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Code book

"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."

  • The file generated by the script run_analysis.R is named "tidyTable.txt" and it contains a table, following the tidy data principles (Each measured variable is in one column, each different observation of that variable is in a different row).

The columns of the file are respectively:

"SUBJECT": the id of the subject under observation

"ACTIVITY": the name of the observed activity (one of "LAYING", "SITTING", "STANDING", "WALKING", "WALKING_DOWNSTAIRS", "WALKING_UPSTAIRS")

and the average of each selected variable for each activity and each subject. The selected variables are only the measurements on the mean and standard deviation for each measurement. For details on single measurements, see below.

A total of 79 features were selected from the original data: the estimated mean and standard deviations, using a grep on "mean|std".

The feature names from the original data have been rewritten, to be more readable:

  • "t"-> "TIME_";
  • "f" -> "FREQUENCY_";
  • "Gravity" -> "GRAVITY"
  • "Gyro" -> "GYROSCOPE"
  • "Acc" -> "ACCELLERATION"
  • "Mag" -> "MAGNITUTE"
  • "Jerk" -> "JERK"
  • "meanFreq" -> "MEANFREQUENCY"
  • "mean" -> "MEAN"
  • "std" -> "STANDARDDEVIATION"
  • "BodyBody" -> "BODY"
  • dashes and parentheses have been removed

A full description of the features is best obtained from the file features_info.txt, included with the original dataset.

"TIME__BODY_ACCELLERATION_MEAN_X"

"TIME__BODY_ACCELLERATION_MEAN_Y"

"TIME__BODY_ACCELLERATION_MEAN_Z"

"TIME__BODY_ACCELLERATION_STANDARDDEVIATION_X"

"TIME__BODY_ACCELLERATION_STANDARDDEVIATION_Y"

"TIME__BODY_ACCELLERATION_STANDARDDEVIATION_Z"

"TIME_GRAVITYACCELLERATION_MEAN_X"

"TIME_GRAVITYACCELLERATION_MEAN_Y"

"TIME_GRAVITYACCELLERATION_MEAN_Z"

"TIME_GRAVITYACCELLERATION_STANDARDDEVIATION_X"

"TIME_GRAVITYACCELLERATION_STANDARDDEVIATION_Y"

"TIME_GRAVITYACCELLERATION_STANDARDDEVIATION_Z"

"TIME__BODY_ACCELLERATIONJERK_MEAN_X"

"TIME__BODY_ACCELLERATIONJERK_MEAN_Y"

"TIME__BODY_ACCELLERATIONJERK_MEAN_Z"

"TIME__BODY_ACCELLERATIONJERK_STANDARDDEVIATION_X"

"TIME__BODY_ACCELLERATIONJERK_STANDARDDEVIATION_Y"

"TIME__BODY_ACCELLERATIONJERK_STANDARDDEVIATION_Z"

"TIME__BODY_GYROSCOPE_MEAN_X"

"TIME__BODY_GYROSCOPE_MEAN_Y"

"TIME__BODY_GYROSCOPE_MEAN_Z"

"TIME__BODY_GYROSCOPE_STANDARDDEVIATION_X"

"TIME__BODY_GYROSCOPE_STANDARDDEVIATION_Y"

"TIME__BODY_GYROSCOPE_STANDARDDEVIATION_Z"

"TIME__BODY_GYROSCOPEJERK_MEAN_X"

"TIME__BODY_GYROSCOPEJERK_MEAN_Y"

"TIME__BODY_GYROSCOPEJERK_MEAN_Z"

"TIME__BODY_GYROSCOPEJERK_STANDARDDEVIATION_X"

"TIME__BODY_GYROSCOPEJERK_STANDARDDEVIATION_Y"

"TIME__BODY_GYROSCOPEJERK_STANDARDDEVIATION_Z"

"TIME__BODY_ACCELLERATIONMAGNITUTE_MEAN_"

"TIME__BODY_ACCELLERATIONMAGNITUTE_STANDARDDEVIATION_"

"TIME_GRAVITYACCELLERATIONMAGNITUTE_MEAN_"

"TIME_GRAVITYACCELLERATIONMAGNITUTE_STANDARDDEVIATION_"

"TIME__BODY_ACCELLERATIONJERKMAGNITUTE_MEAN_"

