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CodeBook

Data Set Information

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.

Attribute Information:

For each record in the dataset 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.

Field Name

  • subject
  • Activity_Label
  • timeBodyAccMean-X
  • timeBodyAccMean-Y
  • timeBodyAccMean-Z
  • timeBodyAcc-std-X
  • timeBodyAcc-std-Y
  • timeBodyAcc-std-Z
  • timeGravityAccMean-X
  • timeGravityAccMean-Y
  • timeGravityAccMean-Z
  • timeGravityAcc-std-X
  • timeGravityAcc-std-Y
  • timeGravityAcc-std-Z
  • timeBodyAccJerkMean-X
  • timeBodyAccJerkMean-Y
  • timeBodyAccJerkMean-Z
  • timeBodyAccJerk-std-X
  • timeBodyAccJerk-std-Y
  • timeBodyAccJerk-std-Z
  • timeBodyGyroMean-X
  • timeBodyGyroMean-Y
  • timeBodyGyroMean-Z
  • timeBodyGyro-std-X
  • timeBodyGyro-std-Y
  • timeBodyGyro-std-Z
  • timeBodyGyroJerkMean-X
  • timeBodyGyroJerkMean-Y
  • timeBodyGyroJerkMean-Z
  • timeBodyGyroJerk-std-X
  • timeBodyGyroJerk-std-Y
  • timeBodyGyroJerk-std-Z
  • timeBodyAccMagnitudeMean
  • timeBodyAccMagnitudeStdDev
  • timeGravityAccMagnitudeMean
  • timeGravityAccMagnitudeStdDev
  • timeBodyAccJerkMagnitudeMean
  • timeBodyAccJerkMagnitudeStdDev
  • timeBodyGyroMagnitudeMean
  • timeBodyGyroMagnitudeStdDev
  • timeBodyGyroJerkMagnitudeMean
  • timeBodyGyroJerkMagnitudeStdDev
  • freqBodyAccMean-X
  • freqBodyAccMean-Y
  • freqBodyAccMean-Z
  • freqBodyAcc-std-X
  • freqBodyAcc-std-Y
  • freqBodyAcc-std-Z
  • freqBodyAccMeanFreq-X
  • freqBodyAccMeanFreq-Y
  • freqBodyAccMeanFreq-Z
  • freqBodyAccJerkMean-X
  • freqBodyAccJerkMean-Y
  • freqBodyAccJerkMean-Z
  • freqBodyAccJerk-std-X
  • freqBodyAccJerk-std-Y
  • freqBodyAccJerk-std-Z
  • freqBodyAccJerkMeanFreq-X
  • freqBodyAccJerkMeanFreq-Y
  • freqBodyAccJerkMeanFreq-Z
  • freqBodyGyroMean-X
  • freqBodyGyroMean-Y
  • freqBodyGyroMean-Z
  • freqBodyGyro-std-X
  • freqBodyGyro-std-Y
  • freqBodyGyro-std-Z
  • freqBodyGyroMeanFreq-X
  • freqBodyGyroMeanFreq-Y
  • freqBodyGyroMeanFreq-Z
  • freqBodyAccMagnitudeMean
  • freqBodyAccMagnitudeStdDev
  • freqBodyAccMagnitudeMeanFreq
  • freqBodyAccJerkMagnitudeMean
  • freqBodyAccJerkMagnitudeStdDev
  • freqBodyAccJerkMagnitudeMeanFreq
  • freqBodyGyroMagnitudeMean
  • freqBodyGyroMagnitudeStdDev
  • freqBodyGyroMagnitudeMeanFreq
  • freqBodyGyroJerkMagnitudeMean
  • freqBodyGyroJerkMagnitudeStdDev
  • freqBodyGyroJerkMagnitudeMeanFreq