features_info.txt

Feature Selection 
=================

The features selected for this database come from the set https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip which is based on data which is avaible at : http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

subject : the subject performing the activity
Activity: the decoded activity of the orginal set, i.e. the activity number is decoded into the activity text for readability.

The 79 feautures are a subset of the original set. Only the feautures indicating a mean or standard deviation were kept:

t: features prefixed with 't' are within the time domain,
f: features prefixed with 'f' are in the frequency domain. This mean a Fast Fourier Transform (FFT) was applied to thos features.

hese 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

Additional vectors obtained by averaging the signals in a signal window sample. These are used on the angle() variable:

gravityMean
tBodyAccMean
tBodyAccJerkMean
tBodyGyroMean
tBodyGyroJerkMean