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Code Book

This is a code book for the final project assignment for Getting and Cleaning Data By Yulia Zamriy

Raw Data information

Provided By

Human Activity Recognition Using Smartphones Dataset Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto.

Raw Data Description

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.

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 features selected for this database come from the accelerometer and gyroscope 3-axial raw signals tAcc-XYZ and tGyro-XYZ. These time domain signals (prefix 't' to denote time) were captured at a constant rate of 50 Hz. Then they were filtered using a median filter and a 3rd order low pass Butterworth filter with a corner frequency of 20 Hz to remove noise. Similarly, the acceleration signal was then separated into body and gravity acceleration signals (tBodyAcc-XYZ and tGravityAcc-XYZ) using another low pass Butterworth filter with a corner frequency of 0.3 Hz.

Subsequently, the body linear acceleration and angular velocity were derived in time to obtain Jerk signals (tBodyAccJerk-XYZ and tBodyGyroJerk-XYZ). Also the magnitude of these three-dimensional signals were calculated using the Euclidean norm (tBodyAccMag, tGravityAccMag, tBodyAccJerkMag, tBodyGyroMag, tBodyGyroJerkMag).

Finally a Fast Fourier Transform (FFT) was applied to some of these signals producing fBodyAcc-XYZ, fBodyAccJerk-XYZ, fBodyGyro-XYZ, fBodyAccJerkMag, fBodyGyroMag, fBodyGyroJerkMag. (Note the 'f' to indicate frequency domain signals).

These signals were used to estimate variables of the feature vector for each pattern:
'-XYZ' is used to denote 3-axial signals in the X, Y and Z directions.

tBodyAcc-XYZ tGravityAcc-XYZ tBodyAccJerk-XYZ tBodyGyro-XYZ tBodyGyroJerk-XYZ tBodyAccMag tGravityAccMag tBodyAccJerkMag tBodyGyroMag tBodyGyroJerkMag fBodyAcc-XYZ fBodyAccJerk-XYZ fBodyGyro-XYZ fBodyAccMag fBodyAccJerkMag fBodyGyroMag fBodyGyroJerkMag

The set of variables that were estimated from these signals are:

mean(): Mean value std(): Standard deviation mad(): Median absolute deviation max(): Largest value in array min(): Smallest value in array sma(): Signal magnitude area energy(): Energy measure. Sum of the squares divided by the number of values. iqr(): Interquartile range entropy(): Signal entropy arCoeff(): Autorregresion coefficients with Burg order equal to 4 correlation(): correlation coefficient between two signals maxInds(): index of the frequency component with largest magnitude meanFreq(): Weighted average of the frequency components to obtain a mean frequency skewness(): skewness of the frequency domain signal kurtosis(): kurtosis of the frequency domain signal bandsEnergy(): Energy of a frequency interval within the 64 bins of the FFT of each window. angle(): Angle between to vectors.

Additional vectors obtained by averaging the signals in a signal window sample. These are used on the angle() variable:

gravityMean tBodyAccMean tBodyAccJerkMean tBodyGyroMean tBodyGyroJerkMean

Processed data information

To process the data the following steps have been performed

  • STEP 1. Read in Feature and Activity Tables
  • STEP 2. Read in test folder
  • STEP 3. Read in train folder
  • STEP 4. Add descriptive information to TEST dataset
  • STEP 5. Add descriptive information to TRAIN dataset
  • STEP 6. Merge TRAIN and TEST data
  • STEP 7. Extracting mean and std for each measurement
  • STEP 8. Creating new dataset with the average for each variable and each subject

All the steps are recorded in: "run analysis.R" file

Final dataset

  • Name: SamsungGalaxySIITest-Average-measurements-for-30-volunteers.txt
  • Format: txt
  • Contains Column Names, but no Row Names
  • Number of observations: 40
  • Number of variables: 68

2 Identifier Variables:

  • "VolunteerNumber": 30 volunteers
  • "ActivityName": 6 activities (LAYING SITTING STANDING WALKING WALKING_DOWNSTAIRS WALKING_UPSTAIRS)

Other variables represent means for each volunteer per activity for selected mean and std measurements: "tBodyAcc.mean...X"
"tBodyAcc.mean...Y"
"tBodyAcc.mean...Z"
"tGravityAcc.mean...X"
"tGravityAcc.mean...Y"
"tGravityAcc.mean...Z"
"tBodyAccJerk.mean...X"
"tBodyAccJerk.mean...Y"
"tBodyAccJerk.mean...Z"
"tBodyGyro.mean...X"
"tBodyGyro.mean...Y"
"tBodyGyro.mean...Z"
"tBodyGyroJerk.mean...X"
"tBodyGyroJerk.mean...Y"
"tBodyGyroJerk.mean...Z"
"tBodyAccMag.mean.."
"tGravityAccMag.mean.."
"tBodyAccJerkMag.mean.."
"tBodyGyroMag.mean.."
"tBodyGyroJerkMag.mean.."
"fBodyAcc.mean...X"
"fBodyAcc.mean...Y"
"fBodyAcc.mean...Z"
"fBodyAccJerk.mean...X"
"fBodyAccJerk.mean...Y"
"fBodyAccJerk.mean...Z"
"fBodyGyro.mean...X"
"fBodyGyro.mean...Y"
"fBodyGyro.mean...Z"
"fBodyAccMag.mean.."
"fBodyBodyAccJerkMag.mean.."
"fBodyBodyGyroMag.mean.."
"fBodyBodyGyroJerkMag.mean.." "tBodyAcc.std...X"
"tBodyAcc.std...Y"
"tBodyAcc.std...Z"
"tGravityAcc.std...X"
"tGravityAcc.std...Y"
"tGravityAcc.std...Z"
"tBodyAccJerk.std...X"
"tBodyAccJerk.std...Y"
"tBodyAccJerk.std...Z"
"tBodyGyro.std...X"
"tBodyGyro.std...Y"
"tBodyGyro.std...Z"
"tBodyGyroJerk.std...X"
"tBodyGyroJerk.std...Y"
"tBodyGyroJerk.std...Z"
"tBodyAccMag.std.."
"tGravityAccMag.std.."
"tBodyAccJerkMag.std.."
"tBodyGyroMag.std.."
"tBodyGyroJerkMag.std.."
"fBodyAcc.std...X"
"fBodyAcc.std...Y"
"fBodyAcc.std...Z"
"fBodyAccJerk.std...X"
"fBodyAccJerk.std...Y"
"fBodyAccJerk.std...Z"
"fBodyGyro.std...X"
"fBodyGyro.std...Y"
"fBodyGyro.std...Z"
"fBodyAccMag.std.."
"fBodyBodyAccJerkMag.std.."
"fBodyBodyGyroMag.std.."
"fBodyBodyGyroJerkMag.std.."