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CodeBook

Getting and Cleaning Data

Course Project

Mackenzie Wildman

1/22/17

CodeBook adapted from:

================================================================== 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. 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.

Notes:

  • Features are normalized and bounded within [-1,1].

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.

ID Variables

  • subject - Indicates who carried out the experiment, subjects are labeled 1-30
  • activity - Indicates which activity is carried out, labeled "WALKING WALKING_UPSTAIRS WALKING_DOWNSTAIRS SITTING STANDING LAYING "

Measurement Variables ("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 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 and retained in the summarydata data set are:

mean(): Mean value std(): Standard deviation meanFreq(): Weighted average of the frequency components to obtain a mean frequency

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

gravityMean tBodyAccMean tBodyAccJerkMean tBodyGyroMean tBodyGyroJerkMean

All features are normalized and bounded within [-1,1].

The following variables are included in the data set summarydata averages for each subject, activity pair:

"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" "fBodyAcc-meanFreq()-X"

"50" "fBodyAcc-meanFreq()-Y"

"51" "fBodyAcc-meanFreq()-Z"

"52" "fBodyAccJerk-mean()-X"

"53" "fBodyAccJerk-mean()-Y"

"54" "fBodyAccJerk-mean()-Z"

"55" "fBodyAccJerk-std()-X"

"56" "fBodyAccJerk-std()-Y"

"57" "fBodyAccJerk-std()-Z"

"58" "fBodyAccJerk-meanFreq()-X"

"59" "fBodyAccJerk-meanFreq()-Y"

"60" "fBodyAccJerk-meanFreq()-Z"

"61" "fBodyGyro-mean()-X"

"62" "fBodyGyro-mean()-Y"

"63" "fBodyGyro-mean()-Z"

"64" "fBodyGyro-std()-X"

"65" "fBodyGyro-std()-Y"

"66" "fBodyGyro-std()-Z"

"67" "fBodyGyro-meanFreq()-X"

"68" "fBodyGyro-meanFreq()-Y"

"69" "fBodyGyro-meanFreq()-Z"

"70" "fBodyAccMag-mean()"

"71" "fBodyAccMag-std()"

"72" "fBodyAccMag-meanFreq()"

"73" "fBodyBodyAccJerkMag-mean()"

"74" "fBodyBodyAccJerkMag-std()"

"75" "fBodyBodyAccJerkMag-meanFreq()"

"76" "fBodyBodyGyroMag-mean()"

"77" "fBodyBodyGyroMag-std()"

"78" "fBodyBodyGyroMag-meanFreq()"

"79" "fBodyBodyGyroJerkMag-mean()"

"80" "fBodyBodyGyroJerkMag-std()"

"81" "fBodyBodyGyroJerkMag-meanFreq()"

"82" "angle(tBodyAccMean,gravity)"

"83" "angle(tBodyAccJerkMean),gravityMean)"

"84" "angle(tBodyGyroMean,gravityMean)"

"85" "angle(tBodyGyroJerkMean,gravityMean)"

"86" "angle(X,gravityMean)"

"87" "angle(Y,gravityMean)"

"88" "angle(Z,gravityMean)"