Datascience Coursera

================================================================== Human Activity Recognition Using Smartphones Dataset

Version 1.0

Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto. Smartlab - Non Linear Complex Systems Laboratory DITEN - Universit? degli Studi di Genova. Via Opera Pia 11A, I-16145, Genoa, Italy. activityrecognition@smartlab.ws www.smartlab.ws

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.

For each record 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.

The dataset includes the following files:

'README.txt'
'features_info.txt': Shows information about the variables used on the feature vector.
'features.txt': List of all features.
'activity_labels.txt': Links the class labels with their activity name.
'train/X_train.txt': Training set.
'train/y_train.txt': Training labels.
'test/X_test.txt': Test set.
'test/y_test.txt': Test labels.

The following files are available for the train and test data. Their descriptions are equivalent.

'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.
'train/Inertial Signals/total_acc_x_train.txt': The acceleration signal from the smartphone accelerometer X axis in standard gravity units 'g'. Every row shows a 128 element vector. The same description applies for the 'total_acc_x_train.txt' and 'total_acc_z_train.txt' files for the Y and Z axis.
'train/Inertial Signals/body_acc_x_train.txt': The body acceleration signal obtained by subtracting the gravity from the total acceleration.
'train/Inertial Signals/body_gyro_x_train.txt': The angular velocity vector measured by the gyroscope for each window sample. The units are radians/second.

Notes:

Features are normalized and bounded within [-1,1].
Each feature vector is a row on the text file.

For more information about this dataset contact: activityrecognition@smartlab.ws

Runnig Analysis:

================

Аnalytical data

Important! Source analytical data files are NOT included in git repository. If /data directory does not exist, script will download and upzip source data.

Silent Mode

To run analysis in silent mode import source file ./run_analysis.R and call run() function.

source("./run_analysis.R")
run()

After successful execution '"Tidy Data saved in ./data/tidy_data.csv file"' will be pinted.

CodeBook

To generate CodeBook I used knitr library. Please, run in R console

library("knitr")
knit2html("CodeBook.Rmd")
browseURL("CodeBook.html")

License:

======== Use of this dataset in publications must be acknowledged by referencing the following publication [1]

[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

This dataset is distributed AS-IS and no responsibility implied or explicit can be addressed to the authors or their institutions for its use or misuse. Any commercial use is prohibited.

Jorge L. Reyes-Ortiz, Alessandro Ghio, Luca Oneto, Davide Anguita. November 2012.

irybakov/datasciencecoursera