The CodeBook.md file contains a description of the data, it includes at the bottom the original features_info.txt text. It further describes the variables selected for this analysis, their new variable names, their structure and the data in this new tidy data set

The files required by the analysis file are expected to be in the current R working directory under the following structure in an unzipped format

1. "UCI HAR Dataset/activity_labels.txt"
2. "UCI HAR Dataset/features.txt"
3. "UCI HAR Dataset/test/subject_test.txt"
4. "UCI HAR Dataset/test/X_test.txt"
5. "UCI HAR Dataset/test/y_test.txt"
6. "UCI HAR Dataset/train/subject_train.txt"
7. "UCI HAR Dataset/train/X_train.txt"
8. "UCI HAR Dataset/train/y_train.txt"

At various stages of the analysis a message is printed to the console. The stages follow:-

• Load packages required
• Read into R all files from the list

• Rename the variable names of the subject and activity data sets
• Column bind the activity.test, subject.test and test.data
• Column bind the activity.train, subject.train and train.data
• Row bind the resulting test and training column accumulations
• Perform a test that the row bind is correct by taking the top of the test data set and the bottom of the training data set and checking if they equal the resulting rows in the binded data set.

• Get the id's from the features of the columns required
• Coerce the id's to match the merged data set
• Add to that the id's of the activity and subject columns
• Split the merged data set into new required data set

• Create activity as a factor column based on the file activity_labels

• Start with the original feature names for the selected columns
• Parse the original names through a serious of regular expressions and alter some of the name
• Each pass alters different parts of the names as new labels
• Add the Activity and Subject names and apply as column names
• Arrange the data by Subject and Activity

#####Comment: An attempt to make the names more readable whilst keeping them close to the orignal names. This helps keep the data owners in tune with the changes without too much confusion.

• Group the data by Subject and Activity
• Generate mean data for each Subject and Activity
• Write to the current working directory this data in a file mean_data_set.txt

##Original ReadMe text

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.

• '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

• '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: [email protected]

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.