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CODEBOOK

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 - Università degli Studi di Genova. Via Opera Pia 11A, I-16145, Genoa, Italy. [email protected] 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 aggregated into a dataset with means of data only.

FEATURES

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 : Body acceleration signal
  • tGravityAcc-XYZ : Gravity acceleration signal
  • tBodyAccJerk-XYZ : Body linear acceleration Jerk signals
  • tBodyGyro-XYZ : Angular velocity
  • tBodyGyroJerk-XYZ : Angular velocity Jerk signals
  • tBodyAccMag : Magnitude of body acceleration, calculated using the Euclidean norm
  • tGravityAccMag : Magnitude of gravity acceleration, calculated using the Euclidean norm
  • tBodyAccJerkMag : Magnitude of body linear acceleration Jerk signals, calculated using the Euclidean norm
  • tBodyGyroMag : Magnitude of angular velocity, calculated using the Euclidean norm
  • tBodyGyroJerkMag : Magnitude of angular velocity Jerk signals, calculated using the Euclidean norm
  • fBodyAcc-XYZ : Fast Fourier Transform applied to tBodyAcc-XYZ
  • fBodyAccJerk-XYZ : Fast Fourier Transform applied to tBodyAccJerk-XYZ
  • fBodyGyro-XYZ : Fast Fourier Transform applied to tBodyGyro-XYZ
  • fBodyAccMag : Fast Fourier Transform applied to tBodyAccMag
  • fBodyAccJerkMag : Fast Fourier Transform applied to tBodyAccJerkMag
  • fBodyGyroMag : Fast Fourier Transform applied to tBodyGyroMag
  • fBodyGyroJerkMag : Fast Fourier Transform applied to tBodyGyroJerkMag

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

  • mean(): Mean value
  • std(): Standard deviation

TRANSFORMATIONS

Source code is documented, but these are the main blocks of the process

  • Load Features
    • From features.txt file
    • Filter Features to get only std and mean variables using grepl looking for "std()" or "mean()" text
  • Load Activity Names
    • From activity_labels.txt file
  • Load Subject Data
    • Merge train (subject_train.txt) and test (subject_test.txt) data, with rbind
    • Rename column V1 to SUBJECT
  • Load Activity Data
    • Merge train (y_train.txt) and test (y_test.txt) data, with rbind
    • Join with Activity Names, using V1 as common field
    • Rename column V2 to ACTIVITY
  • Load Main Data
    • Merge train (X_train.txt) and test (X_test.txt) data, with rbind
    • Filter data to get only std and mean variables
    • Rename columns with descriptions
  • Join all the data by common field (ID)
  • Generate average table
    • Calculate mean for each clomun with colMean function (rows from 1 to 66)
    • Repeat this for every case of "ACTIVITY" and "SUBJECT" using ddply function
    • Write the data to file using write.table