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subject
This variable takes integer values ranging from 1 to 30, corresponding to the id associated to the subject under study
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activity
This variable describes the activity of the subject being monitored
These variables come from differemt measurements performed by an accelerometer and a gyroscope. Their values have been normalized and bounded to the interval [-1, 1]. Then the mean and the standard deviation have been computed (this processing is previous to the action of the script run_analysys.R , alredy integrated in the raw data mentioned in the README.txt file).
The present script run_analysys.R have processed the raw names of these variables for better readibility. The names generated are the following:
- 1 time.body.accelerometer.mean.X
- 2 time.body.accelerometer.mean.Y
- 3 time.body.accelerometer.mean.Z
- 4 time.body.accelerometer.std.X
- 5 time.body.accelerometer.std.Y
- 6 time.body.accelerometer.std.Z
- 7 time.gravity.accelerometer.mean.X
- 8 time.gravity.accelerometer.mean.Y
- 9 time.gravity.accelerometer.mean.Z
- 10 time.gravity.accelerometer.std.X
- 11 time.gravity.accelerometer.std.Y
- 12 time.gravity.accelerometer.std.Z
- 13 time.body.accelerometer.jerk.mean.X
- 14 time.body.accelerometer.jerk.mean.Y
- 15 time.body.accelerometer.jerk.mean.Z
- 16 time.body.accelerometer.jerk.std.X
- 17 time.body.accelerometer.jerk.std.Y
- 18 time.body.accelerometer.jerk.std.Z
- 19 time.body.gyroscope.mean.X
- 20 time.body.gyroscope.mean.Y
- 21 time.body.gyroscope.mean.Z
- 22 time.body.gyroscope.std.X
- 23 time.body.gyroscope.std.Y
- 24 time.body.gyroscope.std.Z
- 25 time.body.gyroscope.jerk.mean.X
- 26 time.body.gyroscope.jerk.mean.Y
- 27 time.body.gyroscope.jerk.mean.Z
- 28 time.body.gyroscope.jerk.std.X
- 29 time.body.gyroscope.jerk.std.Y
- 30 time.body.gyroscope.jerk.std.Z
- 31 time.body.accelerometer.magnitude.mean
- 32 time.body.accelerometer.magnitude.std
- 33 time.gravity.accelerometer.magnitude.mean
- 34 time.gravity.accelerometer.magnitude.std
- 35 time.body.accelerometer.jerk.magnitude.mean
- 36 time.body.accelerometer.jerk.magnitude.std
- 37 time.body.gyroscope.magnitude.mean
- 38 time.body.gyroscope.magnitude.std
- 39 time.body.gyroscope.jerk.magnitude.mean
- 40 time.body.gyroscope.jerk.magnitude.std
- 41 frequency.body.accelerometer.mean.X
- 42 frequency.body.accelerometer.mean.Y
- 43 frequency.body.accelerometer.mean.Z
- 44 frequency.body.accelerometer.std.X
- 45 frequency.body.accelerometer.std.Y
- 46 frequency.body.accelerometer.std.Z
- 47 frequency.body.accelerometer.jerk.mean.X
- 48 frequency.body.accelerometer.jerk.mean.Y
- 49 frequency.body.accelerometer.jerk.mean.Z
- 50 frequency.body.accelerometer.jerk.std.X
- 51 frequency.body.accelerometer.jerk.std.Y
- 52 frequency.body.accelerometer.jerk.std.Z
- 53 frequency.body.gyroscope.mean.X
- 54 frequency.body.gyroscope.mean.Y
- 55 frequency.body.gyroscope.mean.Z
- 56 frequency.body.gyroscope.std.X
- 57 frequency.body.gyroscope.std.Y
- 58 frequency.body.gyroscope.std.Z
- 59 frequency.body.accelerometer.magnitude.mean
- 60 frequency.body.accelerometer.magnitude.std
- 61 frequency.body.accelerometer.jerk.magnitude.mean
- 62 frequency.body.accelerometer.jerk.magnitude.std
- 63 frequency.body.gyroscope.magnitude.mean
- 64 frequency.body.gyroscope.magnitude.std
- 65 frequency.body.gyroscope.jerk.magnitude.mean
- 66 frequency.body.gyroscope.jerk.magnitude.std
The variables with names starting by the string "time" correspond to time domain signals. The variables with names starting by the string "frequency" correspond to frequency domain signals.
Being unable to provide more in-depth details, other than those already contained in the raw files, I content myself with citing literally the file "features_info.txt":
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' "
As explained in README.txt, the file data_summary.txt has been generated by the script run_analysis.R from the object summary. This script performs several transformations of the raw data. The values that we observe in data_summary.txt correspond to the means of the values of the feature variables mentioned above, for each possible combination subject-activity (see README.txt and run_analysis.R for more details).