Coursera assignment
The purpose of this project is to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis.
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.
Check the README.txt file for further details about this dataset.
A video of the experiment including an example of the 6 recorded activities with one of the participants can be seen in the following link: Web Link
An updated version of this dataset can be found at Web Link. It includes labels of postural transitions between activities and also the full raw inertial signals instead of the ones pre-processed into windows.
You can just run source("run_analysis.R") The script downloads an archive with data and stores it in archive.zip file. After that the archive will uncompressed to original folder structure. As soon as all files are available the same function ds runs against test and train folders to create corresponded datasets with merging lists of subjects and actions to create one combined data frame. Later readable action label is added to the data set to use the action labels and subject ids for aggregated result that is stored in data folder with file name avg_per_subj_per_act.txt.
Check CodeBook.md for more details about the script structure and variables.