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
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
fBodyAccJerk.mean()-X
fBodyAccJerk.mean()-Y
fBodyAccJerk.mean()-Z
fBodyAccJerk.std()-X
fBodyAccJerk.std()-Y
fBodyAccJerk.std()-Z
fBodyGyro.mean()-X
fBodyGyro.mean()-Y
fBodyGyro.mean()-Z
fBodyGyro.std()-X
fBodyGyro.std()-Y
fBodyGyro.std()-Z
fBodyAccMag.mean..
fBodyAccMag.std..
fBodyBodyAccJerkMag.mean..
fBodyBodyAccJerkMag.std..
fBodyBodyGyroMag.mean..
fBodyBodyGyroMag.std..
fBodyBodyGyroJerkMag.mean..
fBodyBodyGyroJerkMag.std..
2 more columns are added to the above set of features: Subject and Activity. The output file is the average of the above features, by subject and by activity.