Skip to content

CodeBook.md

Latest commit

 

History

History
101 lines (88 loc) · 3.64 KB

CodeBook.md

File metadata and controls

101 lines (88 loc) · 3.64 KB
Raw

Getting and Cleaning Data - Course Project

Introduction of Original Data

The original data set is about experiments 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.

Data Cleaning Processes

the original data was modifies by

  • Merging the training and the test sets to create one data set.
  • Extracting only the measurements on the mean and standard deviation for each measurement.
  • Useing descriptive activity names to name the activities in the data set
  • Appropriately labeling the data set with descriptive variable names.
  • Creating a second, independent tidy data set with the average of each variable for each activity and each subject.

Descriptions of Processed Data

Identififiers

The first two columns - subjects and activities - are Identifiers.

  • subjects: the ID of the subject
  • activities: the name of the activity performed by the subject when measurements were taken

Units

  • The units of acceleration signal from the smartphone accelerometer are standard gravity units 'g'.
  • The units of angular velocity vector measured by the gyroscope are radians/second.

Measurements of the Processed Data

As mentioned above,the variables remaining are just the calculatd means and standard deviations of these sets of data:

  • tBodyAccMeanX
  • tBodyAccMeanY
  • tBodyAccMeanZ
  • tBodyAccStdX
  • tBodyAccStdY
  • tBodyAccStdZ
  • tGravityAccMeanX
  • tGravityAccMeanY
  • tGravityAccMeanZ
  • tGravityAccStdX
  • tGravityAccStdY
  • tGravityAccStdZ
  • tBodyAccJerkMeanX
  • tBodyAccJerkMeanY
  • tBodyAccJerkMeanZ
  • tBodyAccJerkStdX
  • tBodyAccJerkStdY
  • tBodyAccJerkStdZ
  • tBodyGyroMeanX
  • tBodyGyroMeanY
  • tBodyGyroMeanZ
  • tBodyGyroStdX
  • tBodyGyroStdY
  • tBodyGyroStdZ
  • tBodyGyroJerkMeanX
  • tBodyGyroJerkMeanY
  • tBodyGyroJerkMeanZ
  • tBodyGyroJerkStdX
  • tBodyGyroJerkStdY
  • tBodyGyroJerkStdZ
  • tBodyAccMagMean
  • tBodyAccMagStd
  • tGravityAccMagMean
  • tGravityAccMagStd
  • tBodyAccJerkMagMean
  • tBodyAccJerkMagStd
  • tBodyGyroMagMean
  • tBodyGyroMagStd
  • tBodyGyroJerkMagMean
  • tBodyGyroJerkMagStd
  • fBodyAccMeanX
  • fBodyAccMeanY
  • fBodyAccMeanZ
  • fBodyAccStdX
  • fBodyAccStdY
  • fBodyAccStdZ
  • fBodyAccJerkMeanX
  • fBodyAccJerkMeanY
  • fBodyAccJerkMeanZ
  • fBodyAccJerkStdX
  • fBodyAccJerkStdY
  • fBodyAccJerkStdZ
  • fBodyGyroMeanX
  • fBodyGyroMeanY
  • fBodyGyroMeanZ
  • fBodyGyroStdX
  • fBodyGyroStdY
  • fBodyGyroStdZ
  • fBodyAccMagMean
  • fBodyAccMagStd
  • fBodyBodyAccJerkMagMean
  • fBodyBodyAccJerkMagStd
  • fBodyBodyGyroMagMean
  • fBodyBodyGyroMagStd
  • fBodyBodyGyroJerkMagMean
  • fBodyBodyGyroJerkMagStd