DATA DICTIONARY
activity_description
Descriptive activity names
LAYING
SITTING
STANDING
WALKING
WALKING_DOWNSTAIRS
WALKING_UPSTAIRS
subject
Identifier for the subject who performed the activity for each window sample. 1 .. 30
tBodyAcc-mean()-X-mean
tBodyAcc-mean()-Y-mean
tBodyAcc-mean()-Z-mean
tBodyAcc-std()-X-mean
tBodyAcc-std()-Y-mean
tBodyAcc-std()-Z-mean
tBodyAccJerk-mean()-X-mean
tBodyAccJerk-mean()-Y-mean
tBodyAccJerk-mean()-Z-mean
tBodyAccJerk-std()-X-mean
tBodyAccJerk-std()-Y-mean
tBodyAccJerk-std()-Z-mean
tBodyAccJerkMag-mean()-mean
tBodyAccJerkMag-std()-mean
tBodyAccMag-mean()-mean
tBodyAccMag-std()-mean
tBodyGyro-mean()-X-mean
tBodyGyro-mean()-Y-mean
tBodyGyro-mean()-Z-mean
tBodyGyro-std()-X-mean
tBodyGyro-std()-Y-mean
tBodyGyro-std()-Z-mean
tBodyGyroJerk-mean()-X-mean
tBodyGyroJerk-mean()-Y-mean
tBodyGyroJerk-mean()-Z-mean
tBodyGyroJerk-std()-X-mean
tBodyGyroJerk-std()-Y-mean
tBodyGyroJerk-std()-Z-mean
tBodyGyroJerkMag-mean()-mean
tBodyGyroJerkMag-std()-mean
tBodyGyroMag-mean()-mean
tBodyGyroMag-std()-mean
tGravityAcc-mean()-X-mean
tGravityAcc-mean()-Y-mean
tGravityAcc-mean()-Z-mean
tGravityAcc-std()-X-mean
tGravityAcc-std()-Y-mean
tGravityAcc-std()-Z-mean
tGravityAccMag-mean()-mean
tGravityAccMag-std()-mean
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).
Here are all the mean values based on activity_description and subject.
fBodyAcc-mean()-X-mean
fBodyAcc-mean()-Y-mean
fBodyAcc-mean()-Z-mean
fBodyAcc-std()-X-mean
fBodyAcc-std()-Y-mean
fBodyAcc-std()-Z-mean
fBodyAccJerk-mean()-X-mean
fBodyAccJerk-mean()-Y-mean
fBodyAccJerk-mean()-Z-mean
fBodyAccJerk-std()-X-mean
fBodyAccJerk-std()-Y-mean
fBodyAccJerk-std()-Z-mean
fBodyAccMag-mean()-mean
fBodyAccMag-std()-mean
fBodyBodyAccJerkMag-mean()-mean
fBodyBodyAccJerkMag-std()-mean
fBodyBodyGyroJerkMag-mean()-mean
fBodyBodyGyroJerkMag-std()-mean
fBodyBodyGyroMag-mean()-mean
fBodyBodyGyroMag-std()-mean
fBodyGyro-mean()-X-mean
fBodyGyro-mean()-Y-mean
fBodyGyro-mean()-Z-mean
fBodyGyro-std()-X-mean
fBodyGyro-std()-Y-mean
fBodyGyro-std()-Z-mean
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.
Here are all the mean values based on activity_description and subject.