/Getting-and-Cleaning-Data

Primary LanguageRGNU General Public License v2.0GPL-2.0

================================================================== Getting-and-Cleaning-Data

Making tidy dataset Human Activity Recognition Using Smartphones Dataset

Andrey Eroshkin

Script is a part of Coursera Getting and cleaning data course.

Source of data is "Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012"

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.

For each record it is provided:

  • A 66-feature vector with time and frequency domain variables.
  • Its activity label.
  • An identifier of the subject who carried out the experiment.

The repo includes the following files:

  • 'README.Md'

  • 'Codebook.Md': Shows information about the variables used on the feature vector.

  • 'tidydata.txt': result dataset.

  • 'run_analysis.R': R script.

What script do

  1. Merges the training and the test sets to create one data set.
  2. Extracts only the measurements on the mean and standard deviation for each measurement.
  3. Uses descriptive activity names to name the activities in the data set
  4. Appropriately labels the data set with descriptive variable names.
  5. Creates a second, independent tidy data set with the average of each variable for each activity and each subject.

How to use script

  1. Copy script to local folder.
  2. Load script to R IDE.
  3. Change path in setwd command to script directory path.
  4. Install plyr libruary.
  5. Run script in R.
  6. Result dataset will writting to ./data/tidydata.txt.

Notes:

  • Features are normalized and bounded within [-1,1].
  • Each feature vector is a row on the text file.
  • Data set with the average of each variable for each activity and each subject.