mjeffg / course-project1

Course Project1 - Getting and Tidying Data

Analysis on UCI Human Activity Recognition Using Smartphones data set

Introduction

This assignment uses data from the UC Irvine Machine Learning Repository. The "Human Activity Recognition using Smartphones Data Set" is used for the the assignment:

• Dataset UCI Human Activity Recognition Using Smartphons

• Description Measurements of six activities performed by 30 volunteers within the age bracket of 19-48 years. The voluteers wore a smartphone(Samsung Galazy S II) on the waist. Using embedded accelerometer and gyroscope they captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments were video-recorded to label the activity in the data manually. The dataset was randomly partioned into 70% training data and 30% test data.[1]

    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. [1]
  

• Variables For each record in the dataset 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.[1]

Loading Data into R

* The script assumes the data set is in the working directory and unzipped
* Six text files will be loaded seperately:
	1. Subject_training
	2. X_training
	3. Y_training
	4. Subject_test
	5. X_test
	6. Y_test
* Read.table() will be used to load the data

Processing the Data

* The values in "activity" variable will be renamed: Walking, Walking_Upstairs, Walking_Downstairs,
	Sitting, Standing, Laying
* The measurement variables will be named by their measurement
* The training and test data sets will each be merged by created "id" number which will be deleted after
* The merged training and test data sets will be merged together using rbind()
* The merged data set will be trimmed to include only variables the exact mean and standard deviation
	measures
* A second tidy data set will be created to get the average of each variable for each activity and each
	subject
* The two tidy data sets will be export as a tab delimited text file and uploaded onto Course Project1
	submit box using write.table()

[1] 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