Skip to content

Latest commit

 

History

History
98 lines (75 loc) · 4.6 KB

CodeBook.md

File metadata and controls

98 lines (75 loc) · 4.6 KB

Course Project Code Book

The Code Book reiterates the details on the initial data set used for the analysis and provides additional information on the transformations and analysis done on the data.

Data Set link: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

Transformations of the original data set:

The run_analysis.R script does the following transformations

  1. Sequentially read the Training set and Test set for equipment Data using read.table() and merge them using rbind().
  2. Repeat step 1 for Label and Subject
  3. Read the features data
  4. Using R expressions and grep() function the indexes of the features list containing the mean and std are extracted
  5. The indexes are then used to extract the Data concerning the mean and std only using data frame subsetting
  6. The activities file is read and used to map the label to corressponding activity names
  7. Fianlly a merged data set is obtained by combining the Subject, Label and the extracted Data using cbind()
  8. From the cleaned data another data set is obtained that provides the mean of the measurements for each activity of each subject
  9. the data is then written into a text file using table.write() with row.name=FALSE

Description of the Final Data Set

  • The data set includes 180 obs. of 68 variables
  • The first row provides column names (since row.name was set to false)
  • Provides the mean value for each equipment measurement pertaining to mean and std for each activity of each subject
  • Information of 6 activities of 30 patients
  • Activities: 1.Walking 2.Walking Upstairs 3.Walking Downstairs 4.Sitting 5.Standing 6.Laying

In Original Data Set, for each record it is provided:

  • Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration.
  • Triaxial Angular velocity from the gyroscope.
  • A 561-feature vector with time and frequency domain variables.
  • Its activity label.
  • An identifier of the subject who carried out the experiment.

###The dataset includes the following files:

  • 'README.txt'

  • 'features_info.txt': Shows information about the variables used on the feature vector.

  • 'features.txt': List of all features.

  • 'activity_labels.txt': Links the class labels with their activity name.

  • 'train/X_train.txt': Training set.

  • 'train/y_train.txt': Training labels.

  • 'test/X_test.txt': Test set.

  • 'test/y_test.txt': Test labels.

  • 'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.

Features evaluated

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).

  • 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 set of variables that were estimated from these signals are:

  • mean(): Mean value
  • std(): Standard deviation
  • mad(): Median absolute deviation
  • max(): Largest value in array
  • min(): Smallest value in array
  • sma(): Signal magnitude area
  • energy(): Energy measure. Sum of the squares divided by the number of values.
  • iqr(): Interquartile range
  • entropy(): Signal entropy
  • arCoeff(): Autorregresion coefficients with Burg order equal to 4
  • correlation(): correlation coefficient between two signals
  • maxInds(): index of the frequency component with largest magnitude
  • meanFreq(): Weighted average of the frequency components to obtain a mean frequency
  • skewness(): skewness of the frequency domain signal
  • kurtosis(): kurtosis of the frequency domain signal
  • bandsEnergy(): Energy of a frequency interval within the 64 bins of the FFT of each window.
  • angle(): Angle between to vectors.