##Subject Unique identifier for each subject for which measurements were made Unit: Integer 1..30 Each number represents a different subject
##Activity Description of the activity which took place Unit: character string Possible values are:
LAYING
SITTING
STANDING
WALKING
WALKING_DOWNSTAIRS
WALKING_UPSTAIRS
##MeasurementName Description of the measurement that happened Unit: character string
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.
Only the means and standard deviation values were kept for this dataset.
Possible values are:
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)
##MeanOfMeasurement The mean of the measurements of all observations of the original set Units: time (prefix t-), frequency (prefix f-), angle (prefix angle-) -0.9976661..0.9745087