#Codebook
The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz.
The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.
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 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
These variables were then summerised by Subject and Activity taking the mean of each.
| Variable | Data Type | Desc |
|---|---|---|
| SubjectID | int | An identifier of the subject who carried out the experiment. |
| ActivityName | chr | The activity label |
| ActivityID | num | An identifier of the activity |
| tBodyAcc_mean_X | num | All further values are the mean of the original variables |
| tBodyAcc_mean_Y | num | |
| tBodyAcc_mean_Z | num | |
| tBodyAcc_std_X | num | |
| tBodyAcc_std_Y | num | |
| tBodyAcc_std_Z | num | |
| tGravityAcc_mean_X | num | |
| tGravityAcc_mean_Y | num | |
| tGravityAcc_mean_Z | num | |
| tGravityAcc_std_X | num | |
| tGravityAcc_std_Y | num | |
| tGravityAcc_std_Z | num | |
| tBodyAccJerk_mean_X | num | |
| tBodyAccJerk_mean_Y | num | |
| tBodyAccJerk_mean_Z | num | |
| tBodyAccJerk_std_X | num | |
| tBodyAccJerk_std_Y | num | |
| tBodyAccJerk_std_Z | num | |
| tBodyGyro_mean_X | num | |
| tBodyGyro_mean_Y | num | |
| tBodyGyro_mean_Z | num | |
| tBodyGyro_std_X | num | |
| tBodyGyro_std_Y | num | |
| tBodyGyro_std_Z | num | |
| tBodyGyroJerk_mean_X | num | |
| tBodyGyroJerk_mean_Y | num | |
| tBodyGyroJerk_mean_Z | num | |
| tBodyGyroJerk_std_X | num | |
| tBodyGyroJerk_std_Y | num | |
| tBodyGyroJerk_std_Z | num | |
| tBodyAccMag_mean | num | |
| tBodyAccMag_std | num | |
| tGravityAccMag_mean | num | |
| tGravityAccMag_std | num | |
| tBodyAccJerkMag_mean | num | |
| tBodyAccJerkMag_std | num | |
| tBodyGyroMag_mean | num | |
| tBodyGyroMag_std | num | |
| tBodyGyroJerkMag_mean | num | |
| tBodyGyroJerkMag_std | num | |
| fBodyAcc_mean_X | num | |
| fBodyAcc_mean_Y | num | |
| fBodyAcc_mean_Z | num | |
| fBodyAcc_std_X | num | |
| fBodyAcc_std_Y | num | |
| fBodyAcc_std_Z | num | |
| fBodyAccJerk_mean_X | num | |
| fBodyAccJerk_mean_Y | num | |
| fBodyAccJerk_mean_Z | num | |
| fBodyAccJerk_std_X | num | |
| fBodyAccJerk_std_Y | num | |
| fBodyAccJerk_std_Z | num | |
| fBodyGyro_mean_X | num | |
| fBodyGyro_mean_Y | num | |
| fBodyGyro_mean_Z | num | |
| fBodyGyro_std_X | num | |
| fBodyGyro_std_Y | num | |
| fBodyGyro_std_Z | num | |
| fBodyAccMag_mean | num | |
| fBodyAccMag_std | num | |
| fBodyBodyAccJerkMag_mean | num | |
| fBodyBodyAccJerkMag_std | num | |
| fBodyBodyGyroMag_mean | num | |
| fBodyBodyGyroMag_std | num | |
| fBodyBodyGyroJerkMag_mean | num | |
| fBodyBodyGyroJerkMag_std | num |