| Feature Selection | |
| ================= | |
| 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' | |