerkMean),gravityMean)" "angle(tBodyGyroMean,gravityMean)" "angle(tBodyGyroJerkMean,gravityMean)" "angle(X,gravityMean)" "angle(Y,gravityMean)" "angle(Z,gravityMean)"

#Notes:

  • Features are normalized and bounded within [-1,1].

##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
meanFreq(): Weighted average of the frequency components to obtain a mean frequency
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