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Code Book - Tidy_data.csv

CSV file with 10299 rows of 82 variables

subject

* integer
* Subject ID

activities

* character
* Activity Type

tBodyAcc-mean()-X

* numeric
* See feature description below

tBodyAcc-mean()-Y

* numeric
* See feature description below

tBodyAcc-mean()-Z

* numeric
* See feature description below

tGravityAcc-mean()-X

* numeric
* See feature description below

tGravityAcc-mean()-Y

* numeric
* See feature description below

tGravityAcc-mean()-Z

* numeric
* See feature description below

tBodyAccJerk-mean()-X

* numeric
* See feature description below

tBodyAccJerk-mean()-Y

* numeric
* See feature description below

tBodyAccJerk-mean()-Z

* numeric
* See feature description below

tBodyGyro-mean()-X

* numeric
* See feature description below

tBodyGyro-mean()-Y

* numeric
* See feature description below

tBodyGyro-mean()-Z

* numeric
* See feature description below

tBodyGyroJerk-mean()-X

* numeric
* See feature description below

tBodyGyroJerk-mean()-Y

* numeric
* See feature description below

tBodyGyroJerk-mean()-Z

* numeric
* See feature description below

tBodyAccMag-mean()

* numeric
* See feature description below

tGravityAccMag-mean()

* numeric
* See feature description below

tBodyAccJerkMag-mean()

* numeric
* See feature description below

tBodyGyroMag-mean()

* numeric
* See feature description below

tBodyGyroJerkMag-mean()

* numeric
* See feature description below

fBodyAcc-mean()-X

* numeric
* See feature description below

fBodyAcc-mean()-Y

* numeric
* See feature description below

fBodyAcc-mean()-Z

* numeric
* See feature description below

fBodyAcc-meanFreq()-X

* numeric
* See feature description below

fBodyAcc-meanFreq()-Y

* numeric
* See feature description below

fBodyAcc-meanFreq()-Z

* numeric
* See feature description below

fBodyAccJerk-mean()-X

* numeric
* See feature description below

fBodyAccJerk-mean()-Y

* numeric
* See feature description below

fBodyAccJerk-mean()-Z

* numeric
* See feature description below

fBodyAccJerk-meanFreq()-X

* numeric
* See feature description below

fBodyAccJerk-meanFreq()-Y

* numeric
* See feature description below

fBodyAccJerk-meanFreq()-Z

* numeric
* See feature description below

fBodyGyro-mean()-X

* numeric
* See feature description below

fBodyGyro-mean()-Y

* numeric
* See feature description below

fBodyGyro-mean()-Z

* numeric
* See feature description below

fBodyGyro-meanFreq()-X

* numeric
* See feature description below

fBodyGyro-meanFreq()-Y

* numeric
* See feature description below

fBodyGyro-meanFreq()-Z

* numeric
* See feature description below

fBodyAccMag-mean()

* numeric
* See feature description below

fBodyAccMag-meanFreq()

* numeric
* See feature description below

fBodyBodyAccJerkMag-mean()

* numeric
* See feature description below

fBodyBodyAccJerkMag-meanFreq()

* numeric
* See feature description below

fBodyBodyGyroMag-mean()

* numeric
* See feature description below

fBodyBodyGyroMag-meanFreq()

* numeric
* See feature description below

fBodyBodyGyroJerkMag-mean()

* numeric
* See feature description below

fBodyBodyGyroJerkMag-meanFreq()

* numeric
* See feature description below

tBodyAcc-std()-X

* numeric
* See feature description below

tBodyAcc-std()-Y

* numeric
* See feature description below

tBodyAcc-std()-Z

* numeric
* See feature description below

tGravityAcc-std()-X

* numeric
* See feature description below

tGravityAcc-std()-Y

* numeric
* See feature description below

tGravityAcc-std()-Z

* numeric
* See feature description below

tBodyAccJerk-std()-X

* numeric
* See feature description below

tBodyAccJerk-std()-Y

* numeric
* See feature description below

tBodyAccJerk-std()-Z

* numeric
* See feature description below

tBodyGyro-std()-X

* numeric
* See feature description below

tBodyGyro-std()-Y

* numeric
* See feature description below

tBodyGyro-std()-Z

* numeric
* See feature description below

tBodyGyroJerk-std()-X

* numeric
* See feature description below

tBodyGyroJerk-std()-Y

* numeric
* See feature description below

tBodyGyroJerk-std()-Z

* numeric
* See feature description below

tBodyAccMag-std()

* numeric
* See feature description below

tGravityAccMag-std()

* numeric
* See feature description below

tBodyAccJerkMag-std()

* numeric
* See feature description below

tBodyGyroMag-std()

* numeric
* See feature description below

tBodyGyroJerkMag-std()

* numeric
* See feature description below

fBodyAcc-std()-X

* numeric
* See feature description below

fBodyAcc-std()-Y

* numeric
* See feature description below

fBodyAcc-std()-Z

* numeric
* See feature description below

fBodyAccJerk-std()-X

* numeric
* See feature description below

fBodyAccJerk-std()-Y

* numeric
* See feature description below

fBodyAccJerk-std()-Z

* numeric
* See feature description below

fBodyGyro-std()-X

* numeric
* See feature description below

fBodyGyro-std()-Y

* numeric
* See feature description below

fBodyGyro-std()-Z

* numeric
* See feature description below

fBodyAccMag-std()

* numeric
* See feature description below

fBodyBodyAccJerkMag-std()

* numeric
* See feature description below

fBodyBodyGyroMag-std()

* numeric
* See feature description below

fBodyBodyGyroJerkMag-std()

* numeric
* See feature description below

Feature descriptions

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