Identification variables
subject : identification number of study participant range: 1 - 30 dataset : assignment of the subject to the training or testing data sets values: "testing" "training" activity: types of activities measured by the smartphones values: "WALKING" "WALKING_UPSTAIRS" "WALKING_DOWNSTAIRS" "SITTING" "STANDING" "LAYING"
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