This repository contains codes and documentation for a simple program "run_analysis.R" which calculates mean values by subject and activity for various mean and standard deviation (std) parameters of data recorded from devices worn by subjects in a variety of activities. The output is a tab-delimited file named "tidydata.txt" with column headers included. See CodeBook.md for particulars on the data processing and a description of the processed output.
data description: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones data url: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
Data Set Characteristics: Multivariate, Time-Series
Number of Instances: 10299
Area: Computer
Attribute Characteristics: N/A
Number of Attributes: 561
Date Donated: 2012-12-10
Associated Tasks: Classification, Clustering
Missing Values: N/A
Number of Web Hits: 86233
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.
- Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto.
- Smartlab - Non Linear Complex Systems Laboratory
- DITEN - Università degli Studi di Genova, Genoa I-16145, Italy.
- activityrecognition '@' smartlab.ws
- www.smartlab.ws
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.