"TIME__BODY_ACCELLERATIONJERKMAGNITUTE_STANDARDDEVIATION_"

"TIME__BODY_GYROSCOPEMAGNITUTE_MEAN_"

"TIME__BODY_GYROSCOPEMAGNITUTE_STANDARDDEVIATION_"

"TIME__BODY_GYROSCOPEJERKMAGNITUTE_MEAN_"

"TIME__BODY_GYROSCOPEJERKMAGNITUTE_STANDARDDEVIATION_"

"FREQUENCY__BODY_ACCELLERATION_MEAN_X"

"FREQUENCY__BODY_ACCELLERATION_MEAN_Y"

"FREQUENCY__BODY_ACCELLERATION_MEAN_Z"

"FREQUENCY__BODY_ACCELLERATION_STANDARDDEVIATION_X"

"FREQUENCY__BODY_ACCELLERATION_STANDARDDEVIATION_Y"

"FREQUENCY__BODY_ACCELLERATION_STANDARDDEVIATION_Z"

"FREQUENCY__BODY_ACCELLERATION_MEANFREQUENCY_X"

"FREQUENCY__BODY_ACCELLERATION_MEANFREQUENCY_Y"

"FREQUENCY__BODY_ACCELLERATION_MEANFREQUENCY_Z"

"FREQUENCY__BODY_ACCELLERATIONJERK_MEAN_X"

"FREQUENCY__BODY_ACCELLERATIONJERK_MEAN_Y"

"FREQUENCY__BODY_ACCELLERATIONJERK_MEAN_Z"

"FREQUENCY__BODY_ACCELLERATIONJERK_STANDARDDEVIATION_X"

"FREQUENCY__BODY_ACCELLERATIONJERK_STANDARDDEVIATION_Y"

"FREQUENCY__BODY_ACCELLERATIONJERK_STANDARDDEVIATION_Z"

"FREQUENCY__BODY_ACCELLERATIONJERK_MEANFREQUENCY_X"

"FREQUENCY__BODY_ACCELLERATIONJERK_MEANFREQUENCY_Y"

"FREQUENCY__BODY_ACCELLERATIONJERK_MEANFREQUENCY_Z"

"FREQUENCY__BODY_GYROSCOPE_MEAN_X"

"FREQUENCY__BODY_GYROSCOPE_MEAN_Y"

"FREQUENCY__BODY_GYROSCOPE_MEAN_Z"

"FREQUENCY__BODY_GYROSCOPE_STANDARDDEVIATION_X"

"FREQUENCY__BODY_GYROSCOPE_STANDARDDEVIATION_Y"

"FREQUENCY__BODY_GYROSCOPE_STANDARDDEVIATION_Z"

"FREQUENCY__BODY_GYROSCOPE_MEANFREQUENCY_X"

"FREQUENCY__BODY_GYROSCOPE_MEANFREQUENCY_Y"

"FREQUENCY__BODY_GYROSCOPE_MEANFREQUENCY_Z"

"FREQUENCY__BODY_ACCELLERATIONMAGNITUTE_MEAN_"

"FREQUENCY__BODY_ACCELLERATIONMAGNITUTE_STANDARDDEVIATION_"

"FREQUENCY__BODY_ACCELLERATIONMAGNITUTE_MEANFREQUENCY_"

"FREQUENCY__BODY_ACCELLERATIONJERKMAGNITUTE_MEAN_"

"FREQUENCY__BODY_ACCELLERATIONJERKMAGNITUTE_STANDARDDEVIATION_"

"FREQUENCY__BODY_ACCELLERATIONJERKMAGNITUTE_MEANFREQUENCY_"

"FREQUENCY__BODY_GYROSCOPEMAGNITUTE_MEAN_"

"FREQUENCY__BODY_GYROSCOPEMAGNITUTE_STANDARDDEVIATION_"

"FREQUENCY__BODY_GYROSCOPEMAGNITUTE_MEANFREQUENCY_"

"FREQUENCY__BODY_GYROSCOPEJERKMAGNITUTE_MEAN_"

"FREQUENCY__BODY_GYROSCOPEJERKMAGNITUTE_STANDARDDEVIATION_"

"FREQUENCY__BODY_GYROSCOPEJERKMAGNITUTE_MEANFREQUENCY_"

  • The original measurese comes from the provided dataset in input:

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