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Codebook

Project

The work presented here is part of the course Getting and Cleaning Data from the John Hopkins University's Data Science Specialization for Coursera.

In this file, you will find a brief explanation of the data presented in this repository.

Source

The tidy_data_set.txt file contains a summary of the data found in this link https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

This dataset was generated by the run_analysis.R script. It summarizes the raw data found in the files from the link that were copied to the getting_and_cleaning_data folder by subject and activity

The data

The data is aggregated by subject (ranges from 1 to 30) and activity (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING).

The other columns stand for the mean (mean()) and standard deviation (std()) values measured by accelerometers and gyroscopes in the X, Y and Z axis.

The final dataset contains the following columns:

  • activity
  • subject
  • tBodyAcc
  • tGravityAcc
  • tBodyAccJerk
  • tBodyGyro
  • tBodyGyroJerk
  • tBodyAccMag
  • tGravityAccMag
  • tBodyAccJerkMag
  • tBodyGyroMag
  • tBodyGyroJerkMag
  • fBodyAcc
  • fBodyAccJerk
  • fBodyGyro
  • fBodyAccMag
  • fBodyBodyAccJerkMag
  • fBodyBodyGyroMag
  • fBodyBodyGyroJerkMag

How the original data was obtained

The features selected for this database 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).