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#Human Activity Recognition using Smartphones Data Set

About the Data Set Information:

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 experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data.

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 following actions were performed to the data:

  1. Data frames merged by using rbindand cbind combining Subject, Activity and Measurements observations.

  2. Columns' names are given by extracting them from features.txt file, which is included in the data set.

  3. Columns that contain mean or std are found by using grepl.

  4. IDs and names of activities are read from the activity_labels.txt file. The factor function is used to convert the activity IDs into descriptive names.

  5. Using ddply a second data set is created. This data set includes the average of each variable given each activity and subject.

  6. Finally, write.csv is used to create the tidy data set in the same folder as run_analysis.R script.

About the tidy data set file

CSV file containing 180 observations of 68 variables:

Subject :               int
Activity :              Factor w/ 6 levels
tBodyAcc-mean()-X :     num
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