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Codebook

============

Output file : 'tidydata.txt'

================= The file contains for each subject and each activity the average value of the feature measurements that contains either of the following variables of interest:

  • The mean
  • The standard deviation

As it has been described by [1] - License Reference - the selected features for this database come from samples taken from 30 Subjects using smartphones while experiencing 6 activities:

Activities

================ 1 WALKING

2 WALKING_UPSTAIRS

3 WALKING_DOWNSTAIRS

4 SITTING

5 STANDING

6 LAYING

Conventions

================

###### Origin of the measurements:

  1. The accelerometer(Acc)
  2. The gyroscope (Gyro)

###### How the time measurements have been processed determines:

  1. The prefix 't' to denote time
  2. The prefix 'f' to denote frequency domain signals (calculated by using a FFT - Fast Fourier Transform -)
The acceleration signal were separated into
  1. Body acceleration signal (tBodyAcc)
  2. Gravity acceleration signal (tGravityAcc)

###### A sufix '-XYZ' is used to denote 3-axial signals in the X, Y and Z directions

The body linear acceleration and angular velocity were derived in time to obtain Jerk signals

1.1. tBodyAccJerk-XYZ

1.2. tBodyGyroJerk-XYZ

The magnitude of these signals (calculated using the Euclidean norm) to get:

2.1. tBodyAccMag

2.2. tGravityAccMag

2.3. tBodyAccJerkMag

2.4. tBodyGyroMag

2.5. tBodyGyroJerkMag

###### The variables of interest have been marked as follows:

  1. The mean - mean()
  2. The standard Deviation - std()

The list of the 66 features (in alphabetical order) reported by the file 'tidydata.txt', ordered by Subjects and Activities:

"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"

"fBodyAccMag-mean()"

"fBodyAccMag-std()"

"fBodyBodyAccJerkMag-mean()"

"fBodyBodyAccJerkMag-std()"

"fBodyBodyGyroJerkMag-mean()"

"fBodyBodyGyroJerkMag-std()"

"fBodyBodyGyroMag-mean()"

"fBodyBodyGyroMag-std()"

"fBodyGyro-mean()-X"

"fBodyGyro-mean()-Y"

"fBodyGyro-mean()-Z"

"fBodyGyro-std()-X"

"fBodyGyro-std()-Y"

"fBodyGyro-std()-Z"

"tBodyAcc-mean()-X"

"tBodyAcc-mean()-Y"

"tBodyAcc-mean()-Z"

"tBodyAcc-std()-X"

"tBodyAcc-std()-Y"

"tBodyAcc-std()-Z"

"tBodyAccJerk-mean()-X"

"tBodyAccJerk-mean()-Y"

"tBodyAccJerk-mean()-Z"

"tBodyAccJerk-std()-X"

"tBodyAccJerk-std()-Y"

"tBodyAccJerk-std()-Z"

"tBodyAccJerkMag-mean()"

"tBodyAccJerkMag-std()"

"tBodyAccMag-mean()"

"tBodyAccMag-std()"

"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"

"tBodyGyroJerkMag-mean()"

"tBodyGyroJerkMag-std()"

"tBodyGyroMag-mean()"

"tBodyGyroMag-std()"

"tGravityAcc-mean()-X"

"tGravityAcc-mean()-Y"

"tGravityAcc-mean()-Z"

"tGravityAcc-std()-X"

"tGravityAcc-std()-Y"

"tGravityAcc-std()-Z"

"tGravityAccMag-mean()"

"tGravityAccMag-std()"

License Reference

[1] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012