Getting and Cleaning Data

Mark Addinall - December 2015

This is part of the Johns Hopkins courses that make up the Data Scientist speciality stream.

The program in here, run_analysis.R carries out the following functions.

• Merges the training and the test sets to create one data set.
• Extracts only the measurements on the mean and standard deviation for each measurement.
• Uses descriptive activity names to name the activities in the data set
• Appropriately labels the data set with descriptive variable names.
• Creates a second, independent tidy data set with the average of each variable for each activity and each subject.

The data used for this assignment is described here and more completely in Codebook.md.

The datafiles are stored in the working directory

data\UCI
data\UCI\test
data\UCI\training

The program check for the existance of a file:

data\UCI\tidy.txt

If this file exists, the program will not produce a new one. It must be deleted for it to run. This script should be started from the root here. It changes the working directory to the data area when executing.

Human Activity Recognition Using Smartphones Data Set

Abstract: Human Activity Recognition database built from the recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors.

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

Number of Web Hits:

109723

Source:

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

Data Set Information:

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.

Attribute Information:

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.

Relevant Papers:

N/A

Citation Request:

[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