title	author	date	output
README.MD	Lenette	April 24, 2016	html_document

knitr::opts_chunk$set(echo = TRUE)

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This dataset was created as part of Data Cleaning project under the "Getting and Cleaning Data" course of the Data Science specialization on Coursera, on April 24, 2016.

The original ReadMe is appended below and it begins after the a series of ######### signs

For this dataset, the following modifications were done to the original set located at https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip with a full description located a the original site, http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

Important: The main program file is run_analysis.R and it assumes that the original Samsung data sets are located in folder of the current working directory called .\rawdata

Summary of operations:

1. Column Headings were used and saved in the same file as the data set
2. Activity Names were also incorporated into the actual data sets, instead of just having the activity ids
3. The file of feature vectors was read and converted to numeric
4. The vector data was combined with the activity and subject data to allow the resulting data set to be viewed, sliced and summaried by these dimensions
5. The training and test data were merged
6. Summaries were created as requested, either over the entire dataset or groups by activities and/or subjects
7. Other than the class project upload file, all data sets were saved as csv

This is a list of the new files in this data set and their contents: Note: all the files are in .csv format for ease of loading/parsing

1. combinedhar.csv - this is file combines the test and training data sets, which were originally distributed in separate folders. Also, the data set contains te descriptive variable names in the same file, as headings

2. meanstdactivitynames.csv - this is a subset of the combinedhar.csv file that contains only the measurements on the mean and standard deviation for each measurement

3. combinedactivitynames.csv - this is a further enhancement on the combinedhar.csv, with an additional column containing descriptive activity names

4. byactivitysummary.csv - this contains a summary (mean) of all fields, grouped by activity

5. bysubjectsummary.csv - this contains a summary (mean) of all fields, grouped by subject

6. byactivitysubjectsummary.txt - this contains the summary file (mean) of all fields by both activity and subject, formatted according to the course instructions

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================================================================== 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 - Università 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

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.