| title | output | date |
|---|---|---|
README |
html_document |
July 2015 |
Human Activity Recognition Using Smartphones Dataset
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. See 'features_info.txt' for more details.
Further Description of this research can be obtain at this site http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
###Study design and data processing
####Collection of the raw data The raw data was downloaded into local working directory before running analysis. Data was sourced from https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
####Notes on the original (raw) data Below is the list of files from the raw dataset which are used in this analysis
- features.txt
- activity_labels.txt
- train/X_train.txt
- train/y_train.txt
- train/subject_train.txt
- test/X_test.txt
- test/y_test.txt
- test/subject_test.txt
###Creating the tidy datafile
####Guide to create the tidy data file
- Download the source data zip file using the link provided.
- Unzip this file into a temporary directory, maintaining the original folder stucture.
- Copy the 8 files listed above into the working directory, still maintaining the original folder structure.
- Ensure run_analysis.R file is in this working directory. You may need to copy this file into this directory if necessary.
- run_analysis.R is seperated into few sections. Run all sections.
- The resulting dataset will be exported into result.txt
####Cleaning of the data The R Code is located here https://github.com/kakilima/Module3CourseProject/blob/master/run_analysis.R
Read in Labels / Descriptors
- Read in features.txt to be used as header for dataset
- Read in activity_labels.txt to reference activities
Load Training Data
- Read in test dataset from x_test.txt
- Read in test data activity code from y_test.txt
- Read in test subject data
- All 3 files will be combined to form test data
- Activity code will be look up with activity label, by joining the 2 data frame
Load Training Data
- Read in testing dataset from x_test.txt
- Read in training data activity code from y_train.txt
- Read in training subject data
- All 3 files will be combined to form test data
- Activity code will be look up with activity label, by joining the 2 data frame
Combine & Process both dataset
- Combine both training and test data, using rbind
- Filter out required columns (which is all columns that contain mean and standard deviation)
- Summarise data by subject and activity
- This produced a dataset of 180 rows (30 subjects which perform 6 activities each)
- Write the result into result.txt
To Read Data back into R
- Code in this section can be used to read the result.txt back into R for further analysis
###Description of the variables in the result.txt file The resulting dataset has 180 obversations of 88 variables. Details of each variable can be found in the data dictionary (CodeBook.md) https://github.com/kakilima/Module3CourseProject/blob/master/CodeBook.md