#README for run_analysis.R

This script uses the Samsung wearable data and depends on the "UCI HAR Dataset" folder being saved in the working directory.

This script combines the test and training data, applies descriptive labels to the activities and variables, subsets to only includes variable measurements on the mean and standard deviation, and then produces a summary dataset that includes the mean of each variable summarized by Subject ID and activity.

This subsetting is done using grep to only include measurement variables with "mean()" or "std()" in their name.

This script takes the Samsung data and processes it by doing the following:

1. Merges the training and the test sets to create one data set.
2. Extracts only the measurements on the mean and standard deviation for each measurement.
3. Uses descriptive activity names to name the activities in the data set
4. Appropriately labels the data set with descriptive variable names.
5. From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.

The output is the summary data from step 5

There are 30 subjects labeled by Subject_ID.

There are 6 activities, labeled with factors: [1] WALKING [2] WALKING_UPSTAIRS [3] WALKING_DOWNSTAIRS [4] SITTING [5] STANDING [6] LAYING

In the output table, [1] is Subject_ID, [2] is activity (per above).

There are then 66 variables that represent the average of the underlying variable measurements across each Subject_ID and activity.

The units and specific information are included in the quoted original README.txt that is below.

These variables are:
[3] "tBodyAcc-mean()-X"
[4] "tBodyAcc-mean()-Y"
[5] "tBodyAcc-mean()-Z"
[6] "tBodyAcc-std()-X"
[7] "tBodyAcc-std()-Y"
[8] "tBodyAcc-std()-Z"
[9] "tGravityAcc-mean()-X"
[10] "tGravityAcc-mean()-Y"
[11] "tGravityAcc-mean()-Z"
[12] "tGravityAcc-std()-X"
[13] "tGravityAcc-std()-Y"
[14] "tGravityAcc-std()-Z"
[15] "tBodyAccJerk-mean()-X"
[16] "tBodyAccJerk-mean()-Y"
[17] "tBodyAccJerk-mean()-Z"
[18] "tBodyAccJerk-std()-X"
[19] "tBodyAccJerk-std()-Y"
[20] "tBodyAccJerk-std()-Z"
[21] "tBodyGyro-mean()-X"
[22] "tBodyGyro-mean()-Y"
[23] "tBodyGyro-mean()-Z"
[24] "tBodyGyro-std()-X"
[25] "tBodyGyro-std()-Y"
[26] "tBodyGyro-std()-Z"
[27] "tBodyGyroJerk-mean()-X"
[28] "tBodyGyroJerk-mean()-Y"
[29] "tBodyGyroJerk-mean()-Z"
[30] "tBodyGyroJerk-std()-X"
[31] "tBodyGyroJerk-std()-Y"
[32] "tBodyGyroJerk-std()-Z"
[33] "tBodyAccMag-mean()"
[34] "tBodyAccMag-std()"
[35] "tGravityAccMag-mean()"
[36] "tGravityAccMag-std()"
[37] "tBodyAccJerkMag-mean()"
[38] "tBodyAccJerkMag-std()"
[39] "tBodyGyroMag-mean()"
[40] "tBodyGyroMag-std()"
[41] "tBodyGyroJerkMag-mean()"
[42] "tBodyGyroJerkMag-std()"
[43] "fBodyAcc-mean()-X"
[44] "fBodyAcc-mean()-Y"
[45] "fBodyAcc-mean()-Z"
[46] "fBodyAcc-std()-X"
[47] "fBodyAcc-std()-Y"
[48] "fBodyAcc-std()-Z"
[49] "fBodyAccJerk-mean()-X"
[50] "fBodyAccJerk-mean()-Y"
[51] "fBodyAccJerk-mean()-Z"
[52] "fBodyAccJerk-std()-X"
[53] "fBodyAccJerk-std()-Y"
[54] "fBodyAccJerk-std()-Z"
[55] "fBodyGyro-mean()-X"
[56] "fBodyGyro-mean()-Y"
[57] "fBodyGyro-mean()-Z"
[58] "fBodyGyro-std()-X"
[59] "fBodyGyro-std()-Y"
[60] "fBodyGyro-std()-Z"
[61] "fBodyAccMag-mean()"
[62] "fBodyAccMag-std()"
[63] "fBodyBodyAccJerkMag-mean()" [64] "fBodyBodyAccJerkMag-std()"
[65] "fBodyBodyGyroMag-mean()"
[66] "fBodyBodyGyroMag-std()"
[67] "fBodyBodyGyroJerkMag-mean()" [68] "fBodyBodyGyroJerkMag-std()"

The underlying data is described in the original README.txt that is quoted below.

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

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.