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"Getting and Cleaning Data" Course Project Code Book Initial data for research

The data is taken from UCI HAR Dataset. This dataset provide the following variables for each activity:

subject - ID of participant
activity - ID of activity type
Mean and standart deviation for the following features (other values are presented in initial dataset, but for this reasearch only these parameters were used)
    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 features 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. CodeBook

The following data transformations were conducted to form a tidy dataset:

Added a new feature activitylabel - factor variable for activities with the following levels: WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING.

Tidy dataset was build as a mean values of features grouped by activitylabel and subject - for each subject and activity type determined mean values over all activities of that type.