This dataset is extracted from the "Human Activity Recognition Using Smartphones Data Set" (http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones) which was obtained as follows (extracted from their README file):
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.
Each variable corresponds to a phone's sensor measurement and their names and:
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.
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 script will process the dataset and return a tidy detaset with the mean and standard deviation for each variable for each experiment subject and each activity performed.
The variables that contains the "-mean()" sufix in its name, contains the average of the measurements for that particular subject and activity. The same way, the variables with the "-std()" sufix contains the standard deviation for that subject and activity.
The result is a dataframe with dimensions 180x68 which is stored in disk as tidyData.txt