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Getting and Cleaning Human Activity Recognition Data Project


Introduction

Experiments were 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, the research team at Smartlab (Non Linear Complex Systems Laboratory, Italy) captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz.

The obtained dataset was randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data.Each subject was observed and measurements were recorded across multiple time windows, a vector of features was obtained by calculating variables from the time and frequency domain.

For each record following information was provided in the dataset:

  • 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

Objective

The objective of this project was to tidy up the publicly available raw data set, aggregate data and generate a clean data set that can be used for further analysis and studies.

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

The following related artifacts are included in this repo project

  • run_analysis.R - R script for cleaning the raw data files and generating a clean file

  • CodeBook.md - A reference file describing the variables in the tidy data set and the transformations applied to the raw data for generating the tidy data set.

  • tidydata.txt - Tidy data set containing the aggregated data that was generated by the run.analysis.R script

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Project for Getting and Cleaning Data course

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