This repository contains the project submission for the third course, Getting and Cleaning Data from the Data Science specialization on Coursera by JHU.
Human Activity Recognition Using Smartphones Dataset Version 1.0
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.
- 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.
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'README.txt'
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'features_info.txt': Shows information about the variables used on the feature vector.
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'features.txt': List of all features.
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'activity_labels.txt': Links the class labels with their activity name.
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'train/X_train.txt': Training set.
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'train/y_train.txt': Training labels.
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'test/X_test.txt': Test set.
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'test/y_test.txt': Test labels.
The following files are available for the train and test data. Their descriptions are equivalent.
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'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.
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'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.
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'train/Inertial Signals/body_acc_x_train.txt': The body acceleration signal obtained by subtracting the gravity from the total acceleration.
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'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.
- Features are normalized and bounded within [-1,1].
- Each feature vector is a row on the text file.
- Download and unzip the dataset
- Read train and test dataset into R environment.
- Read variable names into R envrionment.
- Read subject index into R environment.
- Merge the training and the test sets to create one data set.
- Extract only the measurements on the mean and standard deviation for each measurement.
- Uses descriptive activity names to name the activities in the data set Convert activity labels to characters and add a new column as factor
- Appropriately label the data set with descriptive variable names.
- From the data set now formed, create a second, independent tidy data set with the average of each variable for each activity and each subject.
'run_analysis.R' is the code for the R script used to complete the project goals
'CodeBook.md' contains information on the variables, data set, transformations and work that was done to tidy up the data
The new generated dataset containes variables calculated based on the mean and standard deviation. Each row of the dataset is an average of each activity type for all subjects.
'tidydata.txt' is the output from the 'runAnalysis.R' R script