This code book contains original description from the
README.txtfile as well as original description from thefeatures.txtfile, both found on the zip file containing the HCI HAR Dataset
Bellow are some pieces of the original description of the dataset and the experiment from which the data was collected.
================================================================== 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 - Universit? 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 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
The complete list of variables of each feature vector is available in 'features.txt'
The tidy dataset is composed of 180 observations and 88 variables. Besides the PersonId and Activity columns (first two), we selected from the original dataset (merge of the Test and Train datasets) only those that contained mean or std in their names.
This dataset presents the mean value for each subject and for each activity he/she performed.
PersonId (integer variable) represents the subject identification, ranging from 1 to 30.
Activity (categorical variable) represents one of the 6 activities the subjects were performing:
WALKING
WALKING_UPSTAIRS
WALKING_DOWNSTAIRS
SITTING
STANDING
LAYING
All other variables are numeric, representing the measurements (mean and std). They're listed
bellow:
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
fBodyAcc-meanFreq()-X
fBodyAcc-meanFreq()-Y
fBodyAcc-meanFreq()-Z
fBodyAccJerk-mean()-X
fBodyAccJerk-mean()-Y
fBodyAccJerk-mean()-Z
fBodyAccJerk-meanFreq()-X
fBodyAccJerk-meanFreq()-Y
fBodyAccJerk-meanFreq()-Z
fBodyGyro-mean()-X
fBodyGyro-mean()-Y
fBodyGyro-mean()-Z
fBodyGyro-meanFreq()-X
fBodyGyro-meanFreq()-Y
fBodyGyro-meanFreq()-Z
fBodyAccMag-mean()
fBodyAccMag-meanFreq()
fBodyBodyAccJerkMag-mean()
fBodyBodyAccJerkMag-meanFreq()
fBodyBodyGyroMag-mean()
fBodyBodyGyroMag-meanFreq()
fBodyBodyGyroJerkMag-mean()
fBodyBodyGyroJerkMag-meanFreq()
angle(tBodyAccMean,gravity)
angle(tBodyAccJerkMean),gravityMean)
angle(tBodyGyroMean,gravityMean)
angle(tBodyGyroJerkMean,gravityMean)
angle(X,gravityMean)
angle(Y,gravityMean)
angle(Z,gravityMean)
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()
One should refer to the original experiment description for information about the measurement units of these variables.