#TidyDataSet.txt This dataset is a transform and simplification of the Human Activity Recognition(HAR) Using Smartphones Data Set. From the UCI Machine Learning Repository. Source here and download here
##Original Data Set Information:
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.
##Tidy Data Set Information:
The data is reshaped to include mean averages of all features by subject and activity Each row will now consist of mean averages for all features for each subject during each activity An important not is that all STD measures are mean STD's this is not reflected in the variable name in order to prevent names of unweildly length. Further description of which files were included in the tidy data set are documented in run_analysis.R and README.md files in this github repo.
##Original Data Set Feature Description:
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
##Tidy Data Set Feature Description:
The final data set consists of 180 observations of 88 variables.
Each row is denoted by a numeric subject id of 1-30 and 6 activities:
- LAYING
- SITTING
- STANDING
- WALKING
- WALKING_DOWNSTAIRS
- WALKING_UPSTAIRS
With each subject performing each activity we get the final result of 30 subjects accross 6 activities = 180 observations. The feature variables,columns 3:88, are all the variables from the original data set measuring average values and standard deviations, are listed as mean values of the original values calculated by subject and activity. The 86 specific features extracted from the orignal data set are displayed below.
| "tBodyAcc.mean.X" | "tBodyAcc.mean.Y" | "tBodyAcc.mean.Z" | "tBodyAcc.std.X" |
| "tBodyAcc.std.Y" | "tBodyAcc.std.Z" | "tGravityAcc.mean.X" | "tGravityAcc.mean.Y" |
| "tGravityAcc.mean.Z" | "tGravityAcc.std.X" | "tGravityAcc.std.Y" | "tGravityAcc.std.Z" |
| "tBodyAccJerk.mean.X" | "tBodyAccJerk.mean.Y" | "tBodyAccJerk.mean.Z" | "tBodyAccJerk.std.X" |
| "tBodyAccJerk.std.Y" | "tBodyAccJerk.std.Z" | "tBodyGyro.mean.X" | "tBodyGyro.mean.Y" |
| "tBodyGyro.mean.Z" | "tBodyGyro.std.X" | "tBodyGyro.std.Y" | "tBodyGyro.std.Z" |
| "tBodyGyroJerk.mean.X" | "tBodyGyroJerk.mean.Y" | "tBodyGyroJerk.mean.Z" | "tBodyGyroJerk.std.X" |
| "tBodyGyroJerk.std.Y" | "tBodyGyroJerk.std.Z" | "tBodyAccMag.mean" | "tBodyAccMag.std" |
| "tGravityAccMag.mean" | "tGravityAccMag.std" | "tBodyAccJerkMag.mean" | "tBodyAccJerkMag.std" |
| "tBodyGyroMag.mean" | "tBodyGyroMag.std" | "tBodyGyroJerkMag.mean" | "tBodyGyroJerkMag.std" |
| "fBodyAcc.mean.X" | "fBodyAcc.mean.Y" | "fBodyAcc.mean.Z" | "fBodyAcc.std.X" |
| "fBodyAcc.std.Y" | "fBodyAcc.std.Z" | "fBodyAcc.meanFreq.X" | "fBodyAcc.meanFreq.Y" |
| "fBodyAcc.meanFreq.Z" | "fBodyAccJerk.mean.X" | "fBodyAccJerk.mean.Y" | "fBodyAccJerk.mean.Z" |
| "fBodyAccJerk.std.X" | "fBodyAccJerk.std.Y" | "fBodyAccJerk.std.Z" | "fBodyAccJerk.meanFreq.X" |
| "fBodyAccJerk.meanFreq.Y" | "fBodyAccJerk.meanFreq.Z" | "fBodyGyro.mean.X" | "fBodyGyro.mean.Y" |
| "fBodyGyro.mean.Z" | "fBodyGyro.std.X" | "fBodyGyro.std.Y" | "fBodyGyro.std.Z" |
| "fBodyGyro.meanFreq.X" | "fBodyGyro.meanFreq.Y" | "fBodyGyro.meanFreq.Z" | "fBodyAccMag.mean" |
| "fBodyAccMag.std" | "fBodyAccMag.meanFreq" | "fBodyBodyAccJerkMag.mean" | "fBodyBodyAccJerkMag.std" |
| "fBodyBodyAccJerkMag.meanFreq" | "fBodyBodyGyroMag.mean" | "fBodyBodyGyroMag.std" | "fBodyBodyGyroMag.meanFreq" |
| "fBodyBodyGyroJerkMag.mean" | "fBodyBodyGyroJerkMag.std" | "fBodyBodyGyroJerkMag.meanFreq" | "angletBodyAccMean.gravity" |
| "angletBodyAccJerkMean.gravityMean" | "angletBodyGyroMean.gravityMean" | "angletBodyGyroJerkMean.gravityMean" | "angleX.gravityMean" |
| "angleY.gravityMean" | "angleZ.gravityMean" |
Note that special characters, ie. (),underscores and commas have been removed to make the names more R friendly and in accordance with tidy data principles